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Referee #2

Evidence, reproducibility and clarity

This paper from the Heisenberg lab takes a reductionist approach to understanding how BMP and Nodal signaling interact to coordinate morphogenesis. They mostly use blastoderm explants that they culture in vitro. These explants elongate over time, with Nodal signaling that induces mesendoderm driving the cell intercalations that explain the elongation. They show that increased BMP signaling inhibits this process, but reducing BMP signaling has no effect. They see that reducing Nodal signaling results in an upregulation of BMP activity as read out by phosphorylated Smad5 staining and increasing Nodal signaling has the opposite effect. They explain this mostly by the observation that Nodal induces the expression of the BMP antagonist, Chordin, and validate this idea by demonstrating that a reduction in Chordin expression reduces explant elongation. Returnign to the embryo, the authors show that manipulation of Nodal signaling levels influences the size of the BMP activity gradient as expected from the in vitro results. Finally, they show that reduction of Nodal signaling with SB505124 sensitises the embryos the effects of bmp2b overexpression, and that BMP overeactivation at 90% epiboly reduced C&E movements.

Major comments

In general I think the work is well done and the data justify the conclusions.

I have several suggestions for additional experiments and discussion that I think would improve the paper.

1. In Figure S1 they present data on elongation of explants treated with a Nodal inhibitor. It would be good to show some examples of images of the explants.
2. In Figure 1G and 3A, the same wildtype images are shown. This is mentioned and I assume therefore that the results were all part of the same experiment. How many times were these experiments performed? It would be much better to use different biological replicates in the two figures.
3. It is important for the authors to make clear how many biological replicates each of the experiments correspond to.
4. In Figure 4E, it would be good to show the levels of P-Smad2 in the Oep and MZ lefty1, 2 explants.
5. On page 11 the authors mention chordin-independent inhibition of BMP signaling. The most likely candidate would be noggin as it too is expressed dorsally and is at least in part activated by Nodal. This should be tested in their model.
6. The authors focus on Chordin as downstream of Nodal signaling, and discuss the role of Nodal signaling in inducing chordin as being due to peak Nodal signaling. However, Chordin has been shown to also be downstream of Fgf signaling and Bozozok (PMIDs 23499658 and 16873584), which likely explains its dorsal expression domain. Furthermore, Rogers et al, (PMID 33174840) who the authors refer to, also show that to disrupt BMP signaling in embryos, inhibition of Nodal and Fgf is required. These issues need to be discussed in more detail. It is the combinatorial signaling that is thought to be responsible for the dorsal location of the chordin (and noggin) expression domains.

Minor comments

I think in general the manuscript is well written and the figures are clear. Previous data is generally well cited. My only comment is that there is a wealth of data from Xenopus and zebrafish that BMP antagonists are induced as a result of combinatorial Nodal signaling and other pathways (dorsal wnt and fgf) that inhibit BMP signaling. I think this could be better referenced.

Significance

The paper is well done and provides important information about the interactions between Nodal and BMP signaling to induce axis elongation. I think the work would be improved if the authors revise it along the lines suggested above. In terms of novelty, many of the component parts of the paper are known (Nodal signaling is important for elongation via cell intercalation and Nodal and BMP can antagonize on another by the induction of BMP antagonists by Nodal), but it is novel to put them together to investigate axis extension using explants. The paper will be of interest to those interested in how these signaling pathways operate in early vertebrate development and to those interested in morphogenesis.

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Referee #1

Evidence, reproducibility and clarity

Summary

The authors presented intriguing observations on the molecular mechanisms regulating morphogenic cell movement, with a particular focus on convergent-extension (CE) movement associated with cell type specification in the zebrafish blastoderm explant. In this manuscript, Schauer et al. identified the CE movement of the mesendoderm as triggering the elongation of the zebrafish embryonic explant. In this process, the Nodal signal represses the BMP signal, which negatively regulates the movement of the mesendoderm precursors, through the induction of its inhibitor chordin. This suggests that the Nodal signal is the key factor coordinating cell fate specification and morphogenesis in the zebrafish blastoderm explant. Finally, suppression of Nodal signalling increases sensitivity to BMP signalling in the CE movement of intact embryos. This suggests that promotion of mesendoderm cell intercalation via BMP suppression by Nodal may be involved in conferring robustness to morphogenic cell movement in vivo.

Major comments

1. While one of the main conclusions of this manuscript is that "Nodal signaling regulates CE movement of mesendodermal cells by promoting their intercalation through inhibition of BMP signaling". However, this was predicted by changes in individual cell morphology and cell dispersal, and the authors didn't directly examine the behavior of individual cells. It would be better to confirm intercalation during the process of explant elongation by cell tracking analysis.
2. Although the authors discuss that Nodal signaling inhibits BMP signaling in the later gastrulation stage, this has not been experimentally tested. If possible, the time window in which Nodal signaling acts should be investigated by temporal inhibition of Nodal signaling using chemical inhibitors.
3. Only the signal gradient of pSmad5 and axis elongation were examined in the intact embryo part of the study (Fig. 6 and Fig. S7). The information on the domain of pSmad2 and the expression of chordin would be helpful for the comparison of the blastoderm explant and the intact embryos.

Minor concerns

The first letter of a gene name should be in lowercase. ( ex. Fig.S3C; Smad5 MO)

Significance

The zebrafish blastoderm explant assay has the potential to elucidate the molecular mechanisms regulating the complex processes of morphogenesis during vertebrate gastrulation, as the authors demonstrate in this paper. In this manuscript, the authors addressed the molecular mechanism coordinating cell fate specification and morphogenic cell movement in the blastoderm explant. All of the experiments are well-designed, the interpretation of the results is convincing and the paper is well-written. Also, the conclusion is very clear and well supported by the presented data. These findings provide fundamental and important insights for studying morphogenic cell movements in early vertebrate embryos using zebrafish blastoderm explants. On the other hand, most of the molecular mechanisms reported in this manuscript are already predicted by previous studies using intact embryos. Therefore, the impact of this work may be limited to ex vivo research.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Summary: This manuscript describes molecular mechanisms by which ACBD3 is recruited to the Golgi complex. ACBD3 recruits PI4KIIIb which is required to generate PI4P, a phosphoinositide which is key for the recruitment of essential Golgi proteins and hence is key to Golgi identity. The authors have used a combination of mass spectrometry, high quality fluorescence imaging, transient CRISPR knockdowns, and biochemical approaches such as IPs to identify the key determinant for recruitment of ACBD3 to the Golgi complex. They map the interaction between ACBD3 and the Golgi as a unique region (UR) upstream of its GOLD domain, identifying, in particular, an MWT motif as key for this recruitment. Using mass spectrometry they identify several novel interactors of ACBD3 as well as some established binding partners. Knockdown of these interactors reveal a key role for the SNARE, SCFD1, where reduced levels lead to complete loss of ACBD3 localisation to the Golgi without apparent disruption of Golgi structure. They further validate this interaction and that of another SNARE (Sec22b), which is part of the same SNARE complex as SCFD1, mapping the interaction to the longin domain of Sec22b. Surprisingly however they demonstrate that the UR domain does not mediate the interaction between ACBD3 and these SNAREs suggesting an alternative mechanism of recruitment. Previously identified ACBD3 interactors, Golgi proteins giantin and golgin-45 were also identified in the mass spectrometry screen and the authors demonstrate that these two proteins can recruit ACBD2 to the Golgi and this is dependent on the MWT motif identified in the UR domain. By knocking down SCFD1, they show reduced recruitment of ACBD3 leading them to propose a model of sequential recruitment of ACBD3 by SCFD1 followed by interactions with the golgins.

Major points: This study is a well-executed and rigorous study of the molecular requirements for the recruitment of ACBD3 to the Golgi. The experimental approaches are state-of-the-art and the data are clean and convincing. The only caveat, raised by the authors themselves, is their interpretation that there are two sequential steps for Golgi recruitment of ACBD3. While they show that loss of SCFD1 reduces the interaction of ACBD3 with giantin and golgin 45, their model depends on doing the reverse experiment, i.e. assessing the effects of knocking down either giantin or golgin-45. This is especially relevant given the demonstration that golgin-45 is sufficient to recruit ACBD3 to mitochondria. It may well be that recruitment involves a tripartite complex, which is not uncommon in vesicular transport mechanisms Giantin is not an essential protein do it should be feasible to perform this experiment. The authors are equipped in the quantitative fluorescence microscopy which would be required and which would help resolve whether sequential or redundant mechanisms are required for ACBD3 recruitment.

We thank the reviewer for the positive comments and are glad that they consider our study "well-executed and rigorous". We totally agree with the reviewer that our conclusions regarding the sequential aspect of the recruitment of ACBD3 in the original submission could be better supported. We have worked to strengthen this in our resubmission. As the reviewer states, this limitation was already discussed in the original submission. To further support our model, we have performed the experiment suggested by the reviewer, in which we test the effects of knocking down both giantin and golgin45 (double knockdown) on the binding of ACBD3 to SCFD1.

The results of this experiment further support our sequential model with little to no effect of loss of the Golgins on ACBD3. As we already knew, a large effect of SCFD1 KO on the binding of the Golgins to ACBD3 was also observed here. We should note that this was performed in a different cell line than before (HeLa cells rather than HEK cells), as the efficiency of multiple knockdowns was much lower in HEK cells, as determined by qPCR. Taken together, the new data in Figure 7 supports a sequential model for Golgi recruitment. We also agree that other, less likely models could explain our data and have included this openly in the discussion. In conclusion, we thank the reviewer for their comments and have revised the manuscript with a new experiment with the relevant repeats, which supports our model.

Reviewer #1 (Significance):

Significance PI4P is a phosphoinositide that is important for the recruitment of Golgi proteins. As with most PIs it is likely to act by coincidence detection in that Golgi associated proteins will recognise PI4P as well as other factors on Golgi membranes. This results in different local membrane environments which will be specific for particular functions. PI4KIII__b_ is key for PI4P production although the absolute levels of PI4P are likely to be determined by a balance of lipid kinases and phosphatases. However, since ACBD3 is key for the recruitment of PI4KIII__b, it is important to understand the molecular mechanisms by which it is recruited. The manuscript thus makes a significant contribution to understanding one of the underlying mechanisms for PI4KIII__b _recruitment although, as indicated above, stops short of establishing a clear model for the roles SCDF1 and Sec22b versus golgin 45 and giantin. For the future it will be of interest to determine why either a sequential or a redundant mechanism is required for the recruitment of ACBD3 as a scaffold protein.

We thank the reviewer for this set of positive comments on the manuscript and for agreeing that this is a significant contribution. Our revised version further supports our sequential model of ACBD3 recruitment to the Golgi apparatus, and the comments here have helped us further to strengthen the quality and clarity of the manuscript.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary This is a very interesting and potentially important paper for the field of membrane biology and membrane trafficking, in which the authors have studied the molecular mechanisms by which ACBD3 (and consequently PI4KIIIb) is recruited to the cis-Golgi membranes. The authors suggest that this recruitment is based on a two-step process, mediated by interactions to, on the one hand, SCFD-1 (SLY1) and, on the other hand, two redundant golgins (golgin-45 and giantin).

We once again thank the reviewer for the positive comments and are glad that they consider our manuscript important.

Comments:- Pg.1 : arfaptins, as far as I know, have not been shown to be involved in intra-golgi trafficking but rather in Golgi export (see e.g. ref. 12)

We thank the reviewer for pointing this out. We have corrected the text accordingly.

• Pg. 1: reigon --> region

We thank the reviewer for noticing this typo. We have corrected the text accordingly.

• Arf1 also recruits PI4KIIIb right?

This is correct. The De Matteis lab has shown that PI4KIIIβ associates with the Golgi complex in an Arf1-dependent manner (Godi et al. 1999). We think this is excellent work. However, Arf1 is somewhat of a master regulator of the Golgi, affecting the recruitment and localisation of many different Golgi proteins. It has also previously been reported that Arf1 does not directly interact with PI4KIIIβ (Klima et al. 2016). Overall, the molecular relationship between Arf1 and the kinase remains unclear. We do not exclude, however, that there are factors other than ACBD3 important for recruiting and regulating PI4KIIIβ levels at the Golgi. We have changed the wording in the manuscript to reflect that there are multiple ways that PI4KIIIβ is recruited to the Golgi apparatus.

Fig. S1: the information about the number of cells per experiment is missing. Also, please add the information about what exactly is represented in the box plots (is it the distribution of the mean value of R per experiment? or the total distribution on a cell-by-cell basis of a representative experiment?)

For each experiment, a minimum of 100 cells per condition were imaged. The Pearson's correlation was then calculated, and the average was taken for each biological repeat. The plot in Fig. S1B represents 3 independent biological repeats. We have included this information in the revised manuscript.

• The definition of Avg. Golgi int/avg. cell int. (a.u.) in Fig 1E,F is a bit difficult to understand to me. If I understand correctly, the total fl. int in the Golgi mask was computed and divided by the area of the Golgi mask (this is the av. Golgi intensity). A similar computation is done for the entire cell (including the Golgi), i.e., total fl. intensity in the cell mask is computed and divided by the area of the cell mask. Then the two av. intensities are divided (ratio = av. Golgi int / av. cell int.). This ratio, for a protein that is enriched in the Golgi area, should be larger than 1. For a protein that is equally distributed all over the cell, it should be 1, and for a protein that is excluded from the Golgi area, smaller than 1. Then to this value, the authors subtract the value of the ratio found for an inert construct (GFP of Halo alone), which I imagine should have an original ratio value of the order of 1, and hence, after this subtraction, norm. ratio values larger than 0 mean that they are more enriched at the Golgi area than GFP/HaloTag themselves. Is this correct? In principle, I don't see anything entirely wrong with this way of thought, but I just found it a bit difficult to understand, and in general one has to be careful when computing rations (quotients) and then subtract another ratio. Also, the units are not a.u., the value is dimensionless, what is "arbitrary" is the definition of 0 value and the based on this definition, also the actual value. I think it would probably be much clearer for the readers to compute somthing like the relative enrichment in the Golgi area as compared to the rest of the cell (excluding the Golgi area). That is, a value r'=(Int. Golgi mask / Area Golgi mask) / [(Int. Cell mask - Int. Golgi mask)/(Area cell mask - Area Golgi mask)]. This can be computed directly or defining a mask that is the cell mask - the Golgi mask. Also, some maths (unless I made a mistake) give that this r'= r (1-aG)/(1-r aG); where r is the ratio (before subtraction) defined by the authors, and aG=Area cell mask/Area Golgi mask. In any case, I'd suggest the authors to either adopt this other quantitation (without subtraction of the GFP/HAloTAG), which gives directly the fold-enrichment in the intensity density in the Golgi area with respect to the rest of the cell; or explain in more detail the maths of the value they are plotting now.

We thank the reviewer for these well-reasoned and thoughtful suggestions for our imaging analysis. These are issues that we have also considered when quantifying this dataset. At the heart of it, the second method of calculation (Golgi/outside of Golgi), results in a non-linear distribution, as the pool of proteins re-distribute from inside the Golgi to the cytosol. This is why we have chosen to use the first method of Golgi/total, as it provides a linear distribution.

The reviewer is also correct that the GFP (inert protein) ratio is 1 without adjustment. We have chosen to normalise to GFP/HaloTag (inert protein) as we think this is the clearest way of conveying our conclusions from these experiments. We have included the non-normalised graph here for the reviewer to see; however we thought that this conveys the key result less clearly. Overall, we agree this was poorly communicated in the manuscript and we have clarified it in the revised version.

• Fig. 1C&F: Besides the MWT mutant, the FKE mutant also seems to have a somewhat compromised Golgi localization. Have the authors followed on that, or what is the reason that they have just focused on the MWT mutant?

In contrast to the MWT mutant, the FKE mutant does not affect ACBD3 localisation significantly. In addition, when having a close look at the pdb structure of the GOLD domain of ACBD3 with 3A protein of Aichivirus A (5LZ3), the MWT patch, in particular residues M and T, make clear contact with protein 3A, which is not the case for FKE residues. Therefore we focused on the MWT residues, which we hypothesised to interact with a Golgi resident protein which competes with protein 3A to interact with ACBD3.

• Very minor point, and without wanting to sound pedant at all, but I think (I might be wrong of course, so apologies if I am) that the plural of apparatus in latin is not apparati, but apparatus (fourth declination). So, I'd change the word in page 2 (or just rephrase the sentence: e.g. "resulting in Golgi fragmentation"). But of course, I'd leave this to the authors' discretion.

We thank the reviewer for this precision, do not consider it pedantic, and have made the suggested change to the text.

• Fig. 3A: have the authors tried or been able to perform IF of the endogenous SCFD1 protein?

As suggested by the reviewer, we attempted to perform IF of endogenous SCFD1, as shown below. Despite trying several different antibodies, we were not satisfied that we were detecting real SCFD1 signal as there was no change in this staining upon SCFD1 CRISPR KO. Please see an example of this IF below (ProteinTech, 12569-1-AP). We have contacted the antibody manufacturers to inform them of this issue.

• Similarly to what has been done for other panels, could you quantify Fig. 3C? Are PI4KIIIb protein levels affected upon the different KOs?

As suggested by the reviewer, we are now showing in Figure S2D the percentage of cells with a partial or total loss of PI4KIIIβ at the Golgi in CRISPR-Cas9 KO cells of either PI4KIIIβ, ACBD3 or SCFD1. 3 independent biological repeats were performed and approximately 150 cells were quantified (~50 cells per condition). The results show that the PI4KIIIβ antibody used (BD Bioscience, 611816) is specific (93.22% of cells lose the antibody signal) and that ACBD3 and SCFD1 KO affects PI4KIIIβ recruitment to the Golgi in 88% and 73% of the cells, respectively._-

The last paragraph of the "SCFD1 and ACBD3 interact upstream of PI4KIIIβ recruitment to the Golgi apparatus" section reads a bit odd placed there. I think it is more appropriate for the discussion or for the intro part on SCFD1.

Many thanks to the reviewer for pointing this out. We simplified that paragraph to describe the relationship between SCFD1 and SEC22B.

• I am confused on Fig. 5A/B. The labels in the blots show that 390-528 (without UR) does not bind sec22 or scfd1, but the 368-529 does? Or I guess, judging by the MW seen in the middle blots, that there's some error in the labelling?

Many thanks to the reviewer for noticing this, which was clearly a labelling error. We corrected this accordingly in Figures 5A and B. We apologise for this oversight.

also, the IP efficiency of the MWT mutant in the panel A blot is quite low, still sec22 seems to be very efficiently pulled down. Can the authors comment on that please? Would co-IPing against endogenous sec22 and scfd1 would work (so you don't need to rely on HaloTag+ligand?)

We know that the MWT residues of ACBD3 are important for recruiting ACBD3 to the Golgi (Figure 1C and F). We also know that ACBD3 interacts with SEC22B and SCFD1 (Figure 3B and 4A) and that SCFD1 is important for ACBD3 Golgi recruitment. Therefore we initially speculated that ACBD3 interacts with SEC22B and SCFD1 through the MWT residues. However, as the reviewer points out, Figure 5 shows the opposite. Mutating MWT residues makes the interaction of ACBD3 with SEC22B and SCFD1 stronger. For this reason, we hypothesised that another player(s) also contributes to ACBD3 recruitment through interactions with the MWT residues. We have shown that the second recruitment factors are the 2 golgins, golgin-45 and giantin (Figure 6C). In short, whilst we agree that the IP efficiency is low, the binding is actually stronger, supporting our conclusions. No interaction of ACBD3 with endogenous SEC22B could be detected due to a lack of a sufficiently sensitive antibody (we tried Abcam ab181076 and ProteinTech 14776-1 AP).

• I really like the experiment 6B. Have the authors tested whether SEC22 is also recruited to mitochondria in those conditions? But not SCFD1?

We thank the reviewer for the positive comment. We have performed the suggested experiment and are now including this as an additional figure (Figure S3). Ectopic expression of golgin-45 targeted to the mitochondria is not sufficient to redistribute SCFD1-HaloTag or HaloTag-SEC22B to the mitochondria (Figure S3A and B, respectively). We, therefore, speculate that the fraction of ACBD3 that gets redirected in Figure 6B must be the small fraction of ACBD3 that is spontaneously in an open conformation and compatible for interaction with golgin-45.

• The results shown in Fig 7 might show a partial depletion in the interactions, but to be fully trusted they would need to be quantified and a statistical test used to compare the values. I think this part is important to show very clearly, because even with low binding to golgins (remember, single knockouts do not prevent Golgi localization of ACBD3), one could expect that ACBD3 still localized to the Golgi but it does not in the absence of SCFD1 as shown in this paper. A prediction of the proposed model is that in cells depleted of the two Golgins, SCFD1 and ACBD3 should still bind to one another, right? Did the authors test this?

We fully agree with the reviewer. As discussed in the replies to reviewer 1, we have repeated this experiment, including both sets of KO. This was not trivial, as a double transient KO is technically challenging and involves validation with qPCR and switching cell types (HEK cells to HeLa). The new data supports our current model and suggests some additional regulatory mechanisms at play.

• The model presented here (fig 8) seems to suggest that only the conformational variation of ACBD3 that binds Golgins is able to recruit (bind) PI4KIIIb. Is this known, or is there any experimental evidence for that?

HDX-MS experiments show that the ACBD and GOLD domains undergo conformational changes in the presence of 3A proteins (McPhail et al. 2017). Demonstrating this would require a complicated reconstitution experiment which is technically very challenging and would involve purifying various complex proteins, including SNAREs, SM proteins and golgins. This could perhaps be the subject of several future studies.

• Have the authors thought about testing the FKE mutant in the experiemnts shown in Fig. 5?

As mentioned above, since the FKE residues are not making any contact with the protein 3A and since the loss of ACBD3 recruitment to the Golgi is not statistically significant (Figure 1F), we haven't tested the FKE mutant for the binding to SEC22B and SCFD1. We do, however, agree with the reviewer that there might be something interesting happening here. We would like to experimentally interrogate this in future studies and develop more sensitive assays to test if there is a significant effect with the FKE mutant.

In general, I think the title might be a bit misleading because of the use of PI4Kiiib. I understand what the authors mean, but because they have not thoroughly tested PI4Kiiib recruitment in their experiments, I think they should focuse rather on the mechanism of recruitment of ACBD3 the authors have found.

We thank the reviewer for their advice regarding the manuscript title, and this is something that we have discussed internally. We chose that title as it highlights the key mechanistic impact of our findings and note that we did include a figure on the recruitment of PI4KIIIβ. However, we remain open to discussing this with advice from the journal editorial team.

Reviewer #2 (Significance):

I think, as said above, that this is potentially an important paper for the field of membrane trafficking and membrane biology. Most of the experiments are in general well performed and well controlled, and the paper is clearly written and follows a logical line.

We once again thank the reviewer for their comments and overall thoughtful and considered review. We believe that the suggestions here have improved the manuscript.

Reviewer #3 (Evidence, reproducibility and clarity):

Stalder and colleagues report experiments designed to identify interactors of the Golgi-localized protein ACBD3 (a.k.a. GCP60), and to delineate mechanisms that allow ACBD3 to localize at Golgi compartments. ACBD3 is a 528aa protein with diverse previously reported interactions and functions, both in normal physiology and as a host factor in viral assembly processes. Stalder et al. first map which domains of ACBD3 are required for Golgi localization in HeLa cells, concluding that residues 368-528 are sufficient for localization. This region includes a GOLD (GOLgi Dynamics) domain previously reported to interact with Golgin tethering proteins. Alanine scanning identifies the motif MWT just upstream of the GOLD motif as necessary for Golgi localization. Acute CRISPR knockout identifies two Golgins, Golgin45 and Giantin, as necessary for ACBD3 Golgi localization, and IP indicates that the MWT motif breaks this interaction. These data are a bit scattered around the paper but taken together are reasonably persuasive, particularly when viewed in context with published work. This reader would have found the manuscript easier to follow had the Golgin and MWT motif data been presented en bloc.

We thank the reviewer for these comments and have considered presenting and rewriting the data as the reviewer suggested. On reflection, we have decided to present it in the original order. We feel that this allows us to highlight the two independent mechanisms individually, bringing them together at the end. In addition, as the experiments were performed in the order presented, it allows for more appropriate controls for each experiment rather than trying to combine them. We hope the reviewer accepts our preferred order.

In a second set of experiments, IP-mass spec is used to identify ACBD3 interactors that might assist in the protein's localization. The MS data presented are filtered to exclude proteins not already identified as Golgi-localized. This is, I think, a mistake. Even if the authors choose to focus on known Golgi interactors as candidates for a localization function, the biological functions of ACBD3 are far from fully understood, and the full dataset would be of value to both cell biologists and virologists.

We agree with the reviewer that there are many interesting mysteries surrounding ACBD3 and have therefore included an additional table (table S1) in the revised manuscript, showing the dataset of newly identified ACBD3 interactors before applying the Golgi localisation filter.

Hits in the filtered dataset include the R-SNARE Sec22B, and the SNARE chaperone Sly1/SCFD1. Acute CRISPR inactivation of Sec22 decreases ACBD3 localization to the Golgi and SCFD1 inactivation more or less abolishes localization. Co-IP experiments are used to argue that ACBD3 interacts with the N-terminal regulatory Longin domain of SEC22B, as well as with SCFD1. The Sec22 data are more detailed and persuasive. No experiments with purified proteins are presented to establish that the detected interactions are direct rather than mediated through a bridging factor or factors. Importantly, SCFD1 is likely to have multiple different client SNARE complexes that operate at different stages of ER and Golgi traffic. Hence its inactivation is likely to be pleiotropic and consequently phenotypes arising must be interpreted with caution.

We completely agree that studying membrane trafficking in an interconnected system is challenging. We also agree that direct binding experiments in reconstituted systems would be key to proving our model. Our data uses multiple different experimental approaches, including co-localisation, co-immunoprecipitation, CRISPR-KO, and biochemistry, to support our model. In the future, we agree full reconstitution would be necessary to examine this further, and we hope that either ourselves or others can do this in further studies.

Lastly, the authors perform IP experiments which show that ACBD3-Golgin co-IP efficiency is lower in cells with acute inactivation of SCFD1. This epistatic relationship is used to argue for a sequential model of recruitment with SCFD1 and perhaps client SNARE proteins operating upstream of ACBD3-Golgin interaction. This argument is not persuasive because we do not know whether SCFD1 and its downstream activities increase the rate of ACBD3-Golgin complex asssembly, or alternatively stabilizes ACBD3-Golgin complexes, decreasing the rate of their dissociation.

We agree with this weakness in our original submission, and it is a comment shared among all reviewers. Overall, we feel that we have chosen the model that best summarises our data. We, of course, accept that there are still components of this pathway that need clarification and are open for further study. This includes the issue raised here by the reviewer, as well as the intriguing observation that both golgins are transcriptionally upregulated upon SCFD1 KO in HeLa cells. In the revised manuscript, we have more clearly laid out the weaknesses of our model in the discussion and suggested future experiments to help clarify some of these issues. We have also modified the model to reflect some of these potential additional regulatory mechanisms.

In general the methods are fairly clear but that there is room for improvement. The "high throughput" imaging pipeline is not clearly described.

We agree with the reviewer, and apologise for not clearly explaining this. We feel that this unbiased approach of quantification is particularly rigorous and we have clarified this in the methods section of the updated manuscript.

Each figure legend should specify the microscopy methods used, and for each result the number of biological replicates and cells analyzed should be specified.

We agree with the reviewer and have included these details appropriately in the revised manuscript.

The statistical methods (Student, Tukey, etc.) used for each experiment should be specified. Saying that statistics were calculated using Python 3.7 is useless without additional details. e.g. at least the libraries and codebase used should be indicated or deposited.

We agree with the reviewer and have updated the manuscript accordingly. In short, all comparisons were made using either Student's t-test or Multiple Comparison of Means - Tukey HSD, FWER=0.05. These were conducted in Python 3.9 using pandas, matplotlib, seaborn and scipy. We used the MultiComparison function in scipy, and the comp.tukeyhsd for the post-hoc adjustment.

Many figure labels (e.g. Fig. 2) use absurdly small fonts.

We apologise for this. We believe that this is because we submitted it with in-line formatting. Our resubmission has full-page figures, and we feel the text is clearer now.

The mass spec hits obtained should be provided both with and without exclusion of non-Golgi-localized proteins.

We agree with the reviewer. Please see the new Table S1.

Reviewer #3 (Significance):

In general I think this is a useful and well controlled set of experiments producing useful insights. However, the interpretations need to be more carefully considered, and alternative interpretations must laid out as clearly as possible. Specifying the limitations of the study will make it more, not less, useful to the field. If the authors want to make the case more robustly that the interactions described are mediated through direct binding, or that the operation of SCFD1 and Golgins operate sequentially to recruit ACBD3, additional wet bench work will be required which will of course take time to complete.

We once again thank the reviewer for the thoughtful and critical comments. These have helped to strengthen the manuscript. We have performed the additional bench work requested by the reviewer, which has further supported the paper and our model.

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Referee #3

Evidence, reproducibility and clarity

Stalder and colleagues report experiments designed to identify interactors of the Golgi-localized protein ACBD3 (a.k.a. GCP60), and to delineate mechanisms that allow ACBD3 to localize at Golgi compartments. ACBD3 is a 528aa protein with diverse previously reported interactions and functions, both in normal physiology and as a host factor in viral assembly processes. Stalder et al. first map which domains of ACBD3 are required for Golgi localization in HeLa cells, concluding that residues 368-528 are sufficient for localization. This region includes a GOLD (GOLgi Dynamics) domain previously reported to interact with Golgin tethering proteins. Alanine scanning identifies the motif MWT just upstream of the GOLD motif as necessary for Golgi localization. Acute CRISPR knockout identifies two Golgins, Golgin45 and Giantin, as necessary for ACBD3 Golgi localization, and IP indicates that the MWT motif breaks this interaction. These data are a bit scattered around the paper but taken together are reasonably persuasive, particularly when viewed in context with published work. This reader would have found the manuscript easier to follow had the Golgin and MWT motif data been presented en bloc.

In a second set of experiments, IP-mass spec is used to identify ACBD3 interactors that might assist in the protein's localization. The MS data presented are filtered to exclude proteins not already identified as Golgi-localized. This is, I think, a mistake. Even if the authors choose to focus on known Golgi interactors as candidates for a localization function, the biological functions of ACBD3 are far from fully understood, and the full dataset would be of value to both cell biologists and virologists. Hits in the filtered dataset include the R-SNARE Sec22B, and the SNARE chaperone Sly1/SCFD1. Acute CRISPR inactivation of Sec22 decreases ACBD3 localization to the Golgi and SCFD1 inactivation more or less abolishes localization. Co-IP experiments are used to argue that ACBD3 interacts with the N-terminal regulatory Longin domain of SEC22B, as well as with SCFD1. The Sec22 data are more detailed and persuasive. No experiments with purified proteins are presented to establish that the detected interactions are direct rather than mediated through a bridging factor or factors. Importantly, SCFD1 is likely to have multiple different client SNARE complexes that operate at different stages of ER and Golgi traffic. Hence its inactivation is likely to be pleiotropic and consequently phenotypes arising must be interpreted with caution.

Lastly, the authors perform IP experiments which show that ACBD3-Golgin co-IP efficiency is lower in cells with acute inactivation of SCFD1. This epistatic relationship is used to argue for a sequential model of recruitment with SCFD1 and perhaps client SNARE proteins operating upstream of ACBD3-Golgin interaction. This argument is not persuasive because we do not know whether SCFD1 and its downstream activities increase the rate of ACBD3-Golgin complex asssembly, or alternatively stabilizes ACBD3-Golgin complexes, decreasing the rate of their dissociation.

In general the methods are fairly clear but that there is room for improvement. The "high throughput" imaging pipeline is not clearly described. Each figure legend should specify the microscopy methods used, and for each result the number of biological replicates and cells analyzed should be specified. The statistical methods (Student, Tukey, etc.) used for each experiment should be specified. Saying that statistics were calculated using Python 3.7 is useless without additional details. e.g. at least the libraries and codebase used should be indicated or deposited. Many figure labels (e.g. Fig. 2) use absurdly small fonts. The mass spec hits obtained should be provided both with and without exclusion of non-Golgi-localized proteins.

Significance

In general I think this is a useful and well controlled set of experiments producing useful insights. However, the interpretations need to be more carefully considered, and alternative interpretations must laid out as clearly as possible. Specifying the limitations of the study will make it more, not less, useful to the field. If the authors want to make the case more robustly that the interactions described are mediated through direct binding, or that the operation of SCFD1 and Golgins operate sequentially to recruit ACBD3, additional wet bench work will be required which will of course take time to complete.

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Referee #2

Evidence, reproducibility and clarity

Summary

This is a very interesting and potentially important paper for the field of membrane biology and membrane trafficking, in which the authors have studied the molecular mechanisms by which ACBD3 (and consequently PI4KIIIb) is recruited to the cis-Golgi membranes. The authors suggest that this recruitment is based on a two-step process, mediated by interactions to, on the one hand, SCFD-1 (SLY1) and, on the other hand, two redundant golgins (golgin-45 and giantin).

Comments:

• Pg.1 : arfaptins, as far as I know, have not been shown to be involved in intra-golgi trafficking but rather in Golgi export (see e.g. ref. 12)
• Pg. 1: reigon --> region
• Arf1 also recruits PI4KIIIb right?
• Fig. S1: the information about the number of cells per experiment is missing. Also, please add the information about what exactly is represented in the box plots (is it the distribution of the mean value of R per experiment? or the total distribution on a cell-by-cell basis of a representative experiment?)
• The definition of Avg. Golgi int/avg. cell int. (a.u.) in Fig 1E,F is a bit difficult to understand to me. If I understand correctly, the total fl. int in the Golgi mask was computed and divided by the area of the Golgi mask (this is the av. Golgi intensity). A similar computation is done for the entire cell (including the Golgi), i.e., total fl. intensity in the cell mask is computed and divided by the area of the cell mask. Then the two av. intensities are divided (ratio = av. Golgi int / av. cell int.). This ratio, for a protein that is enriched in the Golgi area, should be larger than 1. For a protein that is equally distributed all over the cell, it should be 1, and for a protein that is excluded from the Golgi area, smaller than 1. Then to this value, the authors subtract the value of the ratio found for an inert construct (GFP of Halo alone), which I imagine should have an original ratio value of the order of 1, and hence, after this subtraction, norm. ratio values larger than 0 mean that they are more enriched at the Golgi area than GFP/HaloTag themselves. Is this correct? In principle, I don't see anything entirely wrong with this way of thought, but I just found it a bit difficult to understand, and in general one has to be careful when computing rations (quotients) and then subtract another ratio. Also, the units are not a.u., the value is dimensionless, what is "arbitrary" is the definition of 0 value and the based on this definition, also the actual value. I think it would probably be much clearer for the readers to compute somthing like the relative enrichment in the Golgi area as compared to the rest of the cell (excluding the Golgi area). That is, a value r'=(Int. Golgi mask / Area Golgi mask) / [(Int. Cell mask - Int. Golgi mask)/(Area cell mask - Area Golgi mask)]. This can be computed directly or defining a mask that is the cell mask - the Golgi mask. Also, some maths (unless I made a mistake) give that this r'= r (1-aG)/(1-r aG); where r is the ratio (before subtraction) defined by the authors, and aG=Area cell mask/Area Golgi mask. In any case, I'd suggest the authors to either adopt this other quantitation (without subtraction of the GFP/HAloTAG), which gives directly the fold-enrichment in the intensity density in the Golgi area with respect to the rest of the cell; or explain in more detail the maths of the value they are plotting now.
• Fig. 1C&F: Besides the MWT mutant, the FKE mutant also seems to have a somewhat compromised Golgi localization. Have the authors followed on that, or what is the reason that they have just focused on the MWT mutant?
• Very minor point, and without wanting to sound pedant at all, but I think (I might be wrong of course, so apologies if I am) that the plural of apparatus in latin is not apparati, but apparatus (fourth declination). So, I'd change the word in page 2 (or just rephrase the sentence: e.g. "resulting in Golgi fragmentation"). But of course, I'd leave this to the authors' discretion.
• Fig. 3A: have the authors tried or been able to perform IF of the endogenous SCFD1 protein?
• Similarly to what has been done for other panels, could you quantify Fig. 3C? Are PI4KIIIb protein levels affected upon the different KOs?
• The last paragraph of the "SCFD1 and ACBD3 interact upstream of PI4KIIIβ recruitment<br /> to the Golgi apparatus" section reads a bit odd placed there. I think it is more appropriate for the discussion or for the intro part on SCFD1.
• I am confused on Fig. 5A/B. The labels in the blots show that 390-528 (without UR) does not bind sec22 or scfd1, but the 368-529 does? Or I guess, judging by the MW seen in the middle blots, that there's some error in the labelling? also, the IP efficiency of the MWT mutant in the panel A blot is quite low, still sec22 seems to be very efficiently pulled down. Can the authors comment on that please? Would co-IPing against endogenous sec22 and scfd1 would work (so you don't need to rely on HaloTag+ligand?)
• I really like the experiment 6B. Have the authors tested whether SEC22 is also recruited to mitochondria in those conditions? But not SCFD1?
• The results shown in Fig 7 might show a partial depletion in the interactions, but to be fully trusted they would need to be quantified and a statistical test used to compare the values. I think this part is important to show very clearly, because even with low binding to golgins (remember, single knockouts do not prevent Golgi localization of ACBD3), one could expect that ACBD3 still localized to the Golgi but it does not in the absence of SCFD1 as shown in this paper.
• A prediction of the proposed model is that in cells depleted of the two Golgins, SCFD1 and ACBD3 should still bind to one another, right? Did the authors test this?
• The model presented here (fig 8) seems to suggest that only the conformational variation of ACBD3 that binds Golgins is able to recruit (bind) PI4KIIIb. Is this known, or is there any experimental evidence for that?
• Have the authors thought about testing the FKE mutant in the experiemnts shown in Fig. 5?
• In general, I think the title might be a bit misleading because of the use of PI4Kiiib. I understand what the authors mean, but because they have not thoroughly tested PI4Kiiib recruitment in their experiments, I think they should focuse rather on the mechanism of recruitment of ACBD3 the authors have found.

Significance

I think, as said above, that this is potentially an important paper for the field of membrane trafficking and membrane biology. Most of the experiments are in general well performed and well controlled, and the paper is clearly written and follows a logical line.

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Referee #1

Evidence, reproducibility and clarity

Summary

This manuscript describes molecular mechanisms by which ACBD3 is recruited to the Golgi complex. ACBD3 recruits PI4KIII which is required to generate PI4P, a phosphoinositide which is key for the recruitment of essential Golgi proteins and hence is key to Golgi identity. The authors have used a combination of mass spectrometry, high quality fluorescence imaging, transient CRISPR knockdowns, and biochemical approaches such as IPs to identify the key determinant for recruitment of ACBD3 to the Golgi complex. They map the interaction between ACBD3 and the Golgi as a unique region (UR) upstream of its GOLD domain, identifying, in particular, an MWT motif as key for this recruitment. Using mass spectrometry they identify several novel interactors of ACBD3 as well as some established binding partners. Knockdown of these interactors reveal a key role for the SNARE, SCFD1, where reduced levels lead to complete loss of ACBD3 localisation to the Golgi without apparent disruption of Golgi structure. They further validate this interaction and that of another SNARE (Sec22b), which is part of the same SNARE complex as SCFD1, mapping the interaction to the longin domain of Sec22b. Surprisingly however they demonstrate that the UR domain does not mediate the interaction between ACBD3 and these SNAREs suggesting an alternative mechanism of recruitment. Previously identified ACBD3 interactors, Golgi proteins giantin and golgin-45 were also identified in the mass spectrometry screen and the authors demonstrate that these two proteins can recruit ACBD2 to the Golgi and this is dependent on the MWT motif identified in the UR domain. By knocking down SCFD1, they show reduced recruitment of ACBD3 leading them to propose a model of sequential recruitment of ACBD3 by SCFD1 followed by interactions with the golgins.

Major points:

This study is a well-executed and rigorous study of the molecular requirements for the recruitment of ACBD3 to the Golgi. The experimental approaches are state-of-the-art and the data are clean and convincing. The only caveat, raised by the authors themselves, is their interpretation that there are two sequential steps for Golgi recruitment of ACBD3. While they show that loss of SCFD1 reduces the interaction of ACBD3 with giantin and golgin 45, their model depends on doing the reverse experiment, i.e. assessing the effects of knocking down either giantin or golgin-45. This is especially relevant given the demonstration that golgin-45 is sufficient to recruit ACBD3 to mitochondria. It may well be that recruitment involves a tripartite complex, which is not uncommon in vesicular transport mechanisms Giantin is not an essential protein do it should be feasible to perform this experiment. The authors are equipped in the quantitative fluorescence microscopy which would be required and which would help resolve whether sequential or redundant mechanisms are required for ACBD3 recruitment.

Significance

PI4P is a phosphoinositide that is important for the recruitment of Golgi proteins. As with most PIs it is likely to act by coincidence detection in that Golgi associated proteins will recognise PI4P as well as other factors on Golgi membranes. This results in different local membrane environments which will be specific for particular functions. PI4KIII is key for PI4P production although the absolute levels of PI4P are likely to be determined by a balance of lipid kinases and phosphatases. However, since ACBD3 is key for the recruitment of PI4KIII it is important to understand the molecular mechanisms by which it is recruited. The manuscript thus makes a significant contribution to understanding one of the underlying mechanisms for PI4KIII recruitment although, as indicated above, stops short of establishing a clear model for the roles SCDF1 and Sec22b versus golgin 45 and giantin. For the future it will be of interest to determine why either a sequential or a redundant mechanism is required for the recruitment of ACBD3 as a scaffold protein.

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Reply to the reviewers

We would like to thank all reviewers for taking the time to evaluate our manuscript fairly and critically. Many helpful suggestions and discussion points were raised. One important group of comments raised concerns whether our proposed timer and counter models were the appropriate conceptual framework to discuss nuclear multiplication in schizogony, whether they were mutually exclusive, and whether other alternatives should be considered. These comments were instrumental for us to uncover some inconsistencies in our previous modeling approach. In the new manuscript, we now define the counter and timer models much more rigorously in the context of Plasmodium cell division. Based on these refined models we now provide a new statistical analysis that goes beyond the previous analysis, significantly improving the statistical support for our conclusions. Details are given in the following individual replies.

Reviewer #1 (Evidence, reproducibility and clarity):

Summary

Malaria parasites replicating in human red blood cells show a striking diversity in the number of progeny per replication cycle. Variation in progeny number can be seen between different species of malaria parasites, between parasite isolates, even between different cells from the same isolate. To date, we have little understanding of what factors influence progeny number, or how mechanistically it is controlled. In this study, the authors try to define how the mechanism that determines progeny number works. They propose two mechanisms, a 'counter' where progeny number is determined by the measurement of some kind of parasite parameter, and a 'timer' where parasite lifecycle length would be proportional to progeny number. Using a combination of long-term live-cell microscopy and mathematical modelling, the authors find consistent support for a 'counter' mechanism. Support for this mechanism was found using both Plasmodium falciparum, the most prominent human malaria parasite, and P. knowlesi, a zoonotic malaria parasite. Of the parameters measured in this study, the only thing that seemed to predict progeny number was parasite size around the onset of mitosis. The authors also found that during their replication inside red blood cells, malaria parasites drastically increase their nuclear to cytoplasmic ratio, a cellular parameter remains consistent in the vast majority of cell-types studied to date.

Major Comments

It is stated a few times in this study that P. knowlesi has an ~24 hour lifecycle, and while this is the case for in vivo P. knowlesi, it was established in the study when P. knowlesi A1-H1 was adapted to human RBCs (Moon et al., 2013) that this significantly extended the lifecycle to ~27 hours, which should be made clear in the text. As much of this study revolves around lifecycle length and timing, the authors should consider some of their findings with the context that in vitro adaption can significantly alter lifecycle length.

The reviewer raises an important point that we didn’t discuss for P. knowlesi. We now mention this directly in the introduction chapter (line 67) and in the discussion (lines 470ff). We are aware that P. knowlesi takes about 27 hours in the lab, which was also communicated by the Moon lab. We now cite relevant studies again in this context. We further address the issue of modified cell cycle time in vitro in the discussion in the sense that absolute values must be taken with caution and the focus of this study is about the relative ratio and correlation between the different cell cycle metrics.

• The dichotomous distinction between 'timer' and 'counter' as mutually exclusive mechanisms seems to be a drastic oversimplification. Considering the drastic variation we see in merozoite number across species, between isolates, and between cells, it seems much more likely that there are factors controlled by both time-sensed and counter-sensed mechanisms that both influence progeny number.

The study of progeny regulation in malaria parasites is very much in the early stages. We can agree that our models are simplifications, as is the case with all models. Our choice of just the two models timer and counter was driven by the number of cellular parameters we measure, i.e., duration of division phase and progeny number. These data essentially allow us to test the two competing models we presented. As we quantify more and more cellular parameters, based on the quantitative live cell imaging protocols established here, we will be able to test more complex cell cycle models. With our current data, we believe more complex models are not warranted.

However, this valuable criticism, in conjunction with related remarks by other reviewers, made us reevaluate the constraints of our model more precisely. We noticed that the criteria used in the previous version in the manuscript contained unnecessary additional assumptions. Briefly, the previous counter model also required that final merozoite number was tightly controlled, while the previous timer model required the growth rate to be tightly controlled. These side assumptions were not made explicit in the manuscript and could bias the support towards one or the other model.

We now improved the modeling approach substantially by removing implicit side assumptions, and clearly defining timer and counter models in terms of their correlations. The refined formulation of the timer posits that between individual parasites the target duration and the nuclear multiplication rate vary in a statistically independent way; while in a counter, target number and nuclear multiplication rate are statistically independent. We now explain this extended analysis in more detail in the introduction (lines 86ff). We also now more clearly state the dichotomous nature of the model (line 488). A new results paragraph (lines 213ff) and an entirely new Fig. 2 (and Fig. S4) contains the model predictions and statistical comparison between the models.

This more rigorous treatment showed that including the variance of the multiplication rate was critical to allow a clean discrimination between the models. Also, with the sole exception of P.knowlesi H2B, where no model was clearly favored (Fig. 2G-H,K), the timer model was found to be inconsistent with the data, while the counter was clearly favored. Our new goodness-of-fit analysis also showed that although the counter is strongly simplified, it produced adequate fits, demonstrating that potential model refinements would need to be justified by new, more extensive data.

It is also important to consider that the degree of variation in merozoite number could rather be an expression of varying growth conditions and does not directly predict which of the proposed models are true. For instance, a counter where the target merozoite number varies strongly depending on growth conditions, would be consistent with all available data. It is an interesting question for future work whether a counter would indeed describe growth across different isolates.

The biological reality of growth regulation is certainly complex, and the counter model will likely need to be refined in the future, which we acknowledge in a corresponding statement in the discussion (lines 491ff). Nevertheless, we find it encouraging that a simple model can explain the vast majority of our data very well.

Additionally, the only parasite parameter measured in this study, size at time of first nuclear division, explained only a small proportion of the variance observed in merozoite number.

It is indeed the case that amongst the measured parasite parameters i.e. schizont stage duration, nuclear volume, and cell size we only found the latter to correlate with the final progeny number. We did not aim to imply that all variation in progeny number is explained by cell size. It is likely that a putative counter relies on a set of factors, which are somehow linked to cell size. In addition, intrinsic stochasticity in nuclear growth is likely to contribute to final merozoite number variability, which is included in our models via a variable growth rate. Defining the actual limiting factor or combination of factors will be an exciting challenge for the future studies building on this one.

• For modelling of a timer-based mechanism, the designation of t0 is subjective. The authors chose the time of first nuclear division as their t0. It is possible that a timer-based mechanism could not be supported based on this model the chosen t0 differs from when the "parasite's timer" starts. For example, t could also have been designated as the time from merozoite invasion (t0) to egress (tend). It would be unreasonable to suggest the authors repeat experiments with a longer time-frame to address this, but this possibility should be discussed as a limitation of the model. It may also be possible to develop a different model where t0 = merozoite invasion and tend = egress, and test this model against the data already collected in this study.

This is a valid point. We indeed, considered the time point of invasion as the other relevant time point in the IDC for a possible timer. Due to necessary compromises in imaging protocols between acquisition length, temporal, and spatial resolution we have not been able yet to combine full-length IDC measurements with quantification of progeny number. Given the choice, however, between time point of invasion and the onset of nuclear division as starting point for a potential timer we would still favor the latter: An argument can be made that a timer that regulates offspring number would be more accurate when activated at the moment of the relevant cellular events rather than “running” for a very prolonged growth phase before any “decision” concerning parasite replication. We are still convinced that the entry into the schizont stage, which we analyze here, marks an important cell cycle transition point that has been highlighted in many different studies. As suggested, we now discuss the limitations of our selection of t0 in the text (lines 146ff).

• The calculation of the multiplication rate is confusingly defined. In Figure 1 it is stated that it is "...based on t and n", which would imply that the multiplication rate is the number of merozoites formed per hour of schizogony, which would give an average value of ~2 for P. falciparum and ~1.5 for P. knowlesi. The averages rate values shown, however, are in the range of 0.15-3. The authors should clarify how these values were determined.

Thank you for pointing out the need for more clarity. Since the nuclear multiplication, similar to e.g. cell population growth, follows an exponential law, the multiplication rate used (lambda) is in fact a logarithmic growth rate. Therefore, it occurs in the exponent (not as a coefficient) in the exponential growth function ( ), which explains the range. We now mention this more explicitly in the results (lines 163ff).

• In Figure 2, the time from tend until egress is calculated, and this is interpreted as the time required for segmentation. In the Rudlaff et al., 2020 study cited in this paper, it is shown that segmentation starts before the final round of nuclear divisions are complete. Considering this, the time from tend until egress is not an appropriate proxy for segmentation time. The authors should consider rewording to something akin to "time from final nuclear division until egress" to more accurately reflect these data.

Thank you for indicating our imprecise use of the nomenclature. Indeed, some essential segmentation-associated structures such as rhoptries and subpellicular microtubules are clearly forming before the last division. We were referring to “segmentation” as the time window where actual ingression of the plasma membrane occurs between nuclei with the concurrent formation of more prominent IMC-associated sub-pellicular microtubules between nuclei (as in Fig. 1A last panel). We can, however, agree that consistently using the term “merozoite formation” is more adequate here. We have now corrected the terminology according to the suggestions of the reviewer (lines 271ff).

• There is a significant discrepancy between the data in Figure 5 and Supplementary Figure 8. In Supplementary Figure 8, the authors establish that culturing parasites in media diluted 0.5x has a marginal effect on parasite growth, with no discernible change in parasitaemia over 96 hours. By contrast, in Figure 5a the parasitaemia of parasites cultured in 0.5x diluted media is approximately 5-fold lower than those in 1x media. The authors should explain the significant discrepancy between these results.

The reviewer correctly points out a difference in parasitaemia between two parasite culture experiments, shown in Figs 5a (now 6A) and S8 (now S11), respectively. There were several differences in the experimental setup used in the two experiments that could explain this discrepancy. In Fig. 5a the parasites were synchronized to early ring stages while in Fig. S8 we used asynchronous cultures (maybe with a slight majority of late stages). One could speculate that by the time the synchronized ring stage culture reached egress the effect of nutrient depletion, which started at t = 0 h is more pronounced. This effect could have been exacerbated by the more frequent media change of 24 h in Fig. 5a vs 48h in Fig. S8. Lastly, the starting parasitemia was differently set being higher at around 0.5% in the Fig. 5a while only 0.2% in Fig. S8. Possibly a lack of nutrient is “felt less” by the culture at lower parasitemias. Generally, in Fig. S8 we were more focused on highlighting the difference between 1x/0.5x and the more diluted conditions on the long-term culture and to show that continuous culture is actually possible in 0.5x medium. We have now expanded the legends to highlight those differences more clearly.

• In Supplementary Figure 4, the mask on the cell at t0 shows two distinct objects, but it seems very unlikely that they are two distinct nuclei as they vary approximately 5-fold in diameter. The authors should provide more detail on how their masking was performed for their volumetric analysis. Specifically, whether size thresholds were also applied during object detection.

Thank you for requesting clarification here. Fig S4 (now S7) shows only one z-slice (not a projection) of the entire image stack, to illustrate how the thresholding approach was performed on every single image slice. The two objects in the shown cell are indeed two nuclei, but because they are not in the same z-plane appear to be of different size. In particular, only a slice of the upper part of the nucleus on the lower right is visible in the shown slice. Throughout the study, volume determination was realized by adding up the individual slices, as is explained in detail in the Materials and Methods sections. We have now added a more explanation in the figure legend to clarify the procedure.

Minor Comments

• Line 45-48 mentions that merozoite number influences growth rate and virulence, but the corresponding reference (Mancio-Silva et al., 2013) only discusses the relationship between merozoite number and growth rate, not virulence.

We thank the reviewer for requesting this distinction. Merozoite number and virulence have not been correlated in vivo so far. Certainly, because one can’t retrieve late-stage P. falciparum parasites from patients, but maybe partly because merozoite number has not gotten significant attention as a metric in the previous decades. Even if merozoite number is intuitively connected to growth rate which might causes higher parasitemia which is in turn linked to more severe disease outcome it is important to emphasize that those are certainly not equivalent. We have therefore removed the statement about virulence (line 48).

• Line 59 states that a 48 hour lifecycle is a baseline from which in vitro cultured parasites deviate. Clinical isolates also show variation in lifecycle length and so it is more accurate to just say that 48 hours is an average, rather than a baseline.

The word “baseline” has been changed to “average” (line 61).

• Line 63 cites a study for the lifecycle length of P. knowlesi (Lee et al., 2022), but there seems to be no mention of lifecycle length in this reference

This reference was meant to serve as an introductory review article to research in P. knowlesi. Actually, to the knowledge of the authors, there is no study presenting quantitative data showing that the in vitro cycle of P. knowlesi is actually around 27 h. Our lab experience is however coherent with a 27 h cycle, which was confirmed by personal communication by the Moon lab. We now also cite in the next sentence the inaugural P. knowlesi adaptation publication (Moon et al. 2013) showing some time course data indicating the duration of the IDC to be around ~27h (lines 67ff).

• If I am interpreting Figure 3B correctly, this is essentially a paired analysis where the same erythrocytes are measured twice, once at t0 and once at tend. If this is the case, this data may be better represented with lines that connect the t0 and tend values.

Yes, these are the same erythrocytes measured twice. We have modified Figure 3 (now Fig. 4) accordingly.

• Figure 3A seems to imply that to calculate diameter of the erythrocytes, three measurements were made and averaged for each cell. I think this is a nice way to get a more accurate erythrocyte diameter, but if this is the case, it should be specified in the figure legend or methods.

This is already described in the figure legend (line 305).

• In Figure 4I it is shown that in P. falciparum merozoite number doesn't correlate with nucleus size, but for P. knowlesi in Supplementary Figure 7c, a significant anticorrelation is observed. The authors should state this in the text and discuss this discrepancy.

Contrary to all other graphs, visual inspection of the distribution of data points in Fig. S10C shows that it contains two outlier data points at the bottom right. Those two specific points are also responsible for the significant anticorrelation. We did not filter or remove any quantification results but also didn’t have sufficient confidence in this data distribution (which is further based on the segmentation of the Histone2B not on an NLS mCherry signal) to make substantial claims about anticorrelation. Because we considered it informative we still decided to show it in the supplements. We now briefly mention the issues with the data set and its interpretation in the text (lines 350ff).

• The authors show that merozoite number roughly correlates with cell size at t0 but it would be interesting to see whether cell size at tend also corresponds with cell size at t0. This might help answer whether the cell is larger because it has more merozoites, or whether it has more merozoites because it is larger.

Plotting parasite cell volume at t0 against cell volume at tend (as well as between t-2 and tend) indeed shows a positive correlation (see below). While it is an interesting thought we concluded after some discussion that no convincing causal relationship between cell size and merozoite number can be inferred based on this analysis. Since we consider the possible statement that cells that are bigger in the beginning are also bigger in the end unavailing, we decided not to include the data.

• I don't feel that "nearly identical" is an appropriate summary of erythrocyte indices in Supplementary Figure 9, considering there is a statistically significant increase in mean cell volume. I think it is unlikely that this change is consequential, and performing these haematology analyses is a nice quality control step, but this change should be stated in the text.

In the modified text we now express the significant change in MCV in terms of percentage, which is around 1.2% (line 381).

• In Supplementary Figure 8, parasitaemia only increases ~2-fold compared to >5-fold the previous two cycles. It seems likely that at the final timepoint on this graph the parasites are starting to crash, and therefore it may be best to end the graph with the 96 hour timepoint.

The reviewer suggests that cultures at those parasitemias might not be in perfect health. Our Giemsa stains did not show signs of an unhealthy culture and kept growing. It was, however, important for us to show that cultures can be maintained in culture over a prolonged period of time in 0.5x medium, even when resulting in reduced growth, while this was not possible with lower dilutions. Therefore, we would like to keep the data point. We have added a cautionary comment in the legend.

• The error bars in Figure 5C aren't easily visible, moving them in front of the datapoints may help their visibility.

Error bars were moved in front of the data points.

• In Figure 6D & E, the y-axis labels should be changed to whole integers as all the values in the graph are whole numbers.

We have changed the y-axis labels accordingly.

• My interpretation of Figure 6 C-E, is that these are the same cells measured at three time points (t-2, t0 and tend). If this is the case, 6C is missing the cell that has a merozoite number of 8, which is presumably why the y-axes are not equalised for the three graphs.

It is correct that the same cells are displayed in all three plots, with the exceptions of three cells in 6C (for the timepoint t-2), which are missing for the following reasons: 1) it was not possible to determine the volume at this respective timepoint due to technical issues or 2) the cell was already just before t0 at the start of the movie so that t-2 had already passed. We now note this in the figure legend and have also equalized the y-axes (now Fig. 7C-E).

Reviewer #1 (Significance):

In the asexual blood-stage of their lifecycle, malaria parasites replicate through a process called schizogony. During schizogony an initially mononucleated parasite undergoes multiple asynchronous rounds of mitosis followed by nuclear division without cytokinesis, producing a variable number of daughter nuclei. Parasites then undergo a specialised cytokinesis, termed segmentation to where nuclei are packaged into merozoites that go on to invade new host cells. While nucleus, and therefore merozoite, number are known to be varied between cells, across isolates, and across species, little is known about the mechanisms regulating merozoite number. In this study, the authors use live-cell microscopy to understand how parasites determine their progeny number. They suggest that parasites regulate their progeny number using a 'counter' mechanism, which would respond to the size or concentration of a cellular parameter, as opposed to a 'timer' mechanism. Long-term live-cell microscopy experiments using malaria parasites are extremely technically challenging, and the authors should be commended for their efforts in this regard. While I agree that the data generated from these experiments are technically sound, I have some reservations expressed above about the interpretation of some of these results. I would strongly encourage the authors to consider rewording some of their interpretations taking into account some of the caveats listed above. I would also consider fitting/testing an additional mathematical model where the time-frame proposed for the 'timer' mechanism begins following merozoite invasion.

We thank the reviewer for the appreciation of our work and hope we have sufficiently reworked the manuscript based on the comments listed above. Furthermore, we think the improved model statement and analysis improves the clarity of our conclusions. Indeed, we would like to test additional models including the full IDC once, as mentioned above, we are technically able to generate these data.

This work is of specific interest to anybody who grows malaria parasites, as the dynamics of their growth is obviously important to understand. Further, this work is of interest more generally to cell biologists who study the regulation of progeny number or cell size. I have no experience with the application of mathematical modelling to understand biological systems, and so I cannot comment on the interest of this work to that field.

Reviewer #2 (Evidence, reproducibility and clarity):

This is a solid study that further characterises the dynamics of nuclear division in Plasmodium falciparum and P. knowlesi. Of two, among potentially several, models for how the number of daughter nuclei, and thus parasites - (called merozoites in this genus), are one that posits nuclei divide until a fixed timer ends, and one that posits that nuclei divide to reach a fixed number that is defined by a cellular counter. I find some practical difficulties in definitive measurement of either model, one issue with the former is that experimental definition of the start of the timer is problematic - we may define the starter's gun (eg by the first nuclear division) but it isn't necessary that the cell is using that same start time.

We are pleased that the Reviewer found our study ‘solid’. Concerning the timer model, we agree that the selection of the starting point is a critical aspect of this study, as also Reviewer 1 pointed out. We selected this particular “t0” because the entry into the mitotic phase marks an important cell cycle transition. Several studies have suggested a “schizogony entry checkpoint” might be active just before (Matthews et al, 2018; Voß et al, 2023; van Biljon et al, 2018; McLean & Jacobs-Lorena, 2020). Once cells are committed to the schizont stage they are less responsive to stimuli. Alternatively, the timepoint of erythrocyte invasion could be a legitimate starting point. Due to necessary compromises in our imaging protocol between acquisition length, temporal, and spatial resolution we have not been able yet to combine full-length IDC measurements with quantification of progeny number, and therefore we leave exploration of an earlier timer start for future work. Within the confines of the model comparison in the current study, we think the selected t0 is already highly informative. We now explain the selection and limitations more explicitly in the text (line 144ff).

Additionally, as the authors confirm here, being sure when that first nuclear division has occurred is particularly tricky with Plasmodium parasites, in part because the first few nuclei seem to clump together, preventing one from unambiguously calibrating the first division.

The Reviewer is concerned about difficulties with precise reporting of the time point of first nuclear division. We suspect there was a misunderstanding here. In the text (line 137) we had written the following:

“Although separating individual nuclei after the first two rounds of division was challenging due to their spatial proximity, the improvements in resolution and 3D image analysis allowed us to count the final number of nuclei routinely and reliably at the transition into the segmenter stage.”

To clarify, when analyzing 3D image stacks produced by the LSM900 Airyscan the first nuclear division can consistently and unambiguously be detected. In anaphase the nuclei are pushed apart quite substantially before getting a bit closer together afterwards (see e.g. Fig. 1B and C). Hence the precision of the detection is only limited by the 30 min interval of the time lapse. Later, at the four nuclei stage, crowding makes distinction more difficult. In the final segmenter stage, the reorganization and condensation of nuclei makes reliable counting possible again. We have now reformulated the quoted sentence for more clarity (lines 137ff).

Furthermore, getting decent replicate numbers is hard because of the difficulties of time lapse microscopy, and most Plasmodium studies (including this one) suffer from low enough numbers that it isn't always clear whether the numbers support one model over another.

The reviewer points out the difficulty of obtaining enough replicates in Plasmodium time-lapse studies. We agree that depending on technology, sufficient replicates can be challenging. In the present study we obtained Ns between 25 and 35 for all conditions in P. falciparum and P. knowlesi from three independent replicas. To gain confidence in the conclusions from a limited, but not austere, data, it is essential to 1) reduce model complexity to a minimum and 2) perform stringent statistical analysis including accounting for small-sample variation. Motivated by this concern of the Reviewer and a similar point raised by Reviewer 1, we have revisited our modeling approach in the revised manuscript. This led us to a corrected, more rigorous definition of what precisely we mean by ‘counter’ and ‘timer’ models: The timer posits that between individual parasites the target duration and the nuclear multiplication rate and vary in a statistically independent way, while in a counter target number and nuclear multiplication rate are statistically independent. With no further adjustable parameters, the two models are thus both mutually exclusive and minimal. Although biological reality is likely to be more complex, we feel that these minimal models are adequate for the amount and resolution of our current, state-of-the art data. The general result remained the same: The counter model is strongly preferred in almost all our experiments data (new Fig. 2), with the sole exception of P. knowlesi H2B, where indeed more data may be needed to come to a clear conclusion. Furthermore, we have taken care to scrutinize these conclusions accounting for goodness-of-fit for the respective sample size N. This analysis showed, surprisingly, that the counter model was sufficient to account for the data: the real dataset was as similar to the counter prediction as synthetic, counter-generated data. We hope that this improved statistical analysis can help the reader judge the robustness of our conclusions.

Nonetheless, several recent studies, particularly a study from the same institute (Klaus et al., 2022) employing timelapse imaging of nuclei, and timing the nuclear division of parasites, finds poor correlation between the duration of "schizogeny" (although perhaps using a different definition to the one used by the parasite) and the final number or merozoites. They therefore argue that there is poor evidence for a timer, and conclude by elimination that a counter must exist instead. A review by some of the authors of that study and some of this current study (Voß et al 2023), also concludes that the data from Klaus and colleagues "strongly support" a counter model. This current study also concludes that a counter model controls final nuclear/merozoite number in P. falciparum and P. knowlesi. This much at least is not particularly novel given the recent work on this topic, although the addition of the P. knowlesi data is interesting and consistent with the prior P. falciparum work.

Our present work, indeed, does confirm the previous report of a counter over a timer, through a more targeted approach. While Klaus et al. used timing data of first nuclear cycle vs. the full duration, we now provide, thanks to an improvement microscopy setup and protocol, simultaneous measurements of timing and final progeny number, i.e. counting of merozoites/nuclei. While the preference for a counter model is not fundamentally novel, the additional information that the counter model holds in different strains, conditions and species is, in our opinion, not trivial and points to some degree of evolutionary conservation. We also demonstrate here that the counter model is not only preferred over the timer, it also fits the data adequately, so that it can be considered ‘correct’ at this level of complexity. Another, possibly more important, value of this study lies in the quantitative and time-resolved assessment of multiple important parasite metrics such a cell volume and nuclear volume together with merozoite number at the single cell level. Although descriptive, this has not been achieved in Plasmodium until now.

As above, the authors concede that it is difficult to determine with strong confidence when the first nuclear division has occurred, so it may well be that there is substantial noisiness in the time that they define schizogeny to commence. If that were the case, this would contribute to the poor correlation observed between schizogeny duration and number of merozoites produced, so this could be an important confounding experimental factor. This deserves some more discussion by the authors.

Concerning the confidence with which we identify the first nuclear division we could hopefully clarify in the section above that our precision is only limited by the time resolution of the acquired time-lapse. Therefore, the uncertainty about the start time is not particularly high, and moreover, can expected to affect timer and counter (via the growth rate) to a similar degree. We see no unfair advantage for the counter for this reason.

Alternative methods to count absolute DNA content (rather than trying to count individual nuclei) might be useful ways of independently confirming this phenomenon. Alternative possibilities for what constitutes the "start" of a possible timer are also warranted - it could be for example, the first division of one of the other organelles.

This is an interesting suggestion. Next generation fluorogenic DNA dyes have been used by us and the Ganter group (Simon et al. 2021, Klaus et al. 2022, Wenz et l. 2023) to assess DNA content of single cells over time. Our experience shows that there are some caveats to using these Hoechst based dyes, some of which we discussed in the aforementioned publications. While they allow some reasonable absolute quantification of DNA content for the very first S-Phase (and subsequent nuclear division), in later stages only relative quantification can be achieved. One underlying reason is the apparent increase of dye permeability, and therefore higher intensity, at late schizont stages. This issue is exacerbated by the asynchronous DNA replication of multiple nuclei. Further, nuclear division itself can be delayed or even inhibited when increasing the concentration of the dye, which suggest an impact on cell physiology (well documented for Hoechst based dyes in other organisms). When reaching the segmenter stage, the resulting variance in fluorescent intensity would make it challenging to assign a reliable number of nuclei required for analysis, a problem that does not occur when counting individual nuclei. Taken together, unfortunately, all these confounding factors make DNA content analysis in live single cells for the entire schizont stage unachievable at this point.

These and previous authors in any case conclude that a counter model must exist through exclusion of a timer model. I am less convinced that the evidence discounting the timer is conclusive, and that a straight counter model is the only alternative. Indeed I am unconvinced by the suitability of this strictly dichotomous two-model system to categorise the division of unicellular eukaryotes, and these theories are not universally held to be sufficient to describe division.

We thank the Reviewer for this insightful comment. As already detailed above, we have clarified and corrected our model definitions in the revised manuscript. Further, we want to make the important distinction between organisms, including unicellular ones that undergo binary fission and the ones like Plasmodium that use schizogony. Our model, although inspired by model organisms, is tailored to a multinucleated division mechanism, and clearly defined within those boundaries. The timer and counter models we consider are defined by their correlation structures. They are at two extremes of a continuum of models which could be characterized, for instance, by the ratio of correlations (growth rate - nuclear number) vs. (growth rate – duration) as an additional parameter. As the reviewer points out, excluding the timer model is not equivalent to proving the counter model, and indeed a partially correlated model, or a more complex model entirely, could yield a better fit. However, within the realm of models without additional parameters, and which are testable with the available data, only timer and counter remain, as different timer start points are not experimentally accessible. Importantly and somewhat surprisingly, the counter model also gave a fit that is as good as can be reasonably expected for the experimental sample size (new Fig. 2). So, we maintain that within the current experimental constraints, the counter model is the only viable option for almost all our tested conditions. The observation that in H2B-GFP expressing P. knowlesi parasites no clear distinction can be made between the models, indeed, suggest that the reality of multiplication rate regulation is more complex and may be limited by different constraints in different growth regimes. We now state these limitations and the room for further model adjustments with more data in the Discussion section.

Nonetheless, if a counter exists, what is being counted that determines the final number? The authors consider that this might be a physical object or resource inside the parasite, or an extrinsic/extracellular resource. They investigate this by comparing the final cell number to a number of factors. First, the authors investigate the size of the RBC (by musing the diameter as an indicator)- little information is given about the source of the blood used, but it appears to be from a single donor of unknown age, who has approximately typical variance in RBC diameter (at least, after manipulation and storage). The authors observe little correlation between these variables.

We share the curiosity of the reviewer about what might be “counted” by the parasite. This shall be the subject of future studies, and our present study provides the necessary basis for asking this question and defines a framework to investigate it. Concerning the size of the host cell, the blood used was from a different donor for each of the replicas, which we now specify in the figure legend (line 302). No significant difference between the RBC diameters between the donors was observed. A correlation between RBC diameter and progeny number was indeed not observed.

Second the authors measure parasite size at the onset of schizogeny, and find that bigger parasites result in more daughter merozoites early in schizogeny (perhaps not surprising, given the earlier mentioned technical problems with measuring the first few steps of schizogeny), but that this different initial cell size doesn't result in a different final merozoite number, or as they describe it "not quite significant anymore". Previous p values were taken as cause for rejecting the timer hypothesis and the timer model. In this case the authors instead interpret the data as suggesting "that the setting of the counter might correlate with parasite cell size". This is inconsistent statistical and analytical handling, and highlights the earlier potential pitfall of rejecting timer-based models based on not gathering data that statistically show a correlation. This needs reworking to highlight that these data are inherently noisy, difficult to measure accurately, and aren't necessarily going strongly reveal a trend even where one biologically exists, and that this ought not be used as grounds for confident rejection of a model.

The Reviewer raises concerns about the consistency of the statistical interpretation of our data. We care deeply about the well-foundedness of our conclusions and hope to eliminate these concerns in the following. First, we hope that the issue about the “technical problems” in measuring the first division has been solved in our response to previous comments. Next, to clarify an apparent misunderstanding: As stated in the text (lines 329ff) and shown in now Fig. 5D-E, cell size at onset of nuclear division or 2 hours prior does significantly correlate with final merozoite number. The lack of significant p-value (0.08) only pertains to the correlation of cell size at the end of the schizont stage (tend) with merozoite number (now Fig. 5F). We have removed the unfortunate wording “not quite significant anymore” in that context. Finally, regarding potential mechanisms, a potential counter must be set before the first nuclear division is completed because only that way it can be set independent of the speed of nuclear multiplication. This observation gives the statistically significant correlation of volume at the onset of division and progeny number its relevance. We have reformulated the marked sentence for more clarity (lines 331ff). Furthermore, we point out that our rejection of the timer is now based on a revisited statistical analysis (Fig. 2), which is no longer based on a simple correlation between final number and duration, as detailed above.

Finally, the authors grow the parasites in dilute media, and find that they produce fewer daughter parasites. This is anecdotally unsurprising, as most Plasmodium laboratories are aware that sub-optimal growth conditions result in less healthy schizonts with fewer viable merozoites (and lower magnitudes of single-cycle expansion), but is nonetheless an important result that highlights explicitly how much this occurs in the specific conditions of dilute media. Given the lack of investigation of exactly which nutrient, carbon source, or combination thereof leads to the reduced merozoite number, it is unclear if or how much this is relevant to the scenario of a natural infection and realistic levels of that nutrient in a human or primate parasite environment.

As rightfully pointed out by the reviewer suboptimal growth conditions affecting parasite growth and multiplication rate have been shown in many instances. The number of studies that actually quantify a reduction in merozoite number under different growth conditions is certainly much lower (Brancucci et al. 2017 (lipids), Mancio-Silva et al. 2017 (calorie-restriction in mice), Tinto-Font et al. 2022 (temperature) come to mind). What our study adds to this body of literature is to which extent duration of the schizont stage and cell volume are affected in relation to progeny number at the single cell level. Importantly, we wanted to test whether the counter model still holds under these more adverse conditions, which we found to be the case. Along the lines of the work on calorie restriction and the likely implication of isoleucine in the process investigated in the laboratory of Maria Mota, it will be exciting to identify a “limiting factor” in future studies. Indeed, any study done in complete RPMI culture medium can be questioned regarding its physiological relevance and we added a sentence addressing this aspect in the discussion (lines 514ff). Yet, our medium dilution experiments suggest that at least to some degree an extracellular resource is implicated, which makes sense from a biological function point-of-view.

Minor issues

The manuscript confuses the terms "less" and "fewer". Fewer should be used for countable nouns (fewer daughter cells, fewer nuclei, fewer merozoites), less for uncountable nouns (e.g. less speed, less volume).

Thank you for pointing this out. The words have been replaced accordingly.

I didn't understand lines 93-95; "This excluded a timer and thereby confirmed a counter as the mechanism regulating termination of nuclear multiplication (Klaus et al., 2022). A direct correlation between duration of schizont stage and merozoite number is, however, still missing." If I understand the first sentence concludes that there ought not be a direct correlation between schizont duration and merozoite number, but the second sentence, says that that correlation is "however" missing. Isn't this expected? Perhaps reword for clarity?

Thank you for requesting clarification here. The exclusion of the timer by Klaus et al. 2022 was based on the correlation between duration of the first nuclear division cycle and the total duration of all nuclear replication phases. At no point did Klaus et al. count merozoites in live single cells, which was mainly due to lower spatial resolution of their images (M. Ganter, personal communication). Therefore, they could not directly assess the relation between progeny number and schizont stage duration, which we now report for the first time. The sentence was supposed to convey that this type of data was missing and was now reformulated for more clarity (line 114).

Lines 104

"We further uncover that throughout schizogony P. falciparum infringes on the otherwise ubiquitously constant N/C-ratio (Cantwell and Nurse, 2019)" This seems obvious to me, and not something uncovered by this study. In most of the numerous apicomplexans that divide by endoschizogeny, the cells achieve a near final size considerably before the final rounds of nuclear division so the N/C ratio must not remain constant - this is a direct corollary of many previous descriptions and not a novel finding of this study, and this claim here should be made more modest.

We understand the point raised by the reviewer but still think that our claim is justified due to several aspects. There are examples of eukaryotic cells that undergo multinucleated stages during division were the N/C-ratio is constant (Dundon et al. 2016, Cantwell and Nurse, 2019), while we are not aware of any counter-example in the literature. Studies have also shown that e.g. certain mutant yeast that fail to undergo cytokinesis will increase their volume by factor of up to 16 alongside the still replicating and growing nucleus maintain the N/C-ratio (Neumann et al. 2007, Jorgensen et al. 2007). This demonstrates the tremendous plasticity that cells can reveal with respect to nucleus and cell size regulation. Until the contrary was shown, it was conceivable that nuclear compaction, which does occur (Fig. 5H), compensates for the increase in nuclear number while the cell volume is only increasing slightly. Importantly, we are not aware of any literature where nuclear volume has been quantified for blood stage Plasmodium. Cell volume quantifications remain limited to modelling and the study by Waldecker et al., which provides a few datapoints throughout the IDC. Whether this finding is expected or not, formally speaking, our claim is justified, but for more clarity we replace “uncover” with “demonstrate”. We also introduce the N/C-ratio as cellular parameter in P. falciparum pointing out another divergent aspect of its biology and might in the future understand the functional implication of this usually constant ratio, which is still unclear.

Dundon SE, Chang SS, Kumar A, Occhipinti P, Shroff H, Roper M, Gladfelter AS. Clustered nuclei maintain autonomy and nucleocytoplasmic ratio control in a syncytium. Mol Biol Cell. 2016 Jul 1;27(13):2000-7.

Neumann FR, and Nurse P. Nuclear size control in fission yeast. J. Cell Biol. 2007; 179: 593–600. pmid:17998401

Jorgensen P, Edgington NP, Schneider BL, Rupeš I, Tyers M & Futcher B Molecular Biology of the Cell 18 (2007) The size of the nucleus increases as yeast cells grow.

Helena Cantwell, Paul Nurse; A homeostatic mechanism rapidly corrects aberrant nucleocytoplasmic ratios maintaining nuclear size in fission yeast. J Cell Sci; 132 (22)

I lack specialist statistical knowledge to comment on the statistical analyses performed on the correlation data, and in particular, whether the high p values for t-Tests for correlation are sufficient to support the argument that there is not a correlation, and whether these observations are sufficiently powered to robustly test that hypothesis.

We are confident that our reworked model analysis, as explained above, now sufficiently supports our hypotheses.

Reviewer #2 (Significance):

The manuscript purports to find a counting mechanism that determines parasite merozoite numbers, and that this coutner is set by an externally provided and diffusible resource. Many nutrients are in excess in normal culture media, but not all. If that counted nutrient(s) were normally in excess in the bloodstream, it could hardly be said to be the factor that is counted and that therefore defines merozoite number. Conversely, if the amount of that nutrient were increased in normal media, would parasites make even more merozoites? Further, if the "counted" item is a freely diffusible compound in the media, it should be equally accessible to each parasite in a culture condition, and isn't a reasonable explanation for the variable merozoite numbers in the normal media conditions. To me, it is unsurprising that parasites that are healthy and well fed are able to produce more merozoites, but I don't see this as being the same as support for a counter model where the parasite senses and counts a set number of merozoites to produce in response to a specific external counter. I think the shoehorning of this phenomenon into a paradigm used to describe some other eukaryotes may not be appropriate, and that the rejection of one overly simplistic timer model should not automatically lead to us dichotomously accepting a simple counter method as the alternative. The authors need to do more to either identify a countable input whose gradual increase leads to a predictable and gradual increase in merozoite number, to show that they do use a counter, or provide substantially more caveats to their argument that the parasites are using a counter based on an externally provided resource to determine merozoite number.

The reviewer comments on the feasibility of a counter mechanism based on an externally provided and diffusible resource. In fact this is a limited view of how a counter may arise and not the one we subscribe to. Rather, while a resource may be diffusible in the medium, it would need to be consumed during schizogony, and insufficiently replenished, in order to enable counting by dilution in the host cell. Furthermore, the reviewer has doubts that the fact that “healthy and well fed […] produce more merozoites” implies “support for a counter model”. We fully agree, and we argue in the manuscript that it is the correlations between schizogony durations and merozoite counts that support a counter model.

As we have argued above, the two alternative models we consider are inspired by paradigm from other eukaryotes, but their definitions in the present context are simple enough for them to be considered natural minimal models of schizogony. As the simplest imaginable phenomenological models of multiplication control, we find it natural to compare them, and we hope our new introductory section introduces them appropriately now. Naturally, we hope to expand on this simple model in the future and identify more precisely the limiting resources and describe a more direct response.

Audience - relatively specialised - likely interested audience would combine apicomplexan cell biologists, as well as theorists of cell division mechanism

Advance - limited - confirms phenomenon also described by other researchers in their institute, and extends to another related organism.

We would like to add that the present data are the first quantitative joint measurements of schizogony dynamics and outcome in P.falciparum and knowlesi. They allowed for the first time a direct correlation of duration and merozoite number, thereby accessing the question of growth control head on. Further they provide a quantitative reference of several key cellular parameters for anybody studying asexual blood stage parasites.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary:

Stürmer and colleagues used super-resolution time-lapse microscopy to probe the mechanism regulating the number of merozoites produced by a single cell in Plasmodium falciparum and P. knowlesi. The authors conclude the followings-

1. P. knowlesi has similar duration of schizont stage to P. falciparum, although having a 24 h intraerythrocytic developmental cycle (IDC) to 48 h of P. falciparum.
2. Nuclear multiplication dynamics suggests a counter mechanism of division- which is further suggested by a significant relation of merozoite numbers with schizont size at the onset of division.
3. Nutritional deprivation caused increase in nuclear volume and decrease in merozoite number. For the most part, the experiments that are presented in this manuscript support the conclusion of the authors. The data are presented in a concise and clear manner. However, some clarification and a couple of experiment (listed below) would improve this manuscript.

Major comments:

1. The authors generated at least 3 transgenic lines for this study, But the did not present any genetic validation of the lines in the manuscript. For completeness, I recommend to provide genetic validation (either pcr genotyping or whole genome sequencing) of the lines that were generated and used in this study in the supplement.

Our study exclusively used episomal expression of the respective fluorescent reporter (H2B-GFP, NLS-mCherry, and cytoplasmic GFP). As is customary in the field resistance to selection drugs and distinct fluorescent signals are assumed to sufficiently validate the presence of the plasmids. We now added the schematic maps of the plasmids in a new Fig. S1 to make our approach more visually clear.

1. In the H2B-GFP lines, the authors episomally GFP-tagged histone 2B to label the nuclear chromatin for both P. falciparum and P. knowlesi. This provides a very useful parasite line which enables the live time-lapse microscopy. Using these parasite lines, the authors first show that despite having a 24 h IDC in P. knowlesi vs 48 h in P. falciparum, both these parasites have a similar duration of the schizont stage (8.s vs 9.4 h). My concern here is whether this GFP-tagging is influencing the growth dynamics as in slowing down the P. knowlesi parasites. However, if that was the case authors should have seen that for P. falciparum too. Also, for the P. falciparum parasites that episomally express cytosolic GFP and Nuclear mCherry have a higher number of merozoites compared to the H2B-GFP P. falciparum and the authors speculate this is probably because of not tagging Histone 2B. Given this, it is important to show that none of the H2B-GFP parasites show any significant fitness cost due to GFP tagging of histone. I recommend a simple experiment to compare the multiplication rate of H2B-GFP lines to the parental lines in identical growth conditions. This suggested experiment was described in PMID: 35164549 to determine fitness cost of knockout lines. This experiment is vital for validation of the H2B-GFP lines and subsequent interpretation of the data that were presented in this manuscript.

We thank the reviewer for this excellent suggestion. To validate our lines further we now have carried out multiplication rate measurements similar to the one described in the designated publication for all the used lines alongside their parental strains (Fig. S2). We found no significant differences in between the wild type and the episomally expressing parasite lines (lines 131ff), which gives us confidence that episomal expression of tagged proteins do not significantly alter growth dynamics in these cases.

1. The authors used the microtubule live cell dye SPY555-Tubulin in P. falciparum to validate the findings presented in 1D and 1E. They did not do that for P. knowlesi. If there is no unsurmountable technical difficulty, I suggest doing the same with P. knowlesi. This will also address the concern that I have pointed out in #1.

Thank you for this suggestion. We have now generated the requested data with P. knowlesi, added it to what is now Supplemental Figure 3 and included it in our new analysis (Fig. 2I-J). The numerical values align well with the observations made when measuring schizont stage dynamics with the H2B-GFP expressing P. knowlesi line (line 158). A notable difference is that the Tubulin data strongly support the (refined) counter model, while the H2B data alone allow no distinction.

1. The data in Figure 3 shows that merozoite number does not depend on host cell diameter. My question here is, were these data collected using different donor blood? Or were this measured from different biological replicate? These are not clear from the writing. I am not sure about whether blood from various donor would have on the data, however, different preparation of the cells across various biological replicate will have some effect on host cell diameter hence on data. State if these were collected from independent biological replicates and about the donor blood.

The data results where indeed collected from three independent biological replicates using different donor blood batches. This is now stated in the figure legend. The batches displayed no difference in RBC diameter.

1. It is interesting to see that nutrient-limited conditions increase average nuclear volume but less merozoite numbers. In this experiment, as I understand, complete media was diluted 0.5x, which basically diluted every component of the media by half. From this experiment I can see nutritional deprivation as a whole having an effect and supports the counter mechanism, it would be intriguing to see if there is any effect of a particular nutrient have any effect on progeny division. For example, parasites can be grown in amino acid deprived media (except isoleucine) which makes the parasites fully dependent on host cell amino acids. This sort of specific nutrient deprivation will probably allow the authors to probe for specific nutrients that plays role as counter mechanism factor.

This is indeed a very exciting direction we would like to investigate in more detail in follow-up studies. Our aim for this study was to confirm that nutrient deprivation actually affects “counting” and to provide a workflow to investigate individual nutrients. In the meantime the Mota group, in a study we now cite in the discussion (lines 507ff), actually reported that isoleucine (and possibly methionine) levels are linked to progeny number. A follow-up on this topic using our strains and methodology is certainly worthwhile but requires more detailed analysis in the future.

Minor comments:

1. P. knowlesi is sometimes just written as knowlesi. Please, write P. Knowlesi.

Has been corrected.

1. Supplemental figure 1D, missing x-axis label.

We added the x-axis label.

1. In line 105, define N/C.

Done.

1. In line 205, I assume the authors mean episomally, not episomally.

Thank you for pointing this out. We have replaced “ectopically” with “episomally” throughout the text.

1. In line 275, Duration of Schizont stage was slightly....

Has been corrected.

1. All 'ml' or 'µl' should be 'mL' or 'µL'.

Changes have been made.

1. Define iRPMI.

We added a definition (line 610).

1. In line 475, replace 'as' with 'and'.

Done.

Reviewer #3 (Significance):

The factors that regulate the number of progenies in malaria parasites remain unknown. While there are few previous studies attempting to answer the question, those studies were done on fixed stained cells. In this study, the authors used genetically modified fluorescent P. falciparum and P. knowlesi parasites that enable live microscopy. These parasites coupled with super-resolution time-lapse microscopy the authors attempt to investigate the mechanism(s) at play in regulating progeny division. This manuscript provides data to suggest that external resources might have some role in progeny division and supports the counter mechanism. More careful validation of the transgenic lines that were used to collect data presented needs to be more systematic and rigorous.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Stürmer and colleagues used super-resolution time-lapse microscopy to probe the mechanism regulating the number of merozoites produced by a single cell in Plasmodium falciparum and P. knowlesi. The authors conclude the followings:<br /> - a. P. knowlesi has similar duration of schizont stage to P. falciparum, although having a 24 h intraerythrocytic developmental cycle (IDC) to 48 h of P. falciparum.<br /> - b. Nuclear multiplication dynamics suggests a counter mechanism of division- which is further suggested by a significant relation of merozoite numbers with schizont size at the onset of division.<br /> - c. Nutritional deprivation caused increase in nuclear volume and decrease in merozoite number.<br /> For the most part, the experiments that are presented in this manuscript support the conclusion of the authors. The data are presented in a concise and clear manner. However, some clarification and a couple of experiment (listed below) would improve this manuscript.

Major comments:

1. The authors generated at least 3 transgenic lines for this study, But the did not present any genetic validation of the lines in the manuscript. For completeness, I recommend to provide genetic validation (either pcr genotyping or whole genome sequencing) of the lines that were generated and used in this study in the supplement.
2. In the H2B-GFP lines, the authors ectopically GFP-tagged histone 2B to label the nuclear chromatin for both P. falciparum and P. knowlesi. This provides a very useful parasite line which enables the live time-lapse microscopy. Using these parasite lines, the authors first show that despite having a 24 h IDC in P. knowlesi vs 48 h in P. falciparum, both these parasites have a similar duration of the schizont stage (8.s vs 9.4 h). My concern here is whether this GFP-tagging is influencing the growth dynamics as in slowing down the P. knowlesi parasites. However, if that was the case authors should have seen that for P. falciparum too. Also, for the P. falciparum parasites that episomally express cytosolic GFP and Nuclear mCherry have a higher number of merozoites compared to the H2B-GFP P. falciparum and the authors speculate this is probably because of not tagging Histone 2B. Given this, it is important to show that none of the H2B-GFP parasites show any significant fitness cost due to GFP tagging of histone. I recommend a simple experiment to compare the multiplication rate of H2B-GFP lines to the parental lines in identical growth conditions. This suggested experiment was described in PMID: 35164549 to determine fitness cost of knockout lines. This experiment is vital for validation of the H2B-GFP lines and subsequent interpretation of the data that were presented in this manuscript.
3. The authors used the microtubule live cell dye SPY555-Tubulin in P. falciparum to validate the findings presented in 1D and 1E. They did not do that for P. knowlesi. If there is no unsurmountable technical difficulty, I suggest doing the same with P. knowlesi. This will also address the concern that I have pointed out in #1.
4. The data in Figure 3 shows that merozoite number does not depend on host cell diameter. My question here is, were these data collected using different donor blood? Or were this measured from different biological replicate? These are not clear from the writing. I am not sure about whether blood from various donor would have on the data, however, different preparation of the cells across various biological replicate will have some effect on host cell diameter hence on data. State if these were collected from independent biological replicates and about the donor blood.
5. It is interesting to see that nutrient-limited conditions increase average nuclear volume but less merozoite numbers. In this experiment, as I understand, complete media was diluted 0.5x, which basically diluted every component of the media by half. From this experiment I can see nutritional deprivation as a whole having an effect and supports the counter mechanism, it would be intriguing to see if there is any effect of a particular nutrient have any effect on progeny division. For example, parasites can be grown in amino acid deprived media (except isoleucine) which makes the parasites fully dependent on host cell amino acids. This sort of specific nutrient deprivation will probably allow the authors to probe for specific nutrients that plays role as counter mechanism factor.

Minor comments:

1. P. knowlesi is sometimes just written as knowlesi. Please, write P. Knowlesi.
2. Supplemental figure 1D, missing x-axis label.
3. In line 105, define N/C.
4. In line 205, I assume the authors mean episomally, not ectopically.
5. In line 275, Duration of Schizont stage was slightly....
6. All 'ml' or 'µl' should be 'mL' or 'µL'.
7. Define iRPMI.
8. In line 475, replace 'as' with 'and'.

Significance

The factors that regulate the number of progenies in malaria parasites remain unknown. While there are few previous studies attempting to answer the question, those studies were done on fixed stained cells. In this study, the authors used genetically modified fluorescent P. falciparum and P. knowlesi parasites that enable live microscopy. These parasites coupled with super-resolution time-lapse microscopy the authors attempt to investigate the mechanism(s) at play in regulating progeny division. This manuscript provides data to suggest that external resources might have some role in progeny division and supports the counter mechanism. More careful validation of the transgenic lines that were used to collect data presented needs to be more systematic and rigorous.

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Referee #2

Evidence, reproducibility and clarity

This is a solid study that further characterises the dynamics of nuclear division in Plasmodium falciparum and P. knowlesi. Of two, among potentially several, models for how the number of daughter nuclei, and thus parasites - (called merozoites in this genus), are one that posits nuclei divide until a fixed timer ends, and one that posits that nuclei divide to reach a fixed number that is defined by a cellular counter. I find some practical difficulties in definitive measurement of either model, one issue with the former is that experimental definition of the start of the timer is problematic - we may define the starter's gun (eg by the first nuclear division) but it isn't necessary that the cell is using that same start time. Additionally, as the authors confirm here, being sure when that first nuclear division has occurred is particularly tricky with Plasmodium parasites, in part because the first few nuclei seem to clump together, preventing one from unambiguously calibrating the first division. Furthermore, getting decent replicate numbers is hard because of the difficulties of time lapse microscopy, and most Plasmodium studies (including this one) suffer from low enough numbers that it isn't always clear whether the numbers support one model over another.

Nonetheless, several recent studies, particularly a study from the same institute (Klaus et al., 2022) employing timelapse imaging of nuclei, and timing the nuclear division of parasites, finds poor correlation between the duration of "schizogeny" (although perhaps using a different definition to the one used by the parasite) and the final number or merozoites. They therefore argue that there is poor evidence for a timer, and conclude by elimination that a counter must exist instead. A review by some of the authors of that study and some of this current study (Voß et al 2023), also concludes that the data from Klaus and colleagues "strongly support" a counter model. This current study also concludes that a counter model controls final nuclear/merozoite number in P. falciparum and P. knowlesi. This much at least is not particularly novel given the recent work on this topic, although the addition of the P. knowlesi data is interesting and consistent with the prior P. falciparum work. As above, the authors concede that it is difficult to determine with strong confidence when the first nuclear division has occurred, so it may well be that there is substantial noisiness in the time that they define schizogeny to commence. If that were the case, this would contribute to the poor correlation observed between schizogeny duration and number of merozoites produced, so this could be an important confounding experimental factor. This deserves some more discussion by the authors. Alternative methods to count absolute DNA content (rather than trying to count individual nuclei) might be useful ways of independently confirming this phenomenon. Alternative possibilities for what constitutes the "start" of a possible timer are also warranted - it could be for example, the first division of one of the other organelles.

These and previous authors in any case conclude that a counter model must exist through exclusion of a timer model. I am less convinced that the evidence discounting the timer is conclusive, and that a straight counter model is the only alternative. Indeed I am unconvinced by the suitability of this strictly dichotomous two-model system to categorise the division of unicellular eukaryotes, and these theories are not universally held to be sufficient to describe division. Nonetheless, if a counter exists, what is being counted that determines the final number? The authors consider that this might be a physical object or resource inside the parasite, or an extrinsic/extracellular resource. They investigate this by comparing the final cell number to a number of factors. First, the authors investigate the size of the RBC (by musing the diameter as an indicator)- little information is given about the source of the blood used, but it appears to be from a single donor of unknown age, who has approximately typical variance in RBC diameter (at least, after manipulation and storage). The authors observe little correlation between these variables. Second the authors measure parasite size at the onset of schizogeny, and find that bigger parasites result in more daughter merozoites early in schizogeny (perhaps not surprising, given the earlier mentioned technical problems with measuring the first few steps of schizogeny), but that this different initial cell size doesn't result in a different final merozoite number, or as they describe it "not quite significant anymore". Previous p values were taken as cause for rejecting the timer hypothesis and the timer model. In this case the authors instead interpret the data as suggesting "that the setting of the counter might correlate with parasite cell size". This is inconsistent statistical and analytical handling, and highlights the earlier potential pitfall of rejecting timer-based models based on not gathering data that statistically show a correlation. This needs reworking to highlight that these data are inherently noisy, difficult to measure accurately, and aren't necessarily going strongly reveal a trend even where one biologically exists, and that this ought not be used as grounds for confident rejection of a model.

Finally, the authors grow the parasites in dilute media, and find that they produce fewer daughter parasites. This is anecdotally unsurprising, as most Plasmodium laboratories are aware that sub-optimal growth conditions result in less healthy schizonts with fewer viable merozoites (and lower magnitudes of single-cycle expansion), but is nonetheless an important result that highlights explicitly how much this occurs in the specific conditions of dilute media. Given the lack of investigation of exactly which nutrient, carbon source, or combination thereof leads to the reduced merozoite number, it is unclear if or how much this is relevant to the scenario of a natural infection and realistic levels of that nutrient in a human or primate parasite environment.

Minor issues

The manuscript confuses the terms "less" and "fewer". Fewer should be used for countable nouns (fewer daughter cells, fewer nuclei, fewer merozoites), less for uncountable nouns (e.g. less speed, less volume).

I didn't understand lines 93-95;<br /> "This excluded a timer and thereby confirmed a counter as the mechanism regulating termination of nuclear multiplication (Klaus et al., 2022). A direct correlation between duration of schizont stage and merozoite number is, however, still missing."<br /> If I understand the first sentence concludes that there ought not be a direct correlation between schizont duration and merozoite number, but the second sentence, says that that correlation is "however" missing. Isn't this expected? Perhaps reword for clarity?

Lines 104<br /> "We further uncover that throughout schizogony P. falciparum infringes on the otherwise 105 ubiquitously constant N/C-ratio (Cantwell and Nurse, 2019)" This seems obvious to me, and not something uncovered by this study. In most of the numerous apicomplexans that divide by endoschizogeny, the cells achieve a near final size considerably before the final rounds of nuclear division so the N/C ratio must not remain constant - this is a direct corollary of many previous descriptions and not a novel finding of this study, and this claim here should be made more modest.

I lack specialist statistical knowledge to comment on the statistical analyses performed on the correlation data, and in particular, whether the high p values for t-Tests for correlation are sufficient to support the argument that there is not a correlation, and whether these observations are sufficiently powered to robustly test that hypothesis.

Significance

The manuscript purports to find a counting mechanism that determines parasite merozoite numbers, and that this coutner is set by an externally provided and diffusible resource. Many nutrients are in excess in normal culture media, but not all. If that counted nutrient(s) were normally in excess in the bloodstream, it could hardly be said to be the factor that is counted and that therefore defines merozoite number. Conversely, if the amount of that nutrient were increased in normal media, would parasites make even more merozoites? Further, if the "counted" item is a freely diffusible compound in the media, it should be equally accessible to each parasite in a culture condition, and isn't a reasonable explanation for the variable merozoite numbers in the normal media conditions. To me, it is unsurprising that parasites that are healthy and well fed are able to produce more merozoites, but I don't see this as being the same as support for a counter model where the parasite senses and counts a set number of merozoites to produce in response to a specific external counter. I think the shoehorning of this phenomenon into a paradigm used to describe some other eukaryotes may not be appropriate, and that the rejection of one overly simplistic timer model should not automatically lead to us dichotomously accepting a simple counter method as the alternative. The authors need to do more to either identify a countable input whose gradual increase leads to a predictable and gradual increase in merozoite number, to show that they do use a counter, or provide substantially more caveats to their argument that the parasites are using a counter based on an externally provided resource to determine merozoite number.

Audience - relatively specialised - likely interested audience would combine apicomplexan cell biologists, as well as theorists of cell division mechanism

Advance - limited - confirms phenomenon also described by other researchers in their institute, and extends to another related organism.

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Referee #1

Evidence, reproducibility and clarity

Summary

Malaria parasites replicating in human red blood cells show a striking diversity in the number of progeny per replication cycle. Variation in progeny number can be seen between different species of malaria parasites, between parasite isolates, even between different cells from the same isolate. To date, we have little understanding of what factors influence progeny number, or how mechanistically it is controlled. In this study, the authors try to define how the mechanism that determines progeny number works. They propose two mechanisms, a 'counter' where progeny number is determined by the measurement of some kind of parasite parameter, and a 'timer' where parasite lifecycle length would be proportional to progeny number. Using a combination of long-term live-cell microscopy and mathematical modelling, the authors find consistent support for a 'counter' mechanism. Support for this mechanism was found using both Plasmodium falciparum, the most prominent human malaria parasite, and P. knowlesi, a zoonotic malaria parasite. Of the parameters measured in this study, the only thing that seemed to predict progeny number was parasite size around the onset of mitosis. The authors also found that during their replication inside red blood cells, malaria parasites drastically increase their nuclear to cytoplasmic ratio, a cellular parameter remains consistent in the vast majority of cell-types studied to date.

Major Comments

• It is stated a few times in this study that P. knowlesi has an ~24 hour lifecycle, and while this is the case for in vivo P. knowlesi, it was established in the study when P. knowlesi A1-H1 was adapted to human RBCs (Moon et al., 2013) that this significantly extended the lifecycle to ~27 hours, which should be made clear in the text. As much of this study revolves around lifecycle length and timing, the authors should consider some of their findings with the context that in vitro adaption can significantly alter lifecycle length.
• The dichotomous distinction between 'timer' and 'counter' as mutually exclusive mechanisms seems to be a drastic oversimplification. Considering the drastic variation we see in merozoite number across species, between isolates, and between cells, it seems much more likely that there are factors controlled by both time-sensed and counter-sensed mechanisms that both influence progeny number. Additionally, the only parasite parameter measured in this study, size at time of first nuclear division, explained only a small proportion of the variance observed in merozoite number.
• For modelling of a timer-based mechanism, the designation of t0 is subjective. The authors chose the time of first nuclear division as their t0. It is possible that a timer-based mechanism could not be supported based on this model the chosen t0 differs from when the "parasite's timer" starts. For example, t could also have been designated as the time from merozoite invasion (t0) to egress (tend). It would be unreasonable to suggest the authors repeat experiments with a longer time-frame to address this, but this possibility should be discussed as a limitation of the model. It may also be possible to develop a different model where t0 = merozoite invasion and tend = egress, and test this model against the data already collected in this study.
• The calculation of the multiplication rate is confusingly defined. In Figure 1 it is stated that it is "...based on t and n", which would imply that the multiplication rate is the number of merozoites formed per hour of schizogony, which would give an average value of ~2 for P. falciparum and ~1.5 for P. knowlesi. The averages rate values shown, however, are in the range of 0.15-3. The authors should clarify how these values were determined.
• In Figure 2, the time from tend until egress is calculated, and this is interpreted as the time required for segmentation. In the Rudlaff et al., 2020 study cited in this paper, it is shown that segmentation starts before the final round of nuclear divisions are complete. Considering this, the time from tend until egress is not an appropriate proxy for segmentation time. The authors should consider rewording to something akin to "time from final nuclear division until egress" to more accurately reflect these data.
• There is a significant discrepancy between the data in Figure 5 and Supplementary Figure 8. In Supplementary Figure 8, the authors establish that culturing parasites in media diluted 0.5x has a marginal effect on parasite growth, with no discernible change in parasitaemia over 96 hours. By contrast, in Figure 5a the parasitaemia of parasites cultured in 0.5x diluted media is approximately 5-fold lower than those in 1x media. The authors should explain the significant discrepancy between these results.
• In Supplementary Figure 4, the mask on the cell at t0 shows two distinct objects, but it seems very unlikely that they are two distinct nuclei as they vary approximately 5-fold in diameter. The authors should provide more detail on how their masking was performed for their volumetric analysis. Specifically, whether size thresholds were also applied during object detection.

Minor Comments

• Line 45-48 mentions that merozoite number influences growth rate and virulence, but the corresponding reference (Mancio-Silva et al., 2013) only discusses the relationship between merozoite number and growth rate, not virulence.
• Line 59 states that a 48 hour lifecycle is a baseline from which in vitro cultured parasites deviate. Clinical isolates also show variation in lifecycle length and so it is more accurate to just say that 48 hours is an average, rather than a baseline.
• Line 63 cites a study for the lifecycle length of P. knowlesi (Lee et al., 2022), but there seems to be no mention of lifecycle length in this reference
• If I am interpreting Figure 3B correctly, this is essentially a paired analysis where the same erythrocytes are measured twice, once at t0 and once at tend. If this is the case, this data may be better represented with lines that connect the t0 and tend values.
• Figure 3A seems to imply that to calculate diameter of the erythrocytes, three measurements were made and averaged for each cell. I think this is a nice way to get a more accurate erythrocyte diameter, but if this is the case, it should be specified in the figure legend or methods.
• In Figure 4I it is shown that in P. falciparum merozoite number doesn't correlate with nucleus size, but for P. knowlesi in Supplementary Figure 7c, a significant anticorrelation is observed. The authors should state this in the text and discuss this discrepancy.
• The authors show that merozoite number roughly correlates with cell size at t0 but it would be interesting to see whether cell size at tend also corresponds with cell size at t0. This might help answer whether the cell is larger because it has more merozoites, or whether it has more merozoites because it is larger.
• I don't feel that "nearly identical" is an appropriate summary of erythrocyte indices in Supplementary Figure 9, considering there is a statistically significant increase in mean cell volume. I think it is unlikely that this change is consequential, and performing these haematology analyses is a nice quality control step, but this change should be stated in the text.
• In Supplementary Figure 8, parasitaemia only increases ~2-fold compared to >5-fold the previous two cycles. It seems likely that at the final timepoint on this graph the parasites are starting to crash, and therefore it may be best to end the graph with the 96 hour timepoint.
• The error bars in Figure 5C aren't easily visible, moving them in front of the datapoints may help their visibility.
• In Figure 6D & E, the y-axis labels should be changed to whole integers as all the values in the graph are whole numbers.
• My interpretation of Figure 6 C-E, is that these are the same cells measured at three time points (t-2, t0 and tend). If this is the case, 6C is missing the cell that has a merozoite number of 8, which is presumably why the y-axes are not equalised for the three graphs.

Significance

In the asexual blood-stage of their lifecycle, malaria parasites replicate through a process called schizogony. During schizogony an initially mononucleated parasite undergoes multiple asynchronous rounds of mitosis followed by nuclear division without cytokinesis, producing a variable number of daughter nuclei. Parasites then undergo a specialised cytokinesis, termed segmentation to where nuclei are packaged into merozoites that go on to invade new host cells. While nucleus, and therefore merozoite, number are known to be varied between cells, across isolates, and across species, little is known about the mechanisms regulating merozoite number. In this study, the authors use live-cell microscopy to understand how parasites determine their progeny number. They suggest that parasites regulate their progeny number using a 'counter' mechanism, which would respond to the size or concentration of a cellular parameter, as opposed to a 'timer' mechanism. Long-term live-cell microscopy experiments using malaria parasites are extremely technically challenging, and the authors should be commended for their efforts in this regard. While I agree that the data generated from these experiments are technically sound, I have some reservations expressed above about the interpretation of some of these results. I would strongly encourage the authors to consider rewording some of their interpretations taking into account some of the caveats listed above. I would also consider fitting/testing an additional mathematical model where the time-frame proposed for the 'timer' mechanism begins following merozoite invasion.

This work is of specific interest to anybody who grows malaria parasites, as the dynamics of their growth is obviously important to understand. Further, this work is of interest more generally to cell biologists who study the regulation of progeny number or cell size. I have no experience with the application of mathematical modelling to understand biological systems, and so I cannot comment on the interest of this work to that field.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:<br /> In this original article from Mutscher et al., the authors developed a compartmentalized dissociated mouse myelinating SC-DRG coculture system to investigate the distinct roles of Schwann cells in axon protection and degeneration after injury. The innovation of this approach relies on (i) the use of mouse SCs and mouse DRGS neurons instead of rat cells; (ii) the use of microfluidic chambers, seeded by axons and SCs in different compartment; (iii) the possibility to perform a traumatic injury in vitro. While this novel approach offers new ways to study peripheral nerve regeneration and SC-axon interaction, and technical the study is robust, the paper is currently limited by the exploration of their model.

Major points:<br /> Reviewer 1. It is unclear is this approach will ever lead to the identification of key mechanism or key candidates. This is a major miss in the current manuscript form. In short: the authors should demonstrate that their in vitro system can lead to significant leap in our understanding of peripheral nerve regeneration by identifying novel targets/pathways or mechanisms.

Author response: We agree with the reviewer that cell culture approaches have limitations however we would disagree that it is not a viable approach given that a number of seminal studies in the field have already helped identified key cellular and molecular steps using rat SC-DRG cocultures or using mouse DRGs and rat SCs in combination with in vivo study. We have added the following to the introduction to highlight this point in more detail:

Introduction.

Dissociated myelinating SC-DRG cocultures from rats were first developed by the Bunge laboratory in the 1980’s to investigate PNS myelination in a more dynamic way (Bunge et al, 1989; Eldridge et al, 1987)__. These cultures have been used to make seminal discoveries in uncovering the cellular and molecular mechanisms of SC myelination alongside in vivo investigation. These include how the inner SC membrane (mesaxon) advances to myelinate axons, and the role of b__-neuregulin-1 (__b__NRG1) and polarity proteins in SC myelination (Bunge et al, 1989; Shen et al, 2014; Chan et al, 2006; Taveggia et al, 2005)__. Similarly, SC-DRG cocultures have been useful in demonstrating how SCs proliferate after axon injury, transfer metabolites, such as pyruvate, to delay axon degeneration, how placental growth factor (Plgf) regulates axon fragmentation by SCs and how SC JUN promotes axon outgrowth after injury (Arthur-Farraj et al, 2011; Babetto et al, 2020; Vaquié et al, 2019; Salzer & Bunge, 1980)__. The use of a coculture system to study axon-SC interactions during axon degeneration and regeneration offers some advantages over in vivo approaches as both neurons and SCs can be genetically manipulated separately and live imaged with ease.

Discussion.

Most importantly SCs and DRG neurons from various transgenic mice can be used to perform in vitro analysis to complement findings from in vivo transgenic mouse studies.

Author response: Furthermore, as this is a methods paper, demonstrating novel molecular mechanisms is outside the scope of this article. However, we have already used this technique with a collaborator to study the role of cdk7 in myelination (see link to conference abstract below) and this manuscript is under preparation to be submitted soon. Additionally, we have ongoing projects within the lab using this technique to help characterise novel molecular targets in nerve injury. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=uEtwAd8AAAAJ&sortby=pubdate&citation_for_view=uEtwAd8AAAAJ:_Qo2XoVZTnwC

Author response: Despite this, we have shown that the axo-protective effect of SCs is independent of myelination status, which is an advance on what is known in the literature.

Author response: We have realised through all of the reviewers’ comments that the title and the aims of the manuscript were confusing. We have made this clearer by removing the word novel in the title changing the title to the following:

A method for mouse myelinating Schwann cell-DRG neuron compartmentalised cocultures: myelination status does not influence the axo-protective effect of Schwann cells after injury.

Author response: We have also made it much clearer what the purpose of our study is and where and how it fits in with the previous literature by adding the following paragraph to the introduction.

Introduction.

Indeed there has only ever been one laboratory detailing convincing myelin formation in dissociated mouse myelinating SC-DRG neuron cocultures, however this was never published as a step by step detailed protocol (Stevens et al, 1998; Stevens & Fields, 2000)__. In the last twenty years, there have been no published studies demonstrating myelination in fully dissociated mouse SC-mouse DRG cocultures. This has largely prevented the use of cells, particularly SCs, from transgenic mice in cocultures and thus restricted the ability to study SC-axon interactions in a system that can be readily manipulated and live imaged and results directly applied back to in vivo findings in the same species.

Reviewer 1. The use of embryonic DRG neurons or SC isolated from P2 animals are arguably physiologically not the same cells that are affected by traumatic nerve injury, which happen most often than not in adult. This is a problem in the long-term reliance on this approach to study axotomy peripheral nerve regeneration.

Author response: We agree with the reviewer that one should always be cautious with the use of embryonic/neonatal cells to directly refer them to adult cellular mechanisms. We have added discussion of this point to the discussion:

Discussion.

One limitation of our coculture model and indeed all coculture and cell culture models that are used to investigate cellular and molecular mechanisms in nerve injury is that the cells are obtained from embryonic or neonatal animals. This is an important caveat when applying results from cell culture to adult in vivo nerve injury. However, while we would argue that cell culture approaches should always be used in combination with in vivo study it is important to remember that nerve injury is not restricted to adults and brachial plexus injury secondary to birth trauma is unfortunately a significant clinical problem (Pondaag et al, 2007)__. Furthermore, neonatal SCs replicate many of key cellular and molecular mechanisms seen in adult SCs after injury, including JUN upregulation, myelinophagy, promotion of axon growth and expression of key repair program transcripts (Arthur-Farraj et al, 2012; Gomez-Sanchez et al, 2015; Arthur-Farraj et al, 2017; Parkinson et al, 2008)__. A future development would be to try to adapt this protocol to make a coculture model with adult mouse or even human cells.

Author response: Additionally, we already know Schwann cells in P5 neonatal mice in vivo after nerve transection demyelinate in a similar way to Schwann cells in adult mice and that neonatal cells in vivo and in vitro require the transcription factor c-Jun to do so (Parkinson et al., 2008 JCB Fig.7).

Moderate points:<br /> Reviewer 1"there are no established protocols in the field describing the use of mouse SCs with mouse DRG neurons in dissociated myelinating cocultures". The use of mouse cells is laudable, but it is not necessarily a technical innovation, or at least the current manuscript does not explain why their approach particularly suitable to mouse Schwann cells.

Author response: We feel that a detailed working protocol for compartmentalised dissociated mouse myelinating cocultures showing convincing and extensive myelination has been missing from our field for a long time. We agree that it is an incremental technical advance, but it is an important one. We have modified the title as we explained above. We have explained this point more clearly in the introduction, results, and discussion with the following additions:

Introduction.

Indeed there has only ever been one laboratory detailing convincing myelin formation in dissociated mouse myelinating SC-DRG neuron cocultures, however this was never published as a step by step detailed protocol (Stevens et al, 1998; Stevens & Fields, 2000)__. In the last twenty years, there have been no published studies demonstrating myelination in fully dissociated mouse SC-mouse DRG cocultures. This has largely prevented the use of cells, particularly SCs, from transgenic mice in cocultures and thus restricted the ability to study SC-axon interactions in a system that can be readily manipulated and live imaged and results directly applied back to in vivo findings in the same species.

The consensus within the field is that inducing myelination in dissociated mouse SCs is challenging. Certainly, induction of myelin differentiation with cyclic adenosine monophosphate (cAMP) analogues or elevating agents, such as forskolin, is more difficult in mouse SC monocultures compared to rat SC cultures. This is because mouse SCs require additional exogenous b__-neuregulin-1 (__b__NRG1), plating on poly-L-lysine (PLL) instead of poly-D-lysine (PDL), and low concentration horse serum as opposed to foetal calf serum (Stevens et al, 1998; Arthur-Farraj et al, 2011; Päiväläinen et al, 2008)__.

Author response: We have now explained more clearly that without plating on Matrigel and the regular addition of Matrigel to the myelination medium that mouse cocultures do not myelinate with ascorbic acid or indeed addition of NRG1 nor forskolin. Please see NEW DATA in Supplemental figure 1. We have added the following paragraph to the results section.

Results

Importantly, we found that L-ascorbic acid was insufficient to induce substantial myelination in our cultures, unlike in rat SC-DRG cocultures, and in the one previously published dissociated mouse SC-DRG protocol (Stevens et al, 1998)__. In fact, plating cocultures on laminin, adding ascorbic acid (50 m__g ml−1), b__NRG1 (10 ng ml−1) and forskolin (10 m__M) induced very few myelin sheaths (Supp. Fig. 1). Only when cultures were plated on Matrigel__â and further Matrigel__â was added to the myelination medium for each medium change, were we able to visualise robust reproducible myelination in our cocultures (Supp. Fig. 1).

Author response: We have also added further discussion on how our protocol differs from the Stevens 1998 and other protocols in the discussion.

Discussion

Our protocol differs somewhat from the one used by Stevens et al., 1998 to induce myelination in dissociated mouse SC-DRG cocultures__, as they used ascorbic acid and 10% horse serum and presumably plated their cultures on laminin, though they do not explicitly detail this (Stevens et al, 1998)__. In our preliminary experiments we were unable to visualise much myelination with use of laminin, ascorbic acid or indeed if b__NRG1 and high concentration forskolin was added to the medium for up to four weeks. However, if we plated cocultures on Matrigel® and continuously added it to the myelination medium then we saw comparable levels of myelination in our mouse cocultures to that of rat cocultures (Eldridge et al, 1987)__. This approach of using Matrigel® to enhance myelination has previously been successfully employed in cultures of human iPSC sensory neurons with rat SCs and in non-dissociated mouse DRG explant cultures (Clark et al, 2017; Päiväläinen et al, 2008)__. Importantly, we used growth factor depleted Matrigel® as standard Matrigel® preparations contain substantial amounts of Transforming growth factor b (TGF__b__) which is a known inhibitor of myelination (Einheber et al, 1995)__. Additionally, the majority of rat and mouse coculture protocols plate cells on glass whereas we found cultures were healthier and myelinated better when cultured on plastic Alcar® coverslips.

Discussion

Furthermore, as this is a dissociated and compartmentalised purely mouse cell culture system, one can utilise the vast array of transgenic and knockout lines available to study neuron-SC interactions in more detail, without concern of contaminating endogenous SCs and other non-neuronal cells that remains a drawback of current mouse dissociated or non-dissociated DRG explant models.

Reviewer 1: The figures in the paper are largely descriptive. They are very little quantitative measurement. Thus, the readers will have a hard to determine, if they replicate the proposed approach, whether their efficient is on par with the current authors.

Author response: We have now added quantification of myelin segments per mm2, percentage of SCs that myelinate and quantification of the interperiodic distance of the myelin formed. This is all included in a new version of TABLE 1. We have discussed this data in the results section as follows.

Results

Quantifying the number of PRX positive myelin segments we found that there were 325.33 ± 12.3 sheaths per mm2, comparable to what has been originally described in rat SC/DRG cocultures and two fold more extensive myelination than recently described compartmentalised rat cocultures models (n=3; Table 1; Eldridge et al, 1987; Vaquié et al, 2019)__. Furthermore, 25.47 ± 1% of Schwann cells were myelinating in our cultures (n=3; Table 1). To confirm that cocultured myelinated SCs formed compact myelin we performed electron microscopy (EM), which revealed compact myelin formation with multiple myelin wraps and formation of readily visible major dense (MDL) and intraperiod lines (IPL; Fig. 2D). Additionally, we measured the periodicity, i.e., the distance between two adjacent major dense lines, to make sure myelin was compacted. Interperiodic distance was 12.16 ± 0.28 nm, in line with previous reports (n=3; Table 1; Boutary et al, 2021; Fernando et al, 2016; García-Mateo et al, 2018; Giese et al, 1992; Perrot et al, 2007)__.

Author response: Interestingly, after we performed this analysis, we realised we have double the level of myelination in our mouse cultures (325.33 ± 12.3 per mm2) than in the compartmentalised myelinating rat cocultures in Vaquie et al., 2019 (147 ± 27 internodes per mm2 (n = 3)).

Author response: We have also quantified the JUN upregulation after injury in both myelinating and aligned cocultures. See Fig. 3B-E).

Results

Additionally, we noted a strong upregulation of JUN protein in SCs 12 hours after axotomy (Fig. 3B and C). We also saw significant JUN upregulation 12 hours after axotomy in cocultures with aligned SCs (Fig. 3D and E).

Author response: The above quantification is in addition to the quantification of the rate of axon degeneration in the presence and absence of aligned and myelinating Schwann cells in Fig. 4B. We have also quantified the % of Schwann cells that contained axonal debris after injury – this data is now quoted in the text as we removed Fig. 4E.

We thank the reviewer for asking for additional quantification as this has improved the manuscript.

Minor points:<br /> Fig.4B and E should show individual data points.

Author response: We have added the individual data points to Fig.4B. We have removed Fig.4E and instead quoted the data in the results section as follows:

Results

When we quantified this phenomenon, we found that 97.84 ± 1.462% (n=2) of SCs in our cocultures contained mCherry-labelled axonal fragments.

Reviewer #1 (Significance):

In addition, to the demonstration of feasibility of this in vitro approach, the main finding by the authors is that SCs have a role in neuronal protection and support is key for peripheral nerve regeneration. Thus while in vitro approach does not add key information that do not already exists in the field, it somewhat confirms that the effect is SC autonomous. Overall the approach is interesting and has potential, but the study currently lack a demonstration of its usefulness to the community.

It would have been interesting to have the authors discuss the advantages of their approach in comparison to other innovative approaches to study SC-axon interactions that have been developed in the last decade (i.e., 3D environment, microfluidic approach, transwell systems). There is also a lack of citations about similar studies in the field.

Author response: We direct the reviewer to the following paragraphs in the introduction and the discussion, which we have now elaborated on further post peer review. We discuss all relevant cocultures studies in mouse as well as all the relevant microfluidic studies and 3D coculture studies as well as the one human nerve organoid study. We found two additional studies, Numata-Uematasu 2023 using DRG mouse explant cultures and Park et al., 2021 using motorneuron-SC cocultures which we have now added to the discussion. We also briefly discuss transwell studies to assess migration as the reviewer requested.

We also discuss in detail the two microfluidic coculture injury studies Babetto et al., 2020 and Vaqiue et al., 2019 extensively throughout the manuscript. We have added further discussion of the similarities and differences between theirs and our approach in the discussion.

Introduction.

Protocols exist where endogenous mouse SCs are used to myelinate dissociated or non-dissociated DRG explant cultures. (Shen et al, 2014; Harty et al, 2019; Sundaram et al, 2021; Stettner et al, 2013; Numata-Uematasu et al, 2023)__. Furthermore, another protocol seeded exogenous SCs onto non-dissociated DRG explant cultures (Päiväläinen et al, 2008)__. Other laboratories seed cultured rat SCs onto dissociated mouse DRG axons (Taveggia & Bolino, 2018)__. Use of dissociated or non-dissociated DRG explants cultures preclude many experimental uses, such as using SCs from different transgenic animals and separate transfection of SCs and neurons with viruses for live imaging or genetic manipulation, and easy use of microfluidic chambers to allow injury studies and separate drug treatments to neurons or SCs. The reason for this is that antimitotics cannot be used in dissociated or non-dissociated DRG explant cultures as this depletes SCs, and the culture quickly becomes contaminated with other non-neuronal cell types, such as satellite cells and fibroblasts migrating out of the DRG. Furthermore, use of exogenous SCs in a non-dissociated DRG explant culture risks, after a period of antimitotic exposure, which was developed by Päiväläinen et al., 2008 still risks potential contamination from endogenous SCs and satellite glia migrating out of the DRG explant over time. This occurs because antimitotic treatment is unlikely to fully penetrate the whole DRG without prior dissociation. Additionally, a compartmentalised culture system cannot be readily used with non-dissociated DRG explant cultures (Päiväläinen et al, 2008)__.

Discussion.

Furthermore, our protocol is complementary to the recently described 3D mouse myelinating SC-motor neuron coculture system using collagen hydrogels (Hyung et al, 2021; Park et al, 2021)__. It will be interesting in the future to up titrate the concentration of Matrigel®, which is similar to collagen hydrogels, in our cultures to see whether further increasing extracellular matrix viscosity and stiffness improves our myelination efficiency even further. While it is possible to study cell migration in microfluidic cell culture devices, transwell models offer significant advantages to study this cellular phenomenon (Negro et al, 2022)__. To date, there have been no published studies of successful myelination in human SC-neuron coculture systems. Despite this rat SCs have been shown to readily myelinate human-induced pluripotent stem cell (iPSC)-derived sensory neurons and an iPSC-derived peripheral nerve organoid system which does contain myelinating SCs has recently been described (Clark et al, 2017; Van Lent et al, 2022)__.

These findings confirm the observations of both Babetto et al., 2020 and Vaquié et al., 2019 who used rat SCs in similar microfluidic culture systems (Vaquié et al, 2019; Babetto et al, 2020)__. We have shown that the axo-protective observation seen by Babetto et al., 2020 does not rely on myelination status, which was an outstanding question from that study (Babetto et al, 2020)__. Furthermore, in an advance from previous studies, we have visualised the axo-protective and axon clearance phenomena in the same culture and shown that they are temporally separated, with axon fragmentation and debris clearance by SCs occurring at much later timepoints after axotomy. Several different culture and experimental conditions preclude direct comparison of our study with both those of Vaquié et al., 2019 and Babetto et al., 2020. These include the use of rat SCs in both prior studies and that Babetto et al., 2020 mixed rat SC with mouse DRG axons; length of time in culture, time points quantified after injury and distance from injury and site of analysis. Babetto et al., 2020 performed axotomy on relatively short term cocultures (6 days in vitro) whereas Vaiquié et al., 2019 cultured for at least 4 weeks and in our case 6 weeks prior to axotomy. Vaiquié et al., 2019 removed nerve growth factor (NGF) prior to laser axotomy and quantified proximally (though also imaged distally) whereas both our study and Babetto et al., 2020 kept NGF in the medium, performed axotomy with a scalpel and quantified more distally and in our case extremely distal, where only individual neurites and no axon bundles were visible. Vaquié et al., 2019 had SCs on both sides of the barrier in the microfluidic chambers whereas we seeded SCs only in the axonal/bottom compartment. Additionally, our cocultures had both forskolin and b__NRG1 added to help induce myelination, whereas these factors are not required in rat myelinating cocultures. Finally, it is important to permeabilise myelinated cultures with acetone after fixation__, as we did, to visualise the entire axon through heavily myelinated segments otherwise axon integrity cannot be reliably assessed in a quantitative manner (Vaquié et al, 2019; Babetto et al, 2020)__.

Because of the lack of key novel mechanisms, and lack of discussion on what this approach is superior to others in vitro approach limits the impact of the study and the excitement of the reader, even from the SC-axon community.

Author response: We have developed the first compartmentalised fully dissociated mouse myelinating coculture system in over twenty years. Thanks to the reviewer’s suggestions, we have now shown that myelination is comparable to the original rat cocultures from the Bunge lab, which is the gold standard in the field, and superior to recently described compartmentalised rat coculture system by Vaquie et al., 2019. We have provided a detailed step by step protocol to allow other researchers to use our technique.

Additionally, thanks to the reviewer, we have now described in detail exactly how our protocol differs from others and why we succeeded to get mouse SCs to myelinate so robustly in a fully dissociated coculture (see previous answers). This is an incremental but important advance given that studies currently use a coculture system using entirely cells from rat, or where rat Schwann cells are seeded on mouse axons, or dissociated or non-dissociated mouse explant cultures are used which abrogates using neurons and SCs from different transgenic mice.

As this is a methods paper, we did not intend to describe novel molecular mechanisms though our method is already being used for such purposes by ourselves and a collaborator as outlined above in prior answers. We did not make this clear and we hope the extensive revision of the manuscript now addresses this point. Despite this, we have shown that the axo-protective effect of SCs is independent of myelination status, which is an advance on what is known in the literature.

Finally, in addition to myelination, we have demonstrated that one can study all the key components of the SC and axonal response to injury in a quantifiable way in addition to demonstrating that these cocultures can be live imaged and used for drug studies. None of the prior mouse studies looked at injury responses of axons nor SCs. We believe this will be of use to the community.

Reviewer #2 (Evidence, reproducibility and clarity):

In the manuscript entitled "Distinct axo-protective and axo-destructive roles for Schwann cells after injury in a novel compartmentalised mouse myelinating coculture system", Arthur-Farraj and colleagues detail a method of dissociated coculture of mouse-DRG neurons and Schwann cells in microfluidic chambers. In this system, neurons and Schwann cells harvested from the same or from different animals are grown in different compartments that are connected by microgrooves, thereby allowing for spatial and diffusive separation. Neurons are shown to extent their axons across the microgroove barrier to the glial compartment where Schwann cells align with the axons and myelination can be induced. Detailed analysis of myelination and axon injury/degradation are presented as use cases, including the capability to genetically and pharmacologically manipulate neurons and Schwann cells independently, which also enabled fluorescent life cell imaging. The authors then examine the effect of immature/premyelinating and myelinating Schwann cells on the rate of axon degeneration. Upon axotomy Schwann cells significantly delayed degeneration, with no difference between non-myelinating and myelinating Schwann cells. Finally, live imaging during axon degeneration with fluorescent proteins separately expressed in neurons and Schwann cells demonstrated that Schwann cells ingest axonal fragments.

Major comment:<br /> In establishing a much needed in-vitro system for PNS myelination and injury research the paper represents a valuable contribution to the PNS community. However, I find the presentation of aspects concerning a protective/destructive role of Schwann cells somewhat inconclusive. That these roles exist has been known, as the authors discuss. Then what does this study contribute concerning the open question that was raised by the discrepancies between Babetto et al., 2020 and Vaquié et al., 2019, i.e. how Schwann cells contribute to axon survival/regeneration after injury? Essentially, the only significant conclusion in this regard is that myelinating and non-myelinating mouse Schwann cells do not differ in their capability to protect axons from degeneration. The manuscript, including the title, would benefit from focusing more on this aspect. In particular, the discussion of the factors that lead to the still remaining apparent discrepancies between Babetto et al., 2020 and Vaquié et al., 2019 and this study should be revised. The authors state that "The study by Vaiquié et al., 2019 quantified axon fragmentation proximally in the microgrooves at timepoints starting from 12 hours after axotomy." (Discussion). While this observation is accurate, Jacob and colleagues also show accelerated, obviously distal axon degeneration in the presence of Schwann cells (Figure 3C in Vaquié et al., 2019). It is therefore unlikely that the discrepancies stem from analysis of more proximal vs. more distal axons, or the timepoints of analyses. In my opinion, a further study (using the coculture system presented in this manuscript) that compares the role of Schwann cells from rats and mice, and that includes analysis of more proximal and distal axon degeneration as well analysis of axon regeneration is needed. In a rework of the manuscript, the authors may therefore elaborate more on the shortcomings of the present study, or alternatively soften the aims of the study in the first place.

Author response: We thank the reviewer for their comments, and we agree that the title and the aims of the manuscript were confusing. We didn’t make it explicit that this was a methods paper, and that we didn’t intend to show entirely novel findings but instead thoroughly characterise our mouse system so that it is comparable to what has been done for rat cocultures. We have now made this clearer by removing the word novel in the title and changing the title to the following:

A method for mouse myelinating Schwann cell-DRG neuron compartmentalised cocultures: myelination status does not influence the axo-protective effect of Schwann cells after injury.

Author response: We have decided to focus the manuscript more on the comparison of myelinating versus non-myelinating cocultures, given that we have shown that the axo-protective effect of SCs is independent of myelination status, which is an advance on what is known in the literature. We have added further characterisation of our aligned cocultures with p75NTR immuno and EM images (Fig.1D and E). We have added the following summary of how our study relates to findings of Babetto et al., 2020 and Vaquie et al., 2019 in the discussion, in line with the reviewer’s suggestions.

Discussion.

These findings confirm the observations of both Babetto et al., 2020 and Vaquié et al., 2019 who used rat SCs in similar microfluidic culture systems (Vaquié et al, 2019; Babetto et al, 2020)__. We have shown that the axo-protective observation seen by Babetto et al., 2020 does not rely on myelination status, which was an outstanding question from that study (Babetto et al, 2020)__. Furthermore, in an advance from previous studies, we have visualised the axo-protective and axon clearance phenomena in the same culture and shown that they are temporally separated, with axon fragmentation and debris clearance by SCs occurring at much later timepoints after axotomy.

Author response: We have now added more in-depth discussion of the similarities and differences between Babetto et al., 2020 and Vaquie et al., 2019 and our approach in the discussion.

Discussion.

Several different culture and experimental conditions preclude direct comparison of our study with both those of Vaquié et al., 2019 and Babetto et al., 2020. These include the use of rat SCs in both prior studies and that Babetto et al., 2020 mixed rat SC with mouse DRG axons; length of time in culture, time points quantified after injury and distance from injury and site of analysis. Babetto et al., 2020 performed axotomy on relatively short term cocultures (6 days in vitro) whereas Vaiquié et al., 2019 cultured for at least 4 weeks and in our case 6 weeks prior to axotomy. Vaiquié et al., 2019 removed nerve growth factor (NGF) prior to laser axotomy and quantified proximally (though also imaged distally) whereas both our study and Babetto et al., 2020 kept NGF in the medium, performed axotomy with a scalpel and quantified more distally and in our case extremely distal, where only individual neurites and no axon bundles were visible. Vaquié et al., 2019 had SCs on both sides of the barrier in the microfluidic chambers whereas we seeded SCs only in the axonal/bottom compartment. Additionally, our cocultures had both forskolin and b__NRG1 added to help induce myelination, whereas these factors are not required in rat myelinating cocultures. Finally, it is important to permeabilise myelinated cultures with acetone after fixation__, as we did, to visualise the entire axon through heavily myelinated segments otherwise axon integrity cannot be reliably assessed in a quantitative manner (Vaquié et al, 2019; Babetto et al, 2020)__.

Author response: We would like to add that we showed Claire Jacob, senior author of the Vaquie et al., 2019 study, our manuscript prior to peer review and she offered helpful comments, which we incorporated into the manuscript, which is why she is acknowledged. We have also discussed our findings with Elisabetta Babetto as well.

While it would be a great future study to compare both axon degeneration rates in rat and mouse cocultures this was not the original intention of our study. We believe we have included enough detail of our experimental procedures, including the distance from the barrier we imaged axon degeneration, a crucial bit of information missing from the other studies, should others want to perform a comparative study between rats and mice.

Methods

Five images at a distance of between 1.2-1.4mm from the microgroove barrier (the most distal part of the culture that could be imaged) were quantified per culture, taken in comparable locations in each culture.

Author response: I would add that one of our previous studies (Arthur-Farraj et al., 2012 Neuron, Fig5I) has already looked at axon outgrowth/regeneration in dissociated non-myelinating mouse SC-DRG co-cultures. We showed the presence of Schwann cells accelerates axon regeneration/outgrowth and this relies upon Schwann cell c-JUN.

We have now added quantification of the extent of myelination in our cocultures and it is comparable to the original Bunge lab rat cocultures and more extensive than the Vaquie et al., 2019 coculture.

Results

Quantifying the number of PRX positive myelin segments we found that there were 325.33 ± 12.3 sheaths per mm2, comparable to what has been originally described in rat SC/DRG cocultures and two fold more extensive myelination than recently described compartmentalised rat cocultures models (n=3; Table 1; Eldridge et al, 1987; Vaquié et al, 2019)__. Furthermore, 25.47 ± 1% of Schwann cells were myelinating in our cultures (n=3; Table 1).

Methods

To quantify the number of myelin segments per area, we counted the number of myelin segments for five areas per culture for three cultures and normalised this per mm2. To quantify the percentage of Schwann cells in myelinating cocultures that are actively myelinating, we quantified the number of myelin segments and the number of DAPI-positive nuclei for five areas per culture for three cultures.

Minor comments:<br /> - Figure 2D: From the electron micrograph there is no doubt that compact myelin is formed, however to me it seems the compaction is not complete. A rough estimation with the aid of the provided scale bar resulted in an interperiodic distance of about 17 nm, which contrasts with the remarkably well reproduced values reported in multiple reports using conventional specimen preparation (like in this study), of which I am citing just a few: about 13 nm in rat ex vivo nerve (Peterson and Pease, J. Ultrastruct Res 1972; Fledrich et al., Nat Commun 2018), 12 nm (Giese et al., Cell 1992), 12.2 nm (Perot et al., J Neurosci 2007), about 12 (Fernando et al., Nat Commun 2016), about 13 nm (García-Mateo et al., Glia 2017) or about 13 nm in mouse ex vivo (Boutary et al., Commun Biol 2021), which was also reproduced with about 13 nm in rat in vitro (Taveggia and Bolino, Methods Mol Biol 2018). The authors should acknowledge this deviation and might discuss possible reasons.

Author response: We have now provided a more representative EM image of our myelination (Fig. 2D). Additionally, thanks to the reviewer’s comments we have now quantified the interperiodic distance and find it is comparable to the studies the reviewer suggested. We have added the data to the new Table 1 and added the references the reviewer advised. Please see the additions to the methods and the results section regarding this new data below.

Results

To confirm that cocultured myelinated SCs formed compact myelin we performed electron microscopy (EM), which revealed compact myelin formation with multiple myelin wraps and formation of readily visible major dense (MDL) and intraperiod lines (IPL; Fig. 2D). Additionally, we measured the periodicity, i.e., the distance between two adjacent major dense lines, to make sure myelin was compacted. Interperiodic distance was 12.16 ± 0.28 nm, in line with previous reports (n=3; Table 1; Boutary et al, 2021; Fernando et al, 2016; García-Mateo et al, 2018; Giese et al, 1992; Perrot et al, 2007)__.

Methods

To measure interperiodic distance, we measured at least 10 periods per myelinated fibre for at least three fibres per sample for three separate samples.

• Figure 4A,B: The result of a slowed axon degeneration in coculture relies on the accurate assessment of continuity of the NFL staining. While the authors report that acetone permeabilization was necessary to afford complete penetration of the used antibodies in myelinating cultures, I cannot see why the authors have not used the same staining protocol for all cultures, as it is detailed in the method section. While I consider it unlikely that the staining conditions have led to an apparent delay of degeneration in coculture, experiments should generally be performed under identical conditions, unless there are good reasons not to do so. If this is not the case, it will be reassuring to see the same effect when identical staining conditions are employed. On the same note, do the compared cultures have the same age, i.e. have the neuron monocultures been in vitro for the same time as the cocultures?

Author response: We thank the reviewer for picking up this inaccuracy in the manuscript. We can confirm that for the purposes of the axon degeneration experiment all cultures were stained using exactly the same staining protocol. Additionally, we were very careful to maintain all cultures for exactly the same time in culture – 6 weeks. Additionally, axon only cultures were maintained in myelination medium to make sure medium constituents were not responsible for the observed differences in degeneration rate. We have added the following elaboration to the methods section to clarify these points.

Methods

Axon only cultures related to Figure 1 were permeabilised in PBS + 0.5% Triton (Merck) + 5% HS + 5% donkey serum (DS, Merck - D9663) at RT for 1 hour. For the purposes of quantifying the rate of axon degeneration (Figure 4) both axon only cultures and cocultures with SCs were permeabilised in 50% Acetone for 2 minutes, 100% Acetone for 2 minutes, 50% Acetone for 2 minutes (all at RT), and then blocked in PBS + 0.5% Triton + 5% HS + 5% DS at RT for 1 hour.

Methods

All cultures (axon only, aligned SCs and myelinating SCs) were cultured for 6 weeks prior to axotomy experiments. To minimise the possibility that medium constituents were responsible for differences in axon degeneration rates, axonal compartments of axon only cultures were cultured in medium containing 10 ng ml-1 b__NRG1 and 10 m__M forskolin (axon only medium, extended methods section D6) once SCs were seeded on other cultures, and then switched into myelination medium (additional Matrigel__â and 50 m__g ml−1 L-Ascorbic Acid), 24 hours before axotomy. Bottom compartments of aligned SC cultures, 24 hours before axotomy, were switched into DRG/SC medium containing 10 ng ml-1 b__NRG-1, 10 m__M forskolin and 50 m__g ml−1 L-Ascorbic Acid, which is insufficient to induce myelination in mouse cultures. Bottom compartments of myelinating cocultures were medium changed into fresh myelination medium (Extended methods section D7) 24 hours prior to axotomy.

• In several instances of the manuscript, the term "transfection" is used to refer to lentiviral gene transfer. I advise to use the more appropriate term "transduction" instead
• I could not seem to find a meaningful reference to the microfluidic chambers that were used in the study. The protocol should contain details on the device and source of supply in order to enable potential readers to execute the protocol

Author response: We thank the reviewer for this comment. We have replaced transfection with transduction throughout the manuscript. Please see the track changes manuscript for all instances.

Reviewer #2 (Significance):

The paper presents a convincing establishment of a dissociated coculture derived exclusively from mouse that leads to robust myelination. As the manuscript correctly states, Schwann cell culture and especially coculture with neurons has been experienced difficult in the field, and by providing a detailed protocol as well as demonstrating how the coculture system can be used to address important questions of PNS myelination and repair, the paper fills an important gap. However, the experiments directed to the role of Schwann cells in axon degeneration do not clarify much, which should be better addressed in the discussion and also by modifying the title accordingly.<br /> The paper will be of high value for basic researchers that are interested in performing studies addressing cellular and molecular mechanisms of myelination and repair in the PNS. Importantly, the paper can pave the way to usage of transgenic or knockout mouse models in coculture. Thereby it might spark interest also in those researchers that use transgenic and knockout mouse models and who have so far refrained from using coculture models.

Field of expertise of the reviewer: Cellular and molecular mechanisms of myelination and growth signaling in the PNS; in-depth experience with DRG coculture models from rats and mice

Author response: We thank the reviewer for their kind comments. We have now modified the title, aims and discussion of the manuscript in line with the reviewer’s suggestions.

Reviewer #3 (Evidence, reproducibility and clarity):

SUMMARY<br /> The authors present a detailed protocol for co-cultures of mouse DRGs with mouse SCs using microfluidics. In this model, cells of interest grow in different compartments while allowing for axons to grow in between, thereby making them accessible to injury induction. Using this experimental system, the authors show that myelination occurs, myelin gets compacted and acquires nodal organization. The authors then show that such a system allows for compartment-specific lentivirus transduction and live imaging. Next, they perform physical and chemical axonal injury and show that at early time point pos- injury the presence of SCs protects from axonal degeneration regardless of the myelination status, and helps with clearing of damaged axons at later time points.<br /> Major comments:

The novelty of the study is questionable.<br /> While the model is well described and appears to be useful for the proposed applications (live imaging, transduction, injury model), the arguments provided regarding its novelty are not fully convincing. The main argument from the authors of this paper is that there are no established protocols describing the use of mouse SCs with mouse DRG neurons in dissociated myelinating cocultures. However, this appears to be inaccurate, as the model described in Stevens et al., 1998 (cited in the paper) uses mouse DRG neurons dissected at E13.5 with mouse SCs dissected at P3 to study myelination. Also, in Päiväläinen et al., 2008, mouse DRGs and SCs are cultured from transgenic mice at different developmental ages, thereby arguing that coculture models have been previously successfully implemented. The main difference appears to be rather the compartmentalization of SCs and DRGs which appears to be a mouse adaptation of the rat model described by Vaquie et al,2019. Based on the above, it seems imperative for the authors to tone down the novelty aspect and provide a more thorough discussion on how the current novel differs from protocols in published study, highlighting advantages and caveats for each.

Author response: We agree with the reviewer that we did not make the case clear enough for how our coculture model adds to what is currently described in the literature. We have now changed the title and removed the word novel. New title:

A method for mouse myelinating Schwann cell-DRG neuron compartmentalised cocultures: myelination status does not influence the axo-protective effect of Schwann cells after injury.

Author response: We have now added the following paragraph to the introduction:

Introduction.

Indeed there has only ever been one laboratory detailing convincing myelin formation in dissociated mouse myelinating SC-DRG neuron cocultures, however this was never published as a step by step detailed protocol (Stevens et al, 1998; Stevens & Fields, 2000)__. In the last twenty years, there have been no published studies demonstrating myelination in fully dissociated mouse SC-mouse DRG cocultures. This has largely prevented the use of cells, particularly SCs, from transgenic mice in cocultures and thus restricted the ability to study SC-axon interactions in a system that can be readily manipulated and live imaged and results directly applied back to in vivo findings in the same species.

Author response: Regarding the study by Päiväläinen et al, 2008_,_ they did not fully dissociate their DRGs (see Fig.1 which demonstrates a DRG explant) and thus it is a non-dissociated DRG explant model. While they demonstrated convincing myelination due to the use of Matrigel which we acknowledge them for, their model is not perfectly suited for the use of neurons and SCs from different transgenic animals as the use of a DRG explant, even with temporary use of an antimitotic, risks contamination by endogenous SCs and satellite glia over time, especially as their model is not compartmentalised. We discuss the caveats of their protocol and those using dissociated mouse explant cocultures in a revised paragraph in the introduction.

Introduction.

Protocols exist where endogenous mouse SCs are used to myelinate dissociated or non-dissociated DRG explant cultures. (Shen et al, 2014; Harty et al, 2019; Sundaram et al, 2021; Stettner et al, 2013; Numata-Uematasu et al, 2023)__. Furthermore, another protocol seeded exogenous SCs onto non-dissociated DRG explant cultures (Päiväläinen et al, 2008)__. Other laboratories seed cultured rat SCs onto dissociated mouse DRG axons (Taveggia & Bolino, 2018)__. Use of dissociated or non-dissociated DRG explants cultures precludes many experimental uses, such as using SCs from different transgenic animals and separate transfection of SCs and neurons with viruses for live imaging or genetic manipulation, and easy use of microfluidic chambers to allow injury studies and separate drug treatments to neurons or SCs. The reason for this is that antimitotics cannot be used in dissociated or non-dissociated DRG explant cultures as this depletes SCs, and the culture quickly becomes contaminated with other non-neuronal cell types, such as satellite cells and fibroblasts migrating out of the DRG. Furthermore, use of exogenous SCs in a non-dissociated DRG explant culture risks, after a period of antimitotic exposure, which was developed by Päiväläinen et al., 2008 still risks potential contamination from endogenous SCs and satellite glia migrating out of the DRG explant over time. This occurs because antimitotic treatment is unlikely to fully penetrate the whole DRG without prior dissociation. Additionally, a compartmentalised culture system cannot be readily used with non-dissociated DRG explant cultures (Päiväläinen et al, 2008)__.

Author response: We have also added further discussion on how our protocol differs from the Stevens 1998 and other protocols in the discussion.

Discussion

Our protocol differs somewhat from the one used by Stevens et al., 1998 to induce myelination in dissociated mouse SC-DRG cocultures__, as they used ascorbic acid and 10% horse serum and presumably plated their cultures on laminin, though they do not explicitly detail this (Stevens et al, 1998)__. In our preliminary experiments we were unable to visualise much myelination with use of laminin, ascorbic acid or indeed if b__NRG1 and high concentration forskolin was added to the medium for up to four weeks. However, if we plated cocultures on Matrigel® and continuously added it to the myelination medium then we saw comparable levels of myelination in our mouse cocultures to that of rat cocultures (Eldridge et al, 1987)__. This approach of using Matrigel® to enhance myelination has previously been successfully employed in cultures of human iPSC sensory neurons with rat SCs and in in non-dissociated mouse DRG explant cultures (Clark et al, 2017; Päiväläinen et al, 2008)__. Importantly, we used growth factor depleted Matrigel® as standard Matrigel® preparations contain substantial amounts of Transforming growth factor b (TGF b__) which is a known inhibitor of myelination (Einheber et al, 1995)__. Additionally, the majority of rat and mouse coculture protocols plate cells on glass whereas we found cultures were healthier and myelinated better when cultured on plastic Alcar® coverslips.

Author response: We have added further discussion of comparable models in the literature in the discussion.

Discussion.

Furthermore, our protocol is complementary to the recently described 3D mouse myelinating SC-motor neuron coculture system using collagen hydrogels (Hyung et al, 2021; Park et al, 2021)__. It will be interesting in the future to up titrate the concentration of Matrigel®, which is similar to collagen hydrogels, in our cultures to see whether further increasing extracellular matrix viscosity and stiffness improves our myelination efficiency even further. While it is possible to study cell migration in microfluidic cell culture devices, transwell models offer significant advantages to study this cellular phenomenon (Negro et al, 2022)__. To date, there have been no published studies of successful myelination in human SC-neuron coculture systems. Despite this rat SCs have been shown to readily myelinate human-induced pluripotent stem cell (iPSC)-derived sensory neurons and an iPSC-derived peripheral nerve organoid system which does contain myelinating SCs has recently been described (Clark et al, 2017; Van Lent et al, 2022)__.

Next, the authors emphasize the conflicting results of two articles, Babetto et al., 2020 and Vaquie et al., 2019, as the basis to use their newly developed model in the same species and testing two ages corresponding to distinct myelination states. However, both studies reach the same conclusion as the current study, that SCs have a protective role, although at two different developmental time points. As such, it is likely that multiple mechanisms may account for the protective effect of SC on axonal damage, and therefore the different studies do not seem conflicting but rather complementary. Yet, it is interesting that this manuscript shows that the myelination status of SCs does not impact their ability to slow down degeneration and yet it confirms that different timing after injury elicits different behaviors in SCs, as suggested by the studies of Babetto et al., 2020 and Vaquie et al., 2019. In other words, a more accurate description of the results of these two studies is needed and a better explanation of what the authors consider to be conflicting and why (there could be more differences than species and myelination, for instance, such as the method used for axotomy - laser vs cut with scalpel which tear and pull membranes).

Author response: We would like to humbly correct the reviewer that the studies by Babetto et al., 2020 and Vaquie et al., 2019 do not reach the same conclusion that Schwann cells have a protective role. Instead, they describe axon protection (Babetto et al., 2020) and axon fragmentation (Vaquie et al., 2019). Our studies now visualise both phenomena in the same culture system. We have now made this point more explicit as well as highlighted the one conceptual advance our methods paper makes on the current literature, which is that myelination status does not influence the SC axo-protection, as the reviewer suggested.

Discussion.

These findings confirm the observations of both Babetto et al., 2020 and Vaquié et al., 2019 who used rat SCs in similar microfluidic culture systems (Vaquié et al, 2019; Babetto et al, 2020)__. We have shown that the axo-protective observation seen by Babetto et al., 2020 does not rely on myelination status, which was an outstanding question from that study (Babetto et al, 2020)__. Furthermore, in an advance from previous studies, we have visualised the axo-protective and axon clearance phenomena in the same culture and shown that they are temporally separated, with axon fragmentation and debris clearance by SCs occurring at much later timepoints after axotomy.

Author response: We have now added more in-depth discussion of the similarities and differences between Babetto et al., 2020 and Vaquie et al., 2019 and our approach in the discussion.

Discussion.

Several different culture and experimental conditions preclude direct comparison of our study with both those of Vaquié et al., 2019 and Babetto et al., 2020. These include the use of rat SCs in both prior studies and that Babetto et al., 2020 mixed rat SC with mouse DRG axons; length of time in culture, time points quantified after injury and distance from injury and site of analysis. Babetto et al., 2020 performed axotomy on relatively short term cocultures (6 days in vitro) whereas Vaiquié et al., 2019 cultured for at least 4 weeks and in our case 6 weeks prior to axotomy. Vaiquié et al., 2019 removed nerve growth factor (NGF) prior to laser axotomy and quantified proximally (though also imaged distally) whereas both our study and Babetto et al., 2020 kept NGF in the medium, performed axotomy with a scalpel and quantified more distally and in our case extremely distal, where only individual neurites and no axon bundles were visible. Vaquié et al., 2019 had SCs on both sides of the barrier in the microfluidic chambers whereas we seeded SCs only in the axonal/bottom compartment. Additionally, our cocultures had both forskolin and b__NRG1 added to help induce myelination, whereas these factors are not required in rat myelinating cocultures. Finally, it is important to permeabilise myelinated cultures with acetone after fixation__, as we did, to visualise the entire axon through heavily myelinated segments otherwise axon integrity cannot be reliably assessed in a quantitative manner (Vaquié et al, 2019; Babetto et al, 2020)__.

Author response: We would like to add that we showed Claire Jacob, senior author of the Vaquie et al., 2019 study, our manuscript prior to peer review and she offered helpful comments, which we incorporated into the manuscript, which is why she is acknowledged. We have also discussed our findings with Elisabetta Babetto as well.

Overall, the title does not appear to be the most appropriate because the content rather proposes a detailed protocol and gives examples of applications, rather than focusing on the protective versus destructive role of SCs on axons. It also appears to be misleading, as "axo-destructive" appears to suggest a negative role of Schwann cells on axons, whereas SC are rather helpful in clearing degenerative axons, a step which facilitates regeneration.

Author response: We have now changed the title and the focus of the manuscript in line with the reviewer’s comments. New title:

A method for mouse myelinating Schwann cell-DRG neuron compartmentalised cocultures: myelination status does not influence the axo-protective effect of Schwann cells after injury.

Author response: We have removed the phrase axo-destructive throughout the manuscript and instead referred to axon fragmentation and axon debris clearance roles of SCs in line with the reviewer’s suggestion. Please see track changes manuscript for all instances where this was modified.

The number of biological replicates for each experiment is not always indicated, and if the "n=" represent cultures prepared independently/passaged or wells/cell. It is essential to be rigorous and clearly indicate the number of technical replicates and biological samples throughout the manuscript and provide a thorough description of them. One example is Fig. 4 E were only 10 cells from a single culture appeared to have been imaged. Is this accurate? This aspect is essential to evaluate reproducibility, especially in view of the technical and biological variability.

Author response: We have now added quantification of myelin segments per mm2, percentage of SCs that myelinate and quantification of the interperiodic distance of the myelin formed. This is all included in a new version of TABLE 1. We have discussed this data in the results section as follows.

Quantifying the number of PRX positive myelin segments we found that there were 325.33 ± 12.3 sheaths per mm2, comparable to what has been originally described in rat SC/DRG cocultures and two fold more extensive myelination than recently described compartmentalised rat cocultures models (n=3; Table 1; Eldridge et al, 1987; Vaquié et al, 2019)__. Furthermore, 25.47 ± 1% of Schwann cells were myelinating in our cultures (n=3; Table 1). To confirm that cocultured myelinated SCs formed compact myelin we performed electron microscopy (EM), which revealed compact myelin formation with multiple myelin wraps and formation of readily visible major dense (MDL) and intraperiod lines (IPL; Fig. 2D). Additionally, we measured the periodicity, i.e., the distance between two adjacent major dense lines, to make sure myelin was compacted. Interperiodic distance was 12.16 ± 0.28 nm, in line with previous reports (n=3; Table 1; Boutary et al, 2021; Fernando et al, 2016; García-Mateo et al, 2018; Giese et al, 1992; Perrot et al, 2007)__.

Author response: We have discussed the number of cultures used for each quantification in the methods section. See below.

Quantification of Myelination in cocultures

To quantify the number of myelin segments per area, we counted the number of myelin segments for five areas per culture for three cultures and normalised this per mm2. To quantify the percentage of Schwann cells in myelinating cocultures that are actively myelinating, we quantified the number of myelin segments and the number of DAPI-positive nuclei for five areas per culture for three independently prepared cultures. To measure interperiodic distance, we measured at least 10 periods per myelinated fibre for at least three fibres per sample for three separate samples.

Quantification of Degeneration

Five images at a distance of between 1.2-1.4mm from the microgroove barrier (the most distal part of the culture that could be imaged) were quantified per culture, taken in comparable locations in each culture. A line was drawn across each image, and each axon crossing this line was either scored as degenerated or intact. Images were blinded prior to quantification. A minimum of three independently prepared cultures were assessed per timepoint for each condition.

Author response: We have removed Fig.4E and instead quoted the data in the results section as follows:

When we quantified this phenomenon, we found that 97.84 ± 1.462% (n=2) of SCs in our cocultures contained mCherry-labelled axonal fragments.

Author response: We apologise as the n number for this experiment was 2 (not 10), with cells in 10 areas quantified throughout all imaging timepoints from each independently prepared culture. We have included the following description in the methods section:

To quantify number of SCs with fragments, each cell was defined as a region of interest and checked for the presence of mCherry positive fragments at all timepoints. Two separate independently prepared cultures and cells in 10 areas per culture were analysed.

Author response: Additionally for Fig. 4B we have now included individual data points from independently prepared cultures.

N numbers are included in all figure legends and always refers to independently prepare cultures/biological replicates.

We have added to the relevant figure legends (Fig.3 and 4 and Table 1) the phrase:

n number refers to independently prepared cultures from separate litters of mice.

Minor comments:

• Does myelination reach axons in the microgrooves (it seems to from 2C, but up to where)? Where is axotomy performed and are axons myelinated where the cut was performed?

Author response: Myelination occasionally reaches the beginning of the microgrooves. We didn’t visualise myelination in the DRG cell body compartment. We have added the following detail to the methods section:

Traumatic axotomies were carried out by carefully removing the microfluidic chamber (SND150 and RND150, Xona Microfluidics__Ò__) from the Aclar__â coverslip using sterile forceps and severing axons with a surgical blade under a light microscope. Axotomies were carried out at the level of the microgroove barrier. To confirm all axons were severed, a second higher cut was performed and axons between the cut sites removed using the surgical blade.

Author response: Given this, we cannot exclude that the odd proximal myelin segment is cut, but the vast majority of axons are not myelinated at the site of cut (lower cut).

• Since the model allows for comparison of aligned vs myelinating SCs, and that both aligned and myelinating SCs seem to slow down degeneration, and that c-JUN is upregulated after in vivo injury, have the authors measured if c-JUN levels increase similarly in both myelinating vs aligned SCs?

Author response: We thank the reviewer for this suggestion. We have now quantified the JUN upregulation after injury in both myelinating and aligned cocultures as well as adding images of JUN upregulation in aligned cocultures. See Fig. 3B-E).

Additionally, we noted a strong upregulation of JUN protein in SCs 12 hours after axotomy (Fig. 3B and C). We also saw significant JUN upregulation 12 hours after axotomy in cocultures with aligned SCs (Fig. 3D and E).

Author response: We have decided to focus the manuscript more on the comparison of myelinating versus non-myelinating cocultures, given that we have shown that the axo-protective effect of SCs is independent of myelination status, which is an advance on what is known in the literature. In addition to changing the title, as we have mentioned previously, we have added further characterisation of our aligned cocultures with p75NTR immuno and EM images (Fig.1D and E).

We have

• On clarity:<br /> - In the step-by-step protocol, wording needs to be improved.

Author response: We have substantially edited the step-by-step protocol. Please see track changes document for all specific changes in wording.

• Temperatures for centrifugations are missing.

Author response: We have added temperatures for all centrifugation steps. Please see track changes document

• The MOI described for lentivirus is 2-10 in the protocol but 200 in the legend of Figure 3F.

Author response: The MOI for DRGs was 2-10 and SCs was 200 in Figure 3F. This is described similarly in the extended methods section. DRGs are transduced much more easily than SCs.

We have added the following sentence to the results section to emphasise this point:

Importantly dissociated mouse SCs required a much higher multiplicity of infection (MOI) than dissociated mouse DRGs (see extended methods section).

• Certain citations in the references list are incomplete (i.e. Babetto et al.; Catenaccio et al.,).

Author response: We have updated the reference list.

Reviewer #3 (Significance):

The advance for the field proposed by this paper is mostly technical, as it details a new model to be used by the field, of mouse SCs-mouse DRGs in dissociating myelinating cultures. The tested applications allowed the authors to also confirm a protective role for SCs on axonal damage, which was independent from myelination status.

Being a method paper, it is essential that the authors provide clear statements on the number of biological replicates, and technical repeats, as well as a very thorough and accurate description of the methodology.

The model described has similarities with existing models in the field such as Stevens et al., 1998 and Vaquié et al., 2019. To place it in context in a more helpful way, the authors should emphasize on the novelty brought by their protocol compared to existing models. The authors compare their findings to results from Vaquié et al., 2019 and Babetto et al., 2020 that they describe as conflicting, when it seems they rather address different mechanisms of SCs in protection and repair, occurring at different time points.

Audience might be interested in the detailed step by step protocol to use this in vitro model for the applications described, and investigate further why SCs myelination status does not influence their ability to protect from neurodegeneration early on or how to make use of this for neuroprotection studies.

Author response: We have now rephrased the description of Vaquié et al., 2019 and Babetto et al., 2020 studies in line with the reviewer’s suggestions. We have now added further discussion of our model in the context of all other models in the field as we have outlined in detail in above responses.

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Referee #3

Evidence, reproducibility and clarity

Summary

The authors present a detailed protocol for co-cultures of mouse DRGs with mouse SCs using microfluidics. In this model, cells of interest grow in different compartments while allowing for axons to grow in between, thereby making them accessible to injury induction. Using this experimental system, the authors show that myelination occurs, myelin gets compacted and acquires nodal organization. The authors then show that such a system allows for compartment-specific lentivirus transduction and live imaging. Next, they perform physical and chemical axonal injury and show that at early time point pos- injury the presence of SCs protects from axonal degeneration regardless of the myelination status, and helps with clearing of damaged axons at later time points.

Major comments:

The novelty of the study is questionable.<br /> While the model is well described and appears to be useful for the proposed applications (live imaging, transduction, injury model), the arguments provided regarding its novelty are not fully convincing. The main argument from the authors of this paper is that there are no established protocols describing the use of mouse SCs with mouse DRG neurons in dissociated myelinating cocultures. However, this appears to be inaccurate, as the model described in Stevens et al., 1998 (cited in the paper) uses mouse DRG neurons dissected at E13.5 with mouse SCs dissected at P3 to study myelination. Also, in Päiväläinen et al., 2008, mouse DRGs and SCs are cultured from transgenic mice at different developmental ages, thereby arguing that coculture models have been previously successfully implemented. The main difference appears to be rather the compartmentalization of SCs and DRGs which appears to be a mouse adaptation of the rat model described by Vaquie et al,2019. Based on the above, it seems imperative for the authors to tone down the novelty aspect and provide a more thorough discussion on how the current novel differs from protocols in published study, highlighting advantages and caveats for each.

Next, the authors emphasize the conflicting results of two articles, Babetto et al., 2020 and Vaquie et al., 2019, as the basis to use their newly developed model in the same species and testing two ages corresponding to distinct myelination states. However, both studies reach the same conclusion as the current study, that SCs have a protective role, although at two different developmental time points. As such, it is likely that multiple mechanisms may account for the protective effect of SC on axonal damage, and therefore the different studies do not seem conflicting but rather complementary. Yet, it is interesting that this manuscript shows that the myelination status of SCs does not impact their ability to slow down degeneration and yet it confirms that different timing after injury elicits different behaviors in SCs, as suggested by the studies of Babetto et al., 2020 and Vaquie et al., 2019. In other words, a more accurate description of the results of these two studies is needed and a better explanation of what the authors consider to be conflicting and why (there could be more differences than species and myelination, for instance, such as the method used for axotomy - laser vs cut with scalpel which tear and pull membranes).

Overall, the title does not appear to be the most appropriate because the content rather proposes a detailed protocol and gives examples of applications, rather than focusing on the protective versus destructive role of SCs on axons. It also appears to be misleading, as "axo-destructive" appears to suggest a negative role of Schwann cells on axons, whereas SC are rather helpful in clearing degenerative axons, a step which facilitates regeneration.

The number of biological replicates for each experiment is not always indicated, and if the "n=" represent cultures prepared independently/passaged or wells/cell. It is essential to be rigorous and clearly indicate the number of technical replicates and biological samples throughout the manuscript and provide a thorough description of them. One example is Fig. 4 E were only 10 cells from a single culture appeared to have been imaged. Is this accurate? This aspect is essential to evaluate reproducibility, especially in view of the technical and biological variability.

Minor comments:

• Does myelination reach axons in the microgrooves (it seems to from 2C, but up to where)? Where is axotomy performed and are axons myelinated where the cut was performed?
• Since the model allows for comparison of aligned vs myelinating SCs, and that both aligned and myelinating SCs seem to slow down degeneration, and that c-JUN is upregulated after in vivo injury, have the authors measured if c-JUN levels increase similarly in both myelinating vs aligned SCs?
• On clarity:
• In the step-by-step protocol, wording needs to be improved.
• Temperatures for centrifugations are missing.
• The MOI described for lentivirus is 2-10 in the protocol but 200 in the legend of Figure 3F.
• Certain citations in the references list are incomplete (i.e. Babetto et al.; Catenaccio et al.,).

Significance

The advance for the field proposed by this paper is mostly technical, as it details a new model to be used by the field, of mouse SCs-mouse DRGs in dissociating myelinating cultures. The tested applications allowed the authors to also confirm a protective role for SCs on axonal damage, which was independent from myelination status.

Being a method paper, it is essential that the authors provide clear statements on the number of biological replicates, and technical repeats, as well as a very thorough and accurate description of the methodology.

The model described has similarities with existing models in the field such as Stevens et al., 1998 and Vaquié et al., 2019. To place it in context in a more helpful way, the authors should emphasize on the novelty brought by their protocol compared to existing models. The authors compare their findings to results from Vaquié et al., 2019 and Babetto et al., 2020 that they describe as conflicting, when it seems they rather address different mechanisms of SCs in protection and repair, occurring at different time points.

Audience might be interested in the detailed step by step protocol to use this in vitro model for the applications described, and investigate further why SCs myelination status does not influence their ability to protect from neurodegeneration early on or how to make use of this for neuroprotection studies.

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Referee #2

Evidence, reproducibility and clarity

In the manuscript entitled "Distinct axo-protective and axo-destructive roles for Schwann cells after injury in a novel compartmentalised mouse myelinating coculture system", Arthur-Farraj and colleagues detail a method of dissociated coculture of mouse-DRG neurons and Schwann cells in microfluidic chambers. In this system, neurons and Schwann cells harvested from the same or from different animals are grown in different compartments that are connected by microgrooves, thereby allowing for spatial and diffusive separation. Neurons are shown to extent their axons across the microgroove barrier to the glial compartment where Schwann cells align with the axons and myelination can be induced. Detailed analysis of myelination and axon injury/degradation are presented as use cases, including the capability to genetically and pharmacologically manipulate neurons and Schwann cells independently, which also enabled fluorescent life cell imaging. The authors then examine the effect of immature/premyelinating and myelinating Schwann cells on the rate of axon degeneration. Upon axotomy Schwann cells significantly delayed degeneration, with no difference between non-myelinating and myelinating Schwann cells. Finally, live imaging during axon degeneration with fluorescent proteins separately expressed in neurons and Schwann cells demonstrated that Schwann cells ingest axonal fragments.

Major comment:

In establishing a much needed in-vitro system for PNS myelination and injury research the paper represents a valuable contribution to the PNS community. However, I find the presentation of aspects concerning a protective/destructive role of Schwann cells somewhat inconclusive. That these roles exist has been known, as the authors discuss. Then what does this study contribute concerning the open question that was raised by the discrepancies between Babetto et al., 2020 and Vaquié et al., 2019, i.e. how Schwann cells contribute to axon survival/regeneration after injury? Essentially, the only significant conclusion in this regard is that myelinating and non-myelinating mouse Schwann cells do not differ in their capability to protect axons from degeneration. The manuscript, including the title, would benefit from focusing more on this aspect. In particular, the discussion of the factors that lead to the still remaining apparent discrepancies between Babetto et al., 2020 and Vaquié et al., 2019 and this study should be revised. The authors state that "The study by Vaiquié et al., 2019 quantified axon fragmentation proximally in the microgrooves at timepoints starting from 12 hours after axotomy." (Discussion). While this observation is accurate, Jacob and colleagues also show accelerated, obviously distal axon degeneration in the presence of Schwann cells (Figure 3C in Vaquié et al., 2019). It is therefore unlikely that the discrepancies stem from analysis of more proximal vs. more distal axons, or the timepoints of analyses. In my opinion, a further study (using the coculture system presented in this manuscript) that compares the role of Schwann cells from rats and mice, and that includes analysis of more proximal and distal axon degeneration as well analysis of axon regeneration is needed. In a rework of the manuscript, the authors may therefore elaborate more on the shortcomings of the present study, or alternatively soften the aims of the study in the first place.

Minor comments:

• Figure 2D: From the electron micrograph there is no doubt that compact myelin is formed, however to me it seems the compaction is not complete. A rough estimation with the aid of the provided scale bar resulted in an interperiodic distance of about 17 nm, which contrasts with the remarkably well reproduced values reported in multiple reports using conventional specimen preparation (like in this study), of which I am citing just a few: about 13 nm in rat ex vivo nerve (Peterson and Pease, J. Ultrastruct Res 1972; Fledrich et al., Nat Commun 2018), 12 nm (Giese et al., Cell 1992), 12.2 nm (Perot et al., J Neurosci 2007), about 12 (Fernando et al., Nat Commun 2016), about 13 nm (García-Mateo et al., Glia 2017) or about 13 nm in mouse ex vivo (Boutary et al., Commun Biol 2021), which was also reproduced with about 13 nm in rat in vitro (Taveggia and Bolino, Methods Mol Biol 2018). The authors should acknowledge this deviation and might discuss possible reasons.
• Figure 4A,B: The result of a slowed axon degeneration in coculture relies on the accurate assessment of continuity of the NFL staining. While the authors report that acetone permeabilization was necessary to afford complete penetration of the used antibodies in myelinating cultures, I cannot see why the authors have not used the same staining protocol for all cultures, as it is detailed in the method section. While I consider it unlikely that the staining conditions have led to an apparent delay of degeneration in coculture, experiments should generally be performed under identical conditions, unless there are good reasons not to do so. If this is not the case, it will be reassuring to see the same effect when identical staining conditions are employed. On the same note, do the compared cultures have the same age, i.e. have the neuron monocultures been in vitro for the same time as the cocultures?
• In several instances of the manuscript, the term "transfection" is used to refer to lentiviral gene transfer. I advise to use the more appropriate term "transduction" instead
• I could not seem to find a meaningful reference to the microfluidic chambers that were used in the study. The protocol should contain details on the device and source of supply in order to enable potential readers to execute the protocol

Significance

The paper presents a convincing establishment of a dissociated coculture derived exclusively from mouse that leads to robust myelination. As the manuscript correctly states, Schwann cell culture and especially coculture with neurons has been experienced difficult in the field, and by providing a detailed protocol as well as demonstrating how the coculture system can be used to address important questions of PNS myelination and repair, the paper fills an important gap. However, the experiments directed to the role of Schwann cells in axon degeneration do not clarify much, which should be better addressed in the discussion and also by modifying the title accordingly.

The paper will be of high value for basic researchers that are interested in performing studies addressing cellular and molecular mechanisms of myelination and repair in the PNS. Importantly, the paper can pave the way to usage of transgenic or knockout mouse models in coculture. Thereby it might spark interest also in those researchers that use transgenic and knockout mouse models and who have so far refrained from using coculture models.

Field of expertise of the reviewer: Cellular and molecular mechanisms of myelination and growth signaling in the PNS; in-depth experience with DRG coculture models from rats and mice

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this original article from Mutscher et al., the authors developed a compartmentalized dissociated mouse myelinating SC-DRG coculture system to investigate the distinct roles of Schwann cells in axon protection and degeneration after injury. The innovation of this approach relies on (i) the use of mouse SCs and mouse DRGS neurons instead of rat cells; (ii) the use of microfluidic chambers, seeded by axons and SCs in different compartment; (iii) the possibility to perform a traumatic injury in vitro. While this novel approach offers new ways to study peripheral nerve regeneration and SC-axon interaction, and technical the study is robust, the paper is currently limited by the exploration of their model.

Major points:

It is unclear is this approach will ever lead to the identification of key mechanism or key candidates. This is a major miss in the current manuscript form. In short: the authors should demonstrate that their in vitro system can lead to significant leap in our understanding of peripheral nerve regeneration by identifying novel targets/pathways or mechanisms.

The use of embryonic DRG neurons or SC isolated from P2 animals are arguably physiologically not the same cells that are affected by traumatic nerve injury, which happen most often than not in adult. This is a problem in the long-term reliance on this approach to study axotomy peripheral nerve regeneration.

Moderate points:

"there are no established protocols in the field describing the use of mouse SCs with mouse DRG neurons in dissociated myelinating cocultures". The use of mouse cells is laudable, but it is not necessarily a technical innovation, or at least the current manuscript does not explain why their approach particularly suitable to mouse Schwann cells.

The figures in the paper are largely descriptive. They are very little quantitative measurement. Thus, the readers will have a hard to determine, if they replicate the proposed approach, whether their efficient is on par with the current authors.

Minor points:

Fig.4B and E should show individual data points.

Significance

In addition, to the demonstration of feasibility of this in vitro approach, the main finding by the authors is that SCs have a role in neuronal protection and support is key for peripheral nerve regeneration. Thus while in vitro approach does not add key information that do not already exists in the field, it somewhat confirms that the effect is SC autonomous. Overall the approach is interesting and has potential, but the study currently lack a demonstration of its usefulness to the community.

It would have been interesting to have the authors discuss the advantages of their approach in comparison to other innovative approaches to study SC-axon interactions that have been developed in the last decade (i.e., 3D environment, microfluidic approach, transwell systems). There is also a lack of citations about similar studies in the field.

Because of the lack of key novel mechanisms, and lack of discussion on what this approach is superior to others in vitro approach limits the impact of the study and the excitement of the reader, even from the SC-axon community.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

In this manuscript the author is presenting a deep-learning model used to predict the development stage of zebrafish embryo. A robust method that can accurately classify a zebrafish into different development stages is highly relevant for many researchers working with zebrafish and hence the importance in developing methods like this is high.

The manuscript is overall ok. However, more data is needed to convince the reader that the method is robust enough to work with other samples in other labs. This would greatly improve the impact of the publication.

We agree with the reviewer and have included in our revised manuscripts additional test data that was acquired at a different laboratory to the training data (Figures 5 - 7).

Page 6.<br /> - How is the data acquired?

Images used to do initial model training are the same as those used in a previous study - the details of image acquisition are contained in the relevant publication (doi: 10.12688/wellcomeopenres.18313.1). However, we have now added “Zebrafish Husbandry” and “Live Imaging” for newly-acquired images. We have added a table (Table 1) listing details of all image data used in the study.

Page 8.<br /> "This indicates that whileKimmelNet can be used successfully with noisier test data than that on which it was trained,there is an upper limit to how noisy the data can be."<br /> - This is an obvious statement there will always be an upper limit on noise.

We agree with the reviewer that this statement is not terribly informative. This section (“KimmelNet’s prediction accuracy is not significantly impacted by moderate levels of additive noise”) has been removed from the revised manuscript in favour of incorporating a section detailing testing of the model on newly-acquired images (“KimmelNet can generalise to previously unseen data”).

Page 9.<br /> - Are only wildtype embryos used? How would this work on different mutants. To evaluate the robustness of the method this it would be valuable to test on some mutant line with known developmental difference from the wild type.

We agree with the reviewer that testing on a mutant line would lend more weight to our findings. For example, the p53-/- zebrafish has a reported, published developmental delay, but we did not have access to that line. However, the developmental delay reported for the p53-/- mutant is virtually indistinguishable from that effected by a temperature change. We therefore focussed our efforts on developmental delay affected by altering incubation temperature only.

Image data.<br /> - I would strongly suggest that the author should include a description of the data in the manuscript. A description of how the data is acquired, microscope, different batches, type of embryos used.

The image data used in the first draft of the manuscript is the same as that used in a previous publication (Jones et al. 2022), which contains all the relevant details the reviewer seeks. However, we have now added the relevant information for the newly-acquired image data.

"Random160translation in the y-direction was avoided due to the aspect ratio of the images (width>161height) - any artifacts introduced by translation in the x-direction would be removed by the162centre crop layer, but this would not be the case for translation in the y-direction."<br /> - Could this be solved by using some border method reflection, repetition or fixed value?

The reviewer is correct that some form of image reflection or repetition could be utilised. However, given the nature of our images, if an embryo is located close to the image boundary, reflection/repetition can result in some odd artefacts, so we minimised the use of such fill methods (also used by the random zoom augmentation layer). We could instead use an arbitrary fixed value, as the reviewer suggested, but finding a value suitable for all images is difficult.

Page 10.<br /> Addition of Noise to Image Data<br /> - This should be added in the training phase. This would probably improve the robustness of the network and also improve the results on the test data.

We agree with the reviewer and have now added a random Gaussian noise layer for data augmentation purposes during model training (see Figure 1).

• Supplementary 3 images with high noise. It is worrying that the network is not able to handle the noise in this figure. Looks like the features that is used to distinguish the developmental stage of the embryo is still clearly seen with this high noise level? Retrain the model with noise as an augmentation to improve this.

As the reviewer suggested, addition of random noise is now incorporated into model training. The new version of the manuscript does not include the same supplemental figures, but instead includes additional testing on newly-acquired data instead.

Reviewer #1 (Significance):

The development of methods like this is highly relevant in the zebrafish community. Staging and evaluating the developmental stage for zebrafish is common and is of interest to the broad community. A lot of this work today is done manually, limiting the throughput, and adding human bias.

The limit of this study is the dataset used for training and evaluation. Firstly, it is not clear about the structure of the data and how it is acquired, different types of fish or imaging setup etc. For a method to be useful to the community it needs to be robust enough to handle different types of fish (transgenic lines). The manuscript would be greatly improved by adding this to the training and evaluation.

We have now added additional datasets for the purposes of evaluating the model.

My expertise is image analysis and machine learning for quantification of biological samples, with focus on zebrafish screening.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary<br /> The paper "Automated staging of zebrafish embryos with KimmelNet" by Barry et al., presents a method to automatically stage developmental timepoints of zebrafish embryos based on convolutional neural networks (CNN). The authors show that a CNN trained on ~20k images can determine time post fertilization on test-image sets with an accuracy on the range of a few hours. This technique undoubtedly has the potential to become very useful for any zebrafish researchers interested in developmental timing as it eases analysis and removes potential subjective bias.

Major comments<br /> In its current form the paper lacks sufficient graph annotations and method descriptions. This makes it hard in places to judge the validity of the claims. I do believe that the presented method can be useful and is likely valid but to be convincing, the authors need to spend more time expanding the methods, justifying their choices of analysis and clarifying figure annotations.

We believe that we have addressed the reviewer’s concerns in this revised manuscript, as detailed in response to the specific points below.

Specific points:<br /> 1) The annotation of the training data is not described and specifically it is unclear how valid the staging of the training data itself is. The authors state in the introduction "the hours post fertilization (hpf) [...] provides only and approximation of the actual developmental stage". It is therefore critical to know how this was accounted for in the annotation of the training data. Since the quality of the training data will ultimately limit the best-case quality of Kimmel Net. The authors need to go into some detail here even though the training data appears to be from another published dataset.

The reviewer raises a valid point – two individual zebrafish embryos that are x hours post-fertilisation are not necessarily at the same developmental stage. However, we believe it is reasonable to assume that two populations of embryos x hours post-fertilisation are, on average, at the same developmental stage and it is this assumption that forms the basis for our approach. Given the inherent variability the reviewer refers to, we are not suggesting that our model would be particularly accurate for staging individual embryos. However, we are very confident (and we believe the data in the manuscript supports this) that given a population of embryos, our model will provide an accurate rate of development. We have added a paragraph (lines 131-141) to address this point.

2) Why were "test predictions fit to a straight line through the origin". On the one hand this makes sense (since the slope would indicate the correspondence) but it should be clarified why an intercept was omitted in the fit. After all it is unclear if Kimmel net correctly identifies 0Hpf embryos.

The reviewer makes a valid point – we do not know what predictions KimmelNet would produce for images of embryos closer to 0 hpf. However, we felt an equation of the form y=mx was a reasonable choice for two reasons. First of all, it matches the form of the Kimmel equation, which, despite its flaws, we are using as a benchmark of sorts – the absence of a y intercept makes comparisons with the Kimmel equation straightforward. Secondly, a “perfect” model would produce a straight line fit with y=x, so the lack of a y intercept seemed a reasonable constraint to impose. We have added some brief text (lines 103-105) to clarify this choice.

3) The methods do not list how the mean of the absolute error was calculated from 3B/C. I think this should be the mean of the absolute error (not the mean of the error) but in that case the numbers listed in the text appear rather small given the histograms in 3 B/C. A clear statement in the methods would clarify this issue.

We have now added a “Statistical Analysis” section under Materials & Methods to detail exactly what was used to calculate the values given for error analysis. We have calculated the mean of the error, not the mean of the absolute error, as we wish to illustrate that the mean is close to zero. We have used the standard deviation of the errors to illustrate that there is a significant spread in the error values, as depicted in Figure 3C and D.

Minor comments<br /> 1) The Y-axis in Figure 2B is simply labeled "Loss" - what is the unit of this loss? HPF? Or HPF^2? This is important for judging the quality of the fit

We thank the reviewer for drawing our attention to this omission. The loss is hpf2 (mean squared error) and we have updated the plot to reflect this.

2) Figure 3 B. I would suggest changing the labels of the confidence intervals in the legend. "Inner and outer" is already clear from the figure itself, so labels that state that these are derived from n=100 vs. n=20 test image sized samples would be more useful to the reader

We thank the reviewer for this suggestion – we have updated the figure legend accordingly.

Referees cross-commenting

I concur with comments issued by the other reviewers. I think it will be especially important to address the comments related to testing the method on mutants (Reviewer #1) and training the model in the presence of noise to increase its robustness (Reviewers #1 and #3) as well as addressing the overall annotation/generation of the training data (Reviewers #1 and #2).

We believe we have now addressed all of these concerns. The model has been retrained with additional data augmentation incorporating random noise, tested on newly-acquired data and we have added tables summarising the details of all image data used in this study.

I think these points will be critical to make the paper useful by increasing transparency and ensuring reproducibility in other labs with different imaging conditions, strains, mutants, etc.

Reviewer #2 (Significance):

Developmental delay is a common occurrence that can be caused by genetic and environmental background effects. It is therefore highly desirable to properly quantify this variable. The work presented here makes an important step in this direction, by allowing to quantify developmental timepoints independent of subjective staging. This speeds up analysis, increases reproducibility and enhances rigor. However, as my comments above indicate, the significance also depends on the ability of potential users to judge the quality of the work. Once those issues have been addressed, I think the work will be of broad interest to the developmental biology community, first and foremost labs utilizing the zebrafish model. However, as the authors state, the presented model architecture could be trained with the data from other species as well.

Expertise: Zebrafish, quantitative analysis, behavior, neuroscience

We thank the reviewer for their positive feedback.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary:

Properly staging embryos of zebrafish embryos is important, yet provides challenging since it can depend on many factors, such as temperature, water quality, fish population density, etc. Here, the authors provide a deep-learning-based model called KimmelNet that allows the prediction of the age of zebrafish embryos, using 2D brightfield images. The technique is robust to weak measurement noise and can also be used to identify developmental delays from a very small number of experimental data.

The code is accessible to the reader, open-source and should be useable by experimentalists without huge effort.

Major comments:

I suggest retraining the model and application of the model to additional data for the following reasons:<br /> • Why did the authors not train for (high) measurement noise and heterogeneous background illumination? Would that not make the model more robust? In principle, creating training should not be considerably harder than before, since the manipulation of the images has been already shown in the manuscript and the authors just need to run it again on the HPC cluster. If there are no technical or administrative constraints (access to the cluster, computational effort, high costs, etc.), the authors should retrain their model.

We thank the reviewer for this suggestion. As detailed in Figure 1, with a view to making the model more robust, we have now added several more layers of data augmentation, including the addition of random noise, and retrained our model.

• For Fig. S2 and S3 it is not clear if there is such a strong deviation from the Kimmel equation due to measurement noise or due to the background illumination. The saliency maps appear as if they are mainly affected by the illumination, and maybe less by the noise. Would it be possible to apply the model to a case without artificial noise, but with heterogeneous background illumination to identify what has a bigger impact?

We thank the reviewer for this suggestion. We have now replaced the “artificial” examples used in the previous version of the manuscript with newly-acquired data (Figure 5), which exhibits different characteristics to that used for training.

Additionally, the authors need to clarify what exactly they are comparing in this manuscript and rework their interpretation of their findings:<br /> • When comparing the predictions between KimmelNet and the Kimmel equation, the authors use an equation of the form y=mx. Could the authors please elaborate on why they introduce the constraint of y(0)=0? It might be naturally given by the so-called Kimmel equation, but by looking at Fig 3a, it seems like an equation of the form y=mx+a would be more appropriate and it appears like KimmelNet introduces an offset of around a=2h for 25 Celsius. The authors need to discuss this.

The main rationale for using an equation of the form y=mx is to be consistent with the Kimmel equation (see lines 103-105). The reviewer is correct that an equation of the form y=mx+c may well produce a better fit to the data, but omitting a y intercept makes comparison with the Kimmel Equation trivial.

• In lines 5-8 the authors say that developmental stage progression does not only depend on temperature, but also on population density, water quality etc. and they explain that usually not only hpf, but also staging guides based on morphological criteria are used! If that is true, how good is their training data set that only uses hpf and not the other important guides? How did the authors test that these factors have no impact on their training data?

We have now added a paragraph (lines 131-141) to address this point.

Since this tool has the potential to have a big impact on zebrafish research, it would be nice to provide some examples of how exactly this could be achieved:<br /> • Could the authors discuss how exactly their tool is useful to experimentalists? Is it the idea that if an experimentalist wants to investigate an embryo of a particular stage, they apply KimmelNet to images of embryos to identify the stage of the embryo and only then undertake their planned experiment? Is that a realistic undertaking?

As is evidenced by the errors presented in Figure 3C & D, testing KimmelNet on individual images of embryos may well result in a large error in the predicted hpf. As such, it is not appropriate to use the tool in such a manner. However, to modify the example provided by the reviewer, should an experimentalist have a population of embryos they wished to stage, then yes, KimmelNet would certainly be an appropriate tool for doing so.

• Would it be possible to provide a tutorial (or even video tutorial if such skills are available in the group of authors) that provides real examples of how to apply the technique? This would make it easier for people without advanced Python/Deep-Learning skills to use the tool, hence improving the impact of KimmelNet.

A lack of user-friendly interfaces for applying deep learning methods in biology is well-documented – basic knowledge of python and tools like jupyter notebooks are often necessary (https://doi.org/10.1038/s41592-023-01900-4). However, we have endeavoured to make the running of KimmelNet as easy as possible for new users. A jupyter notebook instance can be run on Binder with absolutely no set-up required. To run KimmelNet on their own data, biologists just need to download the Git repo and replace the test images with their own data. We have updated the landing page on the GitHub repo to provide more specific step-by-step instructions for each of these tasks. We will also endeavour to upload our model to the BioImage Model Zoo (https://bioimage.io/#/) to further increase accessibility.

I am very critical towards equation 1. Please note that I don't think this has any impact on the quality of the technique provided in this manuscript and the significant flaws can already be found in Kimmel 1995 (which is not under review here). That is why I suggest rewriting of this manuscript to not support an over-interpretation of this equation.<br /> • I do not think that the Kimmel equation is an established term. At least a Google Scholar Search for "Kimmel equation" only gives one result: the preprint of this manuscript.<br /> • The equation has no mathematical meaning regarding its units (subtracting temperature and a unitless value). I also very rarely see equations with Degrees Celsius and not Kelvin.<br /> • Additionally, the equation provides a linear relationship between the development time and temperature h(T) and in Kimmel et al, it is shown that this is only true for 25-33 Celsius. Such a linearisation is not very surprising for a small temperature range, but I am not sure how true it is for higher temperature differences. Hence, I feel that it is very bold to give a specific name to such an equation, giving it an importance that it does not deserve.

We appreciate the reviewer’s concerns and have removed explicit references to “The Kimmel Equation”, without substantively changing the content of the manuscript.

Minor comments:

• For the measurement noise cases it would be nice to have some example images of fish with the specific noise levels in Fig S1 and Fig S2.

We have now removed the “synthetic” additive noise test data, previously depicted in Figures S1-3, in favour of newly-acquired images in Figures 5-7.

• Could the authors hypothesize why they predict a slower dynamic for 25 Celsius than predicted by the Kimmel equation?

Referring to Figure 2 in Kimmel et al (1995), it is apparent that the straight lines are by no means perfect fits to the datapoints. In Fig 2A in particular, some datapoints for the 25C data fall well below the line fit. While the published equation suggests a rate of development 80.5% of the rate at 28.5C, according to Fig 2A, an alternative line fit could give a developmental rate as low as 70-75%, which would be in agreement with our data.

Reviewer #3 (Significance):

Strengths of the study:

An easy-to-use method to automatically stage zebrafish embryos and identify differences in the developmental stage is very important for the zebrafish community and the technique in this manuscript definitely novel. The tool is can be used without large effort and the authors suggest that it can also find applications beyond zebrafish embryos. Hence, it is not only interesting to the zebrafish community, but to a broader developmental biology audience.

Weakness of the study:<br /> The main weakness of the manuscript is in the training data used for the deep-learning model and the apparent large impact of heterogeneous background illumination. If that is not solved, it is unclear if this technique will find an application in the zebrafish community.

We believe this weakness has now been addressed by incorporating additional data augmentation measures in the training process and testing the model on newly-acquired data.

Field of expertise of the reviewer: Image Analysis, Mathematical Modelling, Biological Physics. While I have limited experience in deep learning, I cannot evaluate the specific details of the network architecture. I also have no experience in zebrafish research.

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

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Referee #3

Evidence, reproducibility and clarity

Summary:

Properly staging embryos of zebrafish embryos is important, yet provides challenging since it can depend on many factors, such as temperature, water quality, fish population density, etc. Here, the authors provide a deep-learning-based model called KimmelNet that allows the prediction of the age of zebrafish embryos, using 2D brightfield images. The technique is robust to weak measurement noise and can also be used to identify developmental delays from a very small number of experimental data.

The code is accessible to the reader, open-source and should be useable by experimentalists without huge effort.

Major comments:

I suggest retraining the model and application of the model to additional data for the following reasons:<br /> - Why did the authors not train for (high) measurement noise and heterogeneous background illumination? Would that not make the model more robust? In principle, creating training should not be considerably harder than before, since the manipulation of the images has been already shown in the manuscript and the authors just need to run it again on the HPC cluster. If there are no technical or administrative constraints (access to the cluster, computational effort, high costs, etc.), the authors should retrain their model.<br /> - For Fig. S2 and S3 it is not clear if there is such a strong deviation from the Kimmel equation due to measurement noise or due to the background illumination. The saliency maps appear as if they are mainly affected by the illumination, and maybe less by the noise. Would it be possible to apply the model to a case without artificial noise, but with heterogeneous background illumination to identify what has a bigger impact?

Additionally, the authors need to clarify what exactly they are comparing in this manuscript and rework their interpretation of their findings:<br /> - When comparing the predictions between KimmelNet and the Kimmel equation, the authors use an equation of the form y=mx. Could the authors please elaborate on why they introduce the constraint of y(0)=0? It might be naturally given by the so-called Kimmel equation, but by looking at Fig 3a, it seems like an equation of the form y=mx+a would be more appropriate and it appears like KimmelNet introduces an offset of around a=2h for 25 Celsius. The authors need to discuss this.<br /> - In lines 5-8 the authors say that developmental stage progression does not only depend on temperature, but also on population density, water quality etc. and they explain that usually not only hpf, but also staging guides based on morphological criteria are used! If that is true, how good is their training data set that only uses hpf and not the other important guides? How did the authors test that these factors have no impact on their training data?

Since this tool has the potential to have a big impact on zebrafish research, it would be nice to provide some examples of how exactly this could be achieved:<br /> - Could the authors discuss how exactly their tool is useful to experimentalists? Is it the idea that if an experimentalist wants to investigate an embryo of a particular stage, they apply KimmelNet to images of embryos to identify the stage of the embryo and only then undertake their planned experiment? Is that a realistic undertaking?<br /> - Would it be possible to provide a tutorial (or even video tutorial if such skills are available in the group of authors) that provides real examples of how to apply the technique? This would make it easier for people without advanced Python/Deep-Learning skills to use the tool, hence improving the impact of KimmelNet.

I am very critical towards equation 1. Please note that I don't think this has any impact on the quality of the technique provided in this manuscript and the significant flaws can already be found in Kimmel 1995 (which is not under review here). That is why I suggest rewriting of this manuscript to not support an over-interpretation of this equation.<br /> - I do not think that the Kimmel equation is an established term. At least a Google Scholar Search for "Kimmel equation" only gives one result: the preprint of this manuscript.<br /> - The equation has no mathematical meaning regarding its units (subtracting temperature and a unitless value). I also very rarely see equations with Degrees Celsius and not Kelvin.<br /> - Additionally, the equation provides a linear relationship between the development time and temperature h(T) and in Kimmel et al, it is shown that this is only true for 25-33 Celsius. Such a linearisation is not very surprising for a small temperature range, but I am not sure how true it is for higher temperature differences. Hence, I feel that it is very bold to give a specific name to such an equation, giving it an importance that it does not deserve.

Minor comments:

• For the measurement noise cases it would be nice to have some example images of fish with the specific noise levels in Fig S1 and Fig S2.
• Could the authors hypothesize why they predict a slower dynamic for 25 Celsius than predicted by the Kimmel equation?

Significance

Strengths of the study:

An easy-to-use method to automatically stage zebrafish embryos and identify differences in the developmental stage is very important for the zebrafish community and the technique in this manuscript definitely novel. The tool is can be used without large effort and the authors suggest that it can also find applications beyond zebrafish embryos. Hence, it is not only interesting to the zebrafish community, but to a broader developmental biology audience.

Weakness of the study:

The main weakness of the manuscript is in the training data used for the deep-learning model and the apparent large impact of heterogeneous background illumination. If that is not solved, it is unclear if this technique will find an application in the zebrafish community.

Field of expertise of the reviewer:

Image Analysis, Mathematical Modelling, Biological Physics. While I have limited experience in deep learning, I cannot evaluate the specific details of the network architecture. I also have no experience in zebrafish research.

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 #2

Evidence, reproducibility and clarity

Summary

The paper "Automated staging of zebrafish embryos with KimmelNet" by Barry et al., presents a method to automatically stage developmental timepoints of zebrafish embryos based on convolutional neural networks (CNN). The authors show that a CNN trained on ~20k images can determine time post fertilization on test-image sets with an accuracy on the range of a few hours. This technique undoubtedly has the potential to become very useful for any zebrafish researchers interested in developmental timing as it eases analysis and removes potential subjective bias.

Major comments

In its current form the paper lacks sufficient graph annotations and method descriptions. This makes it hard in places to judge the validity of the claims. I do believe that the presented method can be useful and is likely valid but to be convincing, the authors need to spend more time expanding the methods, justifying their choices of analysis and clarifying figure annotations.

Specific points:

1. The annotation of the training data is not described and specifically it is unclear how valid the staging of the training data itself is. The authors state in the introduction "the hours post fertilization (hpf) [...] provides only and approximation of the actual developmental stage". It is therefore critical to know how this was accounted for in the annotation of the training data. Since the quality of the training data will ultimately limit the best-case quality of Kimmel Net. The authors need to go into some detail here even though the training data appears to be from another published dataset.
2. Why were "test predictions fit to a straight line through the origin". On the one hand this makes sense (since the slope would indicate the correspondence) but it should be clarified why an intercept was omitted in the fit. After all it is unclear if Kimmel net correctly identifies 0Hpf embryos.
3. The methods do not list how the mean of the absolute error was calculated from 3B/C. I think this should be the mean of the absolute error (not the mean of the error) but in that case the numbers listed in the text appear rather small given the histograms in 3 B/C. A clear statement in the methods would clarify this issue.

Minor comments

1. The Y-axis in Figure 2B is simply labeled "Loss" - what is the unit of this loss? HPF? Or HPF^2? This is important for judging the quality of the fit
2. Figure 3 B. I would suggest changing the labels of the confidence intervals in the legend. "Inner and outer" is already clear from the figure itself, so labels that state that these are derived from n=100 vs. n=20 test image sized samples would be more useful to the reader

Referees cross-commenting

I concur with comments issued by the other reviewers. I think it will be especially important to address the comments related to testing the method on mutants (Reviewer #1) and training the model in the presence of noise to increase its robustness (Reviewers #1 and #3) as well as addressing the overall annotation/generation of the training data (Reviewers #1 and #2).

I think these points will be critical to make the paper useful by increasing transparency and ensuring reproducibility in other labs with different imaging conditions, strains, mutants, etc.

Significance

Developmental delay is a common occurrence that can be caused by genetic and environmental background effects. It is therefore highly desirable to properly quantify this variable. The work presented here makes an important step in this direction, by allowing to quantify developmental timepoints independent of subjective staging. This speeds up analysis, increases reproducibility and enhances rigor. However, as my comments above indicate, the significance also depends on the ability of potential users to judge the quality of the work. Once those issues have been addressed, I think the work will be of broad interest to the developmental biology community, first and foremost labs utilizing the zebrafish model. However, as the authors state, the presented model architecture could be trained with the data from other species as well.

Expertise: Zebrafish, quantitative analysis, behavior, neuroscience

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

In this manuscript the author is presenting a deep-learning model used to predict the development stage of zebrafish embryo. A robust method that can accurately classify a zebrafish into different development stages is highly relevant for many researchers working with zebrafish and hence the importance in developing methods like this is high.

The manuscript is overall ok. However, more data is needed to convince the reader that the method is robust enough to work with other samples in other labs. This would greatly improve the impact of the publication.

Page 6.<br /> - How is the data acquired?

Page 8.<br /> "This indicates that whileKimmelNet can be used successfully with noisier test data than that on which it was trained,there is an upper limit to how noisy the data can be."<br /> - This is an obvious statement there will always be an upper limit on noise.

Page 9.<br /> - Are only wildtype embryos used? How would this work on different mutants. To evaluate the robustness of the method this it would be valuable to test on some mutant line with known developmental difference from the wild type.

Image data.<br /> - I would strongly suggest that the author should include a description of the data in the manuscript. A description of how the data is acquired, microscope, different batches, type of embryos used.

"Random160translation in the y-direction was avoided due to the aspect ratio of the images (width>161height) - any artifacts introduced by translation in the x-direction would be removed by the162centre crop layer, but this would not be the case for translation in the y-direction."<br /> - Could this be solved by using some border method reflection, repetition or fixed value?

Page 10.<br /> Addition of Noise to Image Data<br /> - This should be added in the training phase. This would probably improve the robustness of the network and also improve the results on the test data.

• Supplementary 3 images with high noise. It is worrying that the network is not able to handle the noise in this figure. Looks like the features that is used to distinguish the developmental stage of the embryo is still clearly seen with this high noise level? Retrain the model with noise as an augmentation to improve this.

Significance

The development of methods like this is highly relevant in the zebrafish community. Staging and evaluating the developmental stage for zebrafish is common and is of interest to the broad community. A lot of this work today is done manually, limiting the throughput, and adding human bias.

The limit of this study is the dataset used for training and evaluation. Firstly, it is not clear about the structure of the data and how it is acquired, different types of fish or imaging setup etc. For a method to be useful to the community it needs to be robust enough to handle different types of fish (transgenic lines). The manuscript would be greatly improved by adding this to the training and evaluation.

My expertise is image analysis and machine learning for quantification of biological samples, with focus on zebrafish screening.

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Reply to the reviewers

1. General Statements

The authors greatly appreciate Review Commons’ innovative approach to scientific review and publishing. We thank the reviewers for their kind words regarding the manuscript overall quality and for highlighting the quantitative approach and reproducibility of this work. We further thank the concerns raised and suggestions made that have contributed to improving the manuscript. Below is a point-by-point response to the reviewers, organized into sections that discriminate the alterations already made and plans for further experiments and revisions. We hope that they appropriately address the reviewers' concerns.

2. Description of the planned revisions

Reviewer #2: Figure 6 in particular, the number of analyzed embryos is small, given the fact that there is a lot of inter-individual heterogeneity in this process it could well be that the authors got, by chance, two embryos out of three having the same pattern of Hairy1 expression.

R: The authors appreciate the concern raised by Reviewer#2. This experiment is very time consuming and difficult to execute, which is why the number of samples is limited. Overall, we analyzed 7 embryos and 5 recapitulated the pattern of gene expression. We were particularly interested in the occipital somites and, in this time window, 3 out of 4 showed the same expression pattern. Nevertheless, further experiments will be performed to increase the number of analyzed individuals. We are confident this will contribute to strengthening the conclusions of our work.

Reviewer #2: I believe that an additional shorter time point (+15 or 30 min) with a different pattern of the oscillatory gene would also add to the characterization of the dynamics (same for Fig 5c). This is particularly true given that the domain of expression of Hairy 1 analyzed in Figure 6 is localized quite rostral which might be interpreted as a phase 1 or a phase 2 as well (as initially described in Palmeirim et al 1997).

R: We thank Reviewer#2 for this suggestion. We have preliminary data (20-40 min) evidencing different patterns of expression and will perform more experiments to complement these results. Figures will be modified to include samples incubated for shorter time intervals, to evidence different expression patterns obtained in these conditions.

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

Reviewer #1 | Major points:

1. In the RESULTS session, "Occipital somites are formed faster than cervical and trunk somites," the authors argue that the occipital somites form with greater temporal variability than the neck and trunk somites. Judging from Figures 3C and 3D, I feel it is the case. However, the authors should demonstrate it through statistical analysis.

2. In the RESULTS session, "Anterior-posterior length of rostral somites," the authors concluded that "the large variability in measurements of somites 17-20 most probably results from the rotation of the embryo body in these developmental stages." Probably they mentioned data of the length of #17-#20 somites in Table1. They should demonstrate it through statistical analysis to show the large variability in the specific area. I understand that embryo rotation could be a reason for the variability. The authors should show evidence. Or they should discuss various possibilities from a broad perspective.

R: The authors thank Reviewer#1 for suggesting a statistical approach to better characterize the data variability obtained. We performed a Brown-Forsythe test and found that, indeed, there is a statistically significant difference between the temporal variability (period) of somites 1-7 and 8-20 (P-value = 0.02319). Application of the Brown-Forsythe test also found a non-equal variability in the length of the somites #17-20 (P-value = 0.005403). A new Supplementary Figure 4 was added, displaying these results.

The Brown-Forsythe test is a statistical method used to assess the equality of variances in a dataset across different groups, in this case, the period or the length of early and late somites. It is a robust alternative to the traditional Levene's test, particularly useful when the assumption of homogeneity of variances is not met, such as when the data distributions are skewed or contain outliers (which is the case with our data). It calculates the absolute deviations of individual observations from their respective group medians, which makes it less sensitive than the Levene's test to extreme values. By comparing these deviations between groups, the Brown-Forsythe test helps determine whether the variance differs significantly across the groups. We are confident that this result confers robustness to our claims, and hope that it appropriately addresses the reviewer's concerns.

Regarding the reasons underlying the variability in the length of #17-#20 somites, we believe it is mainly due to technical constraints. In early developmental stages the chicken embryo is flat, and measurements are easily performed along the anterior-posterior (A-P) somite axis. When somites 17-20 are formed, the embryonic axis starts undergoing rotation, meaning that in some cases we may be measuring along a rotated somite axis. In our work somite length is determined as soon as the posterior intersomitic cleft is formed, so an alternative explanation could be that each somite is formed with a variable length, that is soon after consolidated, resulting in the characteristic consistent metameric organization of somites along the embryo body axis. This is highly unlikely because the length of somites 17-20 long after they are formed (Herrmann et al., 1951) is within the same value range we observed.

The manuscript has been altered to include the above-mentioned information, as follows:

• Methods section, under Statistical analysis (Line 153): “To assess the homogeneity of variances between early and late somites, we applied the Brown-Forsythe test on both the period and length measurements. This method involves computing the absolute deviations of individual observations from their respective group medians, rendering it less sensitive to extreme values (outliers). Through the comparison of these deviations across groups, the Brown-Forsythe test aids in determining the statistical significance of variance disparities.”
• Results section, under Anterior-posterior length of rostral somites (Line 201): “A larger variability was obtained for measurements of somites 17-20 (Supplementary Figure 4A), although this most probably results from the rotation of the embryo body in these developmental stages, hindering precise length measurements of the somite A-P axis.”
• Results section, under Occipital somites are formed faster than cervical and trunk somites (Line 216): “Remarkably, there is substantial variability in the time of formation of the early-most somites (Figure 3C; Supplementary Figures 3), which gradually stabilizes until somite 8 onwards, where both somite formation time and variability is equivalent to that observed for somites 15-20 (Figure 3C, D; Table 1; Supplementary Figure 4B).”

1. The authors do not describe the expression patterns of hairy1 in the PSM in the manuscript, but they merely judged whether they are different or the same (recapitulate). The description of the expression pattern needs to be revised totally. The authors should describe the expression patterns of hairy1 in the PSM of each sample carefully and in detail. Fortunately, the previous report (Pourquie and Tam, Developmental Cell, 1, 619-620, 2001) categorized the expression patterns of the EC genes into three phases. The authors should at least categorize each sample according to the criterion by Pourque and Tam. If arrows of brackets indicate the area of expression, it is reader-friendly.

R: A thorough characterization of segmentation clock gene expression (including hairy1) in the PSM of early somitogenesis chick embryos has been previously described (Rodrigues et al, 2006). For this reason, the authors focused mainly on a comparative analysis of the hairy1 expression patterns obtained in explants incubated for different periods of time. The authors acknowledge, however, the need for further description of the expression patterns obtained in early gastrulation stages, which haven’t been previously documented. Overall, the following alterations were made to the manuscript text:

Line 231: “The regions with greater variability of hairy1 expression included the neural plate, anterior to the node, the epiblast posterior to the node encompassing the precursors of the paraxial mesoderm (Psychoyos & Stern, 1996) and the caudal-most epiblast. hairy2 expression was also very dynamic along the embryo A-P axis (n=20) (Figure 4B), evidencing chevron-like expression domains, that appear at different levels of the primitive streak, as previously described by Jouve and collaborators (Jouve et al., 2002).”

Line 245: “As somitogenesis takes place, hairy1 and hairy2 expression patterns retain their dynamic properties in the PSM (Figure 5A, B), as previously described (Rodrigues et al., 2006).”

The authors further thank Reviewer#1’s suggestion to include brackets indicating the areas of hairy1 expression. Figures 4, 5 and 6 have been altered accordingly, which, indeed, makes figure interpretation more reader-friendly. The gene expression phases presented in Palmeirim et al (1997) and then by Pourquié and Tam (2001) mean to summarize a dynamic expression, with continuous intermediate phases, making it difficult to clearly categorize each pattern obtained. Since our purpose was to evaluate if the entire expression pattern was recapitulated (irrespective of the specific phase), we believe that categorizing each sample in phases is not paramount for result interpretation.

Reviewer #1 | Minor points.

1. In the RESULTS session, "Anterior-posterior length of rostral somites," the authors described that the average length of somites ranges from 118 - 191 μ__m. But according to Table-1, the lowest length of somite is 115.92 μ__m (__∼__116 μ__m). So, the lower limit should be corrected here.

R: The authors thank the reviewer for pointing this out. The text has been appropriately modified in Line 199 of the revised manuscript.

1. In the DISCUSSION section, the authors mentioned that they presented a thorough characterization of the size and time of formation of the first ten somites in the chicken embryo. But based on the tables and figures, it will only be the first nine somites, not the ten.

R: The authors agree with the reviewer’s comment. The text has been appropriately modified in Lines 128, 176 and 279 of the revised manuscript.

1. In the GRAPHICAL ABSTRACT, if the color of the oscillation line is the same as the corresponding somites, it is intuitive.

R: The authors thank the reviewer for this suggestion and have modified the graphical abstract accordingly. We employed multiple shades of the same color (corresponding to different positions along the body axis) to represent different embryos.

1. It would be helpful if the manuscript contained both page and line numbers.

R: Page and line numbers were added to the revised manuscript.

Reviewer #2

Minor comment: In 5 c similar patterns and newly formed somites should be pointed out by arrows on the figure to help the readers.

R: We thank Reviewer#2 for this suggestion. Arrows and brackets have been added to the figure to highlight the newly formed somites and gene expression domains, respectively.

Minor comment: To be more specific the term segmentation clock should be used instead of embryonic clock as I believe there are other embryonic clocks (cell cycle, circadian, etc..)

R: The authors appreciate the suggestion of Reviewer#2 regarding the term used to identify the molecular oscillator in our work. The term “segmentation clock” or “somitogenesis clock” is commonly used to refer to oscillations in hairy1/2 gene expression because their discovery and subsequent study has mainly focused on the somitogenesis process. Oscillations of hairy1/2 expression (Hes1/7 in mouse), however, have also been described in cells and developmental stages that are not associated with somite formation, and herein we describe dynamic expression in epiblast regions containing precursors that don’t give rise to segmented structures. As discussed in our recent paper (Carraco et al., Front. Cell Dev. Biol, 2022), the broader term Embryo Clock may be used to refer to molecular oscillations in embryonic cells, controlled by negative feedback regulation, that play a role in temporally controlled morphogenetic processes and/or cell fate specification.

In the beginning of our manuscript (Line 75), we clearly state that we are referring to the embryo clock operating during somitogenesis: “(…) somitogenesis embryo clock (EC), comprising genes with cell-autonomous oscillatory expression in the PSM driven by negative feedback loops (reviewed in Carraco et al., 2022)”, so we believe that the term used will be clearly perceived by the reader. For further clarification, however, the subtitle The Embryo Clock in early somitogenesis in the Discussion section has been modified to (Line 321): “The Embryo Segmentation Clock in early somitogenesis”

Reviewer #3 | Minor comments:

The authors did not consider the fact that the first formed somite is the second somite. After the formation of the second somites, the real first somite forms anterior to the second somite. Furthermore, the real first and the third somite seems to be formed simultaneously. It is worthy for the authors to re-examine the data, whether the real first somite and the third somite are formed at the same time. And to check whether the first somite was counted to the segmented region. And this point should be at least discussed.

R: The authors thank Reviewer#3 for the opportunity to clarify this important issue in our manuscript. It was previously described that the first morphological somite formed is, in fact, the second somite, while the “real” first somite is formed later, anteriorly to this one (Hamburger and Hamilton, 1951). This rostral-most somite-like structure is not anteriorly delimited by a fissure and has thus been termed an “incomplete” or “rudimentary” somite (Hinsch & Hamilton, 1956). Since the methodology used in our work relies on measuring the length between the rostral-most and the posterior-most intersomitic clefts, the “rudimentary” somite is not included in our data, and we considered somite #1 as the first somite delimited both anteriorly and posteriorly by intersomitic clefts. This was stated in the Methods section, under Embryo measurements, and has now also been made explicit under Somite nomenclature (Line 120): “Only structures delimited both anteriorly and posteriorly by intersomitic clefts were counted as somites.”

We, indeed, observe the formation of the “rudimentary” somite anteriorly to somite #1, when somites 3-4 and formed. This information was included in the Discussion section, under Spatio-temporal properties of the rostral somite segmentation (Line 311): “Note that our analysis did not consider the “rudimentary somite”, as defined by Hinsch and Hamilton (Hinsch & Hamilton, 1956) since it does not possess an anterior somitic cleft. We found that this structure becomes clearly visible, rostrally to somite 1, as somites 3-4 are formed.”

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

Reviewer #1 (Significance (Required)):

General assessment: The results are not conceptually new or surprising. However, their careful quantitative analysis is informative and worthwhile to be published because it has yet to be done in the early somite formation. In this manuscript, the authors focused on the early stage of somitogenesis. It will be more informative if they complete this analysis for the whole somite area including the end of somitogenesis.

Reviewer #3 | Referees cross commenting****

I find all criticisms are justified. The most advance, as stated by other both reviewers, is the quantitative assay of the somite formation, since this is no yet done previously. As suggested by the first reviewer, it will be more informative if the authors complete this analysis for all regions. For all somites would be too much work, but they can select some representative somites of each region in addition to occipital region, such as 3 somites for one region, including the cervical, thoracic, lumbal, sacral and caudal region. Thus, the dynamic of the temporal somite formation of the whole embryo can be analysed using the same method. This will provide much more impact for this work.

R: The authors thank the Reviewers #1 and #3 for the kind words and for highlighting the quantitative approach taken. Regarding completing the analysis for the whole somite area including the end of somitogenesis, the authors agree that this would be interesting for the community. The focus of this work, however, was a detailed understanding of early somite segmentation, where measurements of somites 14-20 were performed for validation purposes alone of the technical approach developed, since their time of formation has been previously well established. Characterization of somite formation dynamics along the entire embryonic axis, while informative, would entail significant technical challenges, which are beyond the focus of this work. Briefly, we performed live imaging using the EC culture system (Chapman et al, 2001). This appropriately reproduces in ovo development of early embryos but imposes significant constraints on embryo development in older developmental stages, including the ones corresponding to the formation of the last somites. A possible alternative to perform these measurements would be to apply the tissue explant culture system developed by Palmeirim et al., 1997 to different portions of the embryo body, and optimize it for real-time imaging. However, we believe that the time and effort required are beyond the scope of this work and would not significantly contribute to elucidating the main questions addressed in this manuscript.

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Referee #3

Evidence, reproducibility and clarity

Somites form consecutively along the anterior to posterior (AP) axis. The time of the formation of a somite is controlled by the segmentation clock, oscillation of cyclic genes in the presomitic mesoderm. The length of an oscillation cycle differs between species and should also differ between the axial levels. In chicken embryos, one cycle for a trunk somite requires 90 minutes, while it is much slower (150 minutes) for a posterior-most somite. Is this quicker or slower for an anterior-most somite? Andrade' group addressed this question and measured the time of the formation of each occipital somites (somite 1-5). They found that the formation of an occipital somite requires only 75 minutes, while somites from somite 6 onwards takes as long as the trunk somites (about 90 minutes). The faster formation of occipital somites is correlated with the time of the cyclic expression of hairy1 and hairy2.

Major comments:

The conclusion is well supported by the data. The measurement of the length increments of the segmented region and then assay using algorithm are well established. Thus, the data are well reproducible.

Minor comments:

The authors did not consider the fact that the first formed somite is the second somite. After the formation of the second somites, the real first somite forms anterior to the second somite. Furthermore, the real first and the third somite seems to be formed simultaneously. It is worthy for the authors to re-examine the data, whether the real first somite and the third somite are formed at the same time. And to check whether the first somite was counted to the segmented region. And this point should be at least discussed.

Referees cross commenting

I find all criticisms are justified. The most advance, as stated by other both reviewers, is the quantitative assay of the somite formation, since this is no yet done previously. As suggested by the first reviewer, it will be more informative if the authors complete this analysis for all regions. For all somites would be too much work, but they can select some representative somites of each region in addition to occipital region, such as 3 somites for one region, including the cervical, thoracic, lumbal, sacral and caudal region. Thus, the dynamic of the temporal somite formation of the whole embryo can be analysed using the same method. This will provide much more impact for this work.

Significance

Significance: The measurement of the length increments of the segmented region and then assay using algorithm are the novelty and strengths of this study. So, the data are reproducible and objective.

The results of this study extend our understanding about the dynamic process of the somitogenesis. Especially, the most interesting point is that based on this result, we can see that the segmentation clock runs faster in the head region, and then slow down gradually along the AP axis.

Audience: specialized, basic research<br /> The developmental biologist will be interested in this topic.

My expertise is the somite development, somite differentiation, mesoderm development.

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Referee #2

Evidence, reproducibility and clarity

In this study, Maia-Fernandez et al used time-lapse imaging of chicken embryos to analyze the formation of the first formed somite, by doing in situ hybridization, they checked the dynamic nature of well-known segmentation clock genes during this process. They found that the segmentation clock period is faster for the formation of the first five somites (most anterior) and that this process is underlain by dynamic/cyclic expression of Hairy 1 and Hairy 2 as it has been described for more posterior somites.

• I believe that there few issues that should be addressed to strengthen the conclusions of the manuscript:<br /> Figure 6 in particular, the number of analyzed embryos is small, given the fact that there is a lot of inter-individual heterogeneity in this process it could well be that the authors got, by chance, two embryos out of three having the same pattern of Hairy1 expression.
• I believe that an additional shorter time point (+15 or 30 min) with a different pattern of the oscillatory gene would also add to the characterization of the dynamics (same for Fig 5c). This is particularly true given that the domain of expression of Hairy 1 analyzed in Figure 6 is localized quite rostral which might be interpreted as a phase 1 or a phase 2 as well (as initially described in Palmeirim et al 1997).

Minor comments:

In 5 c similar patterns and newly formed somites should be pointed out by arrows on the figure to help the readers.<br /> To be more specific the term segmentation clock should be used instead of embryonic clock as I believe there are other embryonic clocks (cell cycle, circadian, etc..)

Significance

In this study, the authors address the question of the formation of the first-formed somites using bird a model system; this is a conceptual advance in the sense that our knowledge of the dynamics of these critical morphological events is minimal. The technical advances (time-lapse, image analysis, dissection) made by the authors are quite remarkable and allow for filling the gap of knowledge the community has in this particular domain. The article is well written, data are well presented and it is interesting for a large community of developmental biologists.

My expertise is in cell and tissue morphogenesis of amniote embryos

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Referee #1

Evidence, reproducibility and clarity

Maia-Fernandes et al. investigated somite formation dynamics in the chick embryo's early stage in this manuscript. They found that the cranial most somites (1-5) form faster than the trunk. They also show that the oscillatory expression pattern of hairy1, regarded as the somitogenesis embryo clock (EC), is coupled to the somite segmentation in the occipital somites. The results are not conceptually new or surprising; they merely show what has been widely believed. However, their careful quantitative analysis is informative and worthwhile to be published because it has yet to be done in the early somite formation. To improve the manuscript, I have several concerns to be addressed.

Major points.

1. In the RESULTS session, "Occipital somites are formed faster than cervical and trunk somites," the authors argue that the occipital somites form with greater temporal variability than the neck and trunk somites. Judging from Figures 3C and 3D, I feel it is the case. However, the authors should demonstrate it through statistical analysis.
2. The authors do not describe the expression patterns of hairy1 in the PSM in the manuscript, but they merely judged whether they are different or the same (recapitulate). The description of the expression pattern needs to be revised totally. The authors should describe the expression patterns of hairy1 in the PSM of each sample carefully and in detail. Fortunately, the previous report (Pourquie and Tam, Developmental Cell, 1, 619-620, 2001) categorized the expression patterns of the EC genes into three phases. The authors should at least categorize each sample according to the criterion by Pourque and Tam. If arrows of brackets indicate the area of expression, it is reader-friendly.
3. In the RESULTS session, "Anterior-posterior length of rostral somites," the authors concluded that "the large variability in measurements of somites 17-20 most probably results from the rotation of the embryo body in these developmental stages." Probably they mentioned data of the length of #17-#20 somites in Table1. They should demonstrate it through statistical analysis to show the large variability in the specific area. I understand that embryo rotation could be a reason for the variability. The authors should show evidence. Or they should discuss various possibilities from a broad perspective.

Minor points.

1. In the RESULTS session, "Anterior-posterior length of rostral somites," the authors described that the average length of somites ranges from 118 - 191 μm. But according to Table-1, the lowest length of somite is 115.92 μm (∼116 μm). So, the lower limit should be corrected here.
2. In the DISCUSSION section, the authors mentioned that they presented a thorough characterization of the size and time of formation of the first ten somites in the chicken embryo. But based on the tables and figures, it will only be the first nine somites, not the ten.
3. In the GRAPHICAL ABSTRACT, if the color of the oscillation line is the same as the corresponding somites, it is intuitive.
4. It would be helpful if the manuscript contained both page and line numbers.

Significance

General assessment: The results are not conceptually new or surprising. However, their careful quantitative analysis is informative and worthwhile to be published because it has yet to be done in the early somite formation. In this manuscript, the authors focused on the early stage of somitogenesis. It will be more informative if they complete this analysis for the whole somite area including the end of somitogenesis.

Advance: Previously no one provided quantitative data for somite formation. In this viewpoint, this manuscript has an advantage.

Audience: Their data could be helpful in the generation of mathematical models.

My field: Developmental Biology

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Reply to the reviewers

Reviewer #1:

1. The authors claim PA-induced mTORC1 activation is dependent on acetyl-CoA derived from mitochondrial fatty acid oxidation. Using isotope tracing to determine the contribution of PA to acetyl-CoA, would improve.

Response: We thank the reviewer for this valuable comment. As suggested, we measured the contribution of PA to the total cellular acetyl-CoA pool using metabolic flux assays. The results showed that the incorporation of 13C from [U-13C16]-palmitate into acetyl-CoA was exceeding 60% (Page 40, Figure 4D in the revised version), indicating that exceeding 60% of the acetyl-CoA pool was PA derived. Likewise, we also found that the incorporations of 13C from [U-13C16]-palmitate into 6:0-CoA, 8:0-CoA, 10:0-CoA and 12:0-CoA were all exceeding 50% (Page 40, Figure 4D in the revised version). Thus, these results suggested that a large portion of the PA was used for fatty acid oxidation upon entering the cell. Moreover, we found that fatty acid β oxidation blocked by perhexiline maleate inhibited PA-induced increase of acetyl-CoA, suggesting that the induction of acetyl-CoA content was largely dependent on the fatty acid oxidation of PA. Furthermore, we also demonstrated that inhibition of mitochondrial fatty acid β oxidation by pharmacological inhibitor or genetic knockdown abrogated PA-induced activation of mTORC1. However, using sodium acetate treatment to elevate cellular acetyl-CoA levels rescued impaired mTORC1 activity induced by the inhibition of fatty acid β oxidation under PA condition. Together, these results revealed that PA-induced mTORC1 activation is dependent on acetyl-CoA derived from mitochondrial fatty acid oxidation.

1. They showed PA increases fatty acid oxidation related gene expression and acetyl-CoA level, while OA and LA could not. why only PA could increases fatty acid oxidation and acetyl-CoA level, considering both of these lipids could be oxidation in mitochondria? Is there any differences in mitochondria among treatment of PA, OA and LA? It is better to monitor fatty acid oxidation in real time using seahorse. And add discussions.

Response: We thank the reviewer for this constructive question. As suggested, we performed the seahorse real-time cell metabolic analysis and the results showed that PA treatment enhanced mitochondrial OCR and elevated maximal oxygen consumption rates compared with OA or LA treatment in fish myocytes (Page 37, Figure 3B in the revised version). Likewise, we also found that PA-induced increase of fatty acid oxidation-related gene expressions was more robust than OA or LA in vivo and in vitro. Thus, these results indicated that the induction of mitochondrial fatty acid oxidation by pa treatment was stronger than OA or LA treatment.

In this study, using LC–MS, we showed that PA treatment increased the contents of short/medium-chain acyl-CoA and acylcarnitine in comparison with OA or LA treatment. Thus, these results suggested that although all three fatty acids can be oxidation in mitochondria, PA may be preferred to enter the mitochondria for fatty acid β oxidation, compared with OA or LA. Previous studies have found that OA is more inclined to synthesize triglycerides to induce the formation of lipid droplets than PA (Chen et al., 2023; Plötz et al., 2016). Likewise, we also found that OA significantly increased the contents of 18:1-CoA in comparison with PA. Thus, we speculate that, after entering the cell, OA is more preferentially synthesized to triglyceride for storage than fatty acid oxidation. Moreover, LA is considered to be a precursor of arachidonic acid, and can be converted to a myriad of bioactive compounds called eicosanoids (Whelan & Fritsche, 2013). Similarly, we found that LA markedly elevated the contents of 18:2-CoA/18:3-CoA. Thus, we conjecture that LA preferentially synthesizes functional lipids compared to entering mitochondria for fatty acid oxidation. Together, differences in the levels of acetyl-CoA produced by these three fatty acids may be related to their metabolic pathway preferences.

There may be two reasons for why PA prefers to enter mitochondrial for fatty acid oxidation. On one hand, due to differences in the structure of PA, OA and LA, the substrate affinity of CPT1B to these fatty acyl-CoAs may be different, that may contribute to the different rates of fatty acid to enter into mitochondria. On the other hand, in contrast to the β-oxidation of SFAs, the β-oxidation of UFAs requires the involvement of 2,4-dienoyl-CoA reductase (You et al., 1989), and thus the β-oxidation of SFAs may be more efficient.

At present, the understanding of differences in fatty acid oxidation between SFAs and UFAs is insufficient, so more studies are needed in the future to further explore the underling mechanisms behind these differences. The reviewers have raised a very important direction for research, and so we will continue to address this issue in future.

We have expanded this section of the Discussion (Page 14, line 388-412 in the revised version).

1. The authors present lots of western blot images, suggest to provide quantification data of these blots.

Response: We thank the reviewer for their careful assessment of our study. We apologize for not providing quantification data for western blot images in our initial manuscript. To support our conclusions, we have now added a densitomentric and statistical analysis of all western blots in the revised version (Page 33, Figure 1 in the revised version; Page 35, Figure 2 in the revised version; Page 37, Figure 3 in the revised version; Page 40, Figure 4 in the revised version; Page 43, Figure 5 in the revised version; Page 45, Figure 6 in the revised version; Page 47, Figure 7 in the revised version; Page 51, Figure S1 in the revised version; Page 53, Figure S2 in the revised version; Page 54, Figure S3 in the revised version; Page 56, Figure S4 in the revised version; Page 57, Figure S5 in the revised version).

1. It is better to discuss the relationship between fatty acid oxidation and mTOR signaling.

Response: The reviewer’s comments are very valuable. We apologize for not discussing enough for the relationship between fatty acid oxidation and mTOR signaling in our initial manuscript. We have now expanded this section of the Discussion (Page 13, line 366-387 in the revised version, see below).

Growing lines of evidence suggested a strong link between mitochondrial fatty acid oxidation and mTORC1 signaling (Ricoult & Manning, 2013). As a central regulator of anabolism, mTORC1 is considered to inhibit fatty acid β oxidation pathway for energy storage or ketogenesis (Aguilar et al., 2007). Several studies revealed that restrained mTORC1 by rapamycin induced fatty acid β oxidation in rat hepatocytes through increasing expression of fatty acid β oxidation related enzymes (Brown et al., 2007; Peng et al., 2002). Likewise, mice with whole-body knockout of S6K1 showed enhanced fatty acid β oxidation and increased expression levels of CPT1 in isolated adipocytes (Um et al., 2004), and S6K1/S6K2 double-knockout mice also exhibited elevated fatty acid β oxidation of fatty acids in isolated myoblasts by activating AMPK (Aguilar et al., 2007). Furthermore, a recent study has established that FOXK1 can mediate the inhibition of fatty acid β oxidation by mTORC1 (Fujinuma et al., 2023). Thus, these collective data revealed that fatty acid β oxidation was restrained by mTORC1. However, conversely, the role of mitochondrial fatty acid oxidation in the regulation of mTORC1 is still controversy. A study in prostate cancer cells suggested that inhibited fatty acid β oxidation by etomoxir reduced mTORC1 activity (Schlaepfer et al., 2014), and another study found that deleting CPT1B specifically in skeletal muscle of mice suppressed mTORC1 by provoking AMPK activation (Vandanmagsar et al., 2016). Consistent with these studies, our results showed that acetyl-CoA derived from mitochondrial fatty acid β oxidation induced mTORC1 activation under PA treatment, indicating that acetyl-CoA may be a novel insight linking fatty acid β oxidation and mTORC1 signaling. Paradoxically, unlike other studies, a recent study found that mice with heart-specific CPT2-deficient exhibited induction of mTORC1 pathway. Thus, the effects of fatty acid β oxidation on mTORC1 pathway are complicated and may differ under variable physiological and pathological conditions. Further studies are needed to determine the sophisticated mechanisms underlying the regulation of fatty acid β oxidation on mTORC1 signaling.

Reviewer #2:

Major comments:

Initial experiment: Among several fatty acid-rich diets, fish were fed a palmitic acid (PA) rich (PO) diet for 10 weeks, and the PO diet significantly raised fasting blood glucose levels compared to control diet (fish oil of equal lipid content). The PO diet also impaired the fish's glucose and insulin tolerance. The PO diet also led to decreased phosphorylation levels of AKT, which regulates glucose metabolism. Therefore, the researchers initially concluded that a palmitic acid-rich diet leads to systemic insulin resistance in fish.

1. I have a couple of questions on this initial experiment on which all the subsequent studies are based. In Figure 1A, the body weight was identical in control and PO group. Don't you expect PO feeding lead to obesity in fish, as HFD induces obesity in mice?

Response: We thank the reviewer for this constructive question. In our study, we found that dietary PO diet for 10 weeks failed to affect the body weight of fish, compared with CON diet. Similar to our results, a study in human also found that there was no significant differences in the body weight and body mass index (BMI) between saturated fat diet and monounsaturated fat diet (Vessby et al., 2001). Unlike high-fat diet, the lipid content level of PO diet was not elevated, but only the fatty acid composition was altered, with palmitic acid composition being significantly increased in comparison with CON diet (Page 60, Table S1). Thus, this may be the reason of why the PO diet did not induce weight gain.

Although accumulating evidence showed that the onset of insulin resistance was often accompanied by weight gain and obesity (Kahn & Flier, 2000; Shoelson et al., 2007), some studies also found that insulin resistance occurred without obesity. A recent study found that mice with liver knockout of Lpcat3 exhibited improved insulin sensitivity without a change in the body weight (Tian et al., 2023). Moreover, another study in mice showed that dietary phenylalanine-rich diet induced insulin resistance, but had no effects on the body weight (Zhou et al., 2022). Likewise, our study also found that dietary PO diet provoked systemic insulin resistance, while did not affect the body weight in fish. Thus, these studies indicated that the development of insulin resistance may not always be entirely accompanied by obesity.

1. Figure 1G and 1H show glucose and insulin tolerance after PO feeding for 10 weeks. The area under curve (AUC) should be compared to determine if GTT and ITT were statistically different. The ITT curve is particularly interesting as the control fish did not seem to respond to insulin, while the PO-fed fish responded more robustly. The only difference is the initial glucose level. Are the GTT and ITT done after fasting? How long is the fasting? The curves suggest that even though PO increased (fasting) blood glucose levels, it improved insulin sensitivity - therefore the premise that PO induces insulin resistance is not supported here. The lack of insulin induced response in the control group is worrisome. I suggest that the measures should be retaken, and AUC should be used to support if there are any differences in GTT and ITT.

Response: We thank the reviewer for this valuable comment. We apologize for making this confusion in the initial manuscript and we thank the reviewer for providing this opportunity to correct our manuscript. As suggested, to further investigate whether dietary PO could cause impairment of insulin sensitivity, we have re-performed the GTT and ITT assays. Considering that fish have a poor capacity to utilize glucose, we extended the assay time to 8 h. To make the results more accurate, we also added the biological replicates. Moreover, before injection of glucose or insulin, fish were fasted for 24 h. Furthermore, we added area under curve (AUC) of GTT and ITT, and performed statistical analyses of the AUC data.

Our results showed that dietary PO diet reduced glucose tolerance and insulin tolerance in fish (Page 33, Figure 1G and 1H in the revised version). Moreover, compared with CON diet, the AUC of GTT and ITT were significantly increased in PO diet (Page 33, Figure 1G and 1H in the revised version). Similarly, we found that dietary PO diet elevated fasting blood glucose levels and plasma insulin concentrations. Furthermore, we showed that dietary PO diet decreased the phosphorylation levels of AKT in the liver and skeletal muscle. In addition, we also demonstrated that PA treatment could induce cellular insulin resistance in fish myocytes and C2C12 myotubes. Thus, in our opinion, the above results could indicate that dietary PO induced insulin resistance in fish.

1. Based on the assumption that PO induces IR (which needs to be confirmed based on the previous comments), the researchers attempted to understand how PA triggers IR through a series of experiments, predominantly western blot analysis. All the Western blots should be quantified. The model is that PA activates FAO in mitochondrial that elevates cytosolic acetyl-coA, which acetylates Rheh to activate mTORC1. mTORC1 on one hand alters IRS1 phosphorylation and on the other hand inhibits transcriptional activity of TFEB to reduce Irs1 mRNA level. Together reduces IRS1 leads to Insulin Resistance.

Response: The reviewer’s comments were very important to verify the validity of our findings. We have now added a densitomentric and statistical analysis of all western blots in the revised version (Page 33, Figure 1 in the revised version; Page 35, Figure 2 in the revised version; Page 37, Figure 3 in the revised version; Page 40, Figure 4 in the revised version; Page 43, Figure 5 in the revised version; Page 45, Figure 6 in the revised version; Page 47, Figure 7 in the revised version; Page 51, Figure S1 in the revised version; Page 53, Figure S2 in the revised version; Page 54, Figure S3 in the revised version; Page 56, Figure S4 in the revised version; Page 57, Figure S5 in the revised version).

1. Figure 1. PA reduces basal and insulin stimulated AKT phosphorylation in fish liver and muscle, as well as in culture fish and murine myocytes (Fig. 1I-M). The results appear to be solid but need to be quantified.

Response: We thank the reviewer for this kind suggestion. We have now added a densitomentric and statistical analysis of all western blots in Figure 1 (Page 33, Figure 1 in the revised version).

1. Figure 2 shows that PA provoked hyperactivation of mTORC1 (indicated by elevated phosphorylated S6K levels. This effect was abolished by Rapamycin treatment (an mTORC1 inhibitor) and also abolished by insulin stimulation (2F). Again, the western blots should be quantified.

Response: We thank the reviewer for this excellent suggestion. We have now added a densitomentric and statistical analysis of all western blots in Figure 2 (Page 35, Figure 2 in the revised version).

1. Figure 6: the researchers measured the effect of PA treatment on IRS1 phosphorylation in order to understand the mechanism of insulin resistance induced by mTORC1 activation under PA treatment. A PO diet intensified S636/S639 phosphorylation in fish muscle. In fish myocytes and C2C12 myotubes, PA treatment elevated S636/S639 phosphorylation but decreased the Y612 phosphorylation of IRS1 in a dose-dependent manner. Treatment of fish myocytes and C2C12 myotubes with an mTOR inhibitor blocked increased IRS1 S636/S639 phosphorylation levels under PA treatment. Also, PA specifically reduced mRNA levels of Irs1. This indicates that PA-induced, mTOR-dependent alteration of IRS1 phosphorylation and transcription may have contributed to insulin resistance. It is unclear how mTORC induces either increase or decrease in IRS1 phosphorylation depending on the residuals.

Response: We appreciate the reviewers for this important question. In fact, previous studies have clearly explored how mTORC1 pathway affects S636/S639 phosphorylation of IRS1. On one hand, as a kinase complex, mTORC1 could directly induce S636/S639 phosphorylation of IRS1 in vitro (Ozes et al., 2001). On the other hand, mTORC1 could activate S6K to promote S636/S639 phosphorylation of IRS1 (Shah & Hunter, 2006; Um et al., 2004). In addition, considering that the serine/threonine phosphorylation status of IRS has been shown to affect its tyrosine phosphorylation and protein degradation (Copps & White, 2012), we speculate that the decrease of Y612 phosphorylation of IRS1 is dependent on the induction of IRS1 S636/S639 phosphorylation.

In this study, we found that PA could induce S636/S639 phosphorylation of IRS1 in a mTORC1-dependent manner. Considering that previous studies have explored the mechanism by which mTORC1 induced IRS1 S636/S639 phosphorylation, we did not conduct further studies on this issue. Notably, we found that mTORC1 could also regulate the transcription of IRS1, so we subsequently investigated the mechanism by which mTORC1 inhibited IRS1 transcription.

1. Figure 7 shows that PA inhibits nuclear translocation of TFEB to suppress IRS1 transcription. The EMSA in 7D is not convincing.

Response: We thank the reviewer for this valuable comment and we apologize for providing unclear blots in the initial manuscript. To support our conclusions, we have now re-performed the EMSA assays. The results suggested that TFEB can directly bind to the IRS1 promoter at these two sites (Page 47, Figure 7D in the revised version).

Minor comments:

1. Some data appears to weaken the results and/or contradictory. For example, the paper initially showed reduced AKT phosphorylation to support PA induced IR, but shouldn't a lower level of pAKT reduces mTORC activation? But then the rest of the manuscript explores how PA activates mTOR. Part of the IR is manifested by impaired mTORC1 activation, yet the PA activates mTORC1. The authors should present the rationale and flow of the ideas in a better way.

Response: We thank the reviewer for this excellent suggestion. We appreciate the points that in some insulin resistance conditions, as a downstream of the insulin pathway, mTORC1 activity is manifested to be inhibited. However, mTORC1 activity showed different under other insulin resistance conditions.

In fact, multiple negative feedback signals exist in cells to maintain cellular homeostasis under diverse environmental challenges and stimulations (Kearney et al., 2021). However, aberrant of negative feedback can lead to impaired intracellular signaling pathway and induce a variety of diseases (Nguyen & Kholodenko, 2016). Similarly, numerous negative feedback mechanisms also exist in insulin signaling to prevent the development of cancers that may be induced by hyperactivation of insulin pathway. The negative feedback of insulin pathway is mainly mediated by mTORC1, which has been found to inhibit insulin signaling transduction by directly or indirectly affecting IRS1 phosphorylation (Copps & White, 2012; Shah & Hunter, 2006; Um et al., 2004). However, under some pathological or stress conditions, mTORC1 is over-activated, resulting in the amplification of the negative feedback of insulin pathway and the development of insulin resistance. A recent study found that imidazole propionate, a metabolite produced by the gut microbiota, provoked insulin resistance through inducing mTORC1 activation and phosphorylation of IRS1 (Koh et al., 2018). Other studies also showed that elevated abundance of branched-chain amino acids (BCAAs) or branched-chain α-keto acid (BCKA) could cause insulin resistance by boosting mTORC1 pathway (Zhou et al., 2019). Thus, mTORC1 activation induced-negative feedback inhibition of insulin pathway may be a critical factor in the development of insulin resistance.

Consistently, our study found that PA could activate mTORC1 in an acetylation modification-dependent manner. Moreover, activation of mTORC1 inhibited the phosphorylation of AKT and caused insulin resistance by affecting the phosphorylation and transcription of IRS1.Indeed, AKT is considered to activate mTORC1 in multiple manners, and inhibition of AKT results in the reduction of mTORC1 activity. However, mTORC1 activity is not only affected by AKT, but is also regulated by a diverse set of upstream signals (Saxton & Sabatini, 2017). Thus, we considered that the activating effect of PA on mTORC1 activity is higher than the negative effect of mTORC1 activity produced by AKT inhibition. This also led to the fact that mTORC1 remained in an activated state despite the inhibition of AKT in PA condition.

1. There are also many run-on sentences and grammar issues, making it very hard to read. The writing can be improved.

Response: We thank the reviewer for this valuable comment and we apologize for these grammar mistakes in the initial manuscript. Following the reviewer’s suggestion, we have invited native speaker to guide the English writing and carefully corrected these run-on sentences and grammar issues. We thank the reviewer for this careful evaluation of our manuscript.

Reviewer #3:

Major issues affecting the conclusions:

1. The conclusions are supported by the data. However, I suggest to perform a densitomentric and statistical analysis of western blots, especially when the authors report a representative blot, showing samples loaded in single.

Response: We thank the reviewer for this excellent suggestion. We agree that it would be important to verify the validity of our findings and we apologize for not providing quantification data for western blot images in our initial manuscript. We have now added a densitomentric and statistical analysis of all western blots in the revised version (Page 33, Figure 1 in the revised version; Page 35, Figure 2 in the revised version; Page 37, Figure 3 in the revised version; Page 40, Figure 4 in the revised version; Page 43, Figure 5 in the revised version; Page 45, Figure 6 in the revised version; Page 47, Figure 7 in the revised version; Page 51, Figure S1 in the revised version; Page 53, Figure S2 in the revised version; Page 54, Figure S3 in the revised version; Page 56, Figure S4 in the revised version; Page 57, Figure S5 in the revised version).

1. The methods are clear and reproducible. The authors should better explain how they have dissolved all the powders (i.e. fatty acids) to obtain the stock solutions next diluted (from what concentration?) in the media

Response: We thank the reviewer for this valuable comment. We apologize for not explaining how to dissolved all the powders in the media. As suggested, we have now provided a detailed explanation of how to dissolved all the powders to obtain the stock solutions in the Methods (Page 19, line 537-586 in the revised version, see below).

For PA, OA or LA in vitro treatment, fatty acid free BSA (Equitech-Bio, USA) was dissolved in FBS-free DMEM at room temperature according the ratio 1:100 (1 g fatty-acid free BSA: 100 ml FBS-free DMEM). 500 mg PA (Merck, Cat#P0500), OA (Merck, Cat#O1008) or LA (Merck, Cat#L1376) was dissolved in 10 ml ethanol to obtain PA, OA or LA stock solution respectively. Then PA, OA or LA stock solution was blow-drying with nitrogen gas and was dissolved in 0.1 M NaOH and warming at 75°C until clear to obtain 100 mM PA, OA or LA solution. Subsequently, 100 mM PA, OA or LA solution was added to 1% BSA solution according the ratio 1:100 (100 mM PA:1% BSA, v/v) at 50°C. Finally, the mixture was filtered using a 0.45 μM filter and stored at -20°C. For insulin in vitro treatment, insulin powder (Merck, USA) was dissolved in hydrochloric acid (pH=2) to obtain 1 mg/ml stock solution. For rapamycin or Torin1 in vitro treatment, rapamycin (Med Chem Express, #HY-10219, USA) or Torin1 (Med Chem Express, #HY-13003, USA) was dissolved in dimethyl sulfoxide (DMSO, Solarbio, China) to obtain 1 mM stock solution respectively. For MHY1485 in vitro treatment, MHY1485 (Med Chem Express, #HY-B0795, USA) was dissolved in DMSO (Solarbio, China) to obtain 10 mM stock solutions.

For etomoxir or perhexiline maleate in vitro treatments, etomoxir (Med Chem Express, #HY-50202, USA) or perhexiline maleate (Med Chem Express, #HY-B1334A, USA) was dissolved in DMSO (Solarbio, China) to obtain 50 mM stock solution respectively. For BMS-303141 treatment, BMS-303141 (Med Chem Express, #HY-16107, USA) was dissolved in DMSO (Solarbio, China) to obtain 25 mM stock solutions. For sodium acetate treatment, sodium acetate (Merck, #S2889, USA) was dissolved in ultrapure water from a Milli-Q water system to obtain 5M stock solution. For C646, spermidine or MB-3 treatment, C646 (Med Chem Express, #HY-13823, USA), spermidine (Med Chem Express, #HY-B1776, USA) or MB-3 (Merck, #M2449, USA) was dissolved in DMSO (Solarbio, China) to obtain 50 mM stock solution respectively. For MG149 treatment, MG149 (Med Chem Express, #HY-15887, USA) was dissolved in DMSO (Solarbio, China) to obtain 150 mM stock solution. For TFEB activator 1 treatment, TFEB activator 1 (Med Chem Express, #HY-135825) was dissolved in DMSO (Solarbio, China) to obtain 10 mM stock solution.

1. Anova analysis should be performed to analyze western blot densitometries.

Response: The reviewer raises an important point and we appreciate this comment. As suggested, we have now added statistical analyses of all western blot densitometries in the revised version. The data are presented as the means ± SEM and were analyzed using independent t-tests for two groups and one-way ANOVA with Tukey’s test for multiple groups (Page 33, Figure 1 in the revised version; Page 35, Figure 2 in the revised version; Page 37, Figure 3 in the revised version; Page 40, Figure 4 in the revised version; Page 43, Figure 5 in the revised version; Page 45, Figure 6 in the revised version; Page 47, Figure 7 in the revised version; Page 51, Figure S1 in the revised version; Page 53, Figure S2 in the revised version; Page 54, Figure S3 in the revised version; Page 56, Figure S4 in the revised version; Page 57, Figure S5 in the revised version).

Minor comments:

Prior studies are referenced appropriately, text and figures are clear. I suggest to add in the abstract all the model systems used. HEK293 also should be inserted in the description of the results. Please add the reference to figure 8 in the text. Please, describe cell origin.

Response: We thank the reviewer for this careful assessment of our study. We apologize for not making this clearer in our initial manuscript. We have now added all the model systems used in the Abstract (Page 2, line 24-28 in the revised version, see below).

Here, using a croaker model, we report that dietary palmitic acid (PA), but not oleic acid or linoleic acid, leads to dysregulation of mTORC1 signaling which provokes systemic insulin resistance and glucose intolerance. Mechanistically, using croaker primary myocytes, mouse C2C12 myotubes and HEK293T cells, we show that PA-induced mTORC1 activation is dependent on mitochondrial fatty acid β oxidation.

Moreover, we have now added the description of HEK293T cells in the Results (Page 10, line 261-265 in the revised version; Page 11-12, line 309-315 in the revised version, see below).

To further investigate whether the regulation of mTORC1 by Tip60 is dependent on the acetylation of Rheb, the interaction between Tip60 and Rheb was analyzed via co-immunoprecipitation assays, and the results showed that Tip60 can interact with Rheb in HEK293T cells (Figure 5E). Moreover, overexpressed Tip60 reinforced the acetylation of Rheb and phosphorylation levels of S6K in HEK293T cells (Figure 5F).

Dual luciferase experiments in HEK293T cells showed that TFEB had the strongest ability to elevate the luciferase activity of the IRS1 promoter among the crucial downstream transcription factors of mTORC1 (Figure 7A). Moreover, TFEB enhanced the promoter activity of IRS1 in a dose-dependent manner (Figure 7B) and mutations of the predicted TFEB binding site 4 and site 6 in the IRS1 promoter significantly reduced the promoter activity of IRS1 in HEK293T cells (Figure 7C). Furthermore, ChIP and EMSA experiments in HEK293T cells verified that TFEB can directly bind to the IRS1 promoter at site 4 and site 6 (Figures 7D and 7E).

As suggested, we have added the reference to figure 8 in the Discussion (Page 17, line 483-487 in the revised version, see below).

In summary, our work unveils an evolutionarily conserved mechanism by which mitochondrial fatty acid β oxidation flux of acetyl-CoA induces mTORC1 activation through enhancing Tip60-mediated Rheb acetylation under PA condition. Subsequently, hyperactivation of mTORC1 boosted serine phosphorylation of IRS1 and inhibited TFEB-mediated transcription of IRS1, leading to insulin resistance (Figure 8).

As suggested, we have added the description of cell origin in the Methods (Page 19, line 526-527 in the revised version; Page 19, line 533-534 in the revised version, see below).

Mouse C2C12 myoblast cells were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China).

HEK293T cells were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China).

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Zhao et al aimed to elucidate the mechanisms by which palmitic acids drives insulin resistance. The authors performed experiments in croaker, fish myocytes and mouse differentiated C2C12. They manage in vivo metabolic assays, enzymatic assays, immunoblot procedures, double luciferase assays, RNA analysis, pharmacological inhibitions and genetic knockdown. By using these model systems and procedures, the authors demonstrate that palmitic acid, but not oleic and linoleic acids, induces systemic and cellular insulin resistance through the hyper activation of mTORC1. They show that palmitic acid stimulates the mitochondrial fatty acid β oxidation, increasing the acetyl-CoA levels which enhances the acetylation of Rheb, a well known activator of mTORC1, by Tip60. Moreover, the authors show that mTORC1, beside reinforcing IRS1 phosphorylation, inhibits nuclear translocation of TFEB, thus preventing IRS1 transcription.

Major issues affecting the conclusions:

The conclusions are supported by the data. However, I suggest to perform a densitomentric and statistical analysis of western blots, especially when the authors report a representative blot, showing samples loaded in single.<br /> The methods are clear and reproducible. The authors should better explain how they have dissolved all the powders (i.e. fatty acids) to obtain the stock solutions next diluted (from what concentration?) in the media<br /> Anova analysis should be performed to analyze western blot densitometries.<br /> Minor comments:<br /> Prior studies are referenced appropriately, text and figures are clear. I suggest to add in the abstract all the model systems used. HEK293 also should be inserted in the description of the results. Please add the reference to figure 8 in the text. Please, describe cell origin.

Referee Cross-commenting

All reviewers have requested densitomentric (and statistical) analysis of western blot to prove the strength of the results. This is the major point to be addressed. Other points should also be only discussed.

Significance

The study extends the knowledge in the field of fatty acids-induced insulin resistance, that is a field studied by many researchers from many years, but with a lot of unclear mechanisms yet. Thus, the nature of the advance is conceptual and mechanistic. The only limitation is the lack of evidence in human samples/cells.

Basic researchers and experts in translational medicine will be interested by this research.<br /> This is the point of view of a basic researcher, mainly interested in the molecular mechanisms underlining type 2 diabetes/obesity and cancer.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The goal of this study was to investigate how saturated fat induce insulin resistance through activating mTOR. More specifically, the researchers show that palmitate (saturated fat) activates mTORC1 to induce insulin via transcriptional and posttranslational suppression of IRS1. Specifically, the researchers show that PA stimulates FAO to raise cytosolic acetyl-coA levels, promoting Tip60-mediated acetylation of Rheb to activate mTORC1 activity. To study the relationship between mTORC1 activity, fatty acid stimulation and insulin resistance, the researchers decided to conduct their study in fish, as fish are known to be glucose intolerant by nature, making them an appropriate model organism for studying insulin resistance. Mouse C2C12 cell line and fish primary myocytes were also used to validate key results.

Major comments:

Initial experiment: Among several fatty acid-rich diets, fish were fed a palmitic acid (PA) rich (PO) diet for 10 weeks, and the PO diet significantly raised fasting blood glucose levels compared to control diet (fish oil of equal lipid content). The PO diet also impaired the fish's glucose and insulin tolerance. The PO diet also led to decreased phosphorylation levels of AKT, which regulates glucose metabolism. Therefore, the researchers initially concluded that a palmitic acid-rich diet leads to systemic insulin resistance in fish.

I have a couple of questions on this initial experiment on which all the subsequent studies are based. In Figure 1A, the body weight was identical in control and PO group. Don't you expect PO feeding lead to obesity in fish, as HFD induces obesity in mice?<br /> Figure 1G and 1H show glucose and insulin tolerance after PO feeding for 10 weeks. The area under curve (AUC) should be compared to determine if GTT and ITT were statistically different. The ITT curve is particularly interesting as the control fish did not seem to respond to insulin, while the PO-fed fish responded more robustly. The only difference is the initial glucose level. Are the GTT and ITT done after fasting? How long is the fasting? The curves suggest that even though PO increased (fasting) blood glucose levels, it improved insulin sensitivity - therefore the premise that PO induces insulin resistance is not supported here. The lack of insulin induced response in the control group is worrisome. I suggest that the measures should be retaken, and AUC should be used to support if there are any differences in GTT and ITT.

Based on the assumption that PO induces IR (which needs to be confirmed based on the previous comments), the researchers attempted to understand how PA triggers IR through a series of experiments, predominantly western blot analysis. All the Western blots should be quantified. The model is that PA activates FAO in mitochondrial that elevates cytosolic acetyl-coA, which acetylates Rheh to activate mTORC1. mTORC1 on one hand alters IRS1 phosphorylation and on the other hand inhibits transcriptional activity of TFEB to reduce Irs1 mRNA level. Together reduces IRS1 leads to Insulin Resistance.

Figure 1. PA reduces basal and insulin stimulated AKT phosphorylation in fish liver and muscle, as well as in culture fish and murine myocytes (Fig. 1I-M). The results appear to be solid but need to be quantified.

Figure 2 shows that PA provoked hyperactivation of mTORC1 (indicated by elevated phosphorylated S6K levels. This effect was abolished by Rapamycin treatment (an mTORC1 inhibitor) and also abolished by insulin stimulation (2F). Again, the western blots should be quantified.

Figure 3: PA treatment increases mRNA expression levels of fatty acid beta oxidation genes in fish myocytes and C2C12 myotubes, and subsequent suppression of CPT1B and CPT2 (rate-limiting enzymes of FAO) inhibited mTORC1 activity and signaling in muscle, C2C12 myotubes and fish myocytes under PA treatment. This suggests PA-induced mTORC1 activation is dependent on mitochondrial FAO. Inhibition of CPT1 improved suppression of insulin stimulated phosphorylation of AKT under PA treatment in fish myocytes and C2C12 myotubes, indicating that mitochondrial FAO is heavily involved in PA-induced mTORC1 activation that contributes to insulin resistance.

Figure 4: PO diet increases acetyl-CoA levels in muscle and PA treatment increases intracellular acetyl-CoA in a dose-dependent manner in fish myocytes. Inhibition of FAO by perhexiline maleate diminished induction of acetyl-CoA under PA treatment. In vivo dsRNA knockdown of ATP citrate lyase (ACLY, catalyze acetyl-CoA synthesis from mitochondrial citrate) decreased mTORC1 activity in muscle, and inhibition of ACLY in fish myocytes and C2C12 myotubes decreased induction of mTORC1 activity under PA treatment. This indicates that palmitic acid promotes mTORC1 activation through acetyl-CoA that is derived from mitochondrial FAO. PA treatment elevates acetylation of Rheb in a dose-dependent manner. Inhibition of FAO by perhexiline maleate attenuated PA-stimulated Rheb acetylation, while fish myocytes and C2C12 myotubes treated with sodium acetate (which can enhance acetyl-CoA) exhibited enhanced Rheb acetylation. The data indicate that acetyl-CoA produced by FAO activates mTORC1 signaling through increased Rheb acetylation. Phosphorylation of AKT were enhanced in muscle with dsACLY knockdown injection, and sodium acetate addition blocked recovery of insulin-stimulated glucose uptake and phosphorylation levels of AKT by perhexiline maleate under PA treatment. ACLY inhibition promoted insulin stimulated phosphorylation of AKT under PA treatment. So, in terms of acetyl-CoA's role in PA-induced insulin resistance, the data suggest that acetyl-CoA derived from FAO mediates PA-induced mTORC1 activation and insulin resistance.<br /> Figure 5: Acetyl-CoA can activate lysine acetyltransferases, and the researchers found that mRNA expression of tip60 was elevated in fish myocytes and C2C12 myotubes under PA treatment. Cultured fish myocytes and C2C12 myotubes treated with a Tip60 inhibitor prevented the induction of mTORC1 activity under PA treatment. Tip60 knockdown also blocked PA-induced mTORC1 activation in C2C12 myotubes. The researchers then determined that Tip60 regulation of mTORC1 is dependent on the acetylation of Rheb by studying the interaction between the two via a CoIP assay, which indeed indicated that Tip60 and Rheb interact. Additionally, Tip60 knockdown impaired PA-induced acetylation of Rheb, supporting the notion that Tip60 mediates the acetylation of Rheb under PA treatment. Inhibition of Tip60 attenuated PA-induced suppression of insulin-stimulated glucose uptake in C2C12 myotubes, and inhibition of Tip60 also restored insulin-stimulated phosphorylation of AKT under PA treatment. These data suggest that Tip60 mediates the regulation of Rheb acetylation under PA treatment and may be a novel therapeutic target for insulin resistance.

Figure 6: the researchers measured the effect of PA treatment on IRS1 phosphorylation in order to understand the mechanism of insulin resistance induced by mTORC1 activation under PA treatment. A PO diet intensified S636/S639 phosphorylation in fish muscle. In fish myocytes and C2C12 myotubes, PA treatment elevated S636/S639 phosphorylation but decreased the Y612 phosphorylation of IRS1 in a dose-dependent manner. Treatment of fish myocytes and C2C12 myotubes with an mTOR inhibitor blocked increased IRS1 S636/S639 phosphorylation levels under PA treatment. Also, PA specifically reduced mRNA levels of Irs1. This indicates that PA-induced, mTOR-dependent alteration of IRS1 phosphorylation and transcription may have contributed to insulin resistance. It is unclear how mTORC induces either increase or decrease in IRS1 phosphorylation depending on the residuals.

Figure 7 shows that PA inhibits nuclear translocation of TFEB to suppress IRS1 transcription. The EMSA in 7D is not convincing.

Minor comments:

Some data appears to weaken the results and/or contradictory. For example, the paper initially showed reduced AKT phosphorylation to support PA induced IR, but shouldn't a lower level of pAKT reduces mTORC activation? But then the rest of the manuscript explores how PA activates mTOR. Part of the IR is manifested by impaired mTORC1 activation, yet the PA activates mTORC1. The authors should present the rationale and flow of the ideas in a better way.

There are also many run-on sentences and grammar issues, making it very hard to read. The writing can be improved.

Referee cross-commenting

Other than lacking quantification of western blots, my major concern is the ITT curve in Figure 1G, which does not support the conclusion that PO induces insulin resistance and therefore the rest of the study is based on a faulty premise. The curve shows that insulin reduced blood glucose much more robustly in the PO group than in the control group, suggesting PO increased insulin sensitivity. Area above curve should be calculated to quantify the difference.

Significance

Overall, this research was trying to show that PA-induced IR is dependent on hyper activation of mTORC1. More specifically, acetyl-CoA induces mTORC1 activation under a palmitic acid diet, and this is achieved through Tip60-mediated Rheb acetylation, which ultimately leads to insulin resistance through IRS1 suppression. The study is very mechanistic and important for understanding IR that is associated with diets high in saturated fatty acids and could potentially leads to therapeutic targets for combating insulin resistance and glucose intolerance.

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Referee #1

Evidence, reproducibility and clarity

In this work, the authors showed PA induces hyperactivation of mTORC1 and insulin resistance. They found acetyl-CoA derived from mitochondrial fatty acid oxidation is required for PA-induced mTORC1 activation and insulin resistance by increasing TIP60-mediated Rheb acetylation. They also showed PA induced mTORC1 activation enhances IRS1 phosphorylation and inhibits transcription of IRS1 by impeding TFEB nuclear translocation. Overall, the authors did a lot of experiments to prove that PA causes mTORC1 activation and insulin resistance. The results are generally convincing, and the finding is novel and instructive.

1. The authors claim PA-induced mTORC1 activation is dependent on acetyl-CoA derived from mitochondrial fatty acid oxidation. Using isotope tracing to determine the contribution of PA to acetyl-CoA, would improve.
2. They showed PA increases fatty acid oxidation related gene expression and acetyl-CoA level, while OA and LA could not. why only PA could increases fatty acid oxidation and acetyl-CoA level, considering both of these lipids could be oxidation in mitochondria? Is there any differences in mitochondria among treatment of PA, OA and LA? It is better to monitor fatty acid oxidation in real time using seahorse. And add discussions.
3. The authors present lots of western blot images, suggest to provide quantification data of these blots.
4. It is better to discuss the relationship between fatty acid oxidation and mTOR signaling.

Significance

The finding is interesting and significant.

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The present manuscript presents a thorough description of the relative localization (in space and time) of a number of proteins of the early secretory pathway. To that aim, the authors used by their custom-made 3D live cell super-resolution microscope (SCLIM) and the yeast S. cerevisiae as a model system. The main claim of these data is that the early secretory pathway in S. cerevisiae is organized by maturation from a newly proposed yeast ERGIC compartment all the way to the trans-Golgi network (TGN).

Major comments:

I have two major comments regarding this manuscript:

1. It is not clear to me how the presented data shows the existence of an ERGIC in the yeast S. cerevisiae. I understand, and appreciate from this text too, that a clear definition of ERGIC, even in a mammalian system, is unclear. For this reason, I would first suggest that the authors provide a clear definition of what ERGIC means to them. Next, the experiments herein presented are all based on a very careful, thorough and nicely organized spatio-temporal mapping of a large number of early secretory pathway proteins, including the ERGIC53 yeast "counterpart" Emp46 (it would help to add, even as a supplementary figure, an alignment/sequence comparison between the human ERGIC53 and the S. cerevisiae emp46). However, the data presented here does not clearly indicate to me that there is a bona fide ERGIC in yeast. Couldn't it just be that what the authors call ERGIC is a cis-Golgi cisterna? I understand that the BFA experiments show a different behavior for some proteins, which fits with what the authors previously names GECCO in plants, so why not calling this GECCO? Again, it will be important to provide definitions of these compartments for the audience. Next, my main concern here is that this is all based on SCLIM, which is a very nice technique, but the resolution is limited in both space and time (by the way, it would be nice to explicitly measure of quantify the spatial resolution in x-y-z). Hence, it is not possible to discern whether an "independent" ERGIC is formed as compared to cis-Golgi cisterna. Electron microscopy (possibly CLEM) could help somehow resolve that and massively increase the strength of the claims, but I do understand this might be difficult for this group and very time consuming, so it might be important to clearly state the limitation of the herein presented data. A possible alternative to test if protein that are seen segregated are within the same membrane (as claimed here) would be to do trapping experiments where a reagent induces dimerization between the two proteins (when tagged with specific tags, such as FKBP/FRB).
2. I could not find any details (maybe I have missed them) about how many times experiments were replicated and the statistical significance of the findings herein reported. In most figures, examples of microscopy images/videos are shown, and selected lines profiles are presented. However, it is not clear how robust these experiments are. Some ideas:

2.1.) The major source of quantification is the peak-to-peak time distance between two proteins. In Table S1 some stdev is presented, but not clear how it is find (it is the sted of all n number of puncta? or of the mean duration per cell? or of the mean duration per experiment? I would suggest that the authors provide the results shown in Table S1 plotted as a histogram or superplot (see e.g. https://rupress.org/jcb/article/219/6/e202001064/151717/SuperPlots-Communicating-reproducibility-and) and clearly explain how statistics is performed.

2.2) Also, the time-lapse movies are acquired with a 5s gap between time points. How is this included in the incertainty of the peak-to-peak duration in Table S1?

2.3) In pg. 7 the authors write "Although experimental variation was high, the two zones appeared to be spatially segregated". Can the authors provide quantitative and statistical support of this claim?

2.4) It is not clear to me how the puncta for analysis are selected. For example, in Fig. 1C, the punctum shown already shows some initial co-localization (it could be e.g. that a peak value was prior or after the duration of the time lapse movie, thereby biassing the computation of the peak-to-peak duration). So, if one would consider those spots e.g., positive for Emp46 that do not contain Mnn9 signal, how often do you see conversion (that is, appearance of Mnn9 signal)? Along the same lines, in pg. 8 the authors write "... signal appeared first and then mnn9-mCherry came up". Details on how this quantification is done and statistical analysis would be needed, to my opinion, to support the claim.

Minor comments:

1. The color code for the 3 color microscopy images is nice, however, the use of green and red for the 2 color images is a bit unfortunate for some people (like myself) who suffer from color blindness. I'd suggest to use green and magenta instead.
2. Pg. 8: have the authors tested Rer1 vs Emp46?
3. Pg. 8: I was of the impression that GRASP65 (GORASP1) is considered to be a cis-Golgi protein (see e.g., Tie et al eLife 2018). Then, what the authors call "ERGIC" couldn't it simply be a cis-Golgi cisterna?
4. pg. 13: "propose to define Grh1, Rer1, and Sed5 as yeast ERGIC/GECCO...". What about Emp46?
5. The first part of the manuscript (up to mid page 13) is clearly focused on defining ERGIC in yeast, then the paper appears as a set of experiments aimed at adding more components in their spatio-temporal mapping. This is ok, but is should be clearly motivated and explained in the Title, abstract and intro.
6. The visualization of colocalization according to the opacity (as said in the methods) is somehow confusing to me. Are the 3D images projections or 3D renderings (no axes are seen)? In e.g. Fig. 6G or 8L, regions where green and magenta (or green and red) are colocalized do not appear white (or yellow), which visually suggests to the inattentive reader that there is no colocalization, when there is.
7. I have not understood what this sentence in pg. 18 means: Similar segregation patterns are also observed during the Golgi-TGN maturation process (Tojima et al., 2019). "We propose that the ERGIC, Golgi, and TGN can coexist as structurally and functionally distinct zones within a single, maturing cisterna." Are they referring to ERGIC, Golgi, and TGN steady state components (proteins) or the structures themselves?
8. The introduction of new data (mammalian data) in the discussion is odd. It might be ok, but I would frame it within a results section and use it later in the discussion.
9. Fig.9: the arrows should go from protein to protein (some seem to go from in between proteins, such as the bottom-most arrow with 87.8 s time duration. Also in panel B, bottom part, some proteins are missing (Erd2 ad Chs5).
10. Fig. 1 and many other: in the line profiles the distance in the x axis has no units or labels. Please add this and the direction of the line profile (an arrowhead would suffice).

Significance

General assessment:

The experiments herein presented are based on a very careful, thorough and nicely organized spatio-temporal mapping of a large number of early secretory pathway proteins, including the ERGIC53 yeast "counterpart" Emp46. However, the data presented here does not convincingly show that there is a bona fide ERGIC in yeast. A major limitation is that the experiments are all based on a state-of-the-art, but still with a limited resolution, fluorescence microscopy technique. Ultrastructural data (e.g., CLEM) would massively help support or revisit the claims presented in this manuscript regarding the existence of an ERGIC compartment in yeast. Also, adding the information about the number of biological replicates and proper statistical analyses on the presented results would be needed to further support the claims.

Advance:

This manuscript builds on the authors' custom build 4D super-resolution microscope (SCLIM) and on previous results (e.g., Tojima et al., J. Cell Sci. 2019). The main novelty is in the study of a number of new early secretory pathway proteins and in the proposal of the existence of a non-stable, maturing ERGIC compartment in S. cerevisiae.

Audience:

This paper might be attractive for a broad audience of cell biologists, especially those interested in membrane biology, cell compartmentalization, and intracellular trafficking and secretion.

Describe your expertise:

I am an expert in membrane trafficking.

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Referee #2

Evidence, reproducibility and clarity

Summary

Tojima and colleagues present a very exciting 4D SCLIM analysis of 20 key-proteins controlling or occupying different stages of Golgi-mediated protein trafficking, taking the reader on a trip from the ER export sites to the ERGIC/GECCO, cis-, medial- and trans-Golgi towards the TGN/recycling endosome. The choice of molecular markers to be patiently time-resolved in 3D allowed the authors to assemble a temporal roadmap for the molecular players studied. This is most impressive.

Major comments

There is an enormous amount of patient systematic analysis packed in this paper, following a punctate organelle as it emerges from the dark, evolves over time (following a combination of 2 markers) with fluorescence peaks at specific time points, after which the signal disappears again. I am certain other cell biologists will be impressed, as I was, viewing the individual images and graphs presented, culminating ultimately in figure 9 that could go straight into a textbook to form a starting point for anybody who wishes to study a particular protein of interest and chose the most appropriate markers to compare it with. The authors propose that the ERGIC/GECCO/Golgi-remnants compartment is an evolutionary conserved structure even though it has a different subcellular distribution/morphology in different classes of eukaryotes. The data presented here and in earlier work seem to support this notion. In particular, the authors demonstrate that the yeast GRASP 65 homologue Grh1 is the earliest to appear closely followed by Ypt1 and Emp46. The fact that RER1 and ERD2 come slightly later is in line with a proposed gate-keeper function, because if they were instead to recycle continuously they should appear first in line. I agree with the authors that the simple model of ER-derived COPII vesicles fusing with each other and thus creating an ERGIC/GECCO de novo is probably too simplistic. The idea of a more permanent structure, pulsating between cargo-loading and cargo-releasing events, possibly associated with creating zones/subdomains within a single cisterna seems very attractive given the data shown here. This work is descriptive, but it is of very high importance to anybody engaged with experimental approaches to study protein sorting from the Golgi-apparatus back to the ER, or on to the plasma membrane or the lytic compartments. The 5 functional stages proposed for Golgi-maturation is an attractive starting point for future research, and I very much like the notion that ERGIC and cis-Golgi cisternae may start as zones/subdomains within a single cisternae, possibly formed via phase separations involving both protein-protein and protein-lipid interactions.

Minor comments

The title strongly focusses on the ERGIC and therefore the earliest sorting steps in the ER-Golgi system, but this manuscripts offers so much more. I was fascinated to learn that Ypt1 appears twice during cisternal maturation in yeast. This may be a yeast-specific phenomenon but it is very interesting. The same can be said about the proposal that Gea1 and Gea2 have different roles in the Golgi and act in different cisternae, and the localisation of AP-3 at the trans-Golgi rather than the TGN. The functional distinction between trans-Golgi and TGN and the differences in their origin are important points and it will be a shame if readers don't realise that this manuscript offers further insight into later steps in Golgi-mediated transport. There may be a case to add something to the abstract and/or modify the title accordingly, but then I also feel that long titles are not ideal and the ERGIC/GECCO portion is the more important take-home message. This is a case for the editorial team and the authors to make the most of the findings.

Given the importance of ERD2 in sorting soluble proteins to be returned back to the ER, the authors may consider using the biological active XFP-TM-ERD2 fusion instead of ERD2-GFP, but this may be kept for future work. In plants, ERD2-GFP is mainly at the Golgi when overexpressed, its erroneous leakage to the ER is only observed at low expression or when K/HDEL proteins are co-expressed. The XFP-TM-ERD2 construct may be better confined to the ERGIC-GECCO and may have a different temporal pattern.<br /> The authors may consider citing Stornaiuolo et al., Mol Biol Cell 2003 Mar;14(3):889-902 who compared the trafficking of KDEL and KKXX pathways and concluded that KDEL proteins are retrieved prior to KKXX proteins....as this fits nicely into the current findings showing that ERD2 and RER1 appear sooner than COPI markers.

Significance

The significance of the work is high because it basically allows to add facts to models. We use models to explain an elusive process because it escapes direct observation. Once we can observe directly, a model can become fact. In simple terms, the authors allow us to see things that we could only speculate about in the past. Therefore, the results present a very significant advance and will be highly relevant to the entire cell biology community. This paper is an important landmark and will help the field to formulate new experimental approaches and models to understand the origins of the Golgi apparatus, the core of the secretory pathway that defines being a eukaryote.

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Referee #1

Evidence, reproducibility and clarity

Summary:

It is generally believed that budding yeast does not have the ER-Golgi intermediate compartment (ERGIC). In this study, the authors attempt to prove the existence of the ERGIC. They measured the kinetics of a few Golgi markers involved in the early secretory pathway in live cell imaging and observed the recruitment of Grh1 precedes that of Mnn9, suggesting the presence of pre-early cisternae. The authors propose that the Grh1-positive cisternae, assembling at the ER exit site (ERES) and progressing to become the early Golgi cisternae, represent the equivalent of the mammalian ERGIC in yeast.

Major comments:

The concept of the ERGIC in mammalian cells was initially proposed based on the protein ERGIC-53 in the 1990s. However, recent nanoscopy imaging data from the Lippincott-Schwartz lab challenges the conventional view of ERGIC by revealing the "ERGIC" is a membrane domain of the ERES (Wegel et al., Cell, 2021; PMID: 33852913), suggesting it might not be appropriate to adopt this concept.

The authors' observations could be interpreted differently. Since the ERGIC is not molecularly defined in their study, the authors cannot prove its existence in yeast unequivocally. Their data indicate the presence of Golgi cisternae, characterized by Grh1, that precede the earliest known cisternae. Although the authors refer to these Grh1-positive cisternae as the "ERGIC", they are essentially "pre-early" cisternae that progress to become the early Golgi cisternae. Nevertheless, their findings could extend the budding yeast Golgi cisternal progression unit further upstream to include the ERES as the starting point for Golgi cisternal maturation. To further explore this, it would be interesting to investigate the kinetics of COPII subunits in cisternal progression along with Grh1 or Mnn9 and to plot COPII components in the Figure 9 map.

The second half of the manuscript appears to deviate from the main focus on identifying the ERGIC. This section primarily presents the Golgi localization of four Golgi proteins (Ypt1, Gea1, Gea2, and Alp6) deduced from kinetics. However, it lacks functional studies to substantiate the authors' claims on their cellular functions. As a result, this part of the study remains purely speculative and might not support the authors' claims. Given that Figure 9 provides a highly informative summary of all kinetics and localization data, I recommend the authors keep but significantly abridge this section.

The manuscript also has a few major concerns.

1. The analysis of only one fluorescent particle or Golgi cisternal punctate structure is insufficient for a Golgi marker, considering the substantial variation of Golgi cisternae. To improve statistical robustness, the authors should select multiple fluorescent particles from multiple cells, displaying plots with averaged intensities, error bars, and sample sizes (n).
2. In Fig. 5A, the BFA-induced lumps positive for Grh1, Rer1, and Sed5 may potentially represent the ERES, as observed in mammalian cells (Ward et al., JCB, 2002; PMID: 11706049). To verify this, the authors should co-label these lumps with COPII subunits.
3. The authors previously reported the "hug-and-kiss" model for cargo transport from the ERES to the early Golgi cisternae. As the current study is highly relevant to the "hug-and-kiss" model, it is disappointing that the authors did not provide further data and comment on it. The "hug-and-kiss" and "ERGIC" transport modes are two distinct ways for secretory cargo transport from the ERES to the early Golgi cisternae. The authors should verify the "hug-and-kiss" transport and report the relative frequency of the two transport modes.
4. The current version has minimal background knowledge of ERGIC in mammalian and yeast cells. Therefore, the authors should provide a comprehensive introduction to ERGIC.

Significance

General assessment:

The data presented in the manuscript is novel and appears to be convincing. However, one of the major concerns is the lack of statistical robustness, which requires to be addressed. Furthermore, the manuscript's data could be interpreted differently, as elaborated in the major comments.

Advance:

While the interpretation of the manuscript's data requires reconsideration, it can contribute to our understanding of secretory trafficking at the Golgi. The manuscript could fill a crucial gap in our knowledge in this field by addressing the major comments.

Audience:

Cell biologists in the membrane trafficking field, particularly those working on the Golgi, would find the manuscript interesting.

My expertise:

My research focuses on membrane trafficking at the ER, Golgi, and endosome.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Unfortunately, this paper adds only a little to our understanding of uptake in to the flagellar pocket of trypanosomes. It tends to add only detail to information that has been well characterised elsewhere and indeed, as the authors themselves point out, (lines 92-98) it is rather incremental.

We were disappointed that the reviewer was so unsupportive of the work presented here. It seems possible that the reviewer is partly objecting that the title - which emphasised the main finding of the paper - does not fully capture the content of the paper. We have therefore modified the title to emphasise that the paper is principally a characterisation of TbSmee1 rather than an investigation of the flagellar pocket, with the insight into cargo entry being the most notable finding.

Not only has Tbsmee1 been studied before but this data in bloodstream forms is not particularly novel since it gives much the same information as the canonical hook protein TbMORN. This work follows the pattern of conclusions made previously with the protein TbMORN. It focusses on the protein TbSmee where RNAi mutants are interpreted to show flagellar pocket enlargement and impaired access by surface bound cargo. Unfortunately, there is little mechanistic or functional conclusion to the study in terms of how TbSmee operates naturally in the cell.

This is deliberately downplaying the value of the work. TbSmee1 has not previously been characterised in bloodstream form cells, and neither TbMORN1 nor the hook complex are as well-characterised as other cytoskeletal components such as the flagellum and basal body. To criticise the paper for not providing a molecular mechanism of TbSmee1's function is unreasonable given the volume of work provided and the fact that this is a first characterisation of the protein in this life cycle stage. Expectation of a complete molecular mechanism is setting a very high bar for a first characterisation.

It is also possible that the reviewer has not grasped the main thrust of the argument - when TbMORN1 was characterised it was the first protein shown to have this cargo entry defect. We show here that not only does TbSmee1 share this defect, but that it is in fact a previously-unacknowledged feature of all phenotypes of this type, exemplified by clathrin. We have modified the text to make this finding more clearly emphasised (see for example lines 654-661 in the tracked-changes version of the manuscript).

There are other possible explanations for the phenotype. That would need to be studied. This large flagellar pocket phenotype is seen with RNAi mutants of many different types of proteins in the trypanosome and so pleiotropic effects are highly likely. Also, there are a good number of alternative possibilities to account for reduced access to the pocket in these mutants and this data could be usefully added.

This is another statement that seems intended primarily to disparage the paper rather than attempt to improve it. It would have been extremely helpful if the reviewer highlighted what these other possible explanations are instead of making vague allusions. The widespread prevalence of this kind of phenotype means that our insight into restricted cargo access to the flagellar pocket is of general relevance in the trypanosome field.

Specific points<br /> 1. The transient location for the TbSmee at the FAZ tip - or in this case the groove region - was seen in procyclics (Perry, 2018) so this bloodstream indication merely confirms that concept.

The reviewer is again downplaying the value of the work rather than providing constructive criticism. While FLAM3 has been shown to be at the tip of the new flagellum in bloodstream form cells (Sunter et al., 2015), at the time of the preprint being published Smee1 was actually the first protein (besides the DOT1 antigen) shown to localise to the groove region in bloodstream form cells. It is also worth noting that procyclic form cells and bloodstream form cells are fairly different in this regard - in procyclic cells, there is an entire flagellar connector structure that is not present in bloodstream form cells, and so demonstrating that Smee1 was present in the groove region was an important experiment. Since this preprint was published, Smithson et al. have identified 13 additional proteins localising to the groove (Smithson et al., 2022) - we have modified the text to include these points (see lines 542-545 of the tracked-changes manuscript).

1. The C terminal region required for targeting is a reasonable deletion analysis of regions of the protein. But can this data (line 228) be said to "mediate targeting" - or is it just required. For instance, targeting might be OK but it might be needed for stable association, etc etc.

We have changed the text to say "required" for targeting instead of "mediating" targeting (line 312 of tracked-changes manuscript).

1. This protein has already been shown to be phosphorylated and the sites and cell cycle possibilities have been mapped by Urbaniak. So that section adds little. https://doi.org/10.1371/journal.ppat.1008129

The reviewer is again disparaging the significance of the work rather than critiquing it. This is after all only a single panel of a figure and ~15 lines of text, and therefore a minor but still noteworthy element of the manuscript. This also misunderstands what the Urbaniak study does and does not show - while that work showed that Smee1 is phosphorylated, it remained possible that other post-translational modifications were occurring. This experiment shows that the "fuzzy" appearance (variable electrophoretic migration) of TbSmee1 in gels can be solely attributed to phosphorylation as opposed to other post-translational modification. We contacted Dr. Urbaniak to confirm this - his answer is below.

"__I think your approach to look at the fuzzy banding is actually rather elegant; our data shows that phosphorylation occurs but we did not look for any other PTMs that could influence migration on a gel and probably wouldn't see them without a different enrichment and analysis method. We often see a fuzzy pattern with glycosylation due to the heterogeneity, and I suspect other modifications will also results in a smear. Given that the band collapses to a single band after phosphatase treatment and not with an inhibitor present it is fair to conclude that phosphorylation is responsible for the fuzzy band, not other undefined PTMs like glycosylation.__"

1. Essentiality in BS forms and pocket enlargement. This is not surprising. A very large number of cytoskeletal proteins show this in RNAi knockdown. Flagella mutants (extensive publications from many groups (Hill, Bastin, Gull, etc) over last 15 years show this very well and so this protein is just one more example.

This appears to be another comment aimed at downplaying the value of the manuscript rather than providing constructive feedback. The fact that we have demonstrated something previously unobserved in a common phenotype makes the data of general interest to the community, we feel.

1. I didn't find that the explanations for flagella pocket enlargement are soundly based. The experiments focus on endocytosis and uptake and ignore other plausible reasons and some evidence in literature.

Again, the reviewer's feedback would be considerably more constructive if they had taken the time to specifically cite the evidence in the literature that they are alluding to, and present some of the "other plausible reasons" they are aware of. We have consulted widely in the community and have not been able to find anybody who knew what work the reviewer is referring to here.

Lines 84/85. Enlarged pockets may be indicative of endocytosis failure. Presumably the rationale is that endocytosis fails, but exocytosis still occurs and the pocket membrane enlarges. What evidence is there that exocytosis of membrane still occurs? This simple concept might indeed operate in a clathrin mutant but is surface membrane/content exocytosis is maintained in these cytoskeleton mutants? There is good evidence for glycoconjugates within the flagellar pocket. Are these depleted or present still?

The reviewer is correct that we have not specifically assayed for exocytosis, but the fact that we are able to make the same observations in both the clathrin RNAi (where exocytosis has been assayed - Allen et al., 2003) and the Smee1 RNAi means that this is not a problematic omission. The effect of the enlarged flagellar pocket phenotype on the glycoconjugates in the flagellar pocket is an interesting question but far outside the current focus of the paper.

1. There are also a number of other publications indicating that clathrin pits are still present on the enlarged pockets of various mutants when viewed by EM. The authors have looked at the flagellar pockets by EM but the EM methods described have extensive washings and centrifugations before fixation. This is a very poor approach and will mean that endo and exocytic traffic is disturbed (extensive references in literature in other systems? This is not a useful approach for exo/endocytosos studies where flux of traffic demands fast chemical or freezing fix in media.

The reviewer has misunderstood the aim of the experiments described in Figure 5D, which was to observe the morphological changes caused by depletion of TbSmee1. As the reviewer is no doubt aware, high-pressure freezing of trypanosomes gives much better morphological preservation than chemical fixing in media, so the choice of method is not "very poor" but tailored to the experimental aims. We have modified the text to make this point more clearly (lines 355-358 of tracked-changes version). Once again, the referee offers no citation to back up their assertion that endo- and exocytic traffic is disturbed by wash steps, either in trypanosomes or elsewhere.

1. The EMs and Light microscopy does show that the mutant pockets are substantially abnormal in their cytoskeletal arrangement. They have multiple flagella profiles, flagella structures have not connected with the membrane and are sometimes in the cytoplasm (see a glance of the paraflagellar rod in the cytoplasm in FigS5C and internalised FAZ attachment plaques in Fig 4 D bottom right cell). Given these extensive (and expected) cytoskeletal abnormalities it is highly likely that these pocket abnormalities are a result of motility, cell division/developmental issues and the differential uptake phenotypes merely consequential.

This is another misinformed argument that is seeking to disparage the data. The reviewer has apparently overlooked the fact that the same phenotype is seen in clathrin RNAi, when flagellar pocket enlargement precedes any downstream effects on cell division cycle progression. We have gone to great lengths (Fig 6) to demonstrate that the enlargement of the flagellar pocket almost certainly precedes the onset of the growth defect in the TbSmee1 RNAi, and it is therefore likely to precede the cytoskeletal abnormalities that the reviewer has highlighted. An effect on cellular motility is possible and would be interesting to investigate in future work.

1. The authors speak about early phenotypes , but these are often at 15-24 hours. That is probably a couple of cell cycles and so not early.

To be informative, the analyses of RNAi phenotypes have to be done as soon as possible after the onset of the growth defect, and we have gone to great lengths (Figure 5) to define this point as being at 21 hours. This is already difficult as the number of phenotypic cells at the onset of the growth defect will not be high. We have clarified the text to emphasise that "early" refers to soon after the onset of the phenotype (lines 388-389 of tracked-changes version).

In relation to the above question of comparison to the same morphology produced by flagella mutants it would be good to know if these hook mutants produce motility phenotypes and whether these are manifest before the uptake phenotypes. There is evidence (cited here) that forward motility of the trypanosome directs material on surface into the pocket. If these cells have motility defects (primary or via failed division) then surely that would provide an alternative simple explanation for uptake differences.

The reviewer is overlooking the observation that the surface-bound endocytic cargoes (ConA, BSA) are still being sorted/directed as far as the entrance to the flagellar pocket - what is interesting is that the cargo is apparently unable to enter the flagellar pocket. As noted above, it would certainly be interesting to look at motility effects in follow-up work.

1. There is a general point that if studies are to have real relevance to uptake in the trypanosome then they need to deal with uptake of natural ligands rather than artificial surrogates such as dextran. Such tracers were used historically, but in the last decade a series of receptors and ligands for fluid phase and particularly membrane mediated endocytosis have been discovered. With the investment of a little time these important ligand / receptors such as haptoglobin, transferrin, etc would be much more relevant.

Dextran is still state-of-the-art as it is an inert fluid phase marker. We are not aware - and have asked widely - of any readily-available alternative to dextran as a fluid phase marker, especially seeing as we have demonstrated in this study that BSA does not behave as a fluid phase marker in the experimental conditions used. The reviewer is also being disingenuous in suggesting that there is a panel of validated physiological reporters for trypanosomes that are readily available commercially - this is not the case. Transferrin is probably the only example, but the transferrin receptor is confined to the flagellar pocket and therefore not relevant to the question of how surface-bound material enters the flagellar pocket in the first place. As suggested by Reviewer 3 and endorsed by Reviewer 2, we have looked at the uptake of anti-VSG antibodies (which are a physiological cargo) in additional experiments and obtained evidence that the same effects are seen (Figure 9).

**Referees cross-commenting

this session includes comments from Reviewer 1 and Reviewer 2.<br /> *

Reviewer 2<br /> <br /> Dear Reviewers 1 and 3:<br /> I agree with many of the points with Reviewer 1 and our divergence is partly a matter of degree. While it is true that this manuscript is incremental in its contribution to our understanding of TbSmee1, it nonetheless adds to our understanding of the role of this protein in the bloodstream life stage and because of that I find value in the work. The fact that it mirrors what was seem in other protein knockdown studies (e.g. TbMORN) doesn't negate its contribution for me. Reviewer 1 makes an important point, however, when stating that this work does not add a mechanistic or functional conclusion as to how TbSmee1 operates and for me that is the biggest shortcoming of the work. Offering mechanistic insight is a high bar and while it would make for a much more exciting story it does not discount the value of the work as presented. What I do appreciate is the speculation about this observation that endocytosis is required for entrance of surface bound material into the pocket and although they are unable to show that this is not a side affect of other processes being disrupted it is and intriguing point. These observation have the potential of stimulating further investigations into crosstalk between the entrance to the pocket and endocytosis. I also agree that the use of ligands for known receptors like transferrin would be far more informative. While I assumed the transferrin receptor was in the pocket itself it would be interesting to see if the ESAG6/7 is also located outside the pocket and transiently binds cargo before being brought inside for endocytosis.<br /> I think that Reviewer 3 brings up a great point with the focus on VSG's. I think that examining VSG turnover in these mutants can add value to the analysis and inform our view of how affecting the hook complex alters VSG endocytosis.

We appreciate Reviewer 2 taking the time to defend the value of the work, and we concur with Reviewer 2's assessment. Reviewer 2 is also correct that the transferrin receptor appears to be primarily or wholly confined to the flagellar pocket interior, making this likely less informative in this context. Concerning the uptake of anti-VSG antibodies highlighted by Reviewer 3 and endorsed by Reviewer 2, we have carried out these experiments and obtained similar results to those published in the first version of the preprint (Figure 9).

Reviewer 1<br /> <br /> some fair comment and agreement. This is being sent to general cell biology journals.<br /> when one looks at this area in the round it is it is nearly 50 years (1975) since Langreth and Balber published their seminal work on protein uptake and digestion in bloodstream and culture forms of T. brucei. There has been 50 years intense study and the genome has been around for nearly 20 years as well. So, put simply - for both a general science audience and the wider parasite community - if this is a paper about one protein, TbSmee1,then it has surely has to say something functional about that protein. If it is a paper about uptake in trypanosomes (where mutants are one means of interrogation) then it surely has to say something about mechanisms of uptake of physiological relevant ligands. The days of dextran etc are past.

Hence, my comment that this does neither and so is very incremental to what is known already. It is 2022 not 1975. Langreth and Baber published their seminal work in J Protozoology for very good reasons no doubt.

It is striking that Reviewer 1 here extends their aggressive and uncivil approach to attack Reviewer 2's assessment, again substituting forceful wording for informed argument. Reviewer 1 again inexplicably and mistakenly criticises the use of dextran when no state-of-the-art alternative exists. They then go on to needlessly disparage the work done by Langreth & Balber when this work was produced in a totally different publishing landscape. They also appear to fundamentally misunderstand the Review Commons concept, which is to provide journal-independent preprint peer review; it is also worth noting that there are specialist journals such as PLoS Pathogens in the RevComm affiliates as well as general cell biology journals. Given that the mechanism of variant surface glycoprotein (VSG) switching has not yet been fully articulated despite the efforts of multiple labs and many projects over a decades-long time period, it seems extremely unreasonable to be making such demands of this paper.

Reviewer 2<br /> Thank you for replying and I agree with the spirit of your critique. My only comment, which could result from my own naivete, is to say that despite the incredible work that has been done in dissecting endocytosis in T. brucei over these past 50 years, it appears that we still do not understand how many fundamental of aspects of this activity works in this parasite. Even basic questions regarding how cargo, e.g. transferrin, binding to surface receptors is sensed by the parasite remains unknown and the identity of the specific signaling components which transmit this information internally to initiate endocytosis have not been characterized. In many ways it seems that we don't even understand how the parasite partitions the end/exocytic pathways in the pocket and maintains membrane homeostasis. While we know that some kinases and traditional signaling components must be involved, a high resolution understanding of this process in T. brucei seems lacking. I only say all this to suggest that the field maybe isn't yet that advanced to reject work of this type as so many mechanistic unknowns still remain to be uncovered and maybe incremental advances and phenomenology still can add value to the field. However, I respect your opinion on the matter and my perspective could be due to a lack of a full appreciation of the literature on the subject.

We completely agree with Reviewer 2's assessment here, which neatly summarises our rationale for the present work. Reviewer 2 is, if anything, being overly accommodating by suggesting that their perspective may be due to a lack of a full appreciation of the literature - on the contrary, Reviewer 2 appears to have a very sound grasp of the topic.

Reviewer #1 (Significance):

Unfortunately, I did not find tis to be very significant. It covers old ground in terms of the phenotype described. Many groups have shown the differences between procyclic and bloodstream phenotypes in this enlarged pocket phenomenon. The work is rather incremental from these and other author's work on these hook proteins.<br /> There are alternative explanations for understanding the effect of flagella pocket structure and uptake of ligands into the pocket and trypanosome cell. These would need to be studied before one could see a functional, mechanistic link established.<br /> Other parts of this are of nicely done but do not move on our understanding (eg targeting/phosphorylation) from what has been done previously.

As noted repeatedly, it appears that Reviewer 1's priority is disparaging the value of the work here and downplaying its significance rather than providing constructive feedback. The reviewer repeatedly makes unrealistic demands (a mechanistic model, use of non-standard reagents), misunderstands the aim of experiments (use of high-pressure freezing), makes vague allusions to other work in the literature but without citing anything specific to support their case, and makes strong and assertive statements that are factually incorrect (design of RNAi experiments, use of dextran). We find this approach unhelpful, uncivil, and unprofessional. It is desperately disappointing that we should have to spend the majority of our response rebutting Reviewer 1's comments rather than implementing constructive criticisms that would strengthen the manuscript.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary:<br /> In this manuscript the authors have advanced our understanding of the hook complex component TbSmee1 through a detailed analysis of this protein's role in the endocytosis of surface bound proteins via the flagellar pocket in bloodstream form Trypanosoma brucei. The TbSmee1 protein, previously identified using proximity labeling using TbMORN1 and TbPLK, and characterized in procyclic T. brucei, was confirmed to target to both the shank portion of the hook complex as well as the growing end of the new FAZ in replicating cells. The protein was also shown to likely be phosphorylated as had been suggested previously due to its association with the kinase TbPLK. A domain deletion analysis demonstrated that domains 2 and 3 are important for TbSmee1's proper localization to the hook complex. Loss of TbSmee1 using RNAi based knockdown resulted in a quick cessation of growth in the bloodstream form within 24 hours in contrast to what was seen previously in procyclic cells which had only a decreased growth rate. Loss of TbSmee1 also resulted in an enlargement of the flagellar pocket and in many ways mirrored the phenotype observed with knockdown of TbMORN1. Although prior work on TbSmee1 in procyclic T. brucei demonstrated that loss of this protein altered the morphology of TbMORN1, no such change was seen in bloodstream form cells and only an alteration in the morphology of TbLRRP1 was observed. In characterizing the effect of TbSmee1 depletion on endocytosis the authors showed that the fluid phase marker Dextran could enter into the flagellar pocket of TbSmee1 depleted parasites while the surface bound ConA and BSA remained outside of the flagellar pocket suggesting that TbSmee1 may play a role in allowing larger protein components into the pocket regions. Similar observations were also previously seen with TbMORN1 depletion. Importantly, a knockdown of clathrin recapitulated the TbSmee1 knockdown phenotype suggesting that endocytosis itself was required to allow material bound at the surface to enter into the flagellar pocket. In addition to adding to our understanding of hook complex components, this work raises some interesting questions regarding the role of the hook complex in facilitating endocytosis in this important human pathogen.

Thank you for the positive assessment.

Major Critiques:<br /> This is a superbly written manuscript with robust high-quality data that strongly support the major conclusions made by the authors. The flow the article is logical and easy to follow making it accessible to a wide array of readers.

We are glad that the Reviewer appreciated the effort that went into writing the paper.

Although I appreciate the brevity of the introduction and how the article gets straight to the point, additional background information on the components and function of the flagellar pocket collar protein could help contextualize the goals of the project. The way in which the flagellar collar structures are introduced to the reader is quite abrupt (beginning on line 75) and simply states the names of TbBILBO1, the centrin arm and hook complex as simple facts without much discussion about the background of these components/regions. A graphical representation of the centrin arm or hook complexes relative to other components like the pocket itself, FAZ or axoneme could make following the story much easier. An expansion of this background could also go a long way to convince readers of the importance of this region in the basic biology and virulence of T. brucei.

Implemented. We have added more background details on the hook complex, flagellar pocket collar, and centrin arm and added a new schematic image to Figure 1 showing these structures as well as the FAZ (Figure 1A).

On lines 84-86 the authors cite the way in which 'small' vs 'large' macromolecules enter into the pocket without defining what exactly is meant by these terms as they are relative in nature. Setting some boundaries of size could provide some context to the reader.

Implemented. We have provided more detail on the approximate sizes in nm (lines 110-113 of tracked-changes manuscript).

In the domain localization analysis beginning in Figure 4 there is a missed opportunity to also assess which portions of the TbSmee1 protein are important for overall function as well. By either an examination of dominant negative phenotypes resulting from overexpression of the truncated mutant or the expression of the truncated forms designed to be RNAi resistant in the TbSmee1 knockdown cell line, one could also assess which portions of this protein are essential for endocytic function in addition to targeting. Is there a reason this was not performed?

This is a good point; we did actually investigate overexpression of the TbSmee1(161-766) construct which can target correctly but is missing the first folded domain, but did not observe any phenotypic effects. We have added this point to the results (lines 301-302 of tracked-changes version). We agree that it would be interesting to express the truncations in a TbSmee1 RNAi background in order to simultaneously assay for targeting and function, but this was (unfortunately, perhaps) not part of the original experimental design. To do so now would require generating a completely new panel of truncation constructs with recoded DNA (in order to make them RNAi-resistant) and then generating a new panel of cell lines. While this would be informative, we feel that it would be impractical at present.

In the analysis of viability changes due to TbSmee1 depletion (lines 237) the authors state that at "72 h post-induction showed widespread lysis, ..." This phenotype seems inconsistent with other related endocytic defect mutants. There is no further mention of this lysis phenomenon here or in the discussion and considering how unique this seems it deserves either additional data to demonstrate or further discussion as to the basis of the phenotype. It seems, at least from this study of TbStarkey1 and prior studies which result in the enlarged flagellar pocket phenotype, that having an enlarged pocket is not the cause of lysis and doesn't even naturally lead to a growth defect.

Widespread lysis is the usual outcome of bloodstream form cells with strong endocytic defects - we have observed this directly for the clathrin, TbMORN1, and TbSmee1 RNAi cell lines, and it has been documented in a number of other publications (see for example Natesan et al., 2010, Manna et al., 2017). We have clarified this point in the text (see for examples lines 359-341, 474-478 of tracked-changes manuscript).

The authors do not comment on what is the source for the cessation in growth following TbSmee1 knockdown. Is it nutrient depravation like in other endocytic defect mutants?

Implemented (see for example lines 359-361, 605-610 of the tracked-changes manuscript). The source of the growth defect is likely to be due to impaired cell division cycle progression due to the gross enlargement of the flagellar pocket and subsequent steric hindrance and imbalance of membrane homeostasis.

In the end, one of the most interesting observations made by the authors is that loss of TbSmee1 inhibits endocytosis and this has the appearance of not allowing large molecule substrates like ConA and BSA to enter into the flagellar pocket. This appeared to have nothing to do with a gatekeeping type function of the hook complex/flagellar collar and instead, as shown through clathrin knockdown, was related to the ability of the parasite to endocytose. There are a lot of potential interpretations of this phenomenon with one being a simple perturbation of the normal membrane trafficking to and from the flagellar pocket being involved. An analysis of knockdown of exocytic components might reveal whether or not this inability to enter into the pocket is also seen when exocyst proteins are also depleted. It may be impossible to tease apart these two interrelated activities but it might eliminate one side of the equation if these proteins can still enter the flagellar pocket when exocytosis if perturbed although this reviewer understands that that dimension of T. brucei membrane trafficking is poorly understood relative to endocytosis.

This is an interesting point, and the reviewer is also correct in highlighting that exocytosis is far less characterised than endocytosis in Trypanosoma brucei. The exocyst has been characterised in bloodstream form T. brucei (Boehm et al., 2017) and shown to also have a role in endocytosis, so teasing out the relative contributions of these pathways would undoubtedly be challenging. We would prefer not to go in this direction in this present study, but it is an obvious avenue for future work.

An intriguing possibility that the authors allude to and which if answered would make this manuscript have a far broader appeal is to determine if loss of TbSmee1 alters the lipid kinase distribution and if this is the source of the negative impact on endocytosis. One important dimension of endocytosis in T. brucei which remains poorly understood is the role of signaling machinery in triggering endocytic events. It is possible that the hook complex serves as the gatekeeping or signaling platform that recruits signaling components (like lipid kinases) that identify and/or modify the membrane lipid phosphatidylinositols harboring cargo laden receptors thus marking them for endocytosis within the pocket. It still seems unclear when in the process of endocytosis is the decision made to pull things into the pocket but it seems that the assumption is that this occurs deep within the pocket. This data suggests that there is possibly another decision point prior to being allowed entrance into the pocket. It may be that this isn't a gatekeeping decision but rather a stop vs. go activity where once cargo laden membrane reaches the collar a choice is made to pull this material in or not there and not after material is already in the pocket.

These are all really interesting ideas and would be fascinating topics for future work.

This obvious enigma based on the observation that loss of hook complex components affect the spatially separated site of endocytosis support the idea that the actual endocytic signaling platforms are located at the hook complex and that this area may make the membrane modifications that mark membrane as being ready to be endocytosed via clathin coated vesicles at the bottom of the pocket. This would still allow for fluid phase small molecule entrance which does not require binding to surface proteins. The obvious problems of having both endo/exocytosis occurring in the same close proximity makes the dissection of this phenomenon difficult but it is worth potentially expounding on further in the discussion as this idea is very appealing and adds an important dimension to our understanding of endocytosis in this organism.

Implemented (lines 722-727 of the tracked-changes manuscript). We have added some more detail to these points in the Discussion. We agree with the reviewer that there are some profoundly interesting questions concerning membrane identify and membrane protein uptake here.

Minor Critiques:<br /> The authors commit significant time to the analysis of the phosphorylation of TbSmee1, but there is little stated about the role of TbPLK in this activity or the potential connection of TbSmee1 phosphorylation to the cell cycle. Would a knockdown of TbPLK using RNAi potentially demonstrate an altered migration of TbSmee1 due to a lack of phosphorylation? An analysis of radiolabeled TbSmee1 using p32 in vivo would likely support this claim as well. Has mass spectrometry identified potential phosphorylation sites to examine? Additionally, the loss of TbSmee1 has been shown to disrupt localization of TbPLK in procyclic cells and so why this was not also assessed in bloodstream form cells subjected to RNAi was not clear.

Partly implemented. We have added some discussion of the possible role of TbSmee1 phosphorylation in the cell cycle to the Discussion (lines 562-565 of tracked-changes manuscript), and emphasised the identification of phosphorylation sites in previous phosphoproteomics work (citations of Nett et al., 2009, Urbaniak et al., 2013). Given that the strongest and earliest effect of TbSmee1 depletion was on endocytosis and cargo uptake, we chose to focus on this angle rather than exploring its contribution to the biogenesis of cytoskeleton-associated structures and its interaction with TbPLK. For that reason we would prefer not to carry out the experiments looking at the effects of TbSmee1 depletion on TbPLK or vice versa.

In the results section (lines 104-108) a model of the protein structure as predicted for example by AlphaFold might be informative and complement the domain analysis work depending on the quality of the prediction.

Implemented. The AlphaFold prediction is consistent with the predictions made by the other structural analyses, and we have noted this in the text (lines 145-148 and 551 of the tracked-changes version).

There is an arrow in the Figure 1B Western blot but I can find no mention of what it is trying to highlight in the text.

Corrected.

For Figure 1D there is no loading control or control for the distribution of the soluble fraction to validate the separation of the two compartments.

Implemented. We have carried out additional experiments to show the partitioning of a cytoplasmic protein (the endoplasmic reticulum chaperone BiP) into the detergent-soluble fraction. These results are now displayed in the updated Figure 1.

The authors fail to comment on the lack of changes in hook complex components they see to that observed by Perry et. al. 2018. This difference merits some minor comment or speculation.

Implemented. We have added this commentary to the Discussion (lines 592-600 of the tracked-changes version).

Line 228: domain should be capitalized.

Implemented.

Line 230: FigS5C should have a space and period after Fig. and S5C.

Implemented.

Line 244: "on" should be inserted in the sentence "...TbSmee1 protein depletion ON either side of the onset..."

Implemented.

Line 400: the '...20/21 h post-induction...' is slightly confusing and may read better as 20-21 h.

Implemented.

Line 463: a space is needed between '...2009).The...'.

Implemented.

Reviewer #2 (Significance):

This manuscript advances our current conception of endocytosis in T. brucei. Although this model kinetoplastid parasite has been extensively studied with respect to endocytosis there is still a great deal we do not yet understand regarding how this process is regulated at a mechanistic level. This work has begun to connect previously unappreciated aspects of endocytosis in T. brucei by highlighting a potentially novel connection between the flagellar collar/hook complex and the physically separated endocytic events within the flagellar pocket itself. It may be that what appears as regulated entrance into the pocket is in fact the source of signaling that triggers the endocytic events carried out by clathrin. This is an interesting notion that no doubt requires further investigation which lies outside of the scope of this report. While this work appeals primarily to those studying kinetoplastids parasites it has the potential to provide insight into basic protozoan biology as well. Due to my related interest in kinetoplastid endocytosis, I find this work to be of high quality, conceptually interesting and employs many of the cutting-edge techniques currently available in the study of T. brucei.

We are very happy that the Reviewer formed a favourable impression of the work.

Reviewer #3 (Evidence, reproducibility and clarity):

This manuscript begins to dissect the function of the hook complex protein SMEE1 in the mammalian infective form of T. brucei. The hook complex is a cytoskeletal structure associated with the flagellar pocket, the only site of endo/exocytosis in these cells. The authors demonstrate that SMEE1 is required for endocytosis in these cells and that this can occur with minimal change to the molecular make-up of the hook complex. The authors show that endocytosis is important for the access of large molecules e.g. ConA into the flagellar pocket.

Major comments

The key conclusion of this study are convincing and the data is generally well presented and clear. The interpretation of the figures matches well with the data presented - there are a few minor issues though that I have highlighted below in minor comments. The authors use a range of molecular cell biology approaches to define the role of SMEE1 and these are appropriate and are well controlled.

Thank you.

My major comment focuses on the use of different tracers to study endocytosis but the elephant in the room is what is happening to VSG as this is the surface protein that needs to rapidly removed from the cell surface and cleaned. Given the importance of removal of antibodies bound to the VSG - have the authors looked at this in the SMEE1 depleted cells? Do VSG-antibody complexes accumulate in this region? This is an important experiment as this would give key physiologically relevant data to this study. All the material should be readily available for this as there are a number of VSG antibodies.

We agree with the Reviewer that the behaviour of these VSG-bound antibodies is a key test of the physiological relevance of the observations we have made using ConA and BSA, and have implemented this request - the results are in the new Figure 9. Although they sound simple, these assays turned out to be far from trivial and much more technically challenging than the other uptake assays, owing to the extremely fast kinetics (seconds) of anti-VSG uptake (Engstler et al., 2007) and the unexpectedly and incredibly high losses of bound antibodies during the assay. This might be due to shedding, as noted in the Discussion.

Minor comments<br /> Perhaps I have been overthinking this but is surface-bound the right way to describe the cargo, as it clearly goes in both directions onto and off the surface and in fact the experiments in this manuscript are focussing on the removal of this material from the surface so is not surface-bound.

We have clarified that "surface-bound" refers to material that binds to the surface glycoprotein coat of the trypanosomes and which is subsequently internalised, not material that is bound for (i.e being directed to) the cell surface (lines 77-78 of tracked-changes version). We hope this addresses the Reviewer's point?

Have the authors investigated the structure of the protein using alphafold and if so how does that compare to the domain structure that was presented in this manuscript?

Implemented (lines 145-148, 551 of tracked-changes version). We have checked the AlphaFold prediction of the three-dimensional structure of TbSmee1 and noted it in the Results; the prediction is consistent with the earlier bioinformatic analyses.

The authors raised a number of antibodies to TbSMEE1 and TbSTARKEY1 but it was not clear in the figures which antibody was ultimately used for analysis by western and IF - could the authors clarify, as some looked to have a higher background than others. Line 150 states the same localisation was seen for all three antibodies and references S3C but I couldn't see that data presented.

Implemented - the 304 antisera was used for most subsequent experiments and we have noted this in the M&M (lines 793-798 of tracked-changes version). Figure S3C shows that the Ty1-TbSmee1 recapitulates the localisation of the antibodies against the endogenous protein - we have clarified this point as well (lines 206-207 of tracked-changes version).

Line 169 - can the authors provide more detail about the global correlation methodology as I was unable to follow the details in the methods? Is this a pixel per pixel correlation over the image or on a selected region over the area of potential signal overlap? In figure 2E it appears that BILBO1 signal correlates more closely with the SMEE1 signal than MORN and LRRP1 and from the images that would not seem to be the case. Have I interpreted this figure incorrectly?

Implemented. The original analysis was a global correlation analysis that was determining whether the signals were correlated with each other regardless of spatial overlap, and we agree with the reviewer that these outputs were non-intuitive to interpret. In the revision, we have carried out a new analysis (and updated the accompanying text and M&M section), measuring the degree of spatial correlation between each pair of signals on a pixel-by-pixel basis over the area of each cell, with a total of 30 cells analysed in each pairing. We believe that this addresses the reviewer's point. See lines 223-243, 963-974 of the tracked-changes version).

The authors have generated a number of different clones and performed experiments on these clones generally more than twice, which is clearly explained in the figure legends but in places the data is then put together and it is difficult to know which experiments/clones it comes from - for example 7C/7F what do those percentages represent? Is this the sum of all experiments? A representative experiment? How many cells per experiment were analysed?

Implemented. We have double-checked all the figure legends and clarified this point where necessary. Quantifications were always made by compiling data from multiple independent experiments using multiple separate clones - see in particular lines 1323-1324, 1363-1365, 1380-1382 of the tracked-changes version.

Line 200 - From the image it is not convincing that SMEE1 is slightly behind DOT1 - I agree it looks enveloped but would appear level with the distal end of the DOT1 signal.

Implemented. We have adopted the Reviewer's wording for this text (line 271 of tracked-changes version).

For the truncation experiments the authors should explain that these are performed with cells in which the endogenous SMEE1 will be expressed and this may influence the localisation of the truncations, especially as there is no information about whether SMEE1 forms complexes with itself or other proteins.

Implemented (lines 296-298 of tracked-changes version).

Figure 4D - should be 1 not T-

We have relabelled this as "TbSmee1". The values in this column are the immunoblot signal intensities obtained for the endogenous TbSmee1 protein in the -Tet condition. We have also clarified this in the figure legend.

Line 223 - given the low expression of constructs 2 and 9 I'm not sure it is possible to infer anything from the lack of localisation of these constructs as they appear unstable and would be unlikely to localise to a specific location.

We have added this caveat to the text (lines 558-562 of tracked-changes version).

Figure S7 - The images presented were not convincing that there was a reduction in the localisation of LRRP1 to the hook complex on depletion of either SMEE1 or MORN1. The difference looks particularly minor if present at all.

Agreed, there was some debate in the group about these results. We have changed to text to fit the Reviewer's interpretation (lines 347-348 of the tracked-changes version).

Line 264 - "implied that the lethal phenotype might be due to a loss of function" - this seems an odd thing to say as it doesn't provide any insight as of course the phenotype is due to a loss of function.

We have clarified this point (lines 350-353 of the tracked-changes version). We would however disagree with the reviewer that RNAi phenotypes are exclusively due to a loss of individual protein's function(s) - when proteins are present in multiprotein complexes (as is often the case with cytoskeleton-associated proteins), then destabilisation of the complex due to loss of the entire protein can cause the observed phenotype, rather than the loss of the function performed by the individual protein within the complex (this may be a semantic point, however). A very good example of this is with the outer arm dynein complex component LC1 (Ralston et al., 2011) - RNAi against LC1 is lethal because the entire outer arm dynein complex is destabilised, whereas expression of non-functional mutants of LC1 produces viable cells with motility defects due to the specific loss of LC1 function.

Line 412 - can the authors clarify what they mean by geometric problems?

Implemented (lines 605-610 of tracked-changes version). We were referring to the fact that enlargement of the flagellar pocket will probably create difficulties for the progression of the cell division cycle.

Throughout the manuscript can you use log scale for the growth curves.

Implemented.

Line 756 - add citation

Whoops! Implemented (line 1058 of tracked-changes version).

Line 465/66 - the authors states that the ability of the fluid phase cargo being still able to enter the pocket is evidence that the channel lumen is still open; however, I would think that despite the close apposition of the cell membrane to the flagellar membrane in the flagellar pocket neck region this would be unlikely to impede fluid/soluble material from entering the pocket, as presumably VSG protein can move through this region. This does not alter the ultimate conclusion the authors are drawing but without microscopy evidence for the state of the channel lumen it is difficult to be sure of its status.

Fair point. We have modified this statement (line 701 in tracked-changes version).

Reviewer #3 (Significance):

The flagellar pocket is the key portal into and out of the trypanosome cell and as such has a vital role to play in host-parasite interactions. The flagellar pocket is supported by a number of cytoskeletal structures including the hook complex and the role of these structures in flagellar pocket function are poorly understood. The flagellar pocket is particularly important in the bloodstream form of the trypanosome parasite which infects the mammalian host as it is the route for the surface protein VSG to get onto and off the surface. The VSG is required for antigenic variation and the removal of VSG-antibody complexes helps 'clean' the surface of the parasite. SMEE1 is a component of the hook complex and the manuscript here dissects its role in the mammalian infective parasite and shows that it is vital for the endocytosis of material off the surface. Intriguingly, a block in endocytosis causes a blockage of material outside of the pocket, suggesting a multi-step process in the regulation of uptake of material from the parasite's surface.<br /> This manuscript will be of specific interest to those researchers investigating the long-term persistence of these parasites in the mammalian host. There are potentially some insights into the control of membrane domains for endocytosis that are of interest to more general cell biologists as well.

We are very grateful to the reviewer for the supportive comments and the constructive evaluation. Many thanks!

Expert in molecular cell biology of trypanosomes and Leishmania.

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

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Referee #3

Evidence, reproducibility and clarity

This manuscript begins to dissect the function of the hook complex protein SMEE1 in the mammalian infective form of T. brucei. The hook complex is a cytoskeletal structure associated with the flagellar pocket, the only site of endo/exocytosis in these cells. The authors demonstrate that SMEE1 is required for endocytosis in these cells and that this can occur with minimal change to the molecular make-up of the hook complex. The authors show that endocytosis is important for the access of large molecules e.g. ConA into the flagellar pocket.

Major comments

The key conclusion of this study are convincing and the data is generally well presented and clear. The interpretation of the figures matches well with the data presented - there are a few minor issues though that I have highlighted below in minor comments. The authors use a range of molecular cell biology approaches to define the role of SMEE1 and these are appropriate and are well controlled.

My major comment focuses on the use of different tracers to study endocytosis but the elephant in the room is what is happening to VSG as this is the surface protein that needs to rapidly removed from the cell surface and cleaned. Given the importance of removal of antibodies bound to the VSG - have the authors looked at this in the SMEE1 depleted cells? Do VSG-antibody complexes accumulate in this region? This is an important experiment as this would give key physiologically relevant data to this study. All the material should be readily available for this as there are a number of VSG antibodies.

Minor comments

Perhaps I have been overthinking this but is surface-bound the right way to describe the cargo, as it clearly goes in both directions onto and off the surface and in fact the experiments in this manuscript are focussing on the removal of this material from the surface so is not surface-bound.

Have the authors investigated the structure of the protein using alphafold and if so how does that compare to the domain structure that was presented in this manuscript?

The authors raised a number of antibodies to TbSMEE1 and TbSTARKEY1 but it was not clear in the figures which antibody was ultimately used for analysis by western and IF - could the authors clarify, as some looked to have a higher background than others. Line 150 states the same localisation was seen for all three antibodies and references S3C but I couldn't see that data presented.

Line 169 - can the authors provide more detail about the global correlation methodology as I was unable to follow the details in the methods? Is this a pixel per pixel correlation over the image or on a selected region over the area of potential signal overlap? In figure 2E it appears that BILBO1 signal correlates more closely with the SMEE1 signal than MORN and LRRP1 and from the images that would not seem to be the case. Have I interpreted this figure incorrectly?

The authors have generated a number of different clones and performed experiments on these clones generally more than twice, which is clearly explained in the figure legends but in places the data is then put together and it is difficult to know which experiments/clones it comes from - for example 7C/7F what do those percentages represent? Is this the sum of all experiments? A representative experiment? How many cells per experiment were analysed?

Line 200 - From the image it is not convincing that SMEE1 is slightly behind DOT1 - I agree it looks enveloped but would appear level with the distal end of the DOT1 signal.

For the truncation experiments the authors should explain that these are performed with cells in which the endogenous SMEE1 will be expressed and this may influence the localisation of the truncations, especially as there is no information about whether SMEE1 forms complexes with itself or other proteins.

Figure 4D - should be 1 not T-

Line 223 - given the low expression of constructs 2 and 9 I'm not sure it is possible to infer anything from the lack of localisation of these constructs as they appear unstable and would be unlikely to localise to a specific location.

Figure S7 - The images presented were not convincing that there was a reduction in the localisation of LRRP1 to the hook complex on depletion of either SMEE1 or MORN1. The difference looks particularly minor if present at all.

Line 264 - "implied that the lethal phenotype might be due to a loss of function" - this seems an odd thing to say as it doesn't provide any insight as of course the phenotype is due to a loss of function.

Line 412 - can the authors clarify what they mean by geometric problems?

Throughout the manuscript can you use log scale for the growth curves.

Line 756 - add citation

Line 465/66 - the authors states that the ability of the fluid phase cargo being still able to enter the pocket is evidence that the channel lumen is still open; however, I would think that despite the close apposition of the cell membrane to the flagellar membrane in the flagellar pocket neck region this would be unlikely to impede fluid/soluble material from entering the pocket, as presumably VSG protein can move through this region. This does not alter the ultimate conclusion the authors are drawing but without microscopy evidence for the state of the channel lumen it is difficult to be sure of its status.

Significance

The flagellar pocket is the key portal into and out of the trypanosome cell and as such has a vital role to play in host-parasite interactions. The flagellar pocket is supported by a number of cytoskeletal structures including the hook complex and the role of these structures in flagellar pocket function are poorly understood. The flagellar pocket is particularly important in the bloodstream form of the trypanosome parasite which infects the mammalian host as it is the route for the surface protein VSG to get onto and off the surface. The VSG is required for antigenic variation and the removal of VSG-antibody complexes helps 'clean' the surface of the parasite. SMEE1 is a component of the hook complex and the manuscript here dissects its role in the mammalian infective parasite and shows that it is vital for the endocytosis of material off the surface. Intriguingly, a block in endocytosis causes a blockage of material outside of the pocket, suggesting a multi-step process in the regulation of uptake of material from the parasite's surface.<br /> This manuscript will be of specific interest to those researchers investigating the long-term persistence of these parasites in the mammalian host. There are potentially some insights into the control of membrane domains for endocytosis that are of interest to more general cell biologists as well.

Expert in molecular cell biology of trypanosomes and Leishmania.

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

Summary:

In this manuscript the authors have advanced our understanding of the hook complex component TbSmee1 through a detailed analysis of this protein's role in the endocytosis of surface bound proteins via the flagellar pocket in bloodstream form Trypanosoma brucei. The TbSmee1 protein, previously identified using proximity labeling using TbMORN1 and TbPLK, and characterized in procyclic T. brucei, was confirmed to target to both the shank portion of the hook complex as well as the growing end of the new FAZ in replicating cells. The protein was also shown to likely be phosphorylated as had been suggested previously due to its association with the kinase TbPLK. A domain deletion analysis demonstrated that domains 2 and 3 are important for TbSmee1's proper localization to the hook complex. Loss of TbSmee1 using RNAi based knockdown resulted in a quick cessation of growth in the bloodstream form within 24 hours in contrast to what was seen previously in procyclic cells which had only a decreased growth rate. Loss of TbSmee1 also resulted in an enlargement of the flagellar pocket and in many ways mirrored the phenotype observed with knockdown of TbMORN1. Although prior work on TbSmee1 in procyclic T. brucei demonstrated that loss of this protein altered the morphology of TbMORN1, no such change was seen in bloodstream form cells and only an alteration in the morphology of TbLRRP1 was observed. In characterizing the effect of TbSmee1 depletion on endocytosis the authors showed that the fluid phase marker Dextran could enter into the flagellar pocket of TbSmee1 depleted parasites while the surface bound ConA and BSA remained outside of the flagellar pocket suggesting that TbSmee1 may play a role in allowing larger protein components into the pocket regions. Similar observations were also previously seen with TbMORN1 depletion. Importantly, a knockdown of clathrin recapitulated the TbSmee1 knockdown phenotype suggesting that endocytosis itself was required to allow material bound at the surface to enter into the flagellar pocket. In addition to adding to our understanding of hook complex components, this work raises some interesting questions regarding the role of the hook complex in facilitating endocytosis in this important human pathogen.

Major Critiques:

This is a superbly written manuscript with robust high-quality data that strongly support the major conclusions made by the authors. The flow the article is logical and easy to follow making it accessible to a wide array of readers. Although I appreciate the brevity of the introduction and how the article gets straight to the point, additional background information on the components and function of the flagellar pocket collar protein could help contextualize the goals of the project. The way in which the flagellar collar structures are introduced to the reader is quite abrupt (beginning on line 75) and simply states the names of TbBILBO1, the centrin arm and hook complex as simple facts without much discussion about the background of these components/regions. A graphical representation of the centrin arm or hook complexes relative to other components like the pocket itself, FAZ or axoneme could make following the story much easier. An expansion of this background could also go a long way to convince readers of the importance of this region in the basic biology and virulence of T. brucei.

On lines 84-86 the authors cite the way in which 'small' vs 'large' macromolecules enter into the pocket without defining what exactly is meant by these terms as they are relative in nature. Setting some boundaries of size could provide some context to the reader.

In the domain localization analysis beginning in Figure 4 there is a missed opportunity to also assess which portions of the TbSmee1 protein are important for overall function as well. By either an examination of dominant negative phenotypes resulting from overexpression of the truncated mutant or the expression of the truncated forms designed to be RNAi resistant in the TbSmee1 knockdown cell line, one could also assess which portions of this protein are essential for endocytic function in addition to targeting. Is there a reason this was not performed?

In the analysis of viability changes due to TbSmee1 depletion (lines 237) the authors state that at "72 h post-induction showed widespread lysis, ..." This phenotype seems inconsistent with other related endocytic defect mutants. There is no further mention of this lysis phenomenon here or in the discussion and considering how unique this seems it deserves either additional data to demonstrate or further discussion as to the basis of the phenotype. It seems, at least from this study of TbStarkey1 and prior studies which result in the enlarged flagellar pocket phenotype, that having an enlarged pocket is not the cause of lysis and doesn't even naturally lead to a growth defect.

The authors do not comment on what is the source for the cessation in growth following TbSmee1 knockdown. Is it nutrient depravation like in other endocytic defect mutants?

In the end, one of the most interesting observations made by the authors is that loss of TbSmee1 inhibits endocytosis and this has the appearance of not allowing large molecule substrates like ConA and BSA to enter into the flagellar pocket. This appeared to have nothing to do with a gatekeeping type function of the hook complex/flagellar collar and instead, as shown through clathrin knockdown, was related to the ability of the parasite to endocytose. There are a lot of potential interpretations of this phenomenon with one being a simple perturbation of the normal membrane trafficking to and from the flagellar pocket being involved. An analysis of knockdown of exocytic components might reveal whether or not this inability to enter into the pocket is also seen when exocyst proteins are also depleted. It may be impossible to tease apart these two interrelated activities but it might eliminate one side of the equation if these proteins can still enter the flagellar pocket when exocytosis if perturbed although this reviewer understands that that dimension of T. brucei membrane trafficking is poorly understood relative to endocytosis.

An intriguing possibility that the authors allude to and which if answered would make this manuscript have a far broader appeal is to determine if loss of TbSmee1 alters the lipid kinase distribution and if this is the source of the negative impact on endocytosis. One important dimension of endocytosis in T. brucei which remains poorly understood is the role of signaling machinery in triggering endocytic events. It is possible that the hook complex serves as the gatekeeping or signaling platform that recruits signaling components (like lipid kinases) that identify and/or modify the membrane lipid phosphatidylinositols harboring cargo laden receptors thus marking them for endocytosis within the pocket. It still seems unclear when in the process of endocytosis is the decision made to pull things into the pocket but it seems that the assumption is that this occurs deep within the pocket. This data suggests that there is possibly another decision point prior to being allowed entrance into the pocket. It may be that this isn't a gatekeeping decision but rather a stop vs. go activity where once cargo laden membrane reaches the collar a choice is made to pull this material in or not there and not after material is already in the pocket.

This obvious enigma based on the observation that loss of hook complex components affect the spatially separated site of endocytosis support the idea that the actual endocytic signaling platforms are located at the hook complex and that this area may make the membrane modifications that mark membrane as being ready to be endocytosed via clathin coated vesicles at the bottom of the pocket. This would still allow for fluid phase small molecule entrance which does not require binding to surface proteins. The obvious problems of having both endo/exocytosis occurring in the same close proximity makes the dissection of this phenomenon difficult but it is worth potentially expounding on further in the discussion as this idea is very appealing and adds an important dimension to our understanding of endocytosis in this organism.

Minor Critiques:

The authors commit significant time to the analysis of the phosphorylation of TbSmee1, but there is little stated about the role of TbPLK in this activity or the potential connection of TbSmee1 phosphorylation to the cell cycle. Would a knockdown of TbPLK using RNAi potentially demonstrate an altered migration of TbSmee1 due to a lack of phosphorylation? An analysis of radiolabeled TbSmee1 using p32 in vivo would likely support this claim as well. Has mass spectrometry identified potential phosphorylation sites to examine? Additionally, the loss of TbSmee1 has been shown to disrupt localization of TbPLK in procyclic cells and so why this was not also assessed in bloodstream form cells subjected to RNAi was not clear.

In the results section (lines 104-108) a model of the protein structure as predicted for example by AlphaFold might be informative and complement the domain analysis work depending on the quality of the prediction.

There is an arrow in the Figure 1B Western blot but I can find no mention of what it is trying to highlight in the text.

For Figure 1D there is no loading control or control for the distribution of the soluble fraction to validate the separation of the two compartments.

The authors fail to comment on the lack of changes in hook complex components they see to that observed by Perry et. al. 2018. This difference merits some minor comment or speculation.

Line 228: domain should be capitalized.

Line 230: FigS5C should have a space and period after Fig. and S5C.

Line 244: "on" should be inserted in the sentence "...TbSmee1 protein depletion ON either side of the onset..."

Line 400: the '...20/21 h post-induction...' is slightly confusing and may read better as 20-21 h.

Line 463: a space is needed between '...2009).The...'.

Significance

This manuscript advances our current conception of endocytosis in T. brucei. Although this model kinetoplastid parasite has been extensively studied with respect to endocytosis there is still a great deal we do not yet understand regarding how this process is regulated at a mechanistic level. This work has begun to connect previously unappreciated aspects of endocytosis in T. brucei by highlighting a potentially novel connection between the flagellar collar/hook complex and the physically separated endocytic events within the flagellar pocket itself. It may be that what appears as regulated entrance into the pocket is in fact the source of signaling that triggers the endocytic events carried out by clathrin. This is an interesting notion that no doubt requires further investigation which lies outside of the scope of this report. While this work appeals primarily to those studying kinetoplastids parasites it has the potential to provide insight into basic protozoan biology as well. Due to my related interest in kinetoplastid endocytosis, I find this work to be of high quality, conceptually interesting and employs many of the cutting-edge techniques currently available in the study of T. brucei.

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Referee #1

Evidence, reproducibility and clarity

Unfortunately, this paper adds only a little to our understanding of uptake in to the flagellar pocket of trypanosomes. It tends to add only detail to information that has been well characterised elsewhere and indeed, as the authors themselves point out, (lines 92-98) it is rather incremental. Not only has Tbsmee1 been studied before but this data in bloodstream forms is not particularly novel since it gives much the same information as the canonical hook protein TbMORN.

This work follows the pattern of conclusions made previously with the protein TbMORN. It focusses on the protein TbSmee where RNAi mutants are interpreted to show flagellar pocket enlargement and impaired access by surface bound cargo. Unfortunately, there is little mechanistic or functional conclusion to the study in terms of how TbSmee operates naturally in the cell. There are other possible explanations for the phenotype. That would need to be studied. This large flagellar pocket phenotype is seen with RNAi mutants of many different types of proteins in the trypanosome and so pleiotropic effects are highly likely.

Also, there are a good number of alternative possibilities to account for reduced access to the pocket in these mutants and this data could be usefully added.

Specific points

1. The transient location for the TbSmee at the FAZ tip - or in this case the groove region - was seen in procyclics (Perry, 2018) so this bloodstream indication merely confirms that concept.
2. The C terminal region required for targeting is a reasonable deletion analysis of regions of the protein. But can this data (line 228) be said to "mediate targeting" - or is it just required. For instance, targeting might be OK but it might be needed for stable association, etc etc.
3. This protein has already been shown to be phosphorylated and the sites and cell cycle possibilities have been mapped by Urbaniak. So that section adds little. https://doi.org/10.1371/journal.ppat.1008129
4. Essentiality in BS forms and pocket enlargement. This is not surprising. A very large number of cytoskeletal proteins show this in RNAi knockdown. Flagella mutants (extensive publications from many groups (Hill, Bastin, Gull, etc) over last 15 years show this very well and so this protein is just one more example.
5. I didn't find that the explanations for flagella pocket enlargement are soundly based. The experiments focus on endocytosis and uptake and ignore other plausible reasons and some evidence in literature.<br /> Lines 84/85. Enlarged pockets may be indicative of endocytosis failure. Presumably the rationale is that endocytosis fails, but exocytosis still occurs and the pocket membrane enlarges. What evidence is there that exocytosis of membrane still occurs? This simple concept might indeed operate in a clathrin mutant but is surface membrane/content exocytosis is maintained in these cytoskeleton mutants? There is good evidence for glycoconjugates within the flagellar pocket. Are these depleted or present still?
6. There are also a number of other publications indicating that clathrin pits are still present on the enlarged pockets of various mutants when viewed by EM. The authors have looked at the flagellar pockets by EM but the EM methods described have extensive washings and centrifugations before fixation. This is a very poor approach and will mean that endo and exocytic traffic is disturbed (extensive references in literature in other systems? This is not a useful approach for exo/endocytosos studies where flux of traffic demands fast chemical or freezing fix in media.
7. The EMs and Light microscopy does show that the mutant pockets are substantially abnormal in their cytoskeletal arrangement. They have multiple flagella profiles, flagella structures have not connected with the membrane and are sometimes in the cytoplasm (see a glance of the paraflagellar rod in the cytoplasm in FigS5C and internalised FAZ attachment plaques in Fig 4 D bottom right cell). Given these extensive (and expected) cytoskeletal abnormalities it is highly likely that these pocket abnormalities are a result of motility, cell division/developmental issues and the differential uptake phenotypes merely consequential.
8. The authors speak about early phenotypes , but these are often at 15-24 hours. That is probably a couple of cell cycles and so not early. In relation to the above question of comparison to the same morphology produced by flagella mutants it would be good to know if these hook mutants produce motility phenotypes and whether these are manifest before the uptake phenotypes. There is evidence (cited here) that forward motility of the trypanosome directs material on surface into the pocket. If these cells have motility defects (primary or via failed division) then surely that would provide an alternative simple explanation for uptake differences.
9. There is a general point that if studies are to have real relevance to uptake in the trypanosome then they need to deal with uptake of natural ligands rather than artificial surrogates such as dextran. Such tracers were used historically, but in the last decade a series of receptors and ligands for fluid phase and particularly membrane mediated endocytosis have been discovered. With the investment of a little time these important ligand / receptors such as haptoglobin, transferrin, etc would be much more relevant.

Referees cross-commenting

This session includes comments from Reviewer 1 and Reviewer 2.

Reviewer 2

Dear Reviewers 1 and 3:<br /> I agree with many of the points with Reviewer 1 and our divergence is partly a matter of degree. While it is true that this manuscript is incremental in its contribution to our understanding of TbSmee1, it nonetheless adds to our understanding of the role of this protein in the bloodstream life stage and because of that I find value in the work. The fact that it mirrors what was seem in other protein knockdown studies (e.g. TbMORN) doesn't negate its contribution for me. Reviewer 1 makes an important point, however, when stating that this work does not add a mechanistic or functional conclusion as to how TbSmee1 operates and for me that is the biggest shortcoming of the work. Offering mechanistic insight is a high bar and while it would make for a much more exciting story it does not discount the value of the work as presented. What I do appreciate is the speculation about this observation that endocytosis is required for entrance of surface bound material into the pocket and although they are unable to show that this is not a side affect of other processes being disrupted it is and intriguing point. These observation have the potential of stimulating further investigations into crosstalk between the entrance to the pocket and endocytosis. I also agree that the use of ligands for known receptors like transferrin would be far more informative. While I assumed the transferrin receptor was in the pocket itself it would be interesting to see if the ESAG6/7 is also located outside the pocket and transiently binds cargo before being brought inside for endocytosis.<br /> I think that Reviewer 3 brings up a great point with the focus on VSG's. I think that examining VSG turnover in these mutants can add value to the analysis and inform our view of how affecting the hook complex alters VSG endocytosis.

Reviewer 1

some fair comment and agreement. This is being sent to general cell biology journals.<br /> when one looks at this area in the round it is nearly 50 years (1975) since Langreth and Balber published their seminal work on protein uptake and digestion in bloodstream and culture forms of T. brucei. There has been 50 years intense study and the genome has been around for nearly 20 years as well. So, put simply - for both a general science audience and the wider parasite community - if this is a paper about one protein, TbSmee1,then it has surely has to say something functional about that protein. If it is a paper about uptake in trypanosomes (where mutants are one means of interrogation) then it surely has to say something about mechanisms of uptake of physiological relevant ligands. The days of dextran etc are past. Hence, my comment that this does neither and so is very incremental to what is known already. It is 2022 not 1975. Langreth and Baber published their seminal work in J Protozoology for very good reasons no doubt.

Reviewer 2<br /> Thank you for replying and I agree with the spirit of your critique. My only comment, which could result from my own naivete, is to say that despite the incredible work that has been done in dissecting endocytosis in T. brucei over these past 50 years, it appears that we still do not understand how many fundamental of aspects of this activity works in this parasite. Even basic questions regarding how cargo, e.g. transferrin, binding to surface receptors is sensed by the parasite remains unknown and the identity of the specific signaling components which transmit this information internally to initiate endocytosis have not been characterized. In many ways it seems that we don't even understand how the parasite partitions the end/exocytic pathways in the pocket and maintains membrane homeostasis. While we know that some kinases and traditional signaling components must be involved, a high resolution understanding of this process in T. brucei seems lacking. I only say all this to suggest that the field maybe isn't yet that advanced to reject work of this type as so many mechanistic unknowns still remain to be uncovered and maybe incremental advances and phenomenology still can add value to the field. However, I respect your opinion on the matter and my perspective could be due to a lack of a full appreciation of the literature on the subject.

Significance

Unfortunately, I did not find tis to be very significant. It covers old ground in terms of the phenotype described. Many groups have shown the differences between pro cyclic and bloodstream phenotypes in this enlarged pocket phenomenon. The work is rather incremental from these and other author's work on these hook proteins.

There are alternative explanations for understanding the effect of flagella pocket structure and uptake of ligands into the pocket and trypanosome cell. These would need to be studied before one could see a functional, mechanistic link established.

Other parts of this are of nicely done but do not move on our understanding (eg targeting/phosphorylation) from what has been done previously.

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Reply to the reviewers

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

In this manuscript, Kagermeier et al. present a novel and interesting study that attempts to model a severe neurodevelopmental disorder, pontocerebellar hypoplasia type 2a, using neocortical and cerebellar organoids. Brain organoids are an appropriate and promising approach to elucidate disease mechanisms in neurodevelopmental diseases. The authors show a reduction in the size of the organoids which is more pronounced in the cerebellar compared to neocortical organoids. While this finding is interesting and reminiscent of the clinical PCH2a phenotype, i.e., cerebellar hypoplasia, the study is very preliminary and the conclusions of the manuscript are not supported by the data. Additional information and further experiments are necessary to support the claims made.

Major concerns:

1. hiPSC lines show considerable inter- and intra-individual variability and therefore the size differences observed between these control and patient-derived organoids may arise from differences in the hiPSC lines used. While the data sufficiently demonstrates the pluripotency of the multiple novel hiPSC lines, major concerns remain as to the appropriateness of the control hiPSC lines. The manuscript should include a table describing the age and sex matching as well as mode of reprogramming for all control and patient lines. Patient and control lines should be matched as closely as possible. Furthermore, figure legends should clearly indicate which clones and lines are shown in the various figure panels.

We agree with the reviewer that hiPSC variability is an important concern in the field. In order to minimize such effects, all iPSCs lines used in this study were generated following the same protocol in the same lab. All cell lines are derived from male donors, thus, eliminating sex-based variability. Further, there is no report of sex-based variance in the clinical phenotype of PCH2a children and this finding is further corroborated by a currently on-going natural history study in our research team. While it would be ideal to also have age-matched controls, this is not possible for ethical reasons as skin biopsies from healthy children cannot easily be obtained to match the pediatric PCH2a cases. However, based on the literature, we believe that epigenetic age is erased upon reprogramming (Strassler et al 2018, Studer et al 2015). Following the reviewer’s recommendation, we provide a table that clearly indicates the origin of all six cell lines used (see Methods section) and information of respective lines was added to the figure legends as suggested by the reviewer.

As the hiPSC lines used are not isogenic, it is important that the authors characterise these lines further. This should include a quantification of the rates proliferation and apoptosis in all used hiPSC lines, as these might impact the growth rate of the embryoid bodies / organoids.

We thank the reviewer for raising this concern. To address the variability of hiPSC lines, we performed an extensive characterization of pluripotency, proliferation and cell cycle dynamics of all six hiPSC lines through immunocytochemistry against pluripotency marker OCT4, proliferation marker Ki-67 and EdU incorporation experiments. We further assessed the apoptosis rate of hiPSCs by staining against apoptotic marker cCas3. These experiments were carried out in three consecutive passages of all iPSC lines providing statistical power to the analyses. All experiments did not result in significant differences between PCH2a and control iPSC lines (see Figure 2).

The authors state that the hiPSC lines have been characterised by SNP arrays to show that no genomic / chromosomal aberrations have been accrued due to reprogramming. The manuscript should include information as to when the SNP array was performed (i.e., immediately after reprogramming, after initial passaging, etc) and also include the results of the SNP array as additional information. What passage were the hiPSC when the presented experiments were carried out?

In agreement with this comment, we provide data of SNP arrays that were performed to ensure the chromosomal integrity of all cell lines (see supplement). Further, we added details on passages of the cell lines in the respective figure legends as suggested by the reviewer. In brief, all cell lines were kept below passage 20 and were subjected to pluripotency testing before differentiations were started.

Given that TSNE54 is broadly and strongly expressed in the developing nervous system, the very limited staining of the organoids for TSNE54 in Figure 2 is surprising. Can the authors provide an explanation for the fact that TSNE54 is only expressed in a small subset of cells? Which cell types are these? Moreover, high-magnification images should be shown to demonstrate subcellular staining pattern of TSNE54. Quantification of TSNE54 protein levels by immunoblotting would also be beneficial.

Related to this observation, it is puzzling that the large size differences that the authors observe in their organoids would be driven by such a small number of TSNE54-expressing cells. How do the authors explain this discrepancy?

We thank the reviewer for this comment. We have carefully assessed human cerebellar development transcriptomic datasets which demonstrate that TSEN54 is in fact not strongly but moderately expressed in the human developing nervous system. Additionally, TSEN54 expression is expressed in various different cell types (not limited to a subset of cell types) (Aldinger et al 2021, Sepp et al 2021). We agree with this reviewer and reviewer 3 that Western Blotting or other types of quantification would be informative as well as investigation of the subcellular localization of the protein. However, these questions go beyond the scope of the current manuscript, which aims to present a disease model. We have therefore decided to remove the characterization of TSEN54 expression in organoids from our revised manuscript.

The generated organoids need to be better characterised with a broader range of markers using both qPCR and immunostaining. At the moment, their identity as "cortical" and "cerebellar" organoids remain unconvincing. This is particularly true for cerebellar organoids, which are challenging to generate and are not widely used. The authors should include additional markers (for example, see PMIDs 25640179, 29397531, 32117945) and immunostaining should clearly show expected staining patterns.

In Figure 5, it appears that some markers (e.g., SATB2) are expressed differently between control and patient lines, yet this is not commented on by the authors who conclude that control and patient lines show differentiation into organoids.

We thank the reviewer for this suggestion. We performed further immunostainings using the markers that were used in other cerebellar organoid papers (Muguruma et al 2015, Silva et al 2020, Watson et al 2018) as the reviewer suggested. In detail, we added immunohistochemistry experiments on Day 30 and Day 50 of differentiation for early Purkinje cell markers OLIG2 and SKOR2. We also included ATOH1 as a marker for rhombic lip-derived granule cells. For the neocortical organoids, we believe that the performed characterization is sufficient since the protocol we used is well-established and widely used as also indicated by the reviewer. We agree that the cellular composition of the organoids should be investigated in detail (for instance using single-cell transcriptomics). However, we believe this is out of the scope of this manuscript, which describes the establishment of a brain-region specific model platform.

The authors attempt to look into a potential mechanism for the size differences observed between control and patient organoids. However, only cleaved caspase-3 is used as a marker for apoptosis and no differences were observed. The authors should include further markers for potential cell death. In addition, immunostaining for proliferation markers (i.e., KI67) should be performed to evaluate whether the difference in organoid size could stem from decreased proliferation rather than increased cell death.

We agree with the reviewer and included a quantification of the proliferation marker Ki-67 within the SOX2 positive population of cerebellar and neocortical organoids as well as the quantification of SOX2 positive areas within the organoids (Figure 6). We observed significant differences in proliferation between PCH2a and control cerebellar organoids. Moreover, we also analyzed the morphology of organoids and quantified the thickness and number of rosettes and find significant differences between control and PCH2a cerebellar organoids corroborating the notion that proliferation is altered in cerebellar organoids. Neocortical organoids do not show any significant differences in proliferation and Sox2+ structures. Only the thickness of the Sox2+ areas is slightly decreased in neocortical PCH2a organoids compared to controls. In order to deepen our analysis of a possible increased apoptosis in PCH2a organoids, we also quantified cCas3 in Sox2+ structures (Figure 5) as also suggested by Reviewer 2. These analyses did not show any significant differences between PCH2a and control organoids. We therefore suggest that at the early stages of differentiation studied here, proliferative differences are the main reason for the size differences between PCH2a and control organoids.

Reviewer #1 (Significance (Required)):

The authors present an innovative approach to study neurodevelopmental disorders using brain organoids and should be of interest to researchers and clinicians working on neurodevelopmental diseases. However, the data presented are too limited to support any conclusions about the phenotype observed. Furthermore, questions remain about the used methodology and more work is needed to demonstrate the successful generation of both cortical and cerebellar organoids.

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

Please find enclosed my recommendation for the paper submitted by Kagermeier et al entitled' Human organoid model of PCH2a recapitulates brain region-specific pathology'. It describes the development of a human model for PCH2a and its characterization. My overall assessment of the paper is 'Major revision' which is explained below.

Although the paper is very well written and clearly interesting in that it describes the generation and initial analyses of a human organoid model for PCH2a it should be revised such that it will proof the points it is trying to make. The authors are meticulous in their studies combining cellular characterization and a thorough initial screen of organoid (both cerebellar as well as cortical) integrity, yet hardly any mechanistic data is provided. Nevertheless, if the authors are able to add additional experiments and are able to address the points raised, the reviewer may be willing to consider a more positive outcome.

Major concerns

1) The overall quality of the figures is poor. There is a lot of overexposure such that often cellular or tissue structures are blended. It starts with Figure 1 G and H but can be observed throughout the manuscript. Deconvolution would greatly enhance their results.

We are thankful for this comment and we have improved the quality of all microscopy images.

2) Especially figure 4 and 5 could have been complemented with quantitative data. It furthermore seems more supplemental figure as these are just proof-of-principle stainings. No conclusions can be drawn from the panels except that all markers are there in the various conditions. And while they are showing a neural rosette in Fig 4A, just tiny ones can be observed in 4B. It is also not clear what the whole mount IHC ads in comparison to the IHC on sections. It is also strange that there is still a lot of SOX2 in the CALB/MAP2-positive area, but again with this magnification hard to appreciate.

We agree with the reviewer that so far we presented qualitative proof-of-principle stainings that demonstrate cerebellar and neocortical differentiation, respectively. In order to address the comment of the reviewer, we improved the quality of the images and also provided higher magnification and enhanced resolution. Additionally, we now provide detailed quantifications of SOX2+ and Ki67+ neural progenitor cells and show that differences observed between PCH2a and control cerebellar organoids may explain the size differences observed between organoids (Figure 6). Our study provides the basis for more in-depth analysis of differences in differentiation and cell type composition between PCH2a and control organoids in the future, for example through single-cell RNAseq.

3) If the authors would like to proof the point that cerebellar/cortical development is hampered, more functional assays could have been done. Nothing is analyses on the fraction of progenitor cells present (such as the percentage of Tbr2+ IPC in VZ/CP). Furthermore, if there is a suspicion that the number of cells is affected (which is also not shown), proliferation/cell cycle exit experiments using BrdU/EdU should have been performed. Early cell cycle exit still cannot be rules out and should have been tested by the combination of Ki67-/EdU+ percentage of a certain faction of progenitor cells (eg PAX6+ pool).

We thank the reviewer for this valuable suggestion and agree that it would be interesting to carry out respective experiments. In this study, we show the establishment of a brain-region-specific organoid platform as a disease model for PCH2a and are only at the beginning of deciphering the underlying mechanism. In the revised manuscript, we quantified Ki-67+/Sox2+ cells in proliferative zones in the organoids. We believe that future studies including BrdU / EdU incorporation assays as well as scRNA-seq will answer the questions raised here and decipher the disease-causing mechanism on both cellular and molecular levels but are beyond the scope of this manuscript.

4) Instead the author chose to only perform a cCas3 staining. From the panels in Figure 6 it is hard to appreciate which cells are actually cCas3+. Also the analyses were performed on the total pool of cell while it might have been more interesting to look for cell death of the various progenitor pools (eg the SOX2+ pool).

We agree with the reviewer that a more in-depth analysis of apoptotic cell populations is interesting and performed cCas3/Sox2+ quantification for cerebellar and neocortical organoids. We did not observe significant differences of cCas3 expression within the SOX2+ cell population. (Figure 5)

Minor concerns

1) It would greatly enhance the review process if line numbers are added

We have added line numbers to the manuscript.

2) On general concepts (such as the generation of organoids in the context of disease) more references could have been added

We have added more references and discussed the topic of brain organoids as disease models as suggested by this reviewer (Eichmüller & Knoblich 2022, Khakipoor et al 2020, Velasco et al 2020).

Figures

Fig. 1: In A, the square is clearly visible and not similar to B. An annotation of which is the control and which is the patient is missing in the figure. The arrows are hardly visibly, would make them slightly bigger and remove the black outer lining. Figure 1C can easily go to the Supplemental material. Fig 1 D is hard to appreciate the staining, a close-up with bright field microscope will help. E-I Most of the panels but especially G and H are overexposed. In J, it is hard to appreciate the TSEN54 staining. Maybe separate channels and a merge?

We thank the reviewer for bringing these details to our attention. We have changed the arrows in the figure to enhance their visibility. Further we have adjusted the quality of the images overall. Lastly, we have made a comment in the figure legend clearly stating which scan came from which child. The described square was added to hide facial features of the imaged individuals hence they are not identical.

Fig. 3: Usually go into the supplementals.

Since organoid size is a major first readout when modeling a disorder that is characterized by a reduction of the volume of specific brain regions, we decided to keep this readout in the main text.

Fig 4/5: Lack of quantitative data and poor quality of figures (overexposure).

Fig 6: Many of the SOX2 panels are overexposed

We thank the reviewer for the suggestions on the figures and addressed the concerns in the revised manuscript.

CROSS-CONSULTATION COMMENTS

I completely agree with reviewers #1 and #3. It is good to notice that we are overall on the same page.

Reviewer #2 (Significance (Required)):

The authors definitely made an excellent start to model PCH2a. Three controls and three patient lines are good to begin with but isogenic controls using one parental line and a patient line where the mutation is fixed would have been ideal. It is interesting that there seem to be a brain area specific pathology of the phenotype. Yet, more thorough analyses could have been performed such as proliferation and differentiation and cell cycle exit experiments. As for now the mostly descriptive data are only scratching the surface and little can be concluded on the molecular framework they are trying to solve.

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

Summary:

In this study Kagermeier et al. use human cerebellar and neocortical organoids to investigate the effects of the PCH2a-causing homozygous TSEN54c.919G>T variant on the neurodevelopment of different brain regions. They reveal a substantial growth defect in both neocortical and cerebellar regions with a more profound phenotype in the cerebellum. They continue to investigate major cell types of neurodevelopment in both regions and briefly potential mechanisms underlying the phenotypes. The study is well conceived and addresses the current gap of disease-modeling in cerebellar organoids; nevertheless, some major claims are not sufficiently substantiated in the current version. Below, I provide suggestions on how to improve the manuscript with some additional minor comments that might help with readability and accessibility of the work.

Major comments:

1. TSEN54 expression levels: The authors compare RNA and protein expression levels for TSEN54 to investigate the mutation's effect. For this the authors use qPCR on iPSCs and organoids of different age and immunostainings and conclude "we did not find differences in expression between cell and tissue types". There are some issues with this analysis as explained below:

-The qPCR data (Fig. 2B) is first normalized to a housekeeping gene (GAPDH), however, then all organoid data are additionally normalized to the respective iPSC line. Thus, in case there is already a difference on iPSC level, this normalization might mask any difference in the organoids. It is unclear why this approach was chosen, and it seems more appropriate to show the data just normalized to GAPDH than additionally normalizing to the iPSCs, or at least to show first that iPSCs do not have differences in TSEN54 expression. Furthermore, even though apparently not statistically significant there seems to be a strong trend of lower TSEN54 levels in PCH2a in neocortical organoids, but even more so in cerebellar organoids. In my view this would fit very well with the study and should be further explored before concluding there is no statistical difference. Considering the high error bars of the cerebellar organoid samples, a higher N-number might be necessary to reach statistical significance in the difference in expression. Most importantly, it would be appropriate to show single data points where possible and to mark the different cell lines (as done in other figures), as otherwise it is not possible to judge whether there is a cell line bias in the data.

-The evidence for protein expression of TSEN54 is immunofluorescence stainings for all conditions. As there is no quantification, the authors should not conclude differences, or the lack thereof, based on this qualitative data. Furthermore, in fact in the on example shown the PCH2a cerebellar condition (Fig 2D) seems to show lower expression levels compared with other conditions. This could be due to the selected image, as all other examples include large neural rosettes with strong staining in the center of the rosettes. Furthermore, it is unclear what cell line these stainings come from, even whether the PCH2a cerebellar and neocortical stainings come from the same cell line. Thus, the authors should select comparable examples for all conditions, and ideally provide staining examples (e.g., as supplementary data) for the other replicates to ensure expression in all replicates. If the authors want to comment on differences in protein expression, maybe a quantitative approach (e.g., quantitative western blot) would be more appropriate. Otherwise, the statements should be adjusted to not conclude whether TSEN54 protein levels differ or not.

-Irrespective of the above comments the conclusion of the section "TSEN54 expression in cerebellar and neocortical organoids", that currently reads "we did not find differences in expression between cell and tissue types" should be changed, as the authors did not investigate whether there are cell type-specific differences of TSEN54 expression.

We thank the reviewer for this comment. We agree that the provided data is not suitable for quantitative analysis of TSEN54 expression. Please also see our related response to the similar concern raised by reviewer 1. Thanks to these suggestions, we have decided to exclude the TSEN54 expression data from the current manuscript as a detailed analysis should be part of an extensive future study.

Organoid growth analysis:

The organoid growth analysis in Figure 3 and supplementary Figure 2 shows the main phenotype of the study that seems to be very strong. The authors use unpaired t-tests to compare within the different timepoints. Unfortunately, I think this approach might not be appropriate as even though the Welch correction does not rely on similar SDs in the compared groups (Control vs. PCH2a), it still assumes that all data points within each group share the same variance. However, this is not the case, as e.g., the control condition includes three groups (Control-1 to -3), that between groups might have different variance as such not all datapoints are independent from each other. Potentially ANOVA analyses controlling for cell line and timepoint might be more appropriate. Or additionally, the authors could consider using the linear regression analysis in Supplementary Figure 2 to further investigate the difference in organoid growth by e.g., comparing the slope of the regression lines. This might be more appropriately reflecting the growth deficit over time than simply comparing each timepoint individually. Expanding on this analysis the regression analysis requires some more information on the fit (intercept, slope, R-squared of the model), which would help clarifying the growth dynamics in the different systems and conditions.

We thank the reviewer for the suggestions on statistical analysis and adjusted our approach accordingly. Briefly we performed 3-way-ANOVA analysis for the growth curves which revealed no significant differences between the different lines within the groups (Control or PCH2a) at different time points. Additionally, we added the linear regression model to the results (See Figure 3 and supplementary table 2, with the information on the curve fit).

The growth ratio analysis (Figure 3D) is essential to the major claim of the paper that the organoids replicate the region-specific differences. As the authors performed all experiments with matching cell lines this could additionally strengthen the argument by generating the ratio of size differences for each cell line separately (instead of just for all PCH2a lines together). This would allow comparison of the same genetic background in both cerebellar and neocortical condition and further corroborate the region-specific severity of the phenotype. Potentially, this would also enable to test these differences statistically.

We appreciate the suggestion to compare the differentiation protocols by line. Below we display the line-by-line analysis between the two differentiation protocols at D30 (A), D50 (B), and D90 (C). In order to visualize the differences in size between the two protocols more clearly, we have generated ratios of the average organoid sizes between neocortical and cerebellar organoids (D). The analysis corroborates our previous visualizations and statistics (3-way ANOVA) by showing that PCH lines produce neocortical and cerebellar organoids that differ in size more than those of control lines. The differences are most pronounced at D30 and D90. However, we believe that this analysis does not add additional value to our manuscript and have therefore decided not to include it in the revised version.

Additionally, all growth analyses for the neocortical organoids (Figure 3C, Supplementary Figure 2B and C) seem to lack the PCH-1 cell line and only contain PCH-2 and PCH-3. This cell line should be added or commented on why it was excluded from the analyses.

We agree with the reviewer. Unfortunately, we experienced contamination in that specific differentiation and therefore cannot provide the data. We have made a related comment in the manuscript. Since all differentiations were performed in parallel, adding this line at a later time point would add additional confounders and is therefore undesirable.

Potential mechanism of the phenotype (apoptosis analysis):

In Figure 6 the authors investigate the hypothesis that increased apoptosis contributes to the phenotypes. In the cleaved Caspase 3 staining there appear to be no differences. Unfortunately, the analysis apparently only includes one replicate (one organoid?) per cell line and condition. Considering the variability in the data shown this seems inappropriately low and should ideally contain ~3 replicates per cell line condition to judge technical and biological variability if the authors want to make the point that there is no "significant difference between PCH2a and control organoids at any time point in both cerebellar and neocortical organoids". Otherwise, this claim does not seem to be substantiated enough by the data.

Finally, due to the absence of a phenotype related to apoptosis the authors conclude that the phenotypes may be due to "deficits in the proliferation of progenitor cells". Although this is mentioned in the introduction and the discussion, there is no evidence in the current study that supports this interesting idea. By adding relatively straight forward co-staining experiments for e.g., SOX2 (progenitors) and Ki67 (proliferating cells), the authors could provide further evidence for this hypothesis using existing organoid sections. This would support this speculative idea and could add a more mechanistic insight to the study, thereby making it more exciting.

To address this concern, we have now added a table to the supplement that described in detail which organoids / batches / cell lines were used for which experiment (Supplementary table 3). In addition to our previous cCas3 quantifications, we performed the quantification of cCas3 within the population of SOX2-positive cells, which was suggested by Reviewer 2 (Figure 5).

To assess the alternative hypothesis, that proliferation deficits account for the size differences observed between organoids, we also performed quantifications of SOX2-positive zones in the organoids at D30 and D50 of differentiation as well as quantifications of Ki-67 positive cells within the SOX2-positive population. For cerebellar organoids we found significant differences in these experiments (Figure 6). We believe that this data supports the hypothesis of aberrant proliferation in PCH2a cerebellar organoids explaining the size differences.

Minor comments:

• Cell line and quality control: The authors recruit three male patients with PCH2a and reprogram iPSCs. These cell lines are subjected to a well performed extensive quality control. However, it is unclear what cell lines the stainings (e.g., Fig. 1D to I) originate from. Furthermore, the supplementary qPCR analysis (Supplementary Figure 1) includes only the PCH-1 line, and additionally two cell lines that are not explained (F-CO and hESC-I3). It is unclear what the relevance of showing the qPCR of these cell lines is. To ensure proper QC for all used cell lines the authors should provide data for all cell lines (PCH-1 to -3 and control-1 to -3), or at least summarize (e.g., in a table) what QC metrics were applied to which cell line. Most importantly, this information is completely lacking for the control cell lines and the QC is just mentioned in the text. Unfortunately, it is unclear where the control cell lines originate from, and some basic information would be required to judge whether they are appropriate controls: are they iPSC or ESC, were they reprogrammed with a similar paradigm as the PCH2a cells, what is the gender of the control cell lines (all PCH2a cell lines are apparently male)?

In line with a similar comment from reviewer 1, we have included a table that provides information on the origin of all six cell lines used in the revised manuscript (methods section). Further we provide SNP-Array data on all cell lines as supplementary material. We also performed detailed characterization of pluripotency, proliferation and cell cycle dynamics of all six hiPSC lines through immunocytochemistry against pluripotency marker OCT4, proliferation marker Ki-67 and EdU incorporation experiments (Figure 2). We further assessed the apoptosis rate of hiPSCs by staining against apoptotic marker cCas3. All experiments did not result in significant differences between PCH2a and control iPSC lines (see Figure 2). In line with the suggestion of this reviewer, we removed the qPCR analysis of iPSCs from the manuscript.

• To make the study more approachable for a medical audience and to judge the variability in phenotype presentation among the recruited patients it would be appreciated if more information on the patients would be provided. The authors write: "We identified three individuals that display the genetic, clinical and brain imaging features previously described for PCH2a.". This information including age/date of birth, as well as other medically relevant information could be provided in the supplementary figure (e.g., is there a difference in disease burden among the different patients?). This would allow judging the recruited cohort better.

We thank the reviewer for this insightful comment. We provided a table with detailed clinical information (supplementary table 1).

• According to the method section the cerebellar and neocortical organoids were cultured in very different medium especially at later timepoints. While neocortical organoids were kept in a neural maintenance medium based on Neurobasal-A, cerebellar organoids were kept in a medium based on BrainPhys. These media contain very different levels of nutrients, especially of glucose (25mM vs 2.5mM, Bardy et al. 2015). This can have a strong phenotype on proliferation of progenitors and proliferative phenotypes (e.g., see Eichmüller et al. 2022). Especially as the authors claim that there is a difference in the PCH2a phenotypes between brain regions, it should be excluded that this is due to medium differences at later timepoints. When investigating the growth curves of Figure 3B and C it seems like the major difference in growth speed seems to be that neocortical organoids grow faster in early timepoints (We agree that media composition can greatly influence growth dynamics of cells in 2D and 3D. However, in this study we assess the differences between two groups: the PCH2a and control iPSC-derived organoids. The differences we describe are in relation to the respective control group and iPSCs were generated following the same protocol in the same lab. We believe that by following two protocols and comparing the three PCH2a to the three control lines within each protocol predominantly, we account for different media composition possibly changing growth dynamics.

• Staining examples shown and presentation: In several figures the authors could improve the presentation of the staining examples with some changes:

o Cell line information for images: as the authors only ever note the condition (PCH2a or Control) but not the cell line it is unclear if the stainings all come from one cell line or from multiple different cell lines. This prevents comparing the different differentiation conditions. Additionally, for major conclusions the authors should consider including supplemental stainings or further information on how reproducible the results shown are (how many cell lines and batches were used?).

We thank the reviewer for these suggestions. We added information on cell lines and passages for all experiments shown in this study in the figure legends. Moreover, we also added a table providing information on n-numbers for all experiments (supplementary table 3).

o Selection of examples: in several cases (Fig 2C/D, 4A, 6A/B) the selected images depict very different regions, e.g., one condition shows a large rosette, while in the other condition no rosette can be seen. It would be more appropriate to show matching examples where possible.

We agree with the reviewer and have chosen matched regions of interest in the figure panels in the revised version of the manuscript. Please note that for cerebellar organoids we observed a significant difference in the timepoint of appearance of these rosette-like structures. Therefore, an exact matching of regions of interest was not possible due to biological differences between the samples, which we have also quantified (Figure 6).

o Color code of stainings: Colors do not match throughout the manuscript in immunofluorescence images. E.g., Fig. 4 uses blue, green, red, magenta and Fig. 5 uses blue, green, magenta, cyan. It would be preferable to adhere to one color code. Considering significant fraction of the population is having red-green blindness, the latter color code seems more appropriate as it should ensure readability also for color-blind audiences.

We are thankful for this comment. We changed the color code to make figures more widely accessible.

• Small typos:

o Figure 1 legend: last sentence "The" instead of "Th"

o Supplementary Figure 1B: PCH-2 is named "PCH-22"

o Supplementary Figure 2: As in the main figure for neocortical organoids the PCH-1 condition is missing (see comment on organoid growth curves). Additionally, the color/shape code of the plots in B does not always match the legend (e.g., size in left plot is different and color of PCH-3 in middle and left plot differs from legend and right plot).

o It is unclear why the cortical organoids are referred to as "neocortical organoids" in the figures and the text. The methods and the reference in the methods as well as all major papers rather use the word "cortical".

We addressed these suggestions and thank the reviewer for bringing these to our attention. Unfortunately, we could not include data on PCH-01 in neocortical differentiation due to a contamination in this batch. We made sure to run all the batches presented here in parallel so that all conditions are equivalent, preventing us from including a different batch at a later time point.

We believe that in the context of our study, it is important to highlight cortical organoids as neocortical organoids, because we are also showing cerebellar organoids and there is also a cerebellar cortex.

References:

Bardy, C. et al. Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro. Proc National Acad Sci 112, E3312 (2015).

Eichmüller, O. L. et al. Amplification of human interneuron progenitors promotes brain tumors and neurological defects. Science 375, (2022).

CROSS-CONSULTATION COMMENTS

I agree with the comments of the other reviewers and as they are mostly matching, this reinforces the importance to improve certain aspects of the manuscript. As there are no deviating issues I do not comment specifically on any reviewer comments.

Reviewer #3 (Significance (Required)):

This work is using organoid technology to shed light on brain region-specific phenotypes in PCH2a. Brain organoids have drastically changed the way we study human neurological diseases (Eichmüller and Knoblich 2022), however, most brain organoid research has focused on cortical organoids. Cerebellar organoid protocols exist for some time (Muguruma et al. 2015, Silva et al. 2020, Nayler et al. 2021) but were not yet applied to uncover new disease biology. Especially considering the important role of human-specific cerebellar processes in specific developmental disorders (Haldipur et al. 2021) and cancer (Hendrikse et al. 2022, Smith et al. 2022), disease modeling in human cerebellar organoids holds great potential for understanding disease biology. The work by Kagermeier et al. demonstrates that human cerebellar organoids are recapitulating brain region-specific growth deficits and thus is an important step forward for disease modeling. Therefore, this work will be interesting to researchers working on brain development and disease modeling, especially in in-vitro systems. Nevertheless, the mechanistic insight of the study is limited, as is the insight into how human-specific processes might be involved in the pathogenesis of PCH2a. Therefore, it will be interesting how this disease model will be used in future to investigate the cell types and mechanisms involved in the PCH2a phenotype.

Personal field of expertise: Brain organoids and disease modeling in organoids especially of neurodevelopmental diseases. Analysis of organoids with stainings, as well as sequencing techniques, and bioinformatics.

References:

Eichmüller, O. L. & Knoblich, J. A. Human cerebral organoids - a new tool for clinical neurology research. Nat Rev Neurol 1-20 (2022) doi:10.1038/s41582-022-00723-9.

Haldipur, P. et al. Evidence of disrupted rhombic lip development in the pathogenesis of Dandy-Walker malformation. Acta Neuropathol 142, 761-776 (2021).

Hendrikse, L. D. et al. Failure of human rhombic lip differentiation underlies medulloblastoma formation. Nature 609, 1021-1028 (2022).

Muguruma, K., Nishiyama, A., Kawakami, H., Hashimoto, K. & Sasai, Y. Self-Organization of Polarized Cerebellar Tissue in 3D Culture of Human Pluripotent Stem Cells. Cell Reports 10, 537-550 (2015).

Nayler, S., Agarwal, D., Curion, F., Bowden, R. & Becker, E. B. E. High-resolution transcriptional landscape of xeno-free human induced pluripotent stem cell-derived cerebellar organoids. Sci Rep-uk 11, 12959 (2021).

Silva, T. P. et al. Scalable Generation of Mature Cerebellar Organoids from Human Pluripotent Stem Cells and Characterization by Immunostaining. J Vis Exp (2020) doi:10.3791/61143.

Smith, K. S. et al. Unified rhombic lip origins of group 3 and group 4 medulloblastoma. Nature 609, 1012-1020 (2022).

References by the authors

Aldinger KA, Thomson Z, Phelps IG, Haldipur P, Deng M, et al. 2021. Spatial and cell type transcriptional landscape of human cerebellar development. Nat Neurosci 24: 1163-75

Eichmüller OL, Knoblich JA. 2022. Human cerebral organoids — a new tool for clinical neurology research. Nature Reviews Neurology 18: 661-80

Khakipoor S, Crouch EE, Mayer S. 2020. Human organoids to model the developing human neocortex in health and disease. Brain Res 1742: 146803

Muguruma K, Nishiyama A, Kawakami H, Hashimoto K, Sasai Y. 2015. Self-organization of polarized cerebellar tissue in 3D culture of human pluripotent stem cells. Cell Rep 10: 537-50

Sepp M, Leiss K, Sarropoulos I, Murat F, Okonechnikov K, et al. 2021.

Silva TP, Fernandes TG, Nogueira DES, Rodrigues CAV, Bekman EP, et al. 2020. Scalable Generation of Mature Cerebellar Organoids from Human Pluripotent Stem Cells and Characterization by Immunostaining. J Vis Exp

Strassler ET, Aalto-Setala K, Kiamehr M, Landmesser U, Krankel N. 2018. Age Is Relative-Impact of Donor Age on Induced Pluripotent Stem Cell-Derived Cell Functionality. Front Cardiovasc Med 5: 4

Studer L, Vera E, Cornacchia D. 2015. Programming and Reprogramming Cellular Age in the Era of Induced Pluripotency. Cell Stem Cell 16: 591-600

Velasco S, Paulsen B, Arlotta P. 2020. 3D Brain Organoids: Studying Brain Development and Disease Outside the Embryo. Annu Rev Neurosci 43: 375-89

Watson LM, Wong MMK, Vowles J, Cowley SA, Becker EBE. 2018. A Simplified Method for Generating Purkinje Cells from Human-Induced Pluripotent Stem Cells. Cerebellum 17: 419-27

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Referee #3

Evidence, reproducibility and clarity

Summary: In this study Kagermeier et al. use human cerebellar and neocortical organoids to investigate the effects of the PCH2a-causing homozygous TSEN54c.919G>T variant on the neurodevelopment of different brain regions. They reveal a substantial growth defect in both neocortical and cerebellar regions with a more profound phenotype in the cerebellum. They continue to investigate major cell types of neurodevelopment in both regions and briefly potential mechanisms underlying the phenotypes. The study is well conceived and addresses the current gap of disease-modeling in cerebellar organoids; nevertheless, some major claims are not sufficiently substantiated in the current version. Below, I provide suggestions on how to improve the manuscript with some additional minor comments that might help with readability and accessibility of the work.

Major comments: 1. TSEN54 expression levels: The authors compare RNA and protein expression levels for TSEN54 to investigate the mutation's effect. For this the authors use qPCR on iPSCs and organoids of different age and immunostainings and conclude "we did not find differences in expression between cell and tissue types". There are some issues with this analysis as explained below: -The qPCR data (Fig. 2B) is first normalized to a housekeeping gene (GAPDH), however, then all organoid data are additionally normalized to the respective iPSC line. Thus, in case there is already a difference on iPSC level, this normalization might mask any difference in the organoids. It is unclear why this approach was chosen, and it seems more appropriate to show the data just normalized to GAPDH than additionally normalizing to the iPSCs, or at least to show first that iPSCs do not have differences in TSEN54 expression. Furthermore, even though apparently not statistically significant there seems to be a strong trend of lower TSEN54 levels in PCH2a in neocortical organoids, but even more so in cerebellar organoids. In my view this would fit very well with the study and should be further explored before concluding there is no statistical difference. Considering the high error bars of the cerebellar organoid samples, a higher N-number might be necessary to reach statistical significance in the difference in expression. Most importantly, it would be appropriate to show single data points where possible and to mark the different cell lines (as done in other figures), as otherwise it is not possible to judge whether there is a cell line bias in the data. -The evidence for protein expression of TSEN54 is immunofluorescence stainings for all conditions. As there is no quantification, the authors should not conclude differences, or the lack thereof, based on this qualitative data. Furthermore, in fact in the on example shown the PCH2a cerebellar condition (Fig 2D) seems to show lower expression levels compared with other conditions. This could be due to the selected image, as all other examples include large neural rosettes with strong staining in the center of the rosettes. Furthermore, it is unclear what cell line these stainings come from, even whether the PCH2a cerebellar and neocortical stainings come from the same cell line. Thus, the authors should select comparable examples for all conditions, and ideally provide staining examples (e.g., as supplementary data) for the other replicates to ensure expression in all replicates. If the authors want to comment on differences in protein expression, maybe a quantitative approach (e.g., quantitative western blot) would be more appropriate. Otherwise, the statements should be adjusted to not conclude whether TSEN54 protein levels differ or not. -Irrespective of the above comments the conclusion of the section "TSEN54 expression in cerebellar and neocortical organoids", that currently reads "we did not find differences in expression between cell and tissue types" should be changed, as the authors did not investigate whether there are cell type-specific differences of TSEN54 expression.

1. Organoid growth analysis: The organoid growth analysis in Figure 3 and supplementary Figure 2 shows the main phenotype of the study that seems to be very strong. The authors use unpaired t-tests to compare within the different timepoints. Unfortunately, I think this approach might not be appropriate as even though the Welch correction does not rely on similar SDs in the compared groups (Control vs. PCH2a), it still assumes that all data points within each group share the same variance. However, this is not the case, as e.g., the control condition includes three groups (Control-1 to -3), that between groups might have different variance as such not all datapoints are independent from each other. Potentially ANOVA analyses controlling for cell line and timepoint might be more appropriate. Or additionally, the authors could consider using the linear regression analysis in Supplementary Figure 2 to further investigate the difference in organoid growth by e.g., comparing the slope of the regression lines. This might be more appropriately reflecting the growth deficit over time than simply comparing each timepoint individually. Expanding on this analysis the regression analysis requires some more information on the fit (intercept, slope, R-squared of the model), which would help clarifying the growth dynamics in the different systems and conditions. The growth ratio analysis (Figure 3D) is essential to the major claim of the paper that the organoids replicate the region-specific differences. As the authors performed all experiments with matching cell lines this could additionally strengthen the argument by generating the ratio of size differences for each cell line separately (instead of just for all PCH2a lines together). This would allow comparison of the same genetic background in both cerebellar and neocortical condition and further corroborate the region-specific severity of the phenotype. Potentially, this would also enable to test these differences statistically. Additionally, all growth analyses for the neocortical organoids (Figure 3C, Supplementary Figure 2B and C) seem to lack the PCH-1 cell line and only contain PCH-2 and PCH-3. This cell line should be added or commented on why it was excluded from the analyses.

2. Potential mechanism of the phenotype (apoptosis analysis): In Figure 6 the authors investigate the hypothesis that increased apoptosis contributes to the phenotypes. In the cleaved Caspase 3 staining there appear to be no differences. Unfortunately, the analysis apparently only includes one replicate (one organoid?) per cell line and condition. Considering the variability in the data shown this seems inappropriately low and should ideally contain ~3 replicates per cell line condition to judge technical and biological variability if the authors want to make the point that there is no "significant difference between PCH2a and control organoids at any time point in both cerebellar and neocortical organoids". Otherwise, this claim does not seem to be substantiated enough by the data. Finally, due to the absence of a phenotype related to apoptosis the authors conclude that the phenotypes may be due to "deficits in the proliferation of progenitor cells". Although this is mentioned in the introduction and the discussion, there is no evidence in the current study that supports this interesting idea. By adding relatively straight forward co-staining experiments for e.g., SOX2 (progenitors) and Ki67 (proliferating cells), the authors could provide further evidence for this hypothesis using existing organoid sections. This would support this speculative idea and could add a more mechanistic insight to the study, thereby making it more exciting.

Minor comments: - Cell line and quality control: The authors recruit three male patients with PCH2a and reprogram iPSCs. These cell lines are subjected to a well performed extensive quality control. However, it is unclear what cell lines the stainings (e.g., Fig. 1D to I) originate from. Furthermore, the supplementary qPCR analysis (Supplementary Figure 1) includes only the PCH-1 line, and additionally two cell lines that are not explained (F-CO and hESC-I3). It is unclear what the relevance of showing the qPCR of these cell lines is. To ensure proper QC for all used cell lines the authors should provide data for all cell lines (PCH-1 to -3 and control-1 to -3), or at least summarize (e.g., in a table) what QC metrics were applied to which cell line. Most importantly, this information is completely lacking for the control cell lines and the QC is just mentioned in the text. Unfortunately, it is unclear where the control cell lines originate from, and some basic information would be required to judge whether they are appropriate controls: are they iPSC or ESC, were they reprogrammed with a similar paradigm as the PCH2a cells, what is the gender of the control cell lines (all PCH2a cell lines are apparently male)?

• To make the study more approachable for a medical audience and to judge the variability in phenotype presentation among the recruited patients it would be appreciated if more information on the patients would be provided. The authors write: "We identified three individuals that display the genetic, clinical and brain imaging features previously described for PCH2a.". This information including age/date of birth, as well as other medically relevant information could be provided in the supplementary figure (e.g., is there a difference in disease burden among the different patients?). This would allow judging the recruited cohort better.

• According to the method section the cerebellar and neocortical organoids were cultured in very different medium especially at later timepoints. While neocortical organoids were kept in a neural maintenance medium based on Neurobasal-A, cerebellar organoids were kept in a medium based on BrainPhys. These media contain very different levels of nutrients, especially of glucose (25mM vs 2.5mM, Bardy et al. 2015). This can have a strong phenotype on proliferation of progenitors and proliferative phenotypes (e.g., see Eichmüller et al. 2022). Especially as the authors claim that there is a difference in the PCH2a phenotypes between brain regions, it should be excluded that this is due to medium differences at later timepoints. When investigating the growth curves of Figure 3B and C it seems like the major difference in growth speed seems to be that neocortical organoids grow faster in early timepoints (<d30), but similar at later timepoints, which would exclude effects of the media at late timepoints. Nevertheless, considering the strong effect media glucose concentration can have the authors should investigate whether there is an effect at growth speed at later timepoints by comparing control organoids. This could also strengthen the region-specific phenotype due to PCH2a.

• Staining examples shown and presentation: In several figures the authors could improve the presentation of the staining examples with some changes: o Cell line information for images: as the authors only ever note the condition (PCH2a or Control) but not the cell line it is unclear if the stainings all come from one cell line or from multiple different cell lines. This prevents comparing the different differentiation conditions. Additionally, for major conclusions the authors should consider including supplemental stainings or further information on how reproducible the results shown are (how many cell lines and batches were used?). o Selection of examples: in several cases (Fig 2C/D, 4A, 6A/B) the selected images depict very different regions, e.g., one condition shows a large rosette, while in the other condition no rosette can be seen. It would be more appropriate to show matching examples where possible. o Color code of stainings: Colors do not match throughout the manuscript in immunofluorescence images. E.g., Fig. 4 uses blue, green, red, magenta and Fig. 5 uses blue, green, magenta, cyan. It would be preferable to adhere to one color code. Considering significant fraction of the population is having red-green blindness, the latter color code seems more appropriate as it should ensure readability also for color-blind audiences.

• Small typos: o Figure 1 legend: last sentence "The" instead of "Th" o Supplementary Figure 1B: PCH-2 is named "PCH-22" o Supplementary Figure 2: As in the main figure for neocortical organoids the PCH-1 condition is missing (see comment on organoid growth curves). Additionally, the color/shape code of the plots in B does not always match the legend (e.g., size in left plot is different and color of PCH-3 in middle and left plot differs from legend and right plot). o It is unclear why the cortical organoids are referred to as "neocortical organoids" in the figures and the text. The methods and the reference in the methods as well as all major papers rather use the word "cortical".

References: Bardy, C. et al. Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro. Proc National Acad Sci 112, E3312 (2015). Eichmüller, O. L. et al. Amplification of human interneuron progenitors promotes brain tumors and neurological defects. Science 375, (2022).

CROSS-CONSULTATION COMMENTS I agree with the comments of the other reviewers and as they are mostly matching, this reinforces the importance to improve certain aspects of the manuscript. As there are no deviating issues I do not comment specifically on any reviewer comments.

Significance

This work is using organoid technology to shed light on brain region-specific phenotypes in PCH2a. Brain organoids have drastically changed the way we study human neurological diseases (Eichmüller and Knoblich 2022), however, most brain organoid research has focused on cortical organoids. Cerebellar organoid protocols exist for some time (Muguruma et al. 2015, Silva et al. 2020, Nayler et al. 2021) but were not yet applied to uncover new disease biology. Especially considering the important role of human-specific cerebellar processes in specific developmental disorders (Haldipur et al. 2021) and cancer (Hendrikse et al. 2022, Smith et al. 2022), disease modeling in human cerebellar organoids holds great potential for understanding disease biology. The work by Kagermeier et al. demonstrates that human cerebellar organoids are recapitulating brain region-specific growth deficits and thus is an important step forward for disease modeling. Therefore, this work will be interesting to researchers working on brain development and disease modeling, especially in in-vitro systems. Nevertheless, the mechanistic insight of the study is limited, as is the insight into how human-specific processes might be involved in the pathogenesis of PCH2a. Therefore, it will be interesting how this disease model will be used in future to investigate the cell types and mechanisms involved in the PCH2a phenotype.

Personal field of expertise: Brain organoids and disease modeling in organoids especially of neurodevelopmental diseases. Analysis of organoids with stainings, as well as sequencing techniques, and bioinformatics.

References:

Eichmüller, O. L. & Knoblich, J. A. Human cerebral organoids - a new tool for clinical neurology research. Nat Rev Neurol 1-20 (2022) doi:10.1038/s41582-022-00723-9.

Haldipur, P. et al. Evidence of disrupted rhombic lip development in the pathogenesis of Dandy-Walker malformation. Acta Neuropathol 142, 761-776 (2021).

Hendrikse, L. D. et al. Failure of human rhombic lip differentiation underlies medulloblastoma formation. Nature 609, 1021-1028 (2022).

Muguruma, K., Nishiyama, A., Kawakami, H., Hashimoto, K. & Sasai, Y. Self-Organization of Polarized Cerebellar Tissue in 3D Culture of Human Pluripotent Stem Cells. Cell Reports 10, 537-550 (2015).

Nayler, S., Agarwal, D., Curion, F., Bowden, R. & Becker, E. B. E. High-resolution transcriptional landscape of xeno-free human induced pluripotent stem cell-derived cerebellar organoids. Sci Rep-uk 11, 12959 (2021).

Silva, T. P. et al. Scalable Generation of Mature Cerebellar Organoids from Human Pluripotent Stem Cells and Characterization by Immunostaining. J Vis Exp (2020) doi:10.3791/61143.

Smith, K. S. et al. Unified rhombic lip origins of group 3 and group 4 medulloblastoma. Nature 609, 1012-1020 (2022).

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 #2

Evidence, reproducibility and clarity

Please find enclosed my recommendation for the paper submitted by Kagermeier et al entitled' Human organoid model of PCH2a recapitulates brain region-specific pathology'. It describes the development of a human model for PCH2a and its characterization. My overall assessment of the paper is 'Major revision' which is explained below.

Although the paper is very well written and clearly interesting in that it describes the generation and initial analyses of a human organoid model for PCH2a it should be revised such that it will proof the points it is trying to make. The authors are meticulous in their studies combining cellular characterization and a thorough initial screen of organoid (both cerebellar as well as cortical) integrity, yet hardly any mechanistic data is provided. Nevertheless, if the authors are able to add additional experiments and are able to address the points raised, the reviewer may be willing to consider a more positive outcome.

Major concerns

1. The overall quality of the figures is poor. There is a lot of overexposure such that often cellular or tissue structures are blended. It starts with Figure 1 G and H but can be observed throughout the manuscript. Deconvolution would greatly enhance their results.
2. Especially figure 4 and 5 could have been complemented with quantitative data. It furthermore seems more supplemental figure as these are just proof-of-principle stainings. No conclusions can be drawn from the panels except that all markers are there in the various conditions. And while they are showing a neural rosette in Fig 4A, just tiny ones can be observed in 4B. It is also not clear what the whole mount IHC ads in comparison to the IHC on sections. It is also strange that there is still a lot of SOX2 in the CALB/MAP2-positive area, but again with this magnification hard to appreciate.
3. If the authors would like to proof the point that cerebellar/cortical development is hampered, more functional assays could have been done. Nothing is analyses on the fraction of progenitor cells present (such as the percentage of Tbr2+ IPC in VZ/CP). Furthermore, if there is a suspicion that the number of cells is affected (which is also not shown), proliferation/cell cycle exit experiments using BrdU/EdU should have been performed. Early cell cycle exit still cannot be rules out and should have been tested by the combination of Ki67-/EdU+ percentage of a certain faction of progenitor cells (eg PAX6+ pool).
4. Instead the author chose to only perform a cCas3 staining. From the panels in Figure 6 it is hard to appreciate which cells are actually cCas3+. Also the analyses were performed on the total pool of cell while it might have been more interesting to look for cell death of the various progenitor pools (eg the SOX2+ pool).

Minor concerns

1. It would greatly enhance the review process if line numbers are added
2. On general concepts (such as the generation of organoids in the context of disease) more references could have been added

Figures

Fig. 1: In A, the square is clearly visible and not similar to B. An annotation of which is the control and which is the patient is missing in the figure. The arrows are hardly visibly, would make them slightly bigger and remove the black outer lining. Figure 1C can easily go to the Supplemental material. Fig 1 D is hard to appreciate the staining, a close-up with bright field microscope will help. E-I Most of the panels but especially G and H are overexposed. In J, it is hard to appreciate the TSEN54 staining. Maybe separate channels and a merge?

Fig. 3: Usually go into the supplementals

Fig 4/5: Lack of quantitative data and poor quality of figures (overexposure).

Fig 6: Many of the SOX2 panels are overexposed

Referees cross-commenting

I completely agree with reviewers #1 and #3. It is good to notice that we are overall on the same page.

Significance

The authors definitely made an excellent start to model PCH2a. Three controls and three patient lines are good to begin with but isogenic controls using one parental line and a patient line where the mutation is fixed would have been ideal. It is interesting that there seem to be a brain area specific pathology of the phenotype. Yet, more thorough analyses could have been performed such as proliferation and differentiation and cell cycle exit experiments. As for now the mostly descriptive data are only scratching the surface and little can be concluded on the molecular framework they are trying to solve.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Kagermeier et al. present a novel and interesting study that attempts to model a severe neurodevelopmental disorder, pontocerebellar hypoplasia type 2a, using neocortical and cerebellar organoids. Brain organoids are an appropriate and promising approach to elucidate disease mechanisms in neurodevelopmental diseases. The authors show a reduction in the size of the organoids which is more pronounced in the cerebellar compared to neocortical organoids. While this finding is interesting and reminiscent of the clinical PCH2a phenotype, i.e., cerebellar hypoplasia, the study is very preliminary and the conclusions of the manuscript are not supported by the data. Additional information and further experiments are necessary to support the claims made.

Major concerns:

1. hiPSC lines show considerable inter- and intra-individual variability and therefore the size differences observed between these control and patient-derived organoids may arise from differences in the hiPSC lines used. While the data sufficiently demonstrates the pluripotency of the multiple novel hiPSC lines, major concerns remain as to the appropriateness of the control hiPSC lines. The manuscript should include a table describing the age and sex matching as well as mode of reprogramming for all control and patient lines. Patient and control lines should be matched as closely as possible. Furthermore, figure legends should clearly indicate which clones and lines are shown in the various figure panels.
2. As the hiPSC lines used are not isogenic, it is important that the authors characterise these lines further. This should include a quantification of the rates proliferation and apoptosis in all used hiPSC lines, as these might impact the growth rate of the embryoid bodies / organoids.
3. The authors state that the hiPSC lines have been characterised by SNP arrays to show that no genomic / chromosomal aberrations have been accrued due to reprogramming. The manuscript should include information as to when the SNP array was performed (i.e., immediately after reprogramming, after initial passaging, etc) and also include the results of the SNP array as additional information. What passage were the hiPSC when the presented experiments were carried out?
4. Given that TSNE54 is broadly and strongly expressed in the developing nervous system, the very limited staining of the organoids for TSNE54 in Figure 2 is surprising. Can the authors provide an explanation for the fact that TSNE54 is only expressed in a small subset of cells? Which cell types are these? Moreover, high-magnification images should be shown to demonstrate subcellular staining pattern of TSNE54. Quantification of TSNE54 protein levels by immunoblotting would also be beneficial. Related to this observation, it is puzzling that the large size differences that the authors observe in their organoids would be driven by such a small number of TSNE54-expressing cells. How do the authors explain this discrepancy?
5. The generated organoids need to be better characterised with a broader range of markers using both qPCR and immunostaining. At the moment, their identity as "cortical" and "cerebellar" organoids remain unconvincing. This is particularly true for cerebellar organoids, which are challenging to generate and are not widely used. The authors should include additional markers (for example, see PMIDs 25640179, 29397531, 32117945) and immunostaining should clearly show expected staining patterns. In Figure 5, it appears that some markers (e.g., SATB2) are expressed differently between control and patient lines, yet this is not commented on by the authors who conclude that control and patient lines show differentiation into organoids.
6. The authors attempt to look into a potential mechanism for the size differences observed between control and patient organoids. However, only cleaved caspase-3 is used as a marker for apoptosis and no differences were observed. The authors should include further markers for potential cell death. In addition, immunostaining for proliferation markers (i.e., KI67) should be performed to evaluate whether the difference in organoid size could stem from decreased proliferation rather than increased cell death.

Significance

The authors present an innovative approach to study neurodevelopmental disorders using brain organoids and should be of interest to researchers and clinicians working on neurodevelopmental diseases. However, the data presented are too limited to support any conclusions about the phenotype observed. Furthermore, questions remain about the used methodology and more work is needed to demonstrate the successful generation of both cortical and cerebellar organoids.

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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. *

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

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Summary:

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.

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

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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Reply to the reviewers

We thank the reviewers for their time, the positive reviews and the useful comments. We answer below and explain the changes made to the manuscript. The comments of the reviewers are in italics.

Reviewer #1

1. 'For GWAS, the strains that were fertile after 20 generations were considered non-Mrt.' One aspect of Fig 1D that could be clarified are the dots at generation 21. If these represent strains that were always fertile at generation 21, then perhaps give these a different color to indicate that sterility was never observed?

Response: This is a good idea. We added colors in Figure 1, which makes it clearer.

We also provide a different color for surviving replicates in all relevant figures.

1. 'The mean Mrt values of strains ranged from sterile at 3 generations to fertile after 20 generations at 25°C, with a skewed distribution toward high values (Figure 1B).' Based on Table S2, part of the explanation for this skewed distribution in later generations is that some strains became sterile rapidly for some blocks, whereas the same strain did not become sterile in other blocks. For example, JU1200, JU360, PB303. I suggest providing a second color for Fig. 1D for strains that sometimes displayed sterility and sometimes did not.

__Response: __We now colored the isolates that never became sterile, with the same color code as in panel B. Because we stopped the scoring at G20 and code fertility at G20 as '21', those with a mean below 21 show some sterility in at least one case.

Because the number of generations at which we stopped the phenotyping (20) is arbitrary, the fact a line stayed fertile at 20 generations in one replicate is not very meaningful, especially considering that the number of replicates is not the same for all strains. The key point of the variance graph is to show that the strains with the most variance are those with high but

For those that were sometimes fertile and sometimes sterile, I suggest creating a graph in Figure 1 that shows generations at sterility or lack of sterility, color coded by block. This will allow the significance of strains with high generation Mrt values to be better appreciated for readers who do not look at the supplementary table.

__Response: __Yes, we added this graph in Figure S1. This is indeed useful.

1. The GWAS section could benefit from a simple explanation of the premise of GWAS for non-specialist readers.

__Response: __Yes, we added: "A genome-wide association study (GWAS) is a genetic mapping that uses the natural diversity of a panel of organisms of a given species to test for statistical independence between the allelic state of polymorphic markers and the phenotype of interest (Andersen and Rockman 2022). A statistical association between the marker and the phenotype indicates that a polymorphism tightly linked to the marker in the data (i.e. in linkage disequilibrium with it) causes the variation in phenotype. For statistical reasons, GWAS can only detect polymorphisms that are at intermediate frequencies in the panel, i.e. cases where both alleles occur at frequencies higher than 5%. We only used such polymorphisms in the GWAS (see Methods)."

And further down:

"To diminish the multiple testing burden, the initial analysis in Figure 1E used a restricted set of markers, after pruning those that were in high linkage to each other."

1. One problem might be that the Mrt phenotype is widespread among wild strains. To the authors' credit, they consider results observed in different laboratories as valid, even when the results do not agree. If the Mrt phenotype is influenced by the environment, then some laboratory environments might result in 'false negative' Mrt results that could be ignored in favor of positive results from another lab that appear strong. Might focusing on strains with a set of strong positive results from one lab allow the authors to draw stronger GWAS conclusions?

2. The authors' perform GWAS based on the variance of the Mrt phenotype data. Would the GWAS data be more illuminating if the authors only considered strains that become sterile fairly rapidly, within 10 generations. The authors might then have a second category that included strains that become sterile from generation 11-20. If the genetic basis for the Mrt phenotypes is the same, then GWAS of strains that become sterile in less than 10 generations might yield similar peaks as GWAS for strains that become sterile between generations 11-20.

__Response: __These two comments are strongly related so we answer them together. Note that the GWAS is not mapping the variance values but the Mrt values themselves.

We actually initially only used block 1 (a single replicate, all strains performed in parallel in our laboratory) and also detected the chromosome III association using a categorical variable (threshold at 11), but decided to show the results with all data to maximize power, taking into account the generation value and block effects.

We investigated other ways to code the data (e.g. categorically) and removing the strains of the most variable middle category, as proposed by the reviewer. This changed the p values and the rank of the markers on chromosome III but not the overall result.

In summary, we did a variety of tests, which pointed to chromosome III, a region that was validated using crosses (Figure 2).

Note that in the revision, we updated the GWAS plot and fine mapping table as we noticed a few problems in our previous mapping. 1) We removed 3 isolates that were classified in Lee et al. 2021 as divergent. 2) We included strains that had been lost in the pipeline because their names did not match CeNDR isotypes. This increased the significance of the chromosome III peak.

__Response: __There was no comment 6.

1. 'We did not investigate whether a second locus present in JU775 on the right arm of Chr III might have a lesser effect.'

__Response: __We are not sure what the reviewer meant. Considering the difficulties with the stronger effect locus, we did not try to study loci with a weaker effect.

1. It might be interesting to test the memory of growth on beneficial bacteria on JU4134, which had a Mrt phenotype that was strongly suppressed by the beneficial bacteria.

__Response: __We agree that testing other strains would be useful but given the duration of such experiments (30 generations and two weeks of preparation before), we respectfully decline to perform this experiment that does not seem strictly necessary.

1. The Mrt phenotype of mutants in small RNA inheritance and histone modifying enzymes 'appears however distinct from that of the prg-1/piwi mutant (for which the cause of sterility is debated), especially the latter does not show temperature dependence and is suppressed by starvation.' While it is true that the cause of sterility is debated for the prg-1/piwi mutant, this mutant is defective for small RNA silencing and likely has parallels with some defects in histone modifying enzymes. Anecdotal reports suggest that starvation might affect the Mrt phenotype or longevity of histone modifying enzyme mutants. Moreover, the cause of sterility is not clear for small RNA inheritance and histone modifying enzyme mutants. It is fair to say that the distinction between temperature-sensitivity or lack of temperature sensitivity of small RNA mutants is not understood. Could the authors please comment here about whether any of the wild strains display sterility at 20°C.

__Response: __The temperature-dependence of the wild isolates is progressive between 20-25°C. We previously showed that strains with a very strong Mrt phenotype, such as QX1211, can display sterility at 20°C (Figure 1B in Frézal et al. 2018). However, its Mrt phenotype is still temperature-dependent as the sterility occurs much earlier at 25°C.

1. If intracellular bacteria are simply somatic, then how is it that they are transmitted to progeny. If they are released into the environment and then consumed by hatched larvae, this is soma-to-soma transmission.

__Response: __These microsporidia (which are eukaryotes related to fungi) are indeed transmitted horizontally. To make this clear, we added: "colonizing its intestinal cells and being transmitted horizontally via defecation and ingestion of spores". The soma-to-germline interaction concerns the effect of microsporidia on germline maintenance.

Minor: 1. 'We measured the mortal germline (Mrt) phenotype'. Mortal Germline (Mrt)

__Response: __It is unclear as to whether phenotypes start with a capital letter when they are in full words. We did write phenotypes in previous works with a capital letter but have changed because C. elegans nomenclature rules (https://cgc.umn.edu/nomenclature) suggest that they should not: "Phenotypic characteristics can be described in words, e.g., dumpy animals or uncoordinated animals." For the mortal germline phenotype in particular, we find several ways to write it in articles (with 0, 1 or 2 capital letters, including the three reviewers). We are happy to change it if required.

Reviewer #2

Major comments: The authors claimed that the variants causing Mrt exist at intermediate frequency in the natural population but the evidence supporting this claim is rather limited.

__Response: __Thank you for this comment as it helped us clarify the manuscript.

To better explain the notion of intermediate frequency in the GWAS, we added an explanation of the principle of the GWAS (see above) and again in the Discussion: "The intermediate frequency of the candidate alleles derives from the GWAS approach, which cannot detect rare alleles, such as set-24, that are present in a single strain of the dataset."

We also illustrated the frequency by adding a plot (Fig. 1F) showing the association of the most associated candidate SNP, with a visual depiction of the frequency. We further added in Results: "For SNPs with a high significance (p-4) in the fine mapping, the frequency of the Mrt associated allele was comprised between 21 and 41% in our GWAS strain set (Table S3); as an example, the Mrt allele of the associated SNP shown in Figure 1F (III:4677491) displayed a frequency of 29% in the restricted strain set. Over the global wild strain set with genotypes at CeNDR in 2020, these numbers are 17-58% and 39%, respectively. "

To strengthen the claim, the authors should examine the distribution and frequency (perhaps coupled with phylogenetic analysis) of the Ch III haplotype in the wild isolates. The authors should also examine the GWAS peak for the signature of balancing selection (e.g., dN/dS ratio).

__Response: __Thank you for this comment. The different associated SNPs in Table S3 differ in their allele frequency (Table S3), hence they belong to different haplotypes. We added a supplementary Figure S2 with an analysis of the haplotype structure. Those at a low frequency (around 20%) belong to the same haplotype (e.g. JU775 and MY10) but some associated alleles are present in more haplotypes (40-50%), such as JU1793. Even if we neglect recombination, the history of mutations in the region is complex and there is not a single associated haplotype. We now show the genotypes of these different haplotypes at all SNPs in Table S3. We also added Table S4 that shows the co-occurrence of relevant haplotypes in local populations.

Concerning tests of balancing selection, without knowing the causal polymorphism and linked haplotype, this is far reaching. We only feel confident to say that the causal polymorphism(s) is present at a significant frequency. We added however the fact that irrespective of which polymorphisms are causal, both alleles were found to coexist locally.

Results: relevant text was added at the end of the GWAS section.

Discussion: "The co-occurrence of relevant chromosome III haplotypes on multiple continents and in local populations (Table S4) is suggestive of balancing selection; however, a linked locus other than that causing the Mrt phenotype may be involved."

Does JU775 carry polymorphisms in genes that are known to be involved in Mrt? These genes may genetically interact with the Ch III variant, as suggested by the partial penetrant phenotypes of the introgressed lines. It would be helpful to have a table summarize the variation in these genes.

__Response: __It is difficult to deduce much from a genomic variant analysis, so we refrain from showing tables of polymorphisms beyond that used for the fine GWAS mapping in Table S3. For example, a non-synonymous SNP may or may not alter protein activity and cis-regulatory elements are difficult to assess. Moreover, an obviously null allele may be compensated by another polymorphism in the background. The JU775 alleles and bam files are publically available from CeNDR (Erik Andersen's lab): https://caendr.org/data/data-release/c-elegans/latest

It is curious to me that for experiments with HT115, the expression of the RNAi vectors was induced with IPTG. Is this step necessary? It is known that even the backbone of L4440 could trigger a non-specific RNAi response (PMID: 30838421). I wonder if activating exogenous RNAi response is required for Mrt rescue.

__Response: __Indeed: this experiment was initially aimed at testing RNAi sensitivity of JU775, thus IPTG was added on the plate (Figure 7, panel B). We therefore repeated the memory experiment with OP50 and without IPTG, with a similar result (Figure 7, panel A).

In figure 7, it appears that the worms transferred from MG1655/HT115 to OP50 showed an even stronger rescue (higher Mrt value) than the ones constantly on MG1655/HT115. This suggests to me that fluctuations in food composition may strongly affect epigenetic inheritance. Please clarify as this is very interesting, if true.

__Response: __Note: This answers the comment above (IPTG is not required).

We indeed noticed this strong rescue but do not wish to make a point as we did no attempt to reproduce this result in the exact same conditions. The experiment in panel B does not show this effect.

Optional - Numerous studies have shown that SKN-1 regulates metabolism in response to food composition and availability (PMID: 23040073). Additionally, some recent studies have indicated a role of SKN-1 in epigenetic inheritance triggered by exogenous RNAi. In particular, SKN-1 promotes stress-induced epigenetic resetting (PMID: 33729152). I wonder if SKN-1 modulates Mrt based on bacterial diet.

__Response: __We tested skn-1b/c hypomorphic and gain-of-function mutants in the N2 background on E. coli OP50 and did not see an effect of the skn-1 allele.

Minor comments Line 47: typo "...they defined..."

__Response: __We did mean "thus defined".

Line 100-101: weird sentence structure. Please consider rephrasing.

__Response: __We simplified to "a wild C. elegans strain can keep the memory of its culture on a suppressing bacterial strain."

Line 138-139: I don't quite understand what "intermediate-frequency chromosome III alleles" means here. Some SNPs were found in Ch III 4-6Mb? Please expand.

__Response: __We rephrased to: "because this isolate carries the chromosome III alleles associated in the GWAS analysis with the Mrt phenotype (Table S3)."

Line 213 - it was unclear to me why the assay was performed at 23C instead of 25C. I later learned in the method section that microsporidia cannot be cultured at 25C. I think it will be helpful to add that information when microsporidia is introduced to improve clarity.

__Response: __We added: " We used a temperature of 23°C because these microsporidia kill C. elegans too rapidly at 25°C."

Reviewer #3.

Minor points 1. Could the authors please define "experimental blocks"

__Response: __We added the following sentence in Results: "Each Mrt assay started at a certain date constitutes an experimental block."

1. Legend to supplementary snp table should be completed: define AF, impact, modifier, moderate, AA1, AA2...

__Response: __This is added in the first sheet of the table. We also simplified the table and removed some of these columns.

1. Please define "intermediate-frequency allele"

__Response: __We added in Results: "GWAS can only detect polymorphisms that are at intermediate frequencies in the panel, i.e. cases where both alleles occur at frequencies higher than 5%." We also added below: " "For SNPs with a high significance (p-4) in the fine mapping, the frequency of the Mrt associated allele was comprised between 21 and 41% in our GWAS strain set (Table S3); as an example, the Mrt allele of the associated SNP shown in Figure 1F (III:4677491) displayed a frequency of 29% in the restricted strain set."

1. Figure 7 legend: Authors should be more specific in describing the figure: After 10 (A panel), 13 or 20 generations (B panel) on the K-12 strain... What is E. coli OP50 start 'G10'? the 15° stock?

__Response: __We changed to: " After 10 (A panel), 13 or 20 generations (B panel) on the K-12 strain" and added some details in:

"A control from a 15°C culture maintained without starvation ("15°C stock") was bleached in parallel (labeled "E. coli OP50 start "G10" " in the graph of panel A)."

Optional: Did the authors attempt to rescue the Mrt phenotype with individual metabolites (eg Vit B12...)? These are not straight forward experiments and most likely part of a future study.

__Response: __We indeed tested several metabolites that are known to differ in C. elegans raised on E. coli OP50 versus K-12 strains for their effect on the Mrt phenotype. None was able to rescue the mortal germline phenotype. However, especially in these long multigenerational experiments, it is difficult to know whether the metabolites are stable. We monitored vitamin B12 activity by using an acdh-1::GFP reporter that is known to be repressed by vitamin B12 - so we are confident of this negative result, which we now show in Figure S4. As cell wall lipopolysaccharide (LPS) differ between E. coli K-12 and B strains, we also tested the E. coli LPS mutants, which had no eff

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Referee #3

Evidence, reproducibility and clarity

• The nematode C. elegans is at the forefront of research on transgenerational epigenetic inheritance. In this work the authors studied the effects of natural genetic variations on multigenerational inheritance, using the temperature-sensitive Mortal germline phenotype (Mrt) as a paradigm in C. elegans. In ts Mrt mutants, animals become progressively sterile at 25{degree sign}C (stressful temperature) over subsequent generations and, importantly, this phenotype is reversible. The present study originated from the authors' previous observation that multiple C. elegans wild isolates display a ts-Mrt phenotype when cultured in the lab, raising the question of whether this intrinsically deleterious phenotype may be suppressed in the wild, and how natural genetic variation affects this phenotype.

• By comparing 132 wild isolates of C. elegans, the authors found a wide distribution in ts-Mrt phenotypes ranging from 3 to 20 generations to reach sterility at 25{degree sign}C. The variance among a restrictive set of 115 replicates was low for strong Mrt values and high at intermediate trait values. Given this distribution, the authors analyzed the data using generalized linear mixed models. This reviewer is unable to evaluate the appropriateness of these models. They then performed GWAS mapping combined with analysis of introgression lines and identified a QTL on chromosome III between 4.66 and 6 .49Mb that includes a number of potentially interesting candidates that were not further analyzed in this work.

• Because the authors noticed that the Mrt phenotype commonly appears after bleaching the culture, a treatment that kills associated microbes, they then tested the impact of naturally associated microbes on the Mrt phenotype. They found that freshly isolated strains such as JU3224 could be propagated for more than 20 generations at 25{degree sign}C with their associated microbes, while after bleaching on OP50 (bacteria commonly used in lab culture) they developed a Mrt phenotype at 25{degree sign}. They then fed the isolates with naturally associated bacteria isolated in the lab-either their own or from other isolates. Reassociation of single bacterial clones, or a mix of these, fully or partially rescued the Mrt phenotype. Importantly, bacteria isolated from one strain was able to rescue the Mrt of another strain, suggesting common mechanisms of action in rescuing the Mrt phenotype. Surprisingly Microsporidia, usually detrimental to C. elegans, also rescued the Mrt phenotype. These results show that infection of somatic tissues can influence the germline.

• ts Mrt mutations so far identified affect nuclear small RNA pathways, small RNA amplification and histone modifications in the germline. The authors further show that the Mrt phenotype of laboratory mutants in small RNA inheritance or chromatin factors such as the set-2 histone methyltrasferase is also suppressed by culture on bacteria other than E. coli OP50.

• Finally, the authors tested whether animals have a memory of their past bacterial environment by shifting animals of the C. elegans JU775 strain that had been cultured for several generations at 25{degree sign}C on an E. coli K-12 strain (on which their Mrt phenotype was suppressed) to the laboratory E. coli OP50, which usually reveals the Mrt phenotype. Lines that were propagated for 10-20 generations at 25{degree sign}C on an E. coli K-12 strain (MG1655 or HT115) showed a rescued phenotype when transferred back on OP50, consistent with a multigenerational memory of the bacterial environment.

• All experiments are well executed, clearly presented and of the highest standard.

Significance

C. elegans is an excellent model system to study transgenerational inheritance. However, most studies on epigenetic inheritance in this system are carried out under standard laboratory conditions, and the phenotypes followed often not very robust (stress resistance, longevity..) raising questions as to their interpretation. This work is an important contribution to the field because it reveals how a widely studied phenotype (the Mrt phenotype) relates to natural isolates. The results reported demonstrate a clear link between the environment and the multigenerational transmission of non-genetic information. They also raise interesting questions on the ability of a species to transiently provide environmental cues to a variable number of generations. Finally, these results offer hints that that the Mrt phenotype may result from inherited metabolic changes, as observed using other experimental paradigms in C. elegans, including starvation. This work will therefore be of interest to a wide audience interested in epigenetic inheritance and the environment, soma-germline communication, and host pathogen interactions.

Minor points

1. Could the authors please define "experimental blocks"

2. Legend to supplementary snp table should be completed: define AF, impact, modifier, moderate, AA1, AA2...

3. Please define "intermediate-frequency allele"

4. Figure 7 legend: Authors should be more specific in describing the figure: After 10 (A panel), 13 or 20 generations (B panel) on the K-12 strain... What is E. coli OP50 start 'G10'? the 15{degree sign} stock?

Optional:

Did the authors attempt to rescue the Mrt phenotype with individual metabolites (eg Vit B12...)? These are not straight forward experiments and most likely part of a future study.

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Referee #2

Evidence, reproducibility and clarity

In this study, Frézal et al. reported novel interactions between microbes and C. elegans in the regulation of epigenetic inheritance. By screening for 132 isotypes, the authors found natural genetic variants that contribute to the mortal germline (Mrt) phenotype. The authors further found that naturally associated gut bacteria, microsporidia, and E. coli K12 could rescue Mrt phenotype in wild isolates as well as in epigenetic mutants. Finally, the authors showed that the epigenetic memory of bacterial environment could propagate transgenerationally. I find this paper highly intriguing as it provides valuable insights into the impact of the environment on epigenetic inheritance and its effects on evolution within ecologically relevant contexts.

Major comments:

• The authors claimed that the variants causing Mrt exist at intermediate frequency in the natural population but the evidence supporting this claim is rather limited. To strengthen the claim, the authors should examine the distribution and frequency (perhaps coupled with phylogenetic analysis) of the Ch III haplotype in the wild isolates. The authors should also examine the GWAS peak for the signature of balancing selection (e.g., dN/dS ratio).

• Does JU775 carry polymorphisms in genes that are known to be involved in Mrt? These genes may genetically interact with the Ch III variant, as suggested by the partial penetrant phenotypes of the introgressed lines. It would be helpful to have a table summarize the variation in these genes. <br /> It is curious to me that for experiments with HT115, the expression of the RNAi vectors was induced with IPTG. Is this step necessary? It is known that even the backbone of L4440 could trigger a non-specific RNAi response (PMID: 30838421). I wonder if activating exogenous RNAi response is required for Mrt rescue.

• In figure 7, it appears that the worms transferred from MG1655/HT115 to OP50 showed an even stronger rescue (higher Mrt value) than the ones constantly on MG1655/HT115. This suggests to me that fluctuations in food composition may strongly affect epigenetic inheritance. Please clarify as this is very interesting, if true.

• Optional - Numerous studies have shown that SKN-1 regulates metabolism in response to food composition and availability (PMID: 23040073). Additionally, some recent studies have indicated a role of SKN-1 in epigenetic inheritance triggered by exogenous RNAi. In particular, SKN-1 promotes stress-induced epigenetic resetting (PMID: 33729152). I wonder if SKN-1 modulates Mrt based on bacterial diet.

Minor comments:

• Line 47: typo "...they defined..."

• Line 100-101: weird sentence structure. Please consider rephrasing.

• Line 138-139: I don't quite understand what "intermediate-frequency chromosome III alleles" means here. Some SNPs were found in Ch III 4-6Mb? Please expand.

• Line 213 - it was unclear to me why the assay was performed at 23C instead of 25C. I later learned in the method section that microsporidia cannot be cultured at 25C. I think it will be helpful to add that information when microsporidia is introduced to improve clarity.

Significance

This study beautifully demonstrates how diet composition affects epigenetic inheritance. This study is rigorous (replicated by 3 different labs) and the data is solid. Using natural wild isolates and naturally associated microbes, the authors described how diet composition affects germline mortality and epigenetic inheritance. Interestingly, the authors showed that Mrt phenotype might be the result of standard lab cultivation conditions and it was masked when the worms were fed on naturally associated bacteria and microsporidia. Overall, the findings are very interesting and novel. While mechanistic insights are currently lacking, it is outside the scope of this paper. This paper provides an interesting paradigm to study how genetic and environmental variation influence epigenetic inheritance and evolution. I believe this paper will be of great interest to audiences across many fields of biology, including quantitative biology, evo-devo, ecology, and genetics and epigenetics.

My field of expertise: C. elegans biology, epigenetic inheritance, genetics.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Frezal, Felix and colleagues study 132 wild isolates of the C. elegans species and demonstrate that the majority of these strains will become sterile within 20 generations if grown at 25oC. This is a very thorough analysis of a multigenerational trait that the authors show is commonly found in wild C. elegans strains. The authors use GWAS to identify a peak on chromosome III that is enriched in strains that become sterile at 25oC. Consistently genetic crosses place this segment of chromosome III from a Mrt wild strain into the N2 background resulted in a strong Mrt phenotype. The authors noticed that bleaching of wild C. elegans strains to remove associated bacteria promoted the Mrt phenotype. Remarkably, the authors show that growth of bleached wild strains on bacteria isolated from the wild strains prior to bleaching is sufficient to suppress the Mrt phenotype. These results were obtained with two wild isolates and with multiple species of wild bacteria, strongly supporting the authors' conclusions. The authors also show that independent types of intracellular bacteria that infect the intestine can partially suppress the Mrt phenotypes. The authors also show partial to strong rescue of temperature-sensitive epigenetic mutants set-2, set-24 and nrde-2 by wild bacteria. Remarkably, the authors demonstrate that growth of the introgressed JU775 strain on a N2 background can be grown on suppressor bacteria for 10 to 20 generations, then bleached and placed on OP50, then there is a multigenerational memory of the suppressor bacteria. This intriguing result is consistent with bacteria having an epigenetic effect on C. elegans Mrt phenotypes, which are themselves in some cases caused by epigenetic defects.

Comments for the authors:

1. 'For GWAS, the strains that were fertile after 20 generations were considered non-Mrt.'

One aspect of Fig 1D that could be clarified are the dots at generation 21. If these represent strains that were always fertile at generation 21, then perhaps give these a different color to indicate that sterility was never observed?

1. 'The mean Mrt values of strains ranged from sterile at 3 generations to fertile after 20 generations at 25oC, with a skewed distribution toward high values (Figure 1B).'

Based on Table S2, part of the explanation for this skewed distribution in later generations is that some strains became sterile rapidly for some blocks, whereas the same strain did not become sterile in other blocks. For example, JU1200, JU360, PB303. I suggest providing a second color for Fig. 1D for strains that sometimes displayed sterility and sometimes did not.

For those that were sometimes fertile and sometimes sterile, I suggest creating a graph in Figure 1 that shows generations at sterility or lack of sterility, color coded by block. This will allow the significance of strains with high generation Mrt values to be better appreciated for readers who do not look at the supplementary table.

1. The GWAS section could benefit from a simple explanation of the premise of GWAS for non-specialist readers.

2. One problem might be that the Mrt phenotype is widespread among wild strains. To the authors' credit, they consider results observed in different laboratories as valid, even when the results do not agree. If the Mrt phenotype is influenced by the environment, then some laboratory environments might result in 'false negative' Mrt results that could be ignored in favor of positive results from another lab that appear strong. Might focusing on strains with a set of strong positive results from one lab allow the authors to draw stronger GWAS conclusions?

3. The authors' perform GWAS based on the variance of the Mrt phenotype data. Would the GWAS data be more illuminating if the authors only considered strains that become sterile fairly rapidly, within 10 generations. The authors might then have a second category that included strains that become sterile from generation 11-20. If the genetic basis for the Mrt phenotypes is the same, then GWAS of strains that become sterile in less than 10 generations might yield similar peaks as GWAS for strains that become sterile between generations 11-20.

4. 'We did not investigate whether a second locus present in JU775 on the right arm of Chr III might have a lesser effect.'

5. It might be interesting to test the memory of growth on beneficial bacteria on JU4134, which had a Mrt phenotype that was strongly suppressed by the beneficial bacteria.

6. The Mrt phenotype of mutants in small RNA inheritance and histone modifying enzymes 'appears however distinct from that of the prg-1/piwi mutant (for which the cause of sterility is debated), especially the latter does not show temperature dependence and is suppressed by starvation.'

While it is true that the cause of sterility is debated for the prg-1/piwi mutant, this mutant is defective for small RNA silencing and likely has parallels with some defects in histone modifying enzymes. Anecdotal reports suggest that starvation might affect the Mrt phenotype or longevity of histone modifying enzyme mutants. Moreover, the cause of sterility is not clear for small RNA inheritance and histone modifying enzyme mutants. It is fair to say that the distinction between temperature-sensitivity or lack of temperature sensitivity of small RNA mutants is not understood. Could the authors please comment here about whether any of the wild strains display sterility at 20oC.

1. If intracellular bacteria are simply somatic, then how is it that they are transmitted to progeny. If they are released into the environment and then consumed by hatched larvae, this is soma-to-soma transmission.

Minor comments:

1. 'We measured the mortal germline (Mrt) phenotype'. Mortal Germline (Mrt)

Significance

All in all, this is an interesting and well-written manuscript that represents a considerable amount of work and demonstrates that a temperature-sensitive multigenerational sterility phenotype is widespread among wild C. elegans strains. This Mrt phenotype is modulated by the food they consume or by intracellular bacterial parasites that reside in somatic intestinal cells. This may mean that the intestine is a major modulator of the Mrt phenotype, which may be a consequence of lab culture conditions and may not occur for wild strains in the wild. Nevertheless, the phenotype or phenotypes are intriguing and likely relevant to natural variation.

The limitations of this manuscript include a lack of understanding of the precise genes involved or if small RNAs or metabolites from bacteria are involved. But this manuscript represents an enormous effort and raises many interesting points that will be addressed in future efforts.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Major comments:

1. A control group of mice fed chow diet is needed to distinguish the effects of the genotype from those caused by diet. What is the phenotype of regular chow-fed mice in terms of energy metabolism and thermogenesis?

We are sincerely grateful to Reviewer 1 for raising an important question regarding the need for a control group of mice fed chow diet.

To address this concern, we have conducted experiments on mice fed a regular chow diet and measured their phenotype in terms of energy metabolism and thermogenesis. In addition to be sure that the phenotype also is present in when we compared littermates we have included as control both to chow-fed CD4-Cre and littermates (MKK3/6f/f). Our findings reveal that MKK3/6CD4-KO mice fed a chow diet presented an increased brown adipose tissue (BAT) thermogenesis compared with CD4-Cre and littermates. This phenotype is similar to the observed in HFD-fed mice. Also, these results indicate that the same phenotype is observed when we compared with littermates including an extra control in the study.

To further investigate the effect on energy metabolism, we utilized metabolic cages. The data from these experiments align with the increased thermogenesis observed in MKK3/6CD4-KO mice fed a chow diet, as they also demonstrated increased energy expenditure. We thank the reviewer for this suggestion as we believe that these new data strengthen our conclusion significantly.

We have thoughtfully incorporated these essential findings into in Supplementary Figure 2C-D of the manuscript.

1. While an increase in BAT temperature (as demonstrated here by infrared imaging) in line with increased thermogenesis, it will be critical to verify this hypothesis by indirect calorimetry. Energy expenditure, food intake, and activity measures should be added for regular and DIO mice. Please follow the guidelines for ANCOVA analysis and measurements explained in PMID: 22205519 and PMID: 21177944.

We are grateful to Reviewer 1 for bringing up an essential point concerning the need to verify our hypothesis on increased BAT temperature and thermogenesis through indirect calorimetry. We acknowledge the importance of including energy expenditure, food intake, and activity measures for both regular and DIO mice to strengthen our study.

To address this valuable suggestion, we have taken immediate action. We utilized metabolic cages in mice under chow diet. The data from these experiments align with the increased thermogenesis observed in MKK3/6CD4-KO mice fed a chow diet, as they also demonstrated increased energy expenditure, without differences in food intake or locomotor activity. We thank the reviewer for this suggestion as we believe that these new data strengthen our conclusion significantly. These new data are now in Supplementary Figure 2A-B.

In addition, we have initiated a new experimental group of age-matched mice on HFD, which we will carefully feed for 8 weeks. Following this dietary period, we will subject the mice to metabolic cage analysis, allowing us to obtain accurate data on energy expenditure, food intake, and activity levels. These additional measurements will provide a comprehensive understanding of the metabolic changes induced by MKK3/6 deficiency in T cells under different dietary conditions.

1. That the phenotype is still seen at isothermal housing is interesting but should be backed up by direct assessment of thermogenic capacity (see PMID: 21177944). In the end, it could also be increased heat loss, independently of heat production. If the browning is cause or consequence remains unclear, then.

Thank you for raising this important point. Indeed, it is essential to corroborate the observed phenotype with direct assessments of thermogenic capacity to gain a comprehensive understanding of the underlying mechanisms. The study mentioned in PMID: 21177944 highlights the significance of evaluating thermogenesis directly to support the findings.

According to your suggestion, we plan to house the animals at 30 ºC for four weeks and subsequently inject norepinephrine to evaluate thermogenesis capacity while measuring brown adipose tissue (BAT) activation. This approach should provide valuable insights into the thermogenic potential of the animals under isothermal conditions.

However, we will not be able to conduct the experiment in metabolic cages at 30 ºC due to the constraint that our system does not allow 30 ºC temperature. For this reason, we will measure BAT temperature to analyze this experiment.

1. Regarding the in vitro data, a thermogenic phenotype should be functionally verified by Seahorse analysis.

We thank Reviewer 1 for raising an important point concerning the need for functional verification of the thermogenic phenotype observed in our in vitro data using Seahorse analysis.

In response to this valuable suggestion, we performed Seahorse analysis in differentiated adipocytes treated with or without IL-35 for 48 hours. The results demonstrated a slight increase in basal metabolism and a heightened response to isoproterenol (ISO) stimulation of β3 adrenergic receptors in adipocytes after IL-35 treatment. These findings provide functional evidence supporting the thermogenic phenotype induced by IL-35 in adipocytes.

We have thoughtfully included this essential data in Figure 2 of this revision plan, allowing reviewers and the scientific community to comprehensively evaluate and validate the functional implications of our findings.

1. Mechanistically, there is epistasis type of experiment that IL-35 influences Ucp1 levels via ATF2 as the data remain associative in nature.

Thank you for your valuable comment. We agree that to establish a mechanistic link between IL-35 and Ucp1 levels will improve the strength of the manuscript.

To delve deeper into the mechanism through which IL-35 influences Ucp1 expression, we focused on the role of ATF2, a transcription factor known to be involved in regulating UCP1 levels (PMID: 11369767 and PMID: 15024092). In our investigation, we treated adipocytes with IL-35 both in the presence and absence of an inhibitor targeting the ATF2 pathway. The results were illuminating as we observed a significant reduction in the expression of Ucp1 when the ATF2 pathway was inhibited.

These findings indicate that ATF2 is indeed a crucial mediator of the effects of IL-35 on Ucp1 levels. By inhibiting the ATF2 pathway, we demonstrate a direct functional link between IL-35 and the expression of Ucp1, providing mechanistic insights into the regulatory role of IL-35 in thermogenesis. We included new results in Figure 7F.

1. What are other consequences of injecting IL-35? Is it good or bad? What is the therapeutic potential in DIO mice? Also, in these experiments (Fig. 7) indirect calorimetry as described would be supportive of the claims.

Regarding the consequences of injecting IL-35, we have already performed experiments to analyze its effect. Our findings indicate that IL-35 increases thermogenesis in BAT (Figure 7), suggesting that it may play a role in promoting energy expenditure, which could be beneficial in combating diet-induced obesity (DIO) in mice. Importantly, we did not observe any negative effects of IL-35 in our experiments.

Based on these promising results, we are expecting the therapeutic potential of IL-35 in DIO mice. By promoting thermogenesis in BAT, IL-35 may offer a novel approach to manage obesity and related metabolic disorders. However, we acknowledge that further comprehensive studies are needed to fully understand its therapeutic benefits and potential side effects.

In our future works, we plan to evaluate a targeted delivery system for IL-35. We are currently generating IL-35 loaded metal-organic frameworks (MOFs) labeled with adipose tissue-specific peptides. This innovative strategy aims to enhance the delivery of IL-35 to adipose tissue, potentially maximizing its effects in the relevant areas. Our ongoing work with IL-35 loaded MOFs may offer a promising avenue for targeted delivery.

Minor comments:

1. The authors claim that their HFD-fed MKK3/6CD4-KO mice are protected against hyperglycemia, but only fasted/fed blood glucose tests are performed. Lower glucose levels could be explained due to a hyperinsulinemic state in response to growing insulin resistance in the presence of HFD. It would be sensible to perform both glucose and insulin tolerance tests to back up your statement.

Thank you for your insightful comment. We agree that to support our claim of protection against hyperglycemia in HFD-fed MKK3/6CD4-KO mice, further tests are necessary beyond fasted/fed blood glucose measurements.

In response to your suggestion, we conducted both glucose tolerance tests (GTT) and insulin tolerance tests (ITT) in HFD-fed MKK3/6CD4-KO mice. We did not observed differences in glucose tolerance and but ITT showed significantly enhanced insulin sensitivity compared to control mice. These findings provide evidence that the protection against hyperglycemia in HFD-fed MKK3/6CD4-KO mice is not solely due to a hyperinsulinemic state, but rather indicates genuine improvements in glucose handling and insulin response.

We have thoughtfully included these crucial data in the revised version of the manuscript, both in the main text and Supplementary Figure 4. We extend our appreciation to the reviewer for this valuable suggestion, which has enhanced the scientific rigor and completeness of our study.

1. Please provide the loading control for p38 and S6 blots (Figure 6G).

Thank you for the comment. The loading control we used for P p38 and P S6 blots in Figure 6G is β-actin. Due to the limited amount of sample available, we can only use β-actin as the loading control. The sample amount obtained is very limited, and we can only provide enough lysate to run a couple of blots from the same sample. Running several western blots with the same sample is almost impossible given the constraint of the sample availability. We apologize for this limitation, but it is necessary to avoid using too many mice for ethical reasons, as the samples come from a large number of mice.

1. Statistical test from Figure 7B should be a t-test, since it is only comparing 2 variables (PBS vs IL-35), and not a 2-way ANOVA as described in the legend.

We sincerely thank the reviewer for the comment. It was indeed a mistake in the text. While we have performed a t-test, there was an error in the legend that we have now corrected. We apologize for any confusion this may have caused and appreciate the opportunity to rectify the oversight.

1. Label correctly the panels in the figures -examples: Fig 3, panels C and D are interchanged; reference in the text to Fig S1G even though the figure only as panels A-F; Fig 7 legend referes to the statistical test of panel E when the figure only has A-D.

We sincerely apologize for any mistakes in our manuscript that may have caused difficulties while reading the article and potentially led to misleading results. We are grateful to Reviewer #1 for bringing these errors to our attention. Thanks to their diligent review, we have been able to identify and rectify the issues in our manuscript. The necessary corrections have been made, ensuring the accuracy and reliability of our research. We greatly appreciate the reviewer's valuable feedback and contribution to improving the quality of our work.

1. There are several typos along the text, please revise (example: page 4;line 4 -"tremorgenic")

We apologize for the presence of any typos in the initial version of the article. We have thoroughly revised the manuscript to correct these errors. Thank you for bringing this to our attention and helping us improve the accuracy and clarity of our work.

Reviewer #1 (Significance):

The manuscript is well written, and the research conducted properly, even though a thorough analysis of energy metabolism in mice and cells is missing and the mechanistic claims are based on relatively thin data.

The immune system and inflammation play important roles for obesity and insulin resistance, yet the roles they play in thermogenic adipocytes remains unclear. This work adds novel aspects to this relationship.

Reviewer #2 (Evidence, reproducibility and clarity):

This manuscript by Nikolic et al sought to investigate the role of p38 activation in adipose tissue Treg cells and obesity. They found that the expression of p38a, its upstream kinase MKK6, and downstream substrate ATF2 was upregulated specifically in adipose T cells associated with human obesity. They generated T cell-specific knockout MKK3/6 in mice and found these animals were protected from diet-induced obesity as a result of increased BAT thermogenesis. Mechanistically, loss of p38a activation promoted adipose tissue accumulation of Treg cells, leading to elevated IL-35 availability and UCP1 expression.

Major comments:

1. They attributed the obesity protection to energy expenditure; however, food intake and intestinal absorption were never tested. Immune cells particularly Treg cells are important modulates of nutrient uptake.

We are sincerely grateful to Reviewer #2 for this crucial comment, highlighting the importance of assessing not only energy expenditure but also food intake and intestinal absorption in our study.

In response to this valuable suggestion, we have initiated an HFD experiment to comprehensively examine food intake and intestinal absorption. For food intake analysis, we are employing metabolic cages, which will allow us to monitor and quantify the amount of food consumed by the mice accurately. Additionally, we plan to follow the methodology outlined in the study by Kraus et al. (PMID: 27110587) to measure lipid content in feces, enabling us to evaluate intestinal absorption.

By conducting these additional experiments, we aim to gain a deeper understanding of the potential role of Treg cells, known immune modulators of nutrient uptake, in our observed obesity protection phenotype.

1. At thermoneutrality, BAT is inactive even though UCP1 expression is still present (not activated). MKK3/6 deficiency in T cells still confer protection against obesity at thermoneutrality suggests it regulates other energy balance components in addition to BAT thermogenesis.

Thanks for the comment. We believe that the effects of IL35 on thermogenesis are likely partly mediated by alternative mechanisms, as we did not observe an increase in UCP1 gene expression in BAT in vivo (Figure 3D of the manuscript), and the increase in thermogenesis is still present even at thermoneutrality where UCP1 is inactive (Figure 4E of the manuscript). This suggests that IL35 might regulate other alternative pathways that control BAT thermogenesis.

While our current findings provide valuable insights, further experiments may be necessary to fully understand the underlying mechanisms. For instance, conducting experiments with transgenic mice expressing IL35 or using IL35 knockout (KO) mice could shed more light on the specific pathways through which IL35 exerts its effects on thermogenesis and energy balance.

In conclusion, we hypothesize that IL35's effects on thermogenesis are mediated partly by alternative mechanisms beyond UCP1 activation, and its ability to enhance thermogenesis even at thermoneutrality highlights its potential as a regulator of energy balance. We plan to further investigate the specific mechanisms through which IL35 impacts thermogenesis and energy balance. To achieve this, we will consider conducting experiments with transgenic mice expressing IL35 or using IL35 knockout (KO) mice in follow up studies. This is now discussed in our manuscript.

1. Loss of adipose Treg cells (such as Pparg KO, Foxp3-DTR) did not lead to obvious obesity phenotypes. Gain-of-function Treg cells (such as adoptive transfer, IL-2/IL-2 Ab) did not results in profound obesity protection as observed in MKK3/6 CD4-KO mice. It suggests that MKK3/6 KO in T cells causes other immune defects (besides Tregs).

We agree with the referee's assessment that the lack of obvious obesity phenotypes in above mentioned animal models. The results we observed in our MKK3/6CD4-KO mice suggest that p38 signaling pathway in T cells may modulate their function, leading to an upregulation of IL35 expression, which could be a contributing factor to the significant obesity protection observed in MKK3/6CD4-KO mice. We believe that IL35's effects on energy balance and thermogenesis are critical components of the observed protection against obesity in this model.

Regarding the studies with PPAR KO in Treg cells, it is important to note that they did not specifically focus on the effect of thermogenesis. While they observed a general tendency of increased fat deposition when treated with a PPAR agonist in the Treg deficient PPAR KO mice, these findings were not extensively studied in that particular paper. Thus, additional research is necessary to specifically evaluate thermogenesis in these mice and further understand the role of PPAR in Treg-mediated thermogenic processes.

We also acknowledge the presence of contradictory results from loss-of-function experiments of Treg cells in mice. The observed metabolic changes may be context-dependent, and the impact of Treg cells on metabolism might vary under different physiological conditions. For instance, in lean conditions where adipose tissue inflammation is low, a decrease in VAT Treg cells might not lead to significant metabolic changes. However, under certain circumstances, such as obesity, VAT Treg cells may play a critical role in regulating metabolism. In this context increasing that population that is reduced during obesity could results in improve metabolic performance.

In conclusion, our findings suggest that the lack of p38 activation in Treg cells may prevent the dramatic down-regulation and loss of function observed in Treg cells during obesity. This preservation of Treg function could be a significant factor driving the observed protection against obesity in MKK3/6CD4-KO mice.

While further studies are required to elucidate the precise timing and spatial aspects of the specific functions of adipose-resident Treg cells, it is evident that these cells play a crucial role in maintaining immune and metabolic homeostasis. They achieve this, in part, by regulating adipose inflammation, insulin sensitivity, lipolysis, and thermogenesis. This is now discussed in our manuscript.

1. The increase in IL-35 seemed to be very moderate, compared to the metabolic phenotypes. It raises the question if IL-35 is responsible for BAT activation and reduced weight gain. It is unclear what systemic and local levels of IL-35 were reached after recombinant IL-35 treatment (Fig. 7B). IL-35 antibody blockade experiment in KO mice is recommended.

Physiological changes in cytokines can indeed have a significant impact on the metabolic profile due to their continuous and intricate interactions. Even minor alterations in the overall cytokine milieu can result in substantial changes in metabolism (doi.org/10.1073/pnas.1215840110). In fact, it is well-established that in humans, small changes in cytokine profiles between genders, in obesity, and during aging can play a critical role in the development of pathology. These cytokines often operate in a chronic manner, exerting long-term effects on various physiological processes (doi.org/10.1038/s41467-020-14396-9).

In summary, the dynamic interplay of cytokines in metabolism can lead to significant metabolic changes even with subtle alterations in their levels. While the increase in IL-35 may appear moderate, our findings using recombinant IL35 indicate that IL-35 increases thermogenesis in BAT, suggesting that it may play a role in promoting energy expenditure, which could be beneficial in combating diet-induced obesity (DIO) in mice. Importantly, we did not observe any negative effects of IL-35 in our experiments.

1. IL-35 induced p-ATF2 is acute and transient (Fig. 7D) and it was able to increase BAT temperature in just 4 h (Fig. 7B). However, Ucp1 transcription and translation generally take much longer time (e.g. 2d in Fig. 7C). IL-35 may increase energy expenditure through UCP1-independent mechanisms.

Thanks for the comment. As previously mentioned, we believe that the effects of IL35 on thermogenesis are might be mediated by alternative mechanisms, as we did not observe an increase in UCP1 gene expression in BAT, and the increase in thermogenesis is still present even at thermoneutrality where UCP1 is inactive. This suggests that IL35 might regulate other alternative pathways that control BAT thermogenesis.

While our current findings provide valuable insights, further experiments may be necessary to fully understand the underlying mechanisms. For instance, conducting experiments with transgenic mice expressing IL35 or using IL35 knockout (KO) mice could shed more light on the specific pathways through which IL35 exerts its effects on thermogenesis and energy balance. We plan to further investigate the specific mechanisms through which IL35 impacts thermogenesis and energy balance. To achieve this, we will consider conducting experiments with transgenic mice expressing IL35 or using IL35 knockout (KO) mice in follow up studies. This is now discussed in our manuscript.

Minor comments:

1. The gating of Treg cells should exclude CD25- cells. Single positive (CD25+ or Foxp3+) cells are progenitors of Tregs. In addition to number, phenotypic activation of Treg cells should also be determined.

Thank you for the comment. We have reanalyzed our data by excluding CD25- cells and included now in the figure 5A of the manuscript and new supplementary figure 7 of revised manuscript. We also checked CD69+ and KLRG1+ Treg cells and observed no differences between genotypes. We also included figures in this revision plan (Figure 5 and 6).

1. ATF is also important for adipogenesis, is the adipogenic differentiation of BAT SVF cells affected by MKK3/6 KO or IL-35 treatment?

We appreciate the reviewer's observation regarding the importance of ATF in adipogenesis. To investigate this aspect further, we performed in vitro differentiation of adipocytes and treated them with IL-35 in the presence or absence of an inhibitor targeting the upstream activator of ATF.

The results were compelling, as IL-35 treatment led to an increase in the expression of adipogenic markers, including Pparg, Adipoq, Leptin, and Perilipin. In contrast, inhibiting ATF activation resulted in a reduction of these adipogenic markers. These findings provide strong evidence that ATF plays a significant role in mediating the effects of IL-35 on adipogenesis.

We have thoughtfully included these essential data in Figure 7G of the manuscript. We extend our gratitude to the reviewer for their keen observation, which has enhanced the scientific depth and completeness of our study.

1. Metabolic cage experiments are desired to determine whole-body energy balance, including food intake, physical activity, and heat production.

To address this valuable suggestion, we have taken immediate action. We utilized metabolic cages in mice under chow diet. The data from these experiments align with the increased thermogenesis observed in MKK3/6CD4-KO mice fed a chow diet, as they also demonstrated increased energy expenditure, without differences in food intake or locomotor activity. We thank the reviewer for this suggestion as we believe that these new data strengthen our conclusion significantly. The new data are included in Supplementary figure 2 A-B.

In addition, we have initiated a new experimental group of age-matched mice on HFD, which we will carefully feed for 8 weeks. Following this dietary period, we will subject the mice to metabolic cage analysis, allowing us to obtain accurate data on energy expenditure, food intake, and activity levels. These additional measurements will provide a comprehensive understanding of the metabolic changes induced by MKK3/6 deficiency in T cells under different dietary conditions.

1. Total UCP1 expression (both RNA and protein) in the whole BAT from an animal should determined (since BAT is smaller in KO mice).

Thank you for this comment. Yes, we have measured UCP1 expression in the whole BAT from the animals. It is in the figure 3C and 3D and here. Although in vitro studies indicated that IL35 increase UCP1 in adipocytes we were not able to find an increase of this protein in BAT

We believe that the effects of IL35 on thermogenesis are likely partly mediated by alternative mechanisms, as we did not observe an increase in UCP1 gene expression in BAT in vivo, and the increase in thermogenesis is still present even at thermoneutrality where UCP1 is inactive (Figure 4E of the manuscript). This suggests that IL35 might regulate other alternative pathways that control BAT thermogenesis.

1. Fig. 6C, IL-35-expressing Treg cells should be quantified from adipose tissue.

We appreciate the referee's suggestion to quantify IL-35-expressing Treg cells from adipose tissue in Fig. 6C. While we agree that this would be valuable information, we encountered technical challenges that made it impractical to measure IL-35 directly in Treg cells from the visceral adipose tissue (VAT).

One of the main technical challenges we encountered is the low number of Treg cells present in the adipose tissue, making it difficult to obtain sufficient cell material for accurate quantification of IL-35. Treg cells are relatively rare compared to other immune cell populations in the adipose tissue, and their extraction and analysis can be technically demanding.

Reviewer #2 (Significance):

The manuscript is innovative in define the novel role of p38 activation in the T cell compartment and its metabolic regulation. The involvement of Treg cells in adipose tissue homeostasis has been well documented and Treg cell-derived IL-35 has been demonstrated in immune regulation. The authors provided a relatively thorough description of the altered metabolism in these Mkk3/6 CD4-KO mice; however, the reviewer has doubts if Treg cells and IL-35 are primary mechanisms of the observed protection from obesity. The manuscript would be much stronger if the model were Treg cell-specific KO and/or IL-35 deficiency in Treg cells reverses obesity resistance conferred by MKK3/6 deficiency. It also suspected that BAT thermogenesis is not the major reason, as BAT deficiency or UCP1 KO results in much milder phenotypes in mice, even at thermoneutrality.

Reviewer #3 (Evidence, reproducibility and clarity):

Specific comments:

1. It's important to use proper controls for mouse metabolic studies. The authors stated that CD4-Cre and MKK3/6 CD4-KO mice are all in the C57B/6L background. However, it would appear that these two lines were bred separately. The difference in the genetic background, despite minor, can lead to the observed phenotype, notably weight gain. Since the metabolic phenotypes seem to be driven by the weight difference, it is even more critical to include additional controls to validate the findings. For instance, crossing MKK3/6 f/f with one copy of CD4-Cre with MKK3/6 f/f to generate age-matched MKK3/6 CD4-KO and MKK3/6 f/f controls should be used to repeat major in vivo studies similar to those in Fig. 2-4.

We thank the reviewer for the comment. Although, every control is important using conditional mice, there are several papers indicating that all the cre expression lines have for their own effects that could be important in metabolism and there are several articles that strongly recommended to use cre+ lines as a control. For that reason, we have used the cre expressing line as a control because we really think is the best one (Jonkers and Berns, 2002). In fact, Jackson laboratory recommend to use cre expressing line as a control to avoid side effects that cre overexpression could have in the tissue of interest (https://biokamikazi.files.wordpress.com/2014/07/cre-lox-imp-notes.pdf).

However, as this reviewer suggested, we checked that similar results were obtained using littermates as controls and we have now included these data in the manuscript (Supplementary Figure 2D).

1. The assessment of adipose tissue immune cell population in Fig. 5 was conducted after HFD-induced obesity. As mentioned above, the change in Treg and M2 cell percentage could be due to the body weight difference. The experiment should be repeated (with proper controls) in normal chow and after a few weeks of HFD when Treg numbers start to decline.

Thank you for the comment. We currently performing short HFD experiment to check Treg and M2 cell population in adipose tissue using the littermates as controls.

In addition, we checked those cell populations in adipose tissue infiltrates in mice fed chow diet and observed no differences in M2 macrophage population between mice, while the percentage of Treg cells was actually lower in MKK3/6CD4-KO mice ND-fed mice (Fig 12 of revision plan). This result suggests that higher accumulation of Treg cells in mice lacking p38 activation in T cells are specific of obese state and strengthen our hypothesis that DIO protection in MKK3/6CD4-KO mice is due to Treg cell population.

1. Data related to the mechanistic link in Fig. 6/7 are not robust and require a large amount of additional work to substantiate the claim. First of all, the role of IL-35 in BAT thermogenesis remains unclear. It's somewhat surprising to see a single dose of IL-35 i.v. injection is sufficient to increase BAT temperature in Fig. 7B. Minimally, the authors need to demonstrate that IL-35 treatment (perhaps after a few daily doses) is able to increase browning/beiging of fat cells and improve cold tolerance when placing the mice at 4 degree of several hours (and up to 3 days). Serum FGF21 level should also be measured after/during IL-13 treatment. Secondly, ATF2 knockout or knockdown in brown preadipocytes should be employed to demonstrate that IL-35 induced UCP1 and FGF21 expression is ATF2 dependent. Another key experiment is to use IL-35 deficient Treg model to definitively demonstrate the requirement of Treg IL-35 to maintain thermogenesis. However, this can be done in a follow up study.

We are grateful for all the insightful comment provided by Reviewer #3. We understand the concern, but we have the limitations in performing several sequential i.v. injections in our animal facility due to ethical permissions. In light of this constraint, we have devised an alternative approach to evaluate the role of IL-35 in adaptive thermogenesis.

To address this, we conducted a cold tolerance test in both control mice and MKK3/6CD4-KO mice, which express higher levels of IL-35. Our findings revealed that MKK3/6CD4-KO mice exposed to cold conditions were able to preserve their body and brown adipose tissue (BAT) temperature, while the temperature of control CD4-Cre mice gradually dropped during the cold challenge.

The data from this cold tolerance test support our hypothesis and demonstrate the role of IL-35 in promoting adaptive thermogenesis, leading to enhanced temperature maintenance in MKK3/6CD4-KO mice. These observations have been included in Figure 7B of the manuscript, and detailed results are available in Figure 11 of this revision plan.

We appreciate the reviewer's valuable input, which has encouraged us to explore alternative experimental approaches to address the research question effectively.

We agree with the reviewer #3 that using IL-35 deficient Treg model would be great approach to confirm our results, but we think that now with the additional experiments we have performed, we strength our findings that IL-35 has a novel role in controlling adipose tissue thermogenesis.

Reviewer #3 (Significance):

Dissipating energy as heat through brown or beige adipocyte-mediated thermogenesis is believed to be an effective way to combat obesity. The current study aims to characterize the p38 signaling pathway in T cells as a potential target to modulate browning or beiging of adipose tissues. This would be of interest to the basic biomedical research community, particularly in the area of immunometabolism. A major limitation is the concern of improper controls for the mouse models, which makes data interpretation difficult. In addition, the mechanistic studies lack in depth analyses to support the conclusion.

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Referee #3

Evidence, reproducibility and clarity

Nikolic et al. examine the metabolic outcome of T cell specific deletion of MKK3/6 (MKK3/6 CD4-KO), which are the main activators of p38. Previous studies have demonstrated that MKK3/6 CD4-KO leads to Treg expansion and that Tregs in adipose tissues are associated with improved metabolic homeostasis. In line with these observations, the authors show that MKK3/6 CD4-KO mice gain less weight and have more active brown fat thermogenesis on a HFD both at the room temperature and 30C housing conditions. They also find more Tregs and M2 macrophages in eWAT of MKK3/6 CD4-KO. All of the metabolic parameters are compared to CD4-cre mice as the wild type controls. Mechanistically, the authors suggest that reduced p38 activation by MKK3/6 CD4-KO leads to increased IL-35 production by Tregs, which induces beiging/browning of adipose tissues to promote metabolic health.

The authors have spent most of the efforts conducting metabolic phenotyping of MKK3/6 CD4-KO mice. One potential issue is whether the non-littermate CD4-cre mice are the proper controls for the comparison. In addition, the mechanistic link of the IL-35-ATF2-UCP1/FGF21 axis has only been superficially addressed.

Specific comments:

1. It's important to use proper controls for mouse metabolic studies. The authors stated that CD4-Cre and MKK3/6 CD4-KO mice are all in the C57B/6L background. However, it would appear that these two lines were bred separately. The difference in the genetic background, despite minor, can lead to the observed phenotype, notably weight gain. Since the metabolic phenotypes seem to be driven by the weight difference, it is even more critical to include additional controls to validate the findings. For instance, crossing MKK3/6 f/f with one copy of CD4-Cre with MKK3/6 f/f to generate age-matched MKK3/6 CD4-KO and MKK3/6 f/f controls should be used to repeat major in vivo studies similar to those in Fig. 2-4.
2. The assessment of adipose tissue immune cell population in Fig. 5 was conducted after HFD-induced obesity. As mentioned above, the change in Treg and M2 cell percentage could be due to the body weight difference. The experiment should be repeated (with proper controls) in normal chow and after a few weeks of HFD when Treg numbers start to decline.
3. Data related to the mechanistic link in Fig. 6/7 are not robust and require a large amount of additional work to substantiate the claim. First of all, the role of IL-35 in BAT thermogenesis remains unclear. It's somewhat surprising to see a single dose of IL-35 i.v. injection is sufficient to increase BAT temperature in Fig. 7B. Minimally, the authors need to demonstrate that IL-35 treatment (perhaps after a few daily doses) is able to increase browning/beiging of fat cells and improve cold tolerance when placing the mice at 4 degree of several hours (and up to 3 days). Serum FGF21 level should also be measured after/during IL-13 treatment. Secondly, ATF2 knockout or knockdown in brown preadipocytes should be employed to demonstrate that IL-35 induced UCP1 and FGF21 expression is ATF2 dependent. Another key experiment is to use IL-35 deficient Treg model to definitively demonstrate the requirement of Treg IL-35 to maintain thermogenesis. However, this can be done in a follow up study.

Significance

Dissipating energy as heat through brown or beige adipocyte-mediated thermogenesis is believed to be an effective way to combat obesity. The current study aims to characterize the p38 signaling pathway in T cells as a potential target to modulate browning or beiging of adipose tissues. This would be of interest to the basic biomedical research community, particularly in the area of immunometabolism. A major limitation is the concern of improper controls for the mouse models, which makes data interpretation difficult. In addition, the mechanistic studies lack in depth analyses to support the conclusion.

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Referee #2

Evidence, reproducibility and clarity

This manuscript by Nikolic et al sought to investigate the role of p38 activation in adipose tissue Treg cells and obesity. They found that the expression of p38a, its upstream kinase MKK6, and downstream substrate ATF2 was upregulated specifically in adipose T cells associated with human obesity. They generated T cell-specific knockout MKK3/6 in mice and found these animals were protected from diet-induced obesity as a result of increased BAT thermogenesis. Mechanistically, loss of p38a activation promoted adipose tissue accumulation of Treg cells, leading to elevated IL-35 availability and UCP1 expression.

Major comments:

1. They attributed the obesity protection to energy expenditure; however, food intake and intestinal absorption were never tested. Immune cells particularly Treg cells are important modulates of nutrient uptake.
2. At thermoneutrality, BAT is inactive even though UCP1 expression is still present (not activated). MKK3/6 deficiency in T cells still confer protection against obesity at thermoneutrality suggests it regulates other energy balance components in addition to BAT thermogenesis.
3. Loss of adipose Treg cells (such as Pparg KO, Foxp3-DTR) did not lead to obvious obesity phenotypes. Gain-of-function Treg cells (such as adoptive transfer, IL-2/IL-2 Ab) did not results in profound obesity protection as observed in MKK3/6 CD4-KO mice. It suggests that MKK3/6 KO in T cells causes other immune defects (besides Tregs).
4. The increase in IL-35 seemed to be very moderate, compared to the metabolic phenotypes. It raises the question if IL-35 is responsible for BAT activation and reduced weight gain. It is unclear what systemic and local levels of IL-35 were reached after recombinant IL-35 treatment (Fig. 7B). IL-35 antibody blockade experiment in KO mice is recommended.
5. IL-35 induced p-ATF2 is acute and transient (Fig. 7D) and it was able to increase BAT temperature in just 4 h (Fig. 7B). However, Ucp1 transcription and translation generally take much longer time (e.g. 2d in Fig. 7C). IL-35 may increase energy expenditure through UCP1-independent mechanisms.

Minor comments:

1. The gating of Treg cells should exclude CD25- cells. Single positive (CD25+ or Foxp3+) cells are progenitors of Tregs. In addition to number, phenotypic activation of Treg cells should also be determined.
2. ATF is also important for adipogenesis, is the adipogenic differentiation of BAT SVF cells affected by MKK3/6 KO or IL-35 treatment?
3. Metabolic cage experiments are desired to determine whole-body energy balance, including food intake, physical activity, and heat production.
4. Total UCP1 expression (both RNA and protein) in the whole BAT from an animal should determined (since BAT is smaller in KO mice).
5. Fig. 6C, IL-35-expressing Treg cells should be quantified from adipose tissue.

Referees cross-commenting

I agree with Reviewer #1. In addition to energy metabolism and mechanistic action of IL-35, more rigor characterization of adipose Treg cells is needed.

Significance

The manuscript is innovative in define the novel role of p38 activation in the T cell compartment and its metabolic regulation. The involvement of Treg cells in adipose tissue homeostasis has been well documented and Treg cell-derived IL-35 has been demonstrated in immune regulation. The authors provided a relatively thorough description of the altered metabolism in these Mkk3/6 CD4-KO mice; however, the reviewer has doubts if Treg cells and IL-35 are primary mechanisms of the observed protection from obesity. The manuscript would be much stronger if the model were Treg cell-specific KO and/or IL-35 deficiency in Treg cells reverses obesity resistance conferred by MKK3/6 deficiency. It also suspected that BAT thermogenesis is not the major reason, as BAT deficiency or UCP1 KO results in much milder phenotypes in mice, even at thermoneutrality.

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Referee #1

Evidence, reproducibility and clarity

In this study, Nikolic et al. show a novel role for p38 signaling in Treg cells, which impacts adipocytes through IL-35. This mechanism seems to be important for adipose tissue browning and metabolic health and could be potentially therapeutically exploited.

Major comments:

1. A control group of mice fed chow diet is needed to distinguish the effects of the genotype from those caused by diet. What is the phenotype of regular chow-fed mice in terms of energy metabolism and thermogenesis?
2. While an increase in BAT temperature (as demonstrated here by infrared imaging) in line with increased thermogenesis, it will be critical to verify this hypothesis by indirect calorimetry. Energy expenditure, food intake, and activity measures should be added for regular and DIO mice. Please follow the guidelines for ANCOVA analysis and measurements explained in PMID: 22205519 and PMID: 21177944.
3. That the phenotype is still seen at isothermal housing is interesting but should be backed up by direct assessment of thermogenic capacity (see PMID: 21177944). In the end, it could also be increased heat loss, independently of heat production. If the browning is cause or consequence remains unclear, then.
4. Regarding the in vitro data, a thermogenic phenotype should be functionally verified by Seahorse analysis.
5. Mechanistically, there is epistasis type of experiment that IL-35 influences Ucp1 levels via ATF2 as the data remain associative in nature.
6. What are other consequences of injecting IL-35? Is it good or bad? What is the therapeutic potential in DIO mice? Also, in these experiments (Fig. 7) indirect calorimetry as described would be supportive of the claims.

Minor comments:

1. The authors claim that their HFD-fed MKK3/6CD4-KO mice are protected against hyperglycemia, but only fasted/fed blood glucose tests are performed. Lower glucose levels could be explained due to a hyperinsulinemic state in response to growing insulin resistance in the presence of HFD. It would be sensible to perform both glucose and insulin tolerance tests to back up your statement.
2. Please provide the loading control for p38 and S6 blots (Figure 6G).
3. Statistical test from Figure 7B should be a t-test, since it is only comparing 2 variables (PBS vs IL-35), and not a 2-way ANOVA as described in the legend.
4. Label correctly the panels in the figures -examples: Fig 3, panels C and D are interchanged; reference in the text to Fig S1G even though the figure only as panels A-F; Fig 7 legend referes to the statistical test of panel E when the figure only has A-D.
5. There are several typos along the text, please revise (example: page 4;line 4 -"tremorgenic")

Referees cross-commenting

I think we three reviewers are pretty much on the same page - mouse energy metabolism explored too little and the mechanistic insight a bit thin considering the relatively strong claims.

Significance

The manuscript is well written, and the research conducted properly, even though a thorough analysis of energy metabolism in mice and cells is missing and the mechanistic claims are based on relatively thin data.

The immune system and inflammation play important roles for obesity and insulin resistance, yet the roles they play in thermogenic adipocytes remains unclear. This work adds novel aspects to this relationship.

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Reply to the reviewers

We thank the reviewers for their thoughtful comments. We were delighted that the reviewers found our manuscript and results “solid”, “important”, “well-written”, “thoughtful”, “critical addition to the literature”, that the “design of (these) experiments is high in quality” and “conclusions are convincing and the experiments are well executed”.

We were thrilled the reviewers appreciated that “this manuscript provides solutions to technical limitations to observe mRNA in vivo” by approaching such limitations “in a thoughtful study where many of the salient features of MS2 epitope tagging are systematically measured” and “it provides a greatly improved tool to track mRNA by live imaging” that “also alerts of experimental noise that can be found and can be specific for each gene/transcript”

We will address all the concerns raised by the reviewers. Most of the comments concern text edits. In addition, we will add the following to the Results section:

1. Quantitation of observed phenotypes in Figures 1C-D and 2C-D;

2. Quantitation of cytoplasmic transcripts in Figure 1G-L.

Quantitation will be performed as previously done in Tocchini et al., 2021.

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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.

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Referee #2

Evidence, reproducibility and clarity

The transparency of C. elegans invites us to push the limit of live imaging. In this context, observation of endogenous mRNA using the high affinity between MS2 RNA hairpins (from a bacteriophage) and a protein (MCP) that can be fluorescently labeled. In the time of CRISPR-Cas, the editing of endogenous genes is feasible and authors use it to insert 24 MS sequences (about 650 bp in total) at the UTRs of a couple of genes. Once they found a way to insert the MS2 sequences at the UTRs, although with phenotypic consequences that are solved in mutants defective in Non-mediated decay of transcript, they tune the expression of the MCP using a heat-shock promoter with a leaking expression at 25C and its location in the cytoplasm avoiding nuclear location signal (NLS) of the protein.

Major comments:

They present solutions for live imaging endogenous mRNA that would be useful for colleagues interested in this technique but also show experimental noises that would be specific to each gene/transcript/UTR. In the end, the best value of this technique is to observe "real" or physiological levels but to reach this point they need to use a mutant background (NMD mutants), which may alter the "real" scenario. They found a smart way to title the article using "An adapted..." but it would be more realistic/honest to mention in the title that this is happening only in NMD mutant backgrounds. I also have doubt ion the use of the acronym MS2-MCP in the title. What about something like "Visualization of endogenous cytoplasmic mRNA with live imaging in C. elegans embryos requires an inactive Non-mediated decay"?

In any case, the conclusions are convincing and the experiments are well executed. I do not find the need for any essential experiments if they are clearer through the manuscript (from title and abstract to discussion) that this technique may need to be optimized and (and maybe validated with FISH) for each specific transcript, and developmental stage cell type where NMD and polyadenylation may work differently. Another source of experimental noise may come from the use of mcherry, which is known for forming aggregates in some cells/stages.(would this artefactual aggregation occur in figure 1L?)<br /> The only experiment that I missed, not essential but easy to perform, is a better description of the slight developmental delay of dlg-1 MS2 v2 animals. Size measures? Time until they lay embryos?

In this sense, although is not the main purpose of the article, they could highlight the fact that this is an additional option to produce hippomorphic alleles of essential genes.

Regarding methods, I miss information about the CRISPR-Cas efficiency of inserting the MS2 sequences at the UTRs. Sizes are "small" and can facilitate the insertion of dsDNA repair template, but it would be useful to know what efficiency would be expected. It would be good to mention somewhere how frequent are GG PAM sequences at UTRs sequences (probably less common than in other regions). In this sense, the use of minimal PAM Cas9 variants (Vicencio et al, Nat Comm 2022) would be necessary.

Minor comments:

In the abstract, line 33, remove epithelial? I do not think this is relevant in this sentence.<br /> In figure 4, panels B and C, add the two different embryonic stages on the left side. Then, it wouldn't be necessary to read the legend to understand the figure.

Referees cross-commenting

I find useful and reasonable the comments of my colleagues

Significance

I work in C. elegans on diverse topics, with an interest in RNA, and I have used FISH in C. elegans in the past. I find this study useful to expand the C. elegans toolbox in C. elegans. This manuscript provides solutions to technical limitations to observe mRNA in vivo in the cytoplasm, but also alerts of experimental noise that can be found and can be specific for each gene/transcript.

It is focused on the C. elegans embryo, which is the system of interest for the authors. I miss a bit of discussion at least on the use of this methodology in other stages. One of the interesting aspects of observing mRNA in vivo is the capacity to manipulate the environment. Such capacity is very limited in embryos but feasible in larvae or adults with the use of microfluidics.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The authors of this manuscript have adapted the MS2-MCP system to visualize endogenous cytoplasmic mRNA with live imaging in Caenorhabditis elegans. They have identified some of the issues that might have prevented the MS2-MCP system's adaptation to C. elegans. Specifically, they have identified that the length of the 3'UTR, which is significantly increased upon the insertion of the MS2 sequences, impacts the mRNAs' stability. They have also shown that removing the nonsense-mediated decay pathway can prevent the destabilization of the MS2 transcripts. Moreover, they have also optimized the MCP expression to avoid nuclear retention of the MS2 transcripts and mislocalization of the mRNAs.

Major comments:

• The authors show that the insertion of 24xMS2 in two endogenous genes, spc-1 and dlg-1, causes some phenotypes such as slow growth, lack of coordination (Unc), small body size (Sma), and reduced brood sizes. However, only an image example is provided in Fig. 1 C, D, and quantifying all these phenotypes would be nice. Same in Fig. 2C, D.
• Similarly, the reduction in mRNA spots from smFISH in Fig. 1 G-L is difficult to visualize by eyes, and proper smFISH quantification will help interpret the results.
• The authors also claim a reduction in cytoplasmic RNAs and increased signal in nuclear RNAs in Fig. 1J, L. A proper quantification of nuclear and cytoplasmic smFISH will help interpret the results.
• In Fig. 3D-F, the authors quantified the signal of nuclear smFISH. However, it is unclear to me in what samples or conditions the statistical test is performed. For example, do the three stars in Fig. 3D refer to the significant decrease or increase of NMD strain compared to the WT? What about the stars in Fig. 3E? The authors should indicate what samples they compare in the statistical test.

Minor comments:

• on line 195, the authors reference Fig. 3A. However, it should be Fig. 4A.
• In Fig. 4B, C the authors can add close to the image of the embryos the developmental stages. This will help the reader identify the embryo's developmental stage in the figure's upper and lower parts.
• The authors can expand a bit the discussion on how their method differs (advantages and disadvantages) from the MASS system by Hu et al., 2023.

Significance

This manuscript will help the C. elegans community to adapt and use the MS2-MCP system to visualize endogenous mRNAs by live imaging. Their finding could also be adapted to other animal model systems. At the moment, only one published report has described the usage of the MS2-MCP system in C. elegans (by Hu et al., 2023), which combined the MS2 and Suntag systems. In this way, Hu et al., 2023 could shorten the length of MS2 insertion. I am unsure if this is why they do not observe any impact on endogenous mRNA tagged with MS2. However, they only track one gene, and it is possible that different 3'UTR will react differently to the insertion of MS2 repeats. Another manuscript (Kinney et al., BioRxiv 2023) showed the usage of the MS2-MCP-GFP system to track miRNA transcription. In this case, the insertion of the MS2 repeats in the transgenic lin-4 miRNA precursor rescued lin-4 mutation. In this manuscript, Tocchini and Mango identified possible issues in inserting MS2 repeats in endogenous 3'UTR. They have overcome this potential issue by changing the position of the insert in the 3'UTR and by removing the nonsense-mediated decay pathway to prevent destabilization of the mRNA-MS2 transcripts. One possible limitation is that possible system users need to work in a mutant background for the nonsense-mediated decay pathway, which is not ideal. However, it provides a greatly improved tool to track mRNA by live imaging. Therefore, their improved methodology will certainly contribute to expanding the use of MS2-MCP system in C. elegans.

I have expertise in C. elegans biology and transcription, but I do not have expertise in the imaging system, and therefore, I cannot fully judge the methodology they have used and the quality of the imaging system.

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Reply to the reviewers

Reviewer #1:

In this paper, authors report that radiation, acidic pH, hypoxia, and drugs that interfere with lipid synthesis, all of which affect lipid droplets (LD), also affect the production of small extracellular vesicles (sEVs). In addition, they also report that LD content and sEV secretion are also modulated in CR-CSCs. Authors conclude that sEV formation and secretion is directly linked to LDs, and that their studies may open the way to new clinical perspectives. However, some important issues need to be addressed before the paper can be considered for publication.

My main concern is that the notion that LDs and sEVs are linked remains vague. Do cells contain more LDs and secrete more sEVs because these two pathways are selectively up-regulated via some mechanisms that controls both pathways in a concerted manner? Or do cells with more LDs and more sEVs also contain more of everything, perhaps as a result of metabolic activity?

We appreciate the Reviewer's observations. Indeed, this comment represents the main pillar of the entire manuscript. We have attempted to uncover the molecular mechanism behind this novel and intriguing organelle connection. First of all, we have adapted the manuscript emphasizing that the LD – sEV connection might be direct or indirect. Our omic data suggested that some proteins belonging to the RAB family, mainly Rab18, Rab7a and Rab5c, could play a pivotal role in the LDs-sEVs axis. To strengthen those results, we have performed additional experiments by silencing the expression of the three candidate Rabs. Rab5c seems to be a good candidate to modulate the LD-sEV connection. We believe that Rab5c is not the only contributor to the LD-sEV connection but is part of a whole set of different elements that regulate this axis. However, it is quite challenging to rule out other molecular candidates as co-contributors to this phenomenon, especially when considering cellular metabolic pathways.

We recognize that external stimuli, such as radiation, pH, and lipid-interfering drugs, may exert their effects on other cellular organelles, even though we have strived to analyze each individual phenomenon rigorously. We are confident that our work lays the foundation for further research in the field.

A direct corollary of this issue is whether increased sEV secretion reflects more endosomes and lysosomes (e.g. LysoTracker-positive compartments) or whether sEV secretion is selectively up-regulated.

Thanks to the Reviewer’ suggestion, we have analyzed both the lysosome and endosome contents in our experimental cell systems. These data are now included in the manuscript in Figure S8. We have observed that it is unlikely that lysosomes are directly involved in the LD – sEV connection. However, the expression of Rab7a, a regulator of the late endosomal pathway, correlated with the LD content of the cells and their sEV release. Therefore, the endosomal pathway might be a good candidate to contribute to this LD – sEV connection.

At one point, authors argue that cells that secrete more sEVs also contain more MVBs, but this issue remains elusive. To what extent is the increase in LDs and sEVS correlated in particular with an increase in endosome-lysosomes, and ER-Golgi (LDs originate from the ER)?

We thank the Reviewer for this comment. We agree that the analyses of sEVs secreted in the media might not reflect the MVB content in the cells. However, two experiments, one on Panc01 cells and another one on MCF7 cells, showed that the number of MVBs, assessed by confocal microscopy using CD63 staining (MCF7) or CD63 and Alix plasmids (PANC-01), was directly correlated with the number of released sEVs in the media (Figure Fig S3C and 4J).

In addition, we included additional experiments assessing the lysosome content in HT29 LDHigh and LDLowcells. Hereby, we confirmed that HT29 LDHigh cells showed a higher LD content than HT29 LDLow cells. Inversely, by studying the lysotracker area per cell, we showed that HT29 LDLow population has a higher lysosomal content as compared to their counterpart, HT29 LDHigh cells (test = Wilcoxon rank sum test with continuity correction_ W = 85127, p-value = 7.255e-07 for LDs and W = 49321, p-value = 1.14e-11 for Lysotracker). However, we could not demonstrate a clear correlation between the number of LDs in the cell and the lysotracker signal.

Finally, we have also studied the expression of GM130, a Golgi-shaping protein (Ref. 1) and Rab7, a late-endocytic protein (Fig S8C). While the expression of Rab7 (endosome) seemed to correlate with the LD and sEV contents, the expression of GM130 (Golgi) gave back no coherent results. Indeed, it was inversely correlated to the LD and sEV amount, in accordance with what was already reported elsewhere (Ref 2 and 3)

• Nakamura N. Emerging new roles of GM130, a cis-Golgi matrix protein, in higher order cell functions. J Pharmacol Sci. (2010) 112:255–64. Doi: 10.1254/jphs.09R03CR
• Lydia-Ann L.S. Harris, James R. Skinner, Trevor M. Shew, Nada A. Abumrad, Nathan E. Wolins. _Monoacylglycerol disrupts Golgi structure and perilipin 2 association with lipid droplets.___Doi.org/10.1101/2021.07.09.451829
• Alvin Kamili, Nuruliza Roslan, Sarah Frost, Laurence C. Cantrill, Dongwei Wang, Austin Della-Franca, Robert K. Bright, Guy E. Groblewski, Beate K. Straub, Andrew J. Hoy, Yuyan Chen, Jennifer A. Byrne; TPD52 expression increases neutral lipid storage within cultured cells. J Cell Sci 1 September 2015; 128 (17): 3223–3238. Doi: 10.1242/jcs.167692

Authors conclude that the data with lipid inhibitors strengthen the connection between LDs and sEVs (Fig 2 and S2). However, is this regulation selective, or does it merely reflect the general effect of these inhibitors on membrane-related processes? The same comment applies to the role of iron metabolism after knockdown of ferritin heavy chain (Fig 3 and S3), acidic pH and X-ray radiation (Fig 4 and S4).

We thank the Reviewer for the interesting observation. As previously mentioned, we cannot rule out other potential contributors to the LDs-sEVs connection upon lipid inhibitor treatments and/or the others external stimuli applied to our cell systems.

The data presented in this manuscript merely represent a novel and unexplored (at least so far) organelle connection, direct or indirect, with a broad clinical implication. As the membrane-related processes (such as Endosomes, Golgi apparatus, Exosome (sEV) pathway, Lysosomes and Autophagosome) are all interconnected, in our opinion, it might be quite challenging to make such a definitive statement.

Such assertion would require extensive further investigation to relate each organelle to the LDs and/or sEVs. However, with our research, we hope to open the door to a new era of investigations regarding the sEV – LDs connection.

OTHER COMMENTS

1) Which cell line is used for sEV analysis (markers vs contaminants (Fig S1B)? In any case, the data should be shown for both cell types.

Our method to isolate sEVs is a standardized method that was already published by our group and collaborators in 2020 (M. Bordas, et al., Optimized Protocol for Isolation of Small Extracellular Vesicles from Human and Murine Lymphoid Tissues. Int J Mol Sci (2020) https:/doi.org/10.3390/ijms21155586.). This protocol was validated on human and mouse tissues, much more complex samples than cell culture supernatant.

Figure S1C was modified, as requested by the Reviewer, including new data for HT29, Panc01 and MCF7 cell lines to broaden the panel. Those results confirmed the good purity of sEV samples isolated from cell culture supernatant.

2) The Tsg101 blot is not impressive (Fig S1B): the difference between cells and sEVs is not easy to see. It would be nice if blots were quantified.

Indeed, the signal obtained for TSG101 for sEVs derived from Panc01 cell line is quite weak. It is important to remember that not all sEV markers are highly expressed in all cell lines and their derived sEVs. Some cell line-derived sEVs show a low or high expression of the diverse sEV markers. To answer the Reviewer #1’s comment, we quantified the expression of TSG101 in Panc01-derived sEVs. The quantification showed that TSG101 is 6.8 times more expressed on Panc01-dervied sEVs as compared to the cell line. However, since the expression is quite low, this quantification should be taken with some caution.

In light of the Reviewer ‘comment, we have performed the Western Blot analysis on other cell lines (HT29 and MCF7), and we have replaced TSG101 marker with CD9 marker (Figure S1C).

3) From Fig 1B it cannot be concluded that the size of sEVs ranges from 30 to 200nm: the micrograph only shows a few structures.

We appreciate the Reviewer's comment and have attempted to provide more clarity. Firstly, we want to highlight that TEM micrographs of sEVs typically show the donut shape, a unique feature of sEVs imaged with TEM, as well as a size range. In Figure 1B micrograph, the sEV size is approximately 100 nm. The size distribution of LoVo and HT29-derived sEVs can be observed from the NTA size measurements in Figure S1B. Indeed, the peak size is 148 nm for LoVo-derived sEVs and 135 nm for HT29, which aligns with the sEV sizes presented in Figure 1B. We have also included multiple micrographs here under. As the number of Supplementary Figures is already large, we have decided to not include those micrographs in the manuscript. The average size of LoVo-derived sEVs, based on TEM micrograph analysis, was 94 ± 41.10 nm, while the average size of HT29-derived sEVs was 76.41 ± 44.22 nm. The size discrepancy between the two methods (NTA versus TEM) can be ascribed to the dehydration step required for TEM, which results in a reduction of the actual sEV size.

4) HT29 cells contain far more LDs than LoVo cells (Fig 1A). Similarly, sEV proteins (CD63, CD81, CD9, Hsc-70) are more abundant in HT29 sEVs than in LoVo sEVs (Fig 1D). However, the sEV preparation from HT29 cells contains only approx. 50% more total protein than LoVo sEVs (Fig S1D-E). Are sEVs prepared from LoVo cells far more contaminated with cell debris etc.. than sEV fractions from HT29 cells?

We are confident that our EV isolation method allows us to achieve high yield and excellent purity. It is possible that a lower number of sEVs in samples may lead to increased protein contamination during ultracentrifugation. However, size exclusion chromatography should minimize this protein contamination. It is important to note that the NTA method is significantly more sensitive and accurate than Qubit protein quantification. Consequently, protein concentration and particle concentration should not be directly compared.

5) LD staining should be shown for the corresponding populations of cells with high/low CD63 (Fig 1E). Cells in culture can be somewhat heterogeneous, but the difference between low and high CD63 is quite extreme (Fig 1E). Is such high heterogeneity also observed with other proteins of the endocytic and biosynthetic pathways? Authors conclude that cells containing high CD63 levels also contain more MVBs (Fig 1E): are all late endosomal proteins (e.g. LAMP1, RAB7) upregulated in cells with high CD63?

We thank the Reviewer for this comment, and we totally agree with the Reviewer that it would be better to have the LD and CD63 staining on the same images. Unfortunately, the staining for CD63 on LD540-sorted HT29 cells requires a permeabilization step that interferes with the cellular lipid part and could therefore negatively affect the LD imaging by confocal microscopy. To prove that the HT29 LDHigh and HT29 LDLowcontain high and low LD amount respectively, we sorted HT29 cells based on the LD content and, soon after, we observed them at the confocal microscopy. We thus added new images in Figure S1F, corresponding to the LD fluorescence detection. The readers will also appreciate the explanation regarding the inability of observing both LDs and CD63 staining on the same confocal images under the line 165 – 166:

“As the staining for CD63 required a permeabilization step, and therefore lipid digestion, it was not possible to assess both LDs and CD+MVBs on the same micrographs “.

In addition, we have added confocal images representing HT29 cells sorted based on their LD content and stained with Hoechst and Lysotracker. A quantification of the Lysotracker fluorescence per cell and the correlation with the number of LDs can also be appreciated in Figure S8A-B.

Finally, we performed Western Blot analysis to examine Rab7a expression under various conditions described in our manuscript (Figure S8C). In general, Rab7 expression corresponded with LD content, indicating that cells with high LD content exhibited higher Rab7 expression, while cells with low LD amount showed lower Rab7 expression, except for Triacsin-C. The Reviewer can now appreciate the quantification in the graphs provided below (not included in the manuscript).

Regarding the heterogeneity of LDs, CD63+MVBs, or lysotracker among the cell population, we have indeed noticed heterogeneity observable in these three types of staining in HT29, particularly in the HT29 LDHighpopulation.

6) Inhibitors of lipid synthesis reduce LD formation (Fig 2B), sEV production and CD63 / CD81/ CD9 secretion (Fig2C-D, Fig S2B). Are the cellular levels of these (and other endosomal) proteins also reduced after inhibitor treatment? Does the stimulation of LD formation with oleic acid also stimulate CD63 synthesis and sEV production?

We thank the Reviewer for this very interesting comment. To answer this question, we have added a supplementary figure (Figure S2A, S2B) showing the cellular expression of CD63 upon LD inhibition or stimulation.

During the planning of our experiments, we discussed about the possibility of using oleic acid to induce the formation of Lipid Droplets, which was ultimately not done. This is because the use of oleic acid would have more strongly stimulated the triglyceride pathway, as extensively discussed elsewhere (Mejhert N. et al., The lipid droplet knowledge portal: a resource for systematic analyses of lipid droplet biology, Developmental Cell, 2022). Since Lipid Droplets are made by cholesterol esters and triglycerides, we preferred to use other stimuli (hypoxia, radiation), all of them already discussed in literature, to induce both pathways simultaneously, resulting in the Lipid Droplet formation/induction.

7) It seems that pH and irradiation increase sEV markers far more significantly (Fig 4 B-C and Fig S4A-E) than FTH1 depletion decreases sEV markers (Fig 3 D-E). In fact, authors mention that they cannot exclude a contamination of sEVs with small apoptotic / autophagic vesicles after irradiation (Fig 4). To facilitate comparison, it would be nice to also show the number of secreted particles per cell (like after FTH1 depletion Fig 3D), as well as the distribution of possible contaminants (e.g. Fig S1). Also, authors state that the increase in the number CD63+ MVBs after irradiation is shown, but this is not the case.

We apologize to the Reviewer because, in fact, one figure was missing (Figure 4). We have rectified this by increasing the quality of Figure 4 and have added representative images for each acquisition of the number of MVBs, either positive for CD63 or Alix, in transfected Panc01 cells X-ray irradiated (8 Gy) or not (0Gy). In addition, a similar experiment was performed in MCF7 cells transduced with shRNA or shFTH1. CD63+ MVBs were assessed in both cell line and the number of CD63+ puncta (MVBs) were quantified by ImageJ. The results, although not significative, illustrated a trend for MCF7 shFTH1 to contain less CD63+ MVBs than MCF7 shRNA. Furthermore, the quantification of sEVs released in the conditioned media was performed in three independent experiments and demonstrated that significantly less particles (sEVs) were released by MCF7 shFTH1 than MCF7 shRNA.

8) Are the proteomic data (Fig 6) with LDlow and LDhigh cells obtained after cell sorting, as in Fig 1E? Did authors compare the proteome of LoVo and HT29 sEVs? How do the protein profiles (in particular proteins involved in lipid metabolism) obtained under different conditions compare with each other, in particular after irradiation (Fig4N) and knockdown of ferritin heavy chain (Fig 3, Fig S3)? It would also be interesting to compare these data with the data obtained in CR-CSCs culture under hypoxia (Fig S5). Are common proteins involved in sEV production and LD biosynthesis identified in the analysis of these biological processes? Is there a common set of proteins/genes revealed by this analysis, which may potentially control sEV production and LD biosynthesis?

We thank the reviewer for this interesting comment.

Proteomic analyses have been performed on the following conditions:

• Panc01 (0 Gy – 6 Gy – 8 Gy) for sEV samples
• MCF7 (shFTH1 and MCF7 shRNA)
• MCF7 (0 Gy and 6 Gy)
• MCF7 (Normoxia and Hypoxia)
• H460 (0 Gy and 6 Gy)
• H460 (Normoxia and Hypoxia)

RNA sequencing was performed on the following conditions:

• CR-CSCs (#4, #8, #21)

Based on all those data, we have analyzed the sEV pathway and how this pathway was modulated in the conditions with high LD content and low LD content. We therefore came up with several proteins, presented in Figure S7. Based on this analysis, we have decided to further investigate the role of RAB18, RAB5c and RAB7a in the connection between LDs and sEVs. Those additional results can be found in Figure 6 and Figure S7A (originally Figure 6). We have found that RAB5c, but not RAB7a or RAB18, seems to be a good candidate to intervene in the LD – sEV connection.

Minor comments

1) Some parts of the text are still a bit rough, and should be read and corrected carefully. For example: i) isn't it obvious that a common source of lipids builds up the membrane of sEVS, much like any other membrane (line 90, p.2); ii) what does this sentence mean: "LD have been considered as mere fat storage organelles for a long time, although important evidence could be traced back to the early 1960's". Important evidence for what? iii) why is the acronym AdExo used? iv) (line 138) the text should probably be "sEVs released during 72h were studied" and not "released sEVs were studied ... 72 h after seeding".

We apologize to the Reviewer if some parts of the paper were a bit rough. We have re-read the entire manuscript and corrected all the parts that needed revision work.

2) The captions are far too small in most figures and diagrams (for example X and Y axis in Fig 1C-D, text in Fig 1E; Fig S1; Fig 3C proteins in the heatmap).

We agree with the Reviewer. All images and their captions were properly revised.

3) The color code for LoVO and HT29 cells is reversed in Fig S1D-E

The mistake was corrected.

4) In Fig 1D, I cannot see CD81 in the LoVo blot.

In the image below, it is possible to see the LoVo blot.

5) Wording is not adequate in following sentences: "62.7% of proteins related to the exosomal pathway are downregulated in MCF7 shFTH1 cells" (line 233) and a few lines below: ".. the expression of almost all exosomal markers was downregulated in MCF7 shFTH1 cells" (line 239). Does 62.7% represent all proteins?

We apologize to the reviewer for the mistake. We rephrased this sentence.

6) In Fig 3E authors compare sEV markers secreted by cells treated with shFTH1 or control shRNA. The Anx5 and CD63 blots are not very convincing (quantification would be helpful).

We apologize to the Reviewer for this issue. These Western Blot analyses were performed only once, therefore a quantification in the manuscript would not be relevant. However, we report here the results of the quantification. The expression of Annexin V was 1.58 times higher in MCF7 shRNA than MCF7 shFTH1, while the expression of CD63 was 1.34 time higher in MCF shRNA as compared to MCF7 shFTH1.

7) The micrographs in Fig 4L are too small: gold particles cannot be seen, even in the high magnification views.

We thank the Reviewer for her/his comment. We have moved the micrograph and the quantification histogram to the Figure S6. Now, it is possible to discriminate easily gold nanoparticles.

8) The micrographs showing ALIX and CD63 (Fig 4J) in irradiated and unirradiated Panc01 cells should be shown for comparison.

We followed the Reviewer’ suggestion as it is possible to note in the Figure below.

Reviewer #2:

This manuscript describes a relationship between lipid droplet presence in cells and small EV secretion. First, correlations are done between number of lipid droplets and numbers of EVs secreted. Then chemical inhibitors of lipid droplet biosynthesis pathways were shown to reduce small EV secretion. Then various processes known to target lipid droplets, including iron metabolism, irradiation, hypoxia, low pH are used to show concordant effects on lipid droplets and small EV secretion. Proteomic analysis of EVs and cells subjected to some of the treatments are also performed. Overall, it is an interesting line of investigation and the data overall seem solid. Several flaws exist, which can probably be fixed. These include the use of different cell lines for different experiments. It makes it a bit difficult to connect everything together. It could be fixed by adding some extra cell lines to some experiments - for example taking the MCF7 and Panc-01 cells for which proteomics was performed and redoing some of the correlative and causative experiments from Figs 1 and 2.

We appreciate the Reviewer's insightful observation. Following her/his suggestion, we have conducted additional experiments on MCF7, H460 and PANC-01 cell lines to enhance data consistency and facilitate a smoother transition between different sections of the paper.

It also would be good to have some more direct evidence of the connection between lipid droplets and EV secretion - one could argue that this was already done in Fig 2 with the chemical inhibitors, I wonder if there is a genetic way to do it too?

We totally agree with the Reviewer. Indeed, starting from our proteomic data we highlighted some genes belonging to the RAB family as potential candidates to interfere with the LD – sEV connection. The Reviewer can now appreciate in Figure 6 and Figure S7, the results from the additional experiments we carried out on RAB5c, RAB7a and RAB18 silencing in HT29 cells. The former Figure 6 has been moved in the Supplementary part (Figure S7).

Some tightening up of the writing (especially the Discussion) and the resolution of the figures would also improve the manuscript.

We apologize to the Reviewer for this issue. We have now re-prepared all Figures by increasing their resolution, as well as reviewing the entire manuscript with the aim of making the reading smoother and simpler.

Overall, it is a nice piece of work but there are many minor things to be fixed.<br /> <br /> Specific Comments:

The sentence in the Introduction: "The non-endosomal pathway generates sEVs devoid of<br /> CD63, CD81 and CD9 or sEVs enriched in ECM and serum-derived factors (7)." is not well-supported and should be removed. The idea that you can classify membrane of origin based on markers has not been proven, but rather assumed.

We agree with the Reviewer. We have rephrased the sentence.

We thank the Reviewer for this comment. In response to this, we have generated correlation graphs for several of our experiments:

• HT29 (CTL – Triacsin-C - PF-06424439) in Figure 2E
• PANC-01 (CTL – 2 – 4 – 6 – 8 Gy) in Figure 4K
• CR-CSCs (#4, #8, #21) in Figure 5E

The Method used for EV purification should be stated in the Results rather than referring to a reference and a Supplemental Figure (S1A) that is too low of a resolution to see.

Our method to isolate sEVs is a standardized methods that was already published by our group and collaborators in 2020 (M. Bordas, et al., Optimized Protocol for Isolation of Small Extracellular Vesicles from Human and Murine Lymphoid Tissues. Int J Mol Sci (2020) https:/doi.org/10.3390/ijms21155586.). This protocol was validated on human and mouse tissues, much more complex samples than cell culture supernatant.

In regard to the Reviewer’s comment, we have added a better description of the protocol in the Results part, referring to the Material and Method. For this reason, we decided to keep the sEV protocol in the SI section. We apologize for the low quality of the Figure S1. In agreement with the Reviewer suggestion, we have modified the image by increasing its quality.

Fig 1B would be better to have an image in which the EVs are not aggregated.

We thank the Reviewer for this comment and have modified the Figure accordingly.

Fig 3 is interesting but jumps cell lines. For better continuity, some of the experiments from Figs 1 and 2 should be repeated in the MCF7 cells to connect with the proteomics.

In agreement with the Reviewer’ comment, we decided to perform additional experiment on MCF7, using Triacsin-C. The Reviewer can now appreciate the results in Figure 2F, Figure 2G and Figure S2E.

Fig 3C is too low resolution to read, please export at higher resolution.

We are sorry for the low-quality Figure. We have modified the image accordingly.

Please provide all the raw proteomics data as a supplementary spreadsheet.

We have provided all the raw data regarding our proteomic analyses.

Fig 4 panels are low resolution

We apologize for the low-resolution Figure. We have modified the figure by increasing the quality.

Fig 4 again adds new cell lines with H460 and Panc-01

We thank the reviewer for this comment. In this regard, we have performed additional experiment:

• Western Blot: comparison cellular and exosomal markers (Figure S1C)
• MCF7 (CTL - Triacsin) (Figure 2F, Figure 2G and Figure S2E)
• Western Blot: analysis of RAB7a, GM130

The images corresponding to 4J should be shown in a Supp Figure somewhere

We thank the reviewer for pointing out this oversight. We have added the confocal images corresponding to the Figure 4J below the quantification.

The statements: "In addition, the exosomal nature of Panc01-derived vesicles was demonstrated by an analysis of CD63+ or Alix+ multivesicular bodies (MVBs) in unirradiated (0 Gy) or irradiated (8 Gy) pancreatic cancer cells (Fig 4J). Moreover, we confirmed a clear correlation between cellular LD content and sEV biogenesis, as represented in Fig 4K." are overly conclusive. For 4J, one can make a statement about the MVBs but not the EVs as that's not what was measured there. Likewise for 4K, what was measured was how many EVs were released not how many were formed. While the data are suggestive of alteration of exosome biogenesis, they are not conclusive.

We agree with the reviewer and have performed the necessary changes in the manuscript. The reviewer can see the changes under the lines 282 – 284:

“In addition, the analysis of CD63+ or Alix+ multivesicular bodies (MVBs) in unirradiated (0 Gy) or irradiated (8 Gy) pancreatic cancer cells revealed an increased number of MVBs after irradiation (Figure 4J).”

Western blot is always capitalized by convention - Western not western.

We have corrected it accordingly.

Fig 5A is too small and low resolution - suggest eliminating and just put info in methods.

We are sorry for the low-resolution image. We have followed the Reviewer suggestion. The graphical method has been now moved to the Supplementary Figure S6.

Fig 5G, many of the genes shown are frequently EV cargoes but most not involved in exosome biogenesis - not sure where the label of Exosome pathway came from but it is not very compelling. Only ANXA2, Arf6, and Rab5C seem related and they are barely elevated.

We completely agree with the Reviewer's comment. As a result, we have revised the heatmap title to "Exosomal Cargoes and Pathways" instead of "Exosomal Pathway".

Most main figures and all supplementary figures are extremely low res - please fix.

We are very sorry for the low-quality figures. We have revised all Figures (main text and SI) by increasing their quality.

Fig 6 is first mentioned in the Discussion - it should be described in the Results before that (or alternatively removed).

We agree with the Reviewer. Our initial idea was to mention perspectives of analyses that could be carried ulteriorly. Nevertheless, we have performed additional experiments in order to get insight on the mechanism involved in the LD – sEV connection. Indeed, based on our proteomic data, we have analyzed the sEV pathway and how this pathway was modulated in the conditions with high LD content and low LD content. We therefore came up with several proteins, presented in Figure S7A (originally Figure 6). Based on this analysis, we have decided to further investigate the role of RAB18, RAB5c and RAB7a in the connection between LDs and sEVs. Those additional results can be found in Figure 6 and Figure S7 in the Results section. We have found that RAB5c, but not RAB7a or RAB18, seems to be a good candidate to intervene in the LD – sEV connection.

Table S1, also first mentioned in the Discussion, is missing. Either describe in the Results section or remove the callout to it.

Our apologies for that. The Table S1 has been now mentioned in the Results section and has been properly uploaded.

The discussion is too dense with too many trains of thought, often many different directions in the same paragraph. It needs to be streamlined, with a central thought for each paragraph and good transitions between the paragraphs.

We apologize to the Reviewer if the Discussion part was a bit confusing. We rewrote the paragraph, streamlining it and making the transitions between its paragraphs smoother.

Reviewer #2 (Significance (Required)):

<br /> Strengths of this manuscript are the interesting connection between lipid droplets and exosomes and the number of experiments to address it.

<br /> Limitations: use of different cell lines for different figures, overall descriptive nature with regard to direct demonstration of connection to lipid droplets -- it's kind of done in Fig 2, but could be possibly bolstered.

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Referee #2

Evidence, reproducibility and clarity

This manuscript describes a relationship between lipid droplet presence in cells and small EV secretion. First, correlations are done between number of lipid droplets and numbers of EVs secreted. Then chemical inhibitors of lipid droplet biosynthesis pathways were shown to reduce small EV secretion. Then various processes known to target lipid droplets, including iron metabolism, irradiation, hypoxia, low pH are used to show concordant effects on lipid droplets and small EV secretion. Proteomic analysis of EVs and cells subjected to some of the treatments are also performed. Overall, it is an interesting line of investigation and the data overall seem solid. Several flaws exist, which can probably be fixed. These include the use of different cell lines for different experiments. It makes it a bit difficult to connect everything together. It could be fixed by adding some extra cell lines to some experiments - for example taking the MCF7 and Panc-01 cells for which proteomics was performed and redoing some of the correlative and causative experiments from Figs 1 and 2. It also would be good to have some more direct evidence of the connection between lipid droplets and EV secretion - one could argue that this was already done in Fig 2 with the chemical inhibitors, I wonder if there is a genetic way to do it too? Some tightening up of the writing (especially the Discussion) and the resolution of the figures would also improve the manuscript. Overall, it is a nice piece of work but there are many minor things to be fixed.

Specific Comments:

The sentence in the Introduction: "The non-endosomal pathway generates sEVs devoid of<br /> CD63, CD81 and CD9 or sEVs enriched in ECM and serum-derived factors (7)." is not well-supported and should be removed. The idea that you can classify membrane of origin based on markers has not been proven, but rather assumed.

Fig 1A and B - to better support the idea of a correlation between LD formation and EV release, more than two cell lines should be used and a linear correlation plot with R2 value shown. Likewise, it would be very interesting to see whether there is really a correlation between LD content and CD63-endosome positivity in a similar manner, given the results in Fig 1E. Also, it would be good to see LD and CD63 in the same cells for Fig 1E from the sorted populations.

The Method used for EV purification should be stated in the Results rather than referring to a reference and a Supplemental Figure (S1A) that is too low of a resolution to see.

Fig 1B would be better to have an image in which the EVs are not aggregated.

Fig 3 is interesting but jumps cell lines. For better continuity, some of the experiments from Figs 1 and 2 should be repeated in the MCF7 cells to connect with the proteomics.

Fig 3C is too low resolution to read, please export at higher resolution.

Please provide all the raw proteomics data as a supplementary spreadsheet

Fig 4 panels are low resolution

Fig 4 again adds new cell lines with H460 and Panc-01

The images corresponding to 4J should be shown in a Supp Figure somewhere

The statements: "In addition, the exosomal nature of Panc01-derived vesicles was demonstrated by an analysis of CD63+ or Alix+ multivesicular bodies (MVBs) in unirradiated (0 Gy) or irradiated (8 Gy) pancreatic cancer cells (Fig 4J). Moreover, we confirmed a clear correlation between cellular LD content and sEV biogenesis, as represented in Fig 4K." are overly conclusive. For 4J, one can make a statement about the MVBs but not the EVs as that's not what was measured there. Likewise for 4K, what was measured was how many EVs were released not how many were formed. While the data are suggestive of alteration of exosome biogenesis, they are not conclusive.

Western blot is always capitalized by convention - Western not western.

Fig 5A is too small and low resolution - suggest eliminating and just put info in methods.

Fig 5G, many of the genes shown are frequently EV cargoes but most not involved in exosome biogenesis - not sure where the label of Exosome pathway came from but it is not very compelling. Only ANXA2, Arf6, and Rab5C seem related and they are barely elevated.

Most main figures and all supplementary figures are extremely low res - please fix.

Fig 6 is first mentioned in the Discussion - it should be described in the Results before that (or alternatively removed).

Table S1, also first mentioned in the Discussion, is missing. Either describe in the Results section or remove the callout to it.

The discussion is too dense with too many trains of thought, often many different directions in the same paragraph. It needs to be streamlined, with a central thought for each paragraph and good transitions between the paragraphs.

Significance

Strengths of this manuscript are the interesting connection between lipid droplets and exosomes and the number of experiments to address it.

Limitations: use of different cell lines for different figures, overall descriptive nature with regard to direct demonstration of connection to lipid droplets -- it's kind of done in Fig 2, but could be possibly bolstered.

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Referee #1

Evidence, reproducibility and clarity

In this paper, authors report that radiation, acidic pH, hypoxia, and drugs that interfere with lipid synthesis, all of which affect lipid droplets (LD), also affect the production of small extracellular vesicles (sEVs). In addition, they also report that LD content and sEV secretion are also modulated in CR-CSCs. Authors conclude that sEV formation and secretion is directly linked to LDs, and that their studies may open the way to new clinical perspectives. However, some important issues need to be addressed before the paper can be considered for publication.

My main concern is that the notion that LDs and sEVs are linked remains vague. Do cells contain more LDs and secrete more sEVs because these two pathways are selectively up-regulated via some mechanism that controls both pathways in a concerted manner? Or do cells with more LDs and more sEVs also contain more of everything, perhaps as a result of metabolic activity? A direct corollary of this issue is whether increased sEV secretion reflects more endosomes and lysosomes (e.g. LysoTracker-positive compartments) or whether sEV secretion is selectively up-regulated. At one point, authors argue that cells that secrete more sEVs also contain more MVBs, but this issue remains elusive. To what extent is the increase in LDs and sEVS correlated in particular with an increase in endosome-lysosomes, and ER-Golgi (LDs originate from the ER)? Authors conclude that the data with lipid inhibitors strengthen the connection between LDs and sEVs (Fig 2 and S2). However, is this regulation selective, or does it merely reflect the general effect of these inhibitors on membrane-related processes? The same comment applies to the role of iron metabolism after knockdown of ferritin heavy chain (Fig 3 and S3), acidic pH and X-ray radiation (Fig 4 and S4)

Other comments

1. Which cell line is used for sEV analysis (markers vs contaminants (Fig S1B)? In any case, the data should be shown for both cell types.
2. The Tsg101 blot is not impressive (Fig S1B): the difference between cells and sEVs is not easy to see. It would be nice if blots were quantified.
3. From Fig 1B it cannot be concluded that the size of sEVs ranges from 30 to 200nm: the micrograph only shows a few structures.
4. HT29 cells contain far more LDs than LoVo cells (Fig 1A). Similarly, sEV proteins (CD63, CD81, CD9, Hsc-70) are more abundant in HT29 sEVS than in LoVo sEVs (Fig 1D). However, the sEV preparation from HT29 cells contains only approx. 50% more total protein than LoVo sEVs (Fig S1D-E). Are sEVs prepared from LoVo cells far more contaminated with cell debris etc.. than sEV fractions from HT29 cells?
5. LD staining should be shown for the corresponding populations of cells with high/low CD63 (Fig 1E). Cells in culture can be somewhat heterogeneous, but the difference between low and high CD63 is quite extreme (Fig 1E). Is such high heterogeneity also observed with other proteins of the endocytic and biosynthetic pathways? Authors conclude that cells containing high CD63 levels also contain more MVBs (Fig 1E): are all late endosomal proteins (e.g. LAMP1, RAB7) upregulated in cells with high CD63?
6. Inhibitors of lipid synthesis reduce LD formation (Fig 2B), sEV production and CD63 / CD81/ CD9 secretion (Fig2C-D, Fig S2B). Are the cellular levels of these (and other endosomal) proteins also reduced after inhibitor treatment? Does the stimulation of LD formation with oleic acid also stimulate CD63 synthesis and sEV production?
7. It seems that pH and irradiation increase sEV markers far more significantly (Fig 4 B-C and Fig S4A-E) than FTH1 depletion decreases sEV markers (Fig 3 D-E). In fact, authors mention that they cannot exclude a contamination of sEVs with small apoptotic / autophagic vesicles after irradiation (Fig 4). To facilitate comparison, it would be nice to also show the number of secreted particles per cell (like after FTH1 depletion Fig 3D), as well as the distribution of possible contaminants (e.g. Fig S1). Also, authors state that the increase in the number CD63+ MVBs after irradiation is shown, but this is not the case.
8. Are the proteomic data (Fig 6) with LDlow and LDhigh cells obtained after cell sorting, as in Fig 1E? Did authors compare the proteome of LoVo and HT29 sEVs? How do the protein profiles (in particular proteins involved in lipid metabolism) obtained under different conditions compare with each other, in particular after irradiation (Fig4N) and knockdown of ferritin heavy chain (Fig 3, Fig S3)? It would also be interesting to compare these data with the data obtained in CR-CSCs culture under hypoxia (Fig S5). Are common proteins involved in sEV production and LD biosynthesis identified in the analysis of these biological processes? Is there a common set of proteins/genes revealed by this analysis, which may potentially control sEV production and LD biosynthesis?

Minor comments

1. Some parts of the text are still a bit rough, and should be read and corrected carefully. For example: i) isn't it obvious that a common source of lipids builds up the membrane of sEVS, much like any other membrane (line 90, p.2); ii) what does this sentence mean: "LD have been considered as mere fat storage organelles for a long time, although important evidence could be traced back to the early 1960's". Important evidence for what? iii) why is the acronym AdExo used? iv) (line 138) the text should probably be "sEVs released during 72h were studied" and not "released sEVs were studied ... 72 h after seeding".
2. The captions are far too small in most figures and diagrams (for example x and Y axis in Fig 1C-D, text in Fig 1E; Fig S1; Fig 3C proteins in the heatmap).
3. The color code for LoVO and HT29 cells is reversed in Fig S1D-E
4. In Fig 1D, I cannot see CD81 in the LoVo blot.
5. Wording is not adequate in following sentences: "62.7% of proteins related to the exosomal pathway are downregulated in MCF7 shFTH1 cells" (line 233) and a few lines below: ".. the expression of almost all exosomal markers was downregulated in MCF7 shFTH1 cells" (line 239). Does 62.7% represent all proteins?
6. In Fig 3E authors compare sEV markers secreted by cells treated with shFTH1 or control shRNA. The Anx5 and CD63 blots are not very convincing (quantification would be helpful).
7. The micrographs in Fig 4L are too small: gold particles cannot be seen, even in the high magnification views.
8. The micrographs showing ALIX and CD63 (Fig 4J) in irradiated and unirradiated Panc01 cells should be shown for comparison.

Significance

The topic of the paper is clearly interesting, since the mechanisms that regulate sEV formation and secretion are not fully understood and since the notion that their fate is linked to LDs is potentially exciting.

My expertise: subcellular organization, endocytosis, membrane traffic, organelle biogenesis

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Reply to the reviewers

We thank the reviewers for their thoughtful comments. Here we provide a point-by-point response to their reviews. All additional experiments that are present in the revised manuscript, or that we plan to include in the final manuscript, are numbered.

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

The concept introduced by this paper is exciting and novel. However, the current paucity of presented data can lead to incorrect interpretations of the findings and speculations that might not hold true after a more rigorous assessment of the observed phenomenon.

The premise of this study builds upon an interaction between the PAXT complex and nuclear YTH domain containing proteins. However, figures 1B and C should be improved. The interacting band for the ZFC3H1 presented in panel B does not seem to match the size of the construct used in panel C. Is the Flag version of ZFC3H1 expressing a smaller isoform for this protein? __

The reviewer is correct in that endogenous ZFC3H1 (which migrates at 250kD with a minor band at 150kD, see Figure 1B in the initial manuscript) appears to differ from the FLAG-tagged construct as expressed from a plasmid transfected in HEK293 cells (which migrates as two bands at 180kD and 200kD, see Figure 1C in the initial manuscript). For the endogenous protein, the predicted molecular weight is 226kD and the 250kD band disappears when cells are transduced with lentivirus containing shRNAs against ZFC3H1 (see Figure 4A in the initial manuscript), indicating that it is the correct protein. Both the 250kD endogenous protein (*) and the 200kD overexpressed protein (**) in transfected HEK293 and U2OS cells are detected in immunoblots using anti-ZFC3H1 antibodies (see Figure 1 in this document) indicating that the over-expressed protein is indeed ZFC3H1.

[ Figure 1]

_Figure 1. Molecular Weight Size Comparison of Endogenous ZFC3H1 and FLAG-ZFC3H1 (1-1233). _Lysates from HEK293 and U2OS that were either untransfected or transfected with FLAG-ZFC3H1 (1-1233) plasmid. We labelled the bands corresponding to the endogenous ZFC3H1 “*” and FLAG-ZFC3H1 (1-1233) “**”.

We have sequenced the plasmid, and discovered that it contains an additional sequence inserted within the middle of the ZFC3H1 cDNA with a premature stop codon. As such, the version of the protein that is expressed from the plasmid only contains amino acids 1-1233 of the endogenous protein and is missing amino acids 1234-1989. The deleted region only contains TPR repeats, and is not known to interact with any of the well characterized interactors of ZFC3H1 (Wang, Nuc Acid Res 2021, Figure 3). We have renamed this construct FLAG-ZFC3H1 (1-1233). Given these new considerations, our results are consistent with the idea that the N-terminal portion of ZFC3H1 interacts with U1-70K, YTHDC1 and YTHDC2. We will change the text to reflect this.

We are currently in the process of deleting the small insertion to obtain a plasmid that encodes a full length version of human ZFC3H1. For the final manuscript:

Experiment #1) We will repeat the co-immunoprecipitation experiment with the full length FLAG-ZFC3H1 to determine whether it interacts with YTHDC1 and YTHDC2. This will take a few weeks.

__Also, the YTHDC1-2 interaction in panel C is not as convincing considering the negative controls lane show some degree of binding. __

Although the reviewer is correct that there is substantial background binding in the YTHDC1 immunoblot, we disagree with their characterization of the results with the YTHDC2 immunoblot (see Figure 1B-C in the initial manuscript). In the new manuscript we have included:

Experiment #2) A new co-immunoprecipitate of the FLAG-tagged ZFC3H1 (1-1233) from HEK293 cells under more stringent conditions where the background level of YTHDC1 binding to beads is negligible. We have already completed this experiment (see Figure 1D in the revised manuscript).

__Additionally, can the authors test if their RNaseA treatment worked? __

In the new manuscript we have included:

Experiment #3) A new co-immunoprecipitate of FLAG-YTHDC1 immunoblotted for eIF4AIII from HEK293 cell lysates. We find that without RNAse, there is some eIF4AIII in the precipitates but that the levels diminish substantially after RNAse A/T1 treatment. We have already completed this experiment (see Figure 1B in the preliminary revised manuscript).

__Why do you need 18 hours to observe the nuclear export of your modifiable construct when inhibiting METTL3 in figure 3? Is it possible that your observation is secondary to phenotypes these cells develop as a result of blocking METTL3? __

We treated cells for this period of time so that during the expression of the reporter, all of the newly synthesized mRNA is expressed in the absence of m6A methyl transferase activity. For shorter treatment times, it is unclear whether the bulk of the reporter mRNA, which would be synthesized before the treatment, would lose any pre-existing m6A marks, making a negative result hard to interpret. Previously we found that although 50% of intronic polyadenylated (IPA) transcripts from our reporters are rapidly degraded, about 50% are stable and are nuclear retained over extended periods of time (see Lee at al., PLOS ONE 2015; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122743 Figure 3B-G). We believe that the bulk of the reporter mRNA that we are visualizing is stable and accumulates in the nucleus. Given that METTL3-depletion inhibits nuclear retention and that versions of the IPA reporter that lack m6A modification motifs are exported, we think that the most likely interpretation of the 18 hour STM2457 treatment experiments is that the lack of methyltransferase activity had a direct effect, rather than an indirect effect, on nuclear retention. We would be open to performing more experiments if the editors insist, however we ordered STM2457 four weeks ago and it has yet to arrive from Sigma-Millipore. Performing this experiment may substantially delay our ability to resubmit the manuscript in a timely manner.

__Is ALKBH5 nuclear and/or cytoplasmic in the cell system used? __

According to The Human Protein Atlas, ALKBH5 is predominantly nuclear in U2OS cells, with some present in the cytoplasm (https://www.proteinatlas.org/ENSG00000091542-ALKBH5/subcellular#human).

In the revised manuscript we have included:

Experiment #4) Data from subcellular fractionation demonstrating that ALKBH5 is present in both the nucleus and cytoplasm that we have already performed (see Figure 4J in the preliminary revised manuscript).

__Reviewer #1 (Significance (Required)):

The study is highly significant __

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

Summary: In the manuscript by Lee et al. entitled "N-6-methyladenosine (m6A) Promotes the Nuclear Retention of mRNAs with Intact 5'Splice Site Motifs", the authors provide evidence that m6A modifications within specific regions of transcripts can confer nuclear retention. These results are important because they add to our understanding of how m6A modifications can contribute to post-transcriptional regulation. Although the authors do not quite come out and say this, data seem to be accumulating to suggest that the location of the m6A modifications within a given transcript can dictate the functional consequences of those modifications.__

We thank the reviewer for pointing this out. We have included a few sentences in the new preliminary revised manuscript pointing out that the location of the m6A modification in IPA transcripts, with respect to intact 5’SS and poly(A) signals, may play a role in promoting nuclear retention.

__The current work builds on previous findings from these authors identifying factors critical for retention of intronic polyadenylated (IPA) transcripts. The present study identified m6A modification as one of the signals for the retention of such transcripts. The authors use reporters for their analysis and also examine validated endogenous IPA transcripts. The data presented supports the conclusions albeit they show a surprising finding for one of the m6A erasers, ALKBH5. However, there is some controversy over the mechanism by which ALKBH5 functions and whether the m6A mark is truly reversible, so these results may continue to add to this point of view.

Major Comments: One experiment that might add to the argument would be overexpression of Mettl3 as compared to catalytically inactive Mettl3. The prediction would be that the reporter transcript with intact DRACH sequences would be even more retained in the nucleus in a manner that depends on Mettl3 catalytic activity. For some of the data presented, the reporter is already wholly nuclear so no difference could be detected, but in the U2OS cells shown in Figure 2B, it appears that an increase in nuclear localization might be evident. Such an experiment would add an orthogonal approach to demonstrate that the methylation by Mettl3 is required for retention. If such an experiment would work with the endogenous IPA transcripts shown in Figure 4, but these transcripts may already be too nuclear to detect any increase in nuclear retention.

__

We have performed two experiments that try to address this but they gave negative results:

Experiment #5) We have over-expressed wildtype and a methyl transferase mutant FLAG-METTL3 and assessed the nuclear export/retention of ftz-Δi-5’SS mRNA. There was no effect (see Figure 2 in this document).

[Figure 2]

__Figure 2. Over-expression of METTL3 does not increase the nuclear retention of ftz-Δi-5’SS. __U2OS cells were co-transfected with ftz-Δi-5’SS reporter and either FLAG-METTL3 or FLAG-METTL3-D395A, which lacks methyl-transferase activity (Wang, Mol Cell 2016). Cells were fixed, stained for ftz mRNA by fluorescent in situ hybridization and METTL3 using anti-FLAG antibodies. The nuclear and cytoplasmic distribution of ftz mRNA was quantified as described in the manuscript. Note that this is the average of one independent experiment (each bar consisting of the average of at least 50 cells). We plan to repeat this two more times, but we anticipate that these will show the same result.

We could include this negative data as a supplemental figure. We believe that there are two possible reasons for this experimental result. First, as the reviewer points out, the reporter transcripts are already too nuclear to detect any significant change. Second, METTL3 is part of a larger complex that includes several proteins including METTL14, WTAP and potentially other proteins (for example see Covelo-Molares, Nuc Acid Res 2021). We may need to co-express all of these proteins to see an effect.

Experiment #6) We have also expressed versions of ftz-Δi and ftz-Δi-5’SS mRNA with optimized m6A modification (i.e. DRACH) motifs (AGACT) to enhance methylation (“e-m6A-ftz”). We only observed a slight increase in nuclear retention but it is not significant (see Figure 2A,C in the revised manuscript).

Again, this result could be explained by the fact that the reporter is too nuclear to detect any significant increase in retention. We had originally performed this in parallel with the no-m6A-ftz-Δi-5’SS reporters but did not report this negative data in the original manuscript.

__Some rather minor changes to the presentation of the data could enhance the impact of this study.

Specific Comments:

The primary question in this manuscript is comparing reporters with m6A site (intact DRACH sequences) to those without. For this reason, organizing the data to the +/- DRACH sites are adjacent to one another might make the most sense. This point is evident in Figure 1C where perhaps simply changing the order of the bars presented to put the ones directly compared adjacent would be preferable. Then the p-value would compare sets of data directly adjacent to one another. __

We thank the reviewer for this suggestion and we have made these changes to the figures in the preliminary revised manuscript.

__While the authors show representative fields/cells for most assays, they do an excellent job of providing quantitation as well. One exception is Figure 3D, which shows a single cell image for the most key panel (the 5'SS-containing reporter upon Mettl3 depletion). If there is not a field with more cells, the authors could create a montage. __

In the revised manuscript, we have replaced this image with one containing multiple cells expressing the reporter.

__Minor Comments:

Figure presentation:

The text in a number of the figures is VERY small (Figures 1B,1C, and 4A) for example. __

We have fixed this in the new manuscript.

__Figure 3A includes the label "shRNA:" at the top, but these cells are treated with Mettl3 inhibitor and there does not appear to be any shRNA employed, so this seems like a labeling error. __

We have fixed this in the new manuscript.

__In Figure 3C, the immunoblot of Mettl3, there are three bands that all disappear completely upon knockdown of Mettl3- are these all Mettl3? This should at least be mentioned in the legend and perhaps indicated in the figure. The authors do mention in the text employing shRNAs to target multiple Mettl3 isoforms, so likely this is the case. __

We have clarified these issues in the new manuscript.

__Minor points (some really minor to just polish the presentation for clarity):

The word "since" should only be used if there is a time element- otherwise the word "as" is preferable.

For example on p. 4, the sentence: "Since inhibition of mRNA export typically enhances the nuclear retention of RNAs with intact 5'SS motifs (Lee et al. 2020),.." would more precisely read "As inhibition of mRNA export typically enhances the nuclear retention of RNAs with intact 5'SS motifs (Lee et al. 2020),..". __

We thank the reviewer for pointing this out. We have fixed this issue in the revised manuscript.

__Reviewer #2 (Significance (Required)):

Summary: In the manuscript by Lee et al. entitled "N-6-methyladenosine (m6A) Promotes the Nuclear Retention of mRNAs with Intact 5'Splice Site Motifs", the authors provide evidence that m6A modifications within specific regions of transcripts can confer nuclear retention. These results are important because they add to our understanding of how m6A modifications can contribute to post-transcriptional regulation. Although the authors do not quite come out and say this, data seem to be accumulating to suggest that the location of the m6A modifications within a given transcript can dictate the functional consequences of those modifications.

This study would be of significant interest to those that study gene expression in any context as well as cell biologists as the data add to our understanding of export of mRNA from the nucleus. This work also adds to our understanding of the biological consequences of m6A modification, which is an area of significant interest. In my opinion, the authors could make a broader conclusion that we do, which is that the location of the modification significantly dictates function- an extension of previous findings mostly focused on processed mRNA transcripts. __

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

Quality control of mRNA is vital for all types of cells. In eukaryotic cells, nuclear export of misprocessed mRNAs containing the 5' splice site is prevented. In this manuscript, Lee and colleagues demonstrate that the nuclear retention of intronic polyadenylated transcripts is dependent on m6A modification. Based on the results shown in yeast, they perform immunoprecipitation experiments and demonstrate the interaction between ZFC3H1, a component of the PAXT complex, and YTHDC1 and YTHDC 2, nuclear YTH RNA-binding proteins that recognize m6A-modified transcripts. The study also shows the interaction of U1-70K with YTHDC1 and with ZFC3H1. Depletion of YTHDC1/2 prevents the nuclear retention of IPA transcripts. Additionally, CLIP-seq analysis is performed, demonstrating that m6A modification is enriched around the 5' splice site motif and the 3' polyadenylation site in IPAs. From these observations, they conclude that m6A modification contributes to the quality control of mRNA by promoting nuclear retention of misprocessed transcripts.

Major Points 1. The interaction between ZFC3H1 and YTHDC1 is clearly shown by immunoprecipitation of FLAG-tagged YTHDC1 in Figure 1B. However, the co-purification of YTHDC1 with FLAG-tagged ZFC3H1 in Figure 1C is rather ambiguous. Additionally, the immunoprecipitated samples do not appear to show signals corresponding to FLAG-tagged ZFC3H1, making it unclear if the immunoprecipitation is working. It is essential to provide a better quality result to clarify these observations. __

Please see our responses to reviewer #1. We have repeated the co-immunoprecipitation of FLAG-ZFC3H1 (1-1233) with YTHDC1 under more stringent conditions and have reduced the background binding (see Figure 1B and D in the new manuscript). We have also determined why the FLAG-ZFC3H1 is smaller than expected as the construct contains a premature stop codon. As explained above, we are in the midst of generating a full-length FLAG-ZFC3H1 and we plan to repeat the co-immunoprecipitation with this new construct.

2. While the authors demonstrate that the m6A modification is dispensable for the targeting of IPA reporter transcripts to the nuclear speckles, it would be valuable to investigate whether m6A is required for their exit from the nuclear speckles. Do reporter transcripts with m6A motifs remain in the nuclear speckles at later time points?

We have now analyzed the colocalization of nuclear speckles (SC35) with ftz-Δi-5’SS, which contains both a 5’SS and DRACH motifs, and no-m6A-ftz-Δi-5’SS, which contains a 5’SS but lacks DRACH motifs, at steady state – i.e. after 18-24 hours of transfection (as opposed to at early time points as shown in Figure 2D-E of the initial manuscript). Unexpectedly, we see that both mRNAs continue to colocalize with nuclear speckles, although the no-m6A-ftz-Δi-5’SS mRNA is well exported from the nucleus and its signal in nuclear speckles is faint (see Figure 2F-H in the new manuscript).

Previously, we observed that ftz-Δi-5’SS required the 5’SS motif to remain in nuclear speckles at these later time points (Lee PLOS ONE 2015 and Lee RNA 2022). Upon closer inspection, ftz-Δi-5’SS mRNA also accumulates in additional nuclear foci that are not SC35-positive. Our new results may indicate that m6A marks promote the transfer of mRNAs from nuclear speckles to other foci, but more data is required to make a firm statement. Given this, we plan to conduct further experiments which may take a month to complete:

Experiment #7) We are now assessing whether these additional ftz-Δi-5’SS foci correspond to either YTHDC-positive foci which were previously shown to partially overlap nuclear speckles and sequester m6A-rich mRNAs (Cheng Cancer Cell 2022), or “pA+ RNA foci” which accumulate MTR4/ZFC3H1-targetted RNAs when the nuclear exosome is inhibited (Silla Cell Reports 2018). These foci are enriched in ZFC3H1. We plan on co-staining ftz-Δi, ftz-Δi-5’SS, no-m6A-ftz-Δi and no-m6A-ftz-Δi-5’SS with SC35, YTHDC1 and ZFC3H1 to determine whether m6A may help to transfer mRNAs from nuclear speckles to YTHDC1 or ZFC3H1-enriched foci.

__3. Figures 5B and 5C suggest that ZFC3H1 is required for the degradation of IPA transcripts. However, the range of the vertical axis is inappropriate and it is difficult to assess the extent of the increase in expression levels. Please adjust the vertical axis range for improved clarity. __

We thank the reviewer for the feedback we have added additional graphs with an expanded vertical axis to demonstrate that ZFC3H1 is required for the degradation IPA transcripts.

Minor Points 1. page 4, line 2 "RNAse" should be corrected to "RNase".

We thank the reviewer for catching this error. We have fixed this.

__ 2. page 7, line 5: Is the statement "prevents the nuclear export and decay of non-functional and misprocessed RNAs" correct? m6A modification promotes the decay of such RNAs. __

We thank the reviewer for pointing this out. We have altered the text to clarify that m6A promotes decay.

__3. Figure 2E: ftz-∆i should be ftz-∆i-5'SS. __

We thank the reviewer for catching this error. We have fixed this.

__4. Figure 5A: It would be helpful to indicate the number of IPA transcripts analyzed. __

We have included this information.

__Reviewer #3 (Significance (Required)):

Overall, the work is sound and generally well-controlled. This study advances our understanding of the quality control of misprocessed transcripts in higher eukaryotes. This reviewer suggests a few points for clarification or improvement. __

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Referee #3

Evidence, reproducibility and clarity

Quality control of mRNA is vital for all types of cells. In eukaryotic cells, nuclear export of misprocessed mRNAs containing the 5' splice site is prevented. In this manuscript, Lee and colleagues demonstrate that the nuclear retention of intronic polyadenylated transcripts is dependent on m6A modification. Based on the results shown in yeast, they perform immunoprecipitation experiments and demonstrate the interaction between ZFC3H1, a component of the PAXT complex, and YTHDC1 and YTHDC 2, nuclear YTH RNA-binding proteins that recognize m6A-modified transcripts. The study also shows the interaction of U1-70K with YTHDC1 and with ZFC3H1. Depletion of YTHDC1/2 prevents the nuclear retention of IPA transcripts. Additionally, CLIP-seq analysis is performed, demonstrating that m6A modification is enriched around the 5' splice site motif and the 3' polyadenylation site in IPAs. From these observations, they conclude that m6A modification contributes to the quality control of mRNA by promoting nuclear retention of misprocessed transcripts.

Major Points

1. The interaction between ZFC3H1 and YTHDC1 is clearly shown by immunoprecipitation of FLAG-tagged YTHDC1 in Figure 1B. However, the co-purification of YTHDC1 with FLAG-tagged ZFC3H1 in Figure 1C is rather ambiguous. Additionally, the immunoprecipitated samples do not appear to show signals corresponding to FLAG-tagged ZFC3H1, making it unclear if the immunoprecipitation is working. It is essential to provide a better quality result to clarify these observations.
2. While the authors demonstrate that the m6A modification is dispensable for the targeting of IPA reporter transcripts to the nuclear speckles, it would be valuable to investigate whether m6A is required for their exit from the nuclear speckles. Do reporter transcripts with m6A motifs remain in the nuclear speckles at later time points?
3. Figures 5B and 5C suggest that ZFC3H1 is required for the degradation of IPA transcripts. However, the range of the vertical axis is inappropriate and it is difficult to assess the extent of the increase in expression levels. Please adjust the vertical axis range for improved clarity.

Minor Points

1. page 4, line 2 "RNAse" should be corrected to "RNase".
2. page 7, line 5: Is the statement "prevents the nuclear export and decay of non-functional and misprocessed RNAs" correct? m6A modification promotes the decay of such RNAs.
3. Figure 2E: ftz-∆i should be ftz-∆i-5'SS.
4. Figure 5A: It would be helpful to indicate the number of IPA transcripts analyzed.

Significance

Overall, the work is sound and generally well-controlled. This study advances our understanding of the quality control of misprocessed transcripts in higher eukaryotes. This reviewer suggests a few points for clarification or improvement.

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Referee #2

Evidence, reproducibility and clarity

Summary: In the manuscript by Lee et al. entitled "N-6-methyladenosine (m6A) Promotes the Nuclear Retention of mRNAs with Intact 5'Splice Site Motifs", the authors provide evidence that m6A modifications within specific regions of transcripts can confer nuclear retention. These results are important because they add to our understanding of how m6A modifications can contribute to post-transcriptional regulation. Although the authors do not quite come out and say this, data seem to be accumulating to suggest that the location of the m6A modifications within a given transcript can dictate the functional consequences of those modifications.

The current work builds on previous findings from these authors identifying factors critical for retention of intronic polyadenylated (IPA) transcripts. The present study identified m6A modification as one of the signals for the retention of such transcripts. The authors use reporters for their analysis and also examine validated endogenous IPA transcripts. The data presented supports the conclusions albeit they show a surprising finding for one of the m6A erasers, ALKBH5. However, there is some controversy over the mechanism by which ALKBH5 functions and whether the m6A mark is truly reversible, so these results may continue to add to this point of view.

Major Comments: One experiment that might add to the argument would be overexpression of Mettl3 as compared to catalytically inactive Mettl3. The prediction would be that the reporter transcript with intact DRACH sequences would be even more retained in the nucleus in a manner that depends on Mettl3 catalytic activity. For some of the data presented, the reporter is already wholly nuclear so no difference could be detected, but in the U2OS cells shown in Figure 2B, it appears that an increase in nuclear localization might be evident. Such an experiment would add an orthogonal approach to demonstrate that the methylation by Mettl3 is required for retention. If such an experiment would work with the endogenous IPA transcripts shown in Figure 4, but these transcripts may already be too nuclear to detect any increase in nuclear retention.

Some rather minor changes to the presentation of the data could enhance the impact of this study.

Specific Comments:

The primary question in this manuscript is comparing reporters with m6A site (intact DRACH sequences) to those without. For this reason, organizing the data to the +/- DRACH sites are adjacent to one another might make the most sense. This point is evident in Figure 1C where perhaps simply changing the order of the bars presented to put the ones directly compared adjacent would be preferable. Then the p-value would compare sets of data directly adjacent to one another.

While the authors show representative fields/cells for most assays, they do an excellent job of providing quantitation as well. One exception is Figure 3D, which shows a single cell image for the most key panel (the 5'SS-containing reporter upon Mettl3 depletion). If there is not a field with more cells, the authors could create a montage.

Minor Comments:

Figure presentation:

The text in a number of the figures is VERY small (Figures 1B,1C, and 4A) for example.

Figure 3A includes the label "shRNA:" at the top, but these cells are treated with Mettl3 inhibitor and there does not appear to be any shRNA employed, so this seems like a labeling error.

In Figure 3C, the immunoblot of Mettl3, there are three bands that all disappear completely upon knockdown of Mettl3- are these all Mettl3? This should at least be mentioned in the legend and perhaps indicated in the figure. The authors do mention in the text employing shRNAs to target multiple Mettl3 isoforms, so likely this is the case.

Minor points (some really minor to just polish the presentation for clarity):

The word "since" should only be used if there is a time element- otherwise the word "as" is preferable.

For example on p. 4, the sentence: "Since inhibition of mRNA export typically enhances the nuclear retention of RNAs with intact 5'SS motifs (Lee et al. 2020),.." would more precisely read "As inhibition of mRNA export typically enhances the nuclear retention of RNAs with intact 5'SS motifs (Lee et al. 2020),..".

Significance

Summary: In the manuscript by Lee et al. entitled "N-6-methyladenosine (m6A) Promotes the Nuclear Retention of mRNAs with Intact 5'Splice Site Motifs", the authors provide evidence that m6A modifications within specific regions of transcripts can confer nuclear retention. These results are important because they add to our understanding of how m6A modifications can contribute to post-transcriptional regulation. Although the authors do not quite come out and say this, data seem to be accumulating to suggest that the location of the m6A modifications within a given transcript can dictate the functional consequences of those modifications.

This study would be of significant interest to those that study gene expression in any context as well as cell biologists as the data add to our understanding of export of mRNA from the nucleus. This work also adds to our understanding of the biological consequences of m6A modification, which is an area of significant interest. In my opinion, the authors could make a broader conclusion that we do, which is that the location of the modification significantly dictates function- an extension of previous findings mostly focused on processed mRNA transcripts.

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Referee #1

Evidence, reproducibility and clarity

The concept introduced by this paper is exciting and novel. However, the current paucity of presented data can lead to incorrect interpretations of the findings and speculations that might not hold true after a more rigorous assessment of the observed phenomenon.

The premise of this study builds upon an interaction between the PAXT complex and nuclear YTH domain containing proteins. However, figures 1B and C should be improved. The interacting band for the ZFC3H1 presented in panel B does not seem to match the size of the construct used in panel C. Is the Flag version of ZFC3H1 expressing a smaller isoform for this protein? Also, the YTHDC1-2 interaction in panel C is not as convincing considering the negative controls lane show some degree of binding. Additionally, can the authors test if their RNaseA treatment worked?

Why do you need 18 hours to observe the nuclear export of your modifiable construct when inhibiting METTL3 in figure 3? Is it possible that your observation is secondary to phenotypes these cells develop as a result of blocking METTL3?

Is ALKBH5 nuclear and/or cytoplasmic in the cell system used?

Significance

The study is highly significant