Reviewer #1 (Public review):

One of the roadblocks in PfEMP1 research has been the challenges in manipulating var genes to incorporate markers to allow the transport of this protein to be tracked and to investigate the interactions taking place within the infected erythrocyte. In addition, the ability of Plasmodium falciparum to switch to different PfEMP1 variants during in vitro culture has complicated studies due to parasite populations drifting from the original (manipulated) var gene expression. Cronshagen et al have provided a useful system with which they demonstrate the ability to integrate a selectable drug marker into several different var genes that allows the PfEMP1 variant expression to be 'fixed'. This on its own represents a useful addition to the molecular toolbox and the range of var genes that have been modified suggests that the system will have broad application. As well as incorporating a selectable marker, the authors have also used selective linked integration (SLI) to introduce markers to track the transport of PfEMP1, investigate the route of transport and probe interactions with PfEMP1 proteins in the infected host cell.

One of the major strengths of this paper is that the authors have not only put together a robust system for further functional studies, but they have used it to produce a range of interesting findings including:

Co-activation of rif and var genes when in a head-to-head orientation.

The reduced control of expression of var genes in the 3D7-MEED parasite line.

More support for the PTEX transport route for PfEMP1.<br /> Identification of new proteins involved in PfEMP1 interactions in the infected erythrocyte, including some required for cytoadherence.

In most cases the experimental evidence is straightforward, and the data support the conclusions strongly. The authors have been very careful in the depth of their investigation, and where unexpected results have been obtained, they have looked carefully at why these have occurred.

A weakness of the paper is, as mentioned above, that the results are sometimes not as clear as might have been expected, for example, in the requirement for panning modified parasites to produce binding to EPCR. Where this has happened, the authors take a robust and thoughtful approach, and acknowledge that (as in most research) there are more questions to address. Being able to select specific var gene switches using drug markers will provide some useful starting points to understand how switching happens in P. falciparum. However, our trypanosome colleagues might remind us that forcing switches may show us some mechanisms, but perhaps not all.

Despite these sometimes complicated findings, the authors have achieved their aim as stated in the title of the paper, and in doing so have provided an excellent resource to themselves and other researchers in the field to answer some important questions.

Overall, the authors have produced a useful and robust system to support functional studies on PfEMP1, which provides a platform for future studies manipulating the domain content in var genes. They have used this system to produce a range of interesting findings and to support its use by the research community.

Comments on revisions:

I have no further recommendations for changes by the authors. They have addressed my concerns, and the paper reads very well.

Reviewer #2 (Public review):

Summary

Croshagen et al develop a range of tools based on selection-linked integration (SLI) to study PfEMP1 function in P. falciparum. PfEMP1 is encoded by a family of ~60 var genes subject to mutually exclusive expression. Switching expression between different family members can modify the binding properties of the infected erythrocyte while avoiding the adaptive immune response. Although critical to parasite survival and Malaria disease pathology, PfEMP1 proteins are difficult to study owing to their large size and variable expression between parasites within the same population. The SLI approach previously developed by this group for genetic modification of P. falciparum is employed here to selectively and stably activate expression of target var genes at the population level. Using this strategy, the binding properties of specific PfEMP1 variants were measured for several distinct var genes with a novel semi-automated pipeline to increase throughput and reduce bias. Activation of similar var genes in both the common lab strain 3D7 and the cytoadhesion competent FCR3/IT4 strain revealed higher binding for several PfEMP1 IT4 variants with distinct receptors, indicating this strain provides a superior background for studying PfEMP1 binding. SLI also enables modifications to target var gene products to study PfEMP1 trafficking and identify interacting partners by proximity-labeling proteomics, revealing two novel exported proteins required for cytoadherence. Overall, the data demonstrate a range of SLI-based approaches for studying PfEMP1 that will be broadly useful for understanding the basis for cytoadhesion and parasite virulence.

Comments:

While the capability of SLI to active selected var gene expression was initially reported by Omelianczyk et al., the present study greatly expands the utility of this approach. Several distinct var genes are activated in two different P. falciparum strains and shown to modify the binding properties of infected RBCs to distinct endothelial receptors; development of SLI2 enables multiple SLI modifications in the same parasite line; SLI is used to modify target var genes to study PfEMP1 trafficking and determine PfEMP1 interactomes with BioID. Along the way, the authors also demonstrate a new selection marker for P. falciparum transfection (a mutant FNT lactate transporter that provides resistance to the compound BH267.meta). Curiously, Omelianczyk et al activated a single var (Pf3D7_0421300) and observed elevated expression of an adjacent var arranged in a head to tail manner, possibly resulting from local chromatin modifications enabling expression of the neighboring gene. In contrast, the present study observed activation of neighboring genes with head to head but not head to tail arrangement, which may be the result of shared promoter regions. The reason for these differing results is unclear although it should be noted that the two studies examined different var loci.

The IT4var19 panned line that became binding-competent showed increased expression of both paralogs of ptp3 (as well as a phista and gbp), suggesting that overexpression of PTP3 may improve PfEMP1 display and binding. Interestingly, IT4 appears to be the only known P. falciparum strain (only available in PlasmoDB) that encodes more than one ptp3 gene (PfIT_140083100 and PfIT_140084700). PfIT_140084700 is almost identical to the 3D7 PTP3 (except for a ~120 residue insertion in 3D7 beginning at residue 400). In contrast, while the C-terminal region of PfIT_140083100 shows near perfect conservation with 3D7 PTP3 beginning at residue 450, the N-terminal regions between the PEXEL and residue 450 are quite different. This may indicate the generally stronger receptor binding observed in IT4 relative to 3D7 results from increased PTP3 activity due to multiple isoforms or that specialized trafficking machinery exists for some PfEMP1 proteins.

Revisions:

The authors thoughtfully addressed all the reviewer comments.

Reviewer #3 (Public review):

Summary:

The submission from Cronshagen and colleagues describes the application of a previously described method (selection linked integration) to the systematic study of PfEMP1 trafficking in the human malaria parasite Plasmodium falciparum. PfEMP1 is the primary virulence factor and surface antigen of infected red blood cells and is therefore a major focus of research into malaria pathogenesis. Since the discovery of the var gene family that encodes PfEMP1 in the late 1990s, there have been multiple hypotheses for how the protein is trafficked to the infected cell surface, crossing multiple membranes along the way. One difficulty in studying this process is the large size of the var gene family and the propensity of the parasites to switch which var gene is expressed, thus preventing straightforward gene modification-based strategies for tagging the expressed PfEMP1. Here the authors solve this problem by forcing expression of a targeted var gene by fusing the PfEMP1 coding region with a drug selectable marker separated by a skip peptide. This enabled them to generate relatively homogenous populations of parasites all expressing tagged (or otherwise modified) forms of PfEMP1 suitable for study. They then applied this method to study various aspects of PfEMP1 trafficking.

Strengths:

The study is very thorough, and the data are well presented. The authors used SLI to target multiple var genes, thus demonstrating the robustness of their strategy. They then perform experiments to investigate possible trafficking through PTEX, they knockout proteins thought to be involved in PfEMP1 trafficking and observe defects in cytoadherence, and they perform proximity labeling to further identify proteins potentially involved in PfEMP1 export. These are independent and complimentary approaches that together tell a very compelling story.

Weaknesses:

(1) When the authors targeted IT4var19, they were successful in transcriptionally activating the gene, however they did not initially obtain cytoadherent parasites. To observe binding to ICAM-1 and EPCR, they had to perform selection using panning. This is an interesting observation and potentially provides insights into PfEMP1 surface display, folding, etc. However, it also raises questions about other instances in which cytoadherence was not observed. Would panning of these other lines have successfully selected for cytoadherent infected cells? Did the authors attempt panning of their 3D7 lines? Given that these parasites do export PfEMP1 to the infected cell surface (Figure 1D), it is possible that panning would similarly rescue binding. Likewise, the authors knocked out PTP1, TryThrA and EMPIC3 and detected a loss of cytoadhesion, but they did not attempt panning to see if this could rescue binding. The strong selection that panning exerts on parasite populations could result in selection of compensatory changes that enable cytoadherence, which could be very informative, although the analysis could potentially be quite complicated and beyond the scope of the current paper. Nonetheless, these are important concepts to consider when assessing these phenotypes.

(2) The authors perform a series of trafficking experiments to help discern whether PfEMP1 is trafficked through PTEX. While the results were not entirely definitive, they make a strong case for PTEX in PfEMP1 export. The authors then used BioID to obtain a proxiome for PfEMP1 and identified proteins they suggest are involved in PfEMP1 trafficking. However, it seemed that components of PTEX were missing from the list of interacting proteins. Is this surprising and does this observation shed any additional light on the possibility of PfEMP1 trafficking through PTEX? This warrants a comment or discussion.

Comments on revisions:

The authors have responded thoroughly and constructively to suggestions and comments in the initial review. I have no additional comments. This is a great contribution to the literature.

Author response:

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

eLife Assessment:

This study introduces an important approach using selection linked integration (SLI) to generate Plasmodium falciparum lines expressing single, specific surface adhesins PfEMP1 variants, enabling precise study of PfEMP1 trafficking, receptor binding, and cytoadhesion. By moving the system to different parasite strains and introducing an advanced SLI2 system for additional genomic edits, this work provides compelling evidence for an innovative and rigorous platform to explore PfEMP1 biology and identify novel proteins essential for malaria pathogenesis including immune evasion.

Reviewer #1 (Public review):

One of the roadblocks in PfEMP1 research has been the challenges in manipulating var genes to incorporate markers to allow the transport of this protein to be tracked and to investigate the interactions taking place within the infected erythrocyte. In addition, the ability of Plasmodium falciparum to switch to different PfEMP1 variants during in vitro culture has complicated studies due to parasite populations drifting from the original (manipulated) var gene expression. Cronshagen et al have provided a useful system with which they demonstrate the ability to integrate a selectable drug marker into several different var genes that allows the PfEMP1 variant expression to be 'fixed'. This on its own represents a useful addition to the molecular toolbox and the range of var genes that have been modified suggests that the system will have broad application. As well as incorporating a selectable marker, the authors have also used selective linked integration (SLI) to introduce markers to track the transport of PfEMP1, investigate the route of transport, and probe interactions with PfEMP1 proteins in the infected host cell.

What I particularly like about this paper is that the authors have not only put together what appears to be a largely robust system for further functional studies, but they have used it to produce a range of interesting findings including:

Co-activation of rif and var genes when in a head-to-head orientation.

The reduced control of expression of var genes in the 3D7-MEED parasite line.

More support for the PTEX transport route for PfEMP1.

Identification of new proteins involved in PfEMP1 interactions in the infected erythrocyte, including some required for cytoadherence.

In most cases the experimental evidence is straightforward, and the data support the conclusions strongly. The authors have been very careful in the depth of their investigation, and where unexpected results have been obtained, they have looked carefully at why these have occurred.

We thank the reviewer for the kind assessment and the comments to improve the paper.

(1) In terms of incorporating a drug marker to drive mono-variant expression, the authors show that they can manipulate a range of var genes in two parasite lines (3D7 and IT4), producing around 90% expression of the targeted PfEMP1. Removal of drug selection produces the expected 'drift' in variant types being expressed. The exceptions to this are the 3D7-MEED line, which looks to be an interesting starting point to understand why this variant appears to have impaired mutually exclusive var gene expression and the EPCR-binding IT4var19 line. This latter finding was unexpected and the modified construct required several rounds of panning to produce parasites expressing the targeted PfEMP1 and bind to EPCR. The authors identified a PTP3 deficiency as the cause of the lack of PfEMP1 expression, which is an interesting finding in itself but potentially worrying for future studies. What was not clear was whether the selected IT4var19 line retained specific PfEMP1 expression once receptor panning was removed.

We do not have systematic long-term data for the Var19 line but do have medium-term data. After panning the Var19 line, the binding assays were done within 3 months without additional panning. The first binding assay was 2 months after the panning and the last binding assays three weeks later, totaling about 3 months without panning. While there is inherent variation in these assays that precludes detection of smaller changes, the last assay showed the highest level of binding, giving no indication for rapid loss of the binding phenotype. Hence, we can say that the binding phenotype appears to be stable for many weeks without panning the cells again and there was no indication for a rapid loss of binding in these parasites.

Systematic long-term experiments to assess how long the Var19 parasites retain binding would be interesting, but given that the binding-phenotype appears to remain stable over many weeks or even months, this would only make sense if done over a much longer time frame. Such data might arise if the line is used over extended times for a specific project in which case it might be advisable to monitor continued binding. We included a statement in the discussion that the binding phenotype was stable over many weeks but that if long-term work with this line is planned, monitoring the binding phenotype might be advisable: “In the course of this work the binding phenotype of the IT4var19 expressor line remained stable over many weeks without further panning. However, given that initial panning had been needed for this particular line, it might be advisable for future studies to monitor the binding phenotype if the line is used for experiments requiring extended periods of cultivation.”

(2) The transport studies using the mDHFR constructs were quite complicated to understand but were explained very clearly in the text with good logical reasoning.

We are aware of this being a complex issue and are glad this was nevertheless understandable.

(3) By introducing a second SLI system, the authors have been able to alter other genes thought to be involved in PfEMP1 biology, particularly transport. An example of this is the inactivation of PTP1, which causes a loss of binding to CD36 and ICAM-1. It would have been helpful to have more insight into the interpretation of the IFAs as the anti-SBP1 staining in Figure 5D (PTP-TGD) looks similar to that shown in Figure 1C, which has PTP intact. The anti-EXP2 results are clearly different.

We realize the description of the PTP1-TGD IFA data and that of the other TGDs (see also response to Recommendation to authors point 4 and reviewer 2, major points 6 and 7) was rather cursory. The previously reported PTP1 phenotype is a fragmentation of the Maurer’s clefts into what in IFA appear to be many smaller pieces (Rug et al 2014, referenced in the manuscript). The control in Fig. 5D has 13 Maurer’s cleft spots (previous work indicates an average of ~15 MC per parasite, see e.g. the originally co-submitted eLife preprint doi.org/10.7554/eLife.103633.1 and references therein). The control mentioned by the reviewer in Fig. 1C has about 22 Maurer’s clefts foci, at the upper end of the typical range, but not unusual. In contrast, the PTP1-TGD in Fig. 5D, has more than 30 foci with an additional cytoplasmic pool and additional smaller, difficult to count foci. This is consistent with the published phenotype in Rug et al 2014. The EXP1 stained cell has more than 40 Maurer’s cleft foci, again beyond what typically is observed in controls. Therefore, these cells show a difference to the control in Fig. 5 but also to Fig. 1C. Please note that we are looking at two different strains, in Fig. 1 it is 3D7 and in Fig. 5 IT4. While we did not systematically assess this, the Maurer’s clefts number per cell seemed to be largely comparable between these strains (Fig. 10C and D in the other eLife preprint doi.org/10.7554/eLife.103633.1). 

Overall, as the PTP1 loss phenotype has already been reported, we did not go into more experimental detail. However, we now modified the text to more clearly describe how the phenotype in the PTP1-TGD parasites was different to control: “IFAs showed that in the PTP1-TGD parasites, SBP1 and PfEMP1 were found in many small foci in the host cell that exceeded the average number of ~ 15 Maurer’s clefts typically found per infected RBC [66] (Fig. 5D). This phenotype resembled the previously reported Maurer’s clefts phenotype of the PTP1 knock out in CS2 parasites [39].”

(4) It is good to see the validation of PfEMP1 expression includes binding to several relevant receptors. The data presented use CHO-GFP as a negative control, which is relevant, but it would have been good to also see the use of receptor mAbs to indicate specific adhesion patterns. The CHO system if fine for expression validation studies, but due to the high levels of receptor expression on these cells, moving to the use of microvascular endothelial cells would be advisable. This may explain the unexpected ICAM-1 binding seen with the panned IT4var19 line.

We agree with the reviewer that it is desirable to have better binding systems for studying individual binding interactions. As the main purpose of this paper was to introduce the system and provide proof of principle that the cells show binding, we did not move to more complicated binding systems. However, we would like to point out that the CSA binding was done on receptor alone in addition to the CSA-expressing HBEC-5i cells and was competed successfully with soluble CSA. In addition, apart from the additional ICAM1-binding of the Var19 line, all binding phenotypes were conform with expectations. We therefore hope the tools used for binding studies are acceptable at this stage of introducing the system while future work interested in specific PfEMP1 receptor interactions may use better systems, tailored to the specific question (e.g. endothelial organoid models and engineered human capillaries and inhibitory antibodies or relevant recombinant domains for competition).

(5) The proxiome work is very interesting and has identified new leads for proteins interacting with PfEMP1, as well as suggesting that KAHRP is not one of these. The reduced expression seen with BirA* in position 3 is a little concerning but there appears to be sufficient expression to allow interactions to be identified with this construct. The quantitative impact of reduced expression for proxiome experiments will clearly require further work to define it.

This is a valid point. Clearly there seems to be some impact on binding when BirA* is placed in the extracellular domain (either through reduced presentation or direct reduction of binding efficiency of the modified PfEMP1; please see also minor comment 10 reviewer 2). The exact quantitative impact on the proxiome is difficult to assess but we note that the relative enrichment of hits to each other is rather similar to the other two positions (Fig. 6H-J). We therefore believe the BioIDs with the 3 PfEMP1-BirA* constructs are sufficient to provide a general coverage of proteins proximal to PfEMP1 and hope this will aid in the identification of further proteins involved in PfEMP1 transport and surface display as illustrated with two of the hits targeted here.

The impact of placing a domain on the extracellular region of PfEMP1 will have to be further evaluated if needed in other studies. But the finding that a large folded domain can be placed into this part at all, even if binding was reduced, in our opinion is a success (it was not foreseeable whether any such change would be tolerated at all).

(6) The reduced receptor binding results from the TryThrA and EMPIC3 knockouts were very interesting, particularly as both still display PfEMP1 on the surface of the infected erythrocyte. While care needs to be taken in cross-referencing adhesion work in P. berghei and whether the machinery truly is functionally orthologous, it is a fair point to make in the discussion. The suggestion that interacting proteins may influence the "correct presentation of PfEMP1" is intriguing and I look forward to further work on this.

We hope future work will be able to shed light on this.

Overall, the authors have produced a useful and reasonably robust system to support functional studies on PfEMP1, which may provide a platform for future studies manipulating the domain content in the exon 1 portion of var genes. They have used this system to produce a range of interesting findings and to support its use by the research community. Finally, a small concern. Being able to select specific var gene switches using drug markers could provide some useful starting points to understand how switching happens in P. falciparum. However, our trypanosome colleagues might remind us that forcing switches may show us some mechanisms but perhaps not all.

Point noted! From non-systematic data with the Var01 line that has been cultured for extended periods of time (several years), it seems other non-targeted vars remain silent in our SLI “activation” lines but how much SLI-based var-expression “fixing” tampers with the integrity of natural switching mechanisms is indeed very difficult to gage at this stage. We now added a statement to the discussion that even if mutually exclusive expression is maintained, it is not certain the mechanisms controlling var expression all remain intact: “However, it should be noted that it is not known whether all mechanisms controlling mutually exclusive expression and switching remain intact in parasites with SLI-activated var genes.”

Reviewer #2 (Public review):

Summary

Croshagen et al develop a range of tools based on selection-linked integration (SLI) to study PfEMP1 function in P. falciparum. PfEMP1 is encoded by a family of ~60 var genes subject to mutually exclusive expression. Switching expression between different family members can modify the binding properties of the infected erythrocyte while avoiding the adaptive immune response. Although critical to parasite survival and Malaria disease pathology, PfEMP1 proteins are difficult to study owing to their large size and variable expression between parasites within the same population. The SLI approach previously developed by this group for genetic modification of P. falciparum is employed here to selectively and stably activate the expression of target var genes at the population level. Using this strategy, the binding properties of specific PfEMP1 variants were measured for several distinct var genes with a novel semi-automated pipeline to increase throughput and reduce bias. Activation of similar var genes in both the common lab strain 3D7 and the cytoadhesion competent FCR3/IT4 strain revealed higher binding for several PfEMP1 IT4 variants with distinct receptors, indicating this strain provides a superior background for studying PfEMP1 binding. SLI also enables modifications to target var gene products to study PfEMP1 trafficking and identify interacting partners by proximity-labeling proteomics, revealing two novel exported proteins required for cytoadherence. Overall, the data demonstrate a range of SLI-based approaches for studying PfEMP1 that will be broadly useful for understanding the basis for cytoadhesion and parasite virulence.

We thank the reviewer for the kind assessment and the comments to improve the paper.

Comments

(1) While the capability of SLI to actively select var gene expression was initially reported by Omelianczyk et al., the present study greatly expands the utility of this approach. Several distinct var genes are activated in two different P. falciparum strains and shown to modify the binding properties of infected RBCs to distinct endothelial receptors; development of SLI2 enables multiple SLI modifications in the same parasite line; SLI is used to modify target var genes to study PfEMP1 trafficking and determine PfEMP1 interactomes with BioID. Curiously, Omelianczyk et al activated a single var (Pf3D7_0421300) and observed elevated expression of an adjacent var arranged in a head-to-tail manner, possibly resulting from local chromatin modifications enabling expression of the neighboring gene. In contrast, the present study observed activation of neighboring genes with head-to-head but not head-totail arrangement, which may be the result of shared promoter regions. The reason for these differing results is unclear although it should be noted that the two studies examined different var loci.

The point that we are looking at different loci is very valid and we realize this is not mentioned in the discussion. We now added to the discussion that it is unclear if our results and those cited may be generalized and that different var gene loci may respond differently

“However, it is unclear if this can be generalized and it is possible that different var loci respond differently.”

(2) The IT4var19 panned line that became binding-competent showed increased expression of both paralogs of ptp3 (as well as a phista and gbp), suggesting that overexpression of PTP3 may improve PfEMP1 display and binding. Interestingly, IT4 appears to be the only known P. falciparum strain (only available in PlasmoDB) that encodes more than one ptp3 gene (PfIT_140083100 and PfIT_140084700). PfIT_140084700 is almost identical to the 3D7 PTP3 (except for a ~120 residue insertion in 3D7 beginning at residue 400). In contrast, while the C-terminal region of PfIT_140083100 shows near-perfect conservation with 3D7 PTP3 beginning at residue 450, the N-terminal regions between the PEXEL and residue 450 are quite different. This may indicate the generally stronger receptor binding observed in IT4 relative to 3D7 results from increased PTP3 activity due to multiple isoforms or that specialized trafficking machinery exists for some PfEMP1 proteins.

We thank the reviewer for pointing this out, the exact differences between the two PTP3s of IT4 and that of other strains definitely should be closely examined if the function of these proteins in PfEMP1 binding is analysed in more detail. 

It is an interesting idea that the PTP3 duplication could be a reason for the superior binding of IT4. We always assumed that IT4 had better binding because it was less culture adapted but this does not preclude that PTP3(s) is(are) a reason for this. However, at least in our 3D7 PTP3 can’t be the reason for the poor binding, as our 3D7 still has PfEMP1 on the surface while in the unpanned IT4-Var19 line and in the Maier et al., Cell 2008 ptp3 KO (PMID: 18614010)) PfEMP1 is not on the surface anymore. 

Testing the impact of having two PTP3s would be interesting, but given the “mosaic” similarity of the two PTP3s isoforms, a simple add-on experiment might not be informative. Nevertheless, it will be interesting in future work to explore this in more detail.

Reviewer #3 (Public review):

Summary:

The submission from Cronshagen and colleagues describes the application of a previously described method (selection linked integration) to the systematic study of PfEMP1 trafficking in the human malaria parasite Plasmodium falciparum. PfEMP1 is the primary virulence factor and surface antigen of infected red blood cells and is therefore a major focus of research into malaria pathogenesis. Since the discovery of the var gene family that encodes PfEMP1 in the late 1990s, there have been multiple hypotheses for how the protein is trafficked to the infected cell surface, crossing multiple membranes along the way. One difficulty in studying this process is the large size of the var gene family and the propensity of the parasites to switch which var gene is expressed, thus preventing straightforward gene modification-based strategies for tagging the expressed PfEMP1. Here the authors solve this problem by forcing the expression of a targeted var gene by fusing the PfEMP1 coding region with a drug-selectable marker separated by a skip peptide. This enabled them to generate relatively homogenous populations of parasites all expressing tagged (or otherwise modified) forms of PfEMP1 suitable for study. They then applied this method to study various aspects of PfEMP1 trafficking.

Strengths:

The study is very thorough, and the data are well presented. The authors used SLI to target multiple var genes, thus demonstrating the robustness of their strategy. They then perform experiments to investigate possible trafficking through PTEX, they knock out proteins thought to be involved in PfEMP1 trafficking and observe defects in cytoadherence, and they perform proximity labeling to further identify proteins potentially involved in PfEMP1 export. These are independent and complimentary approaches that together tell a very compelling story.

We thank the reviewer for the kind assessment and the comments to improve the paper.

Weaknesses:

(1)  When the authors targeted IT4var19, they were successful in transcriptionally activating the gene, however, they did not initially obtain cytoadherent parasites. To observe binding to ICAM-1 and EPCR, they had to perform selection using panning. This is an interesting observation and potentially provides insights into PfEMP1 surface display, folding, etc. However, it also raises questions about other instances in which cytoadherence was not observed. Would panning of these other lines have been successfully selected for cytoadherent infected cells? Did the authors attempt panning of their 3D7 lines? Given that these parasites do export PfEMP1 to the infected cell surface (Figure 1D), it is possible that panning would similarly rescue binding. Likewise, the authors knocked out PTP1, TryThrA, and EMPIC3 and detected a loss of cytoadhesion, but they did not attempt panning to see if this could rescue binding. To ensure that the lack of cytoadhesion in these cases is not serendipitous (as it was when they activated IT4var19), they should demonstrate that panning cannot rescue binding.

These are very important considerations. Indeed, we had repeatedly attempted to pan 3D7 when we failed to get the SLI-generated 3D7 PfEMP1 expressor lines to bind, but this had not been successful. The lack of binding had been a major obstacle that had held up the project and was only solved when we moved to IT4 which readily bound (apart from Var19 which was created later in the project). After that we made no further efforts to understand why 3D7 does not bind but the fact that PfEMP1 is on the surface indicates this is not a PTP3 issue because loss of PTP3 also leads to loss of PfEMP1 surface display. Also, as the parent 3D7 could not be panned, we assumed this issue is not easily fixed in the SLI var lines we made in 3D7.

Panning the TGD lines: we see the reasoning for conducting panning experiments with the TGD lines. However, on second thought, we are unsure this should be attempted. The outcome might not be easily interpretable as at least two forces will contribute to the selection in panning experiments with TGD lines that do not bind anymore:

Firstly, panning would work against the SLI of the TGD, resulting in a tug of war between the TGD-SLI and binding. This is because a small number of parasites will loop out the TGD plasmid (revert) and would normally be eliminated during standard culturing due to the SLI drug used for the TGD. These revertant cells would bind and the panning would enrich them. Hence, panning and SLI are opposed forces in the case of a TGD abolishing binding. It is unclear how strong this effect would be, but this would for sure lead to mixed populations that complicate interpretations. 

The second selecting force are possible compensatory changes to restore binding. These can be due to different causes: (i) reversal of potential independent changes that may have occurred in the TGD parasites and that are in reality causing the binding loss (i.e. such as ptp3 loss or similar, the concern of the reviewer) or (ii) new changes to compensate the loss of the TGD target (in this case the TGD is the cause of the binding loss but for instance a different change ameliorates it by for instance increasing PfEMP1 expression or surface display). As both TGDs show some residual binding and have VAR01 on the surface to at least some extent, it is possible that new compensatory changes might indeed occur that indirectly increase binding again. 

In summary, even if more binding occurs after panning of the lines, it is not clear whether this is due to a compensatory change ameliorating the TGD or reversal of an unrelated change or are counter-selections against the SLI. To determine the cause, the panned TGD lines would need to be subjected to a complex and time-consuming analysis (WGS, RNASeq, possibly Maurer’s clefts phenotype) to find out whether they were SLI-revertants, or had an unrelated chance that was reverted or a new compensatory change that helps binding. This might be further muddled if a mix of cells come out of the selection that have different changes of the options indicated above. In that case, it might even require scRNASeq to make sense of the panning experiment. Due to the envisaged difficulty in interpreting the outcome, we did not attempt this panning.

To exclude loss of ptp3 expression as the reason for binding loss (something we would not have seen in the WGS if it is only due to a transcriptional change), we now carried out RNASeq with the TGD lines that have a binding phenotype. While we did not generate replicas to obtain quantitative data, the results show that both ptp3 copies were expressed in these TGDs comparable to other parasite lines that do bind with the same SLI-activated var gene, indicating that the effect is not due to ptp3 (see response to point 4 on PTP3 expression in the Recommendations for the authors). While we can’t fully exclude other changes in the TGDs that might affect binding, the WGS did not show any obvious alterations that could be responsible for this. 

(2) The authors perform a series of trafficking experiments to help discern whether PfEMP1 is trafficked through PTEX. While the results were not entirely definitive, they make a strong case for PTEX in PfEMP1 export. The authors then used BioID to obtain a proxiome for PfEMP1 and identified proteins they suggest are involved in PfEMP1 trafficking. However, it seemed that components of PTEX were missing from the list of interacting proteins. Is this surprising and does this observation shed any additional light on the possibility of PfEMP1 trafficking through PTEX? This warrants a comment or discussion.

This is an interesting point and we agree that this warrants to be discussed. A likely reason why PTEX components are not picked up as interactors is that BirA* is expected to be unfolded when it passes through the channel and in that state can’t biotinylate. Labelling likely would only be possible if PfEMP1 lingered at the PTEX translocation step before BirA* became unfolded to go through the channel which we would not expect under physiological conditions. We added the following sentences to the discussion: “While our data indicates PfEMP1 uses PTEX to reach the host cell, this could be expected to have resulted in the identification of PTEX components in the PfEMP1 proxiomes, which was not the case. However, as BirA* must be unfolded to pass through PTEX, it likely is unable to biotinylate translocon components unless PfEMP1 is stalled during translocation. For this reason, a lack of PTEX components in the PfEMP1 proxiomes does not necessarily exclude passage through PTEX.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Most of my comments are in the public section. I would just highlight a few things:

(1) In the binding studies section you talk about "human brain endothelial cells (HBEC-5i)". These cells do indeed express CSA but this is a property of their immortalisation rather than being brain endotheliium, which does not express CSA. I think this could be confusing to readers so I think you might want to reword this sentence to focus on CSA expressing the cell line rather than other features.

We thank the reviewer for pointing this out, we now modified the sentence to focus on the fact these are CSA expressing cells and provided a reference for it.

(2) As I said in the public section, CHO cells are great for proof of concept studies, but they are not endothelium. Not a problem for this paper.

Noted! Please also see our response to the public review.

(3) I wonder whether your comment about how well tolerated the Bir3* insertion is may be a bit too strong. I might say "Nonetheless, overall the BirA* modified PfEMP1 were functional."

Changed as requested.

(4) I'm not sure how you explain the IFA staining patterns to the uninitiated, but perhaps you could explain some of the key features you are looking for.

We apologise for not giving an explanation of the IFA staining patterns in the first place. Please see detailed response to public review of this reviewer (point 3 on PTP1-TGD phenotype) and to reviewer 2 (Recommendations to the authors, points 6 and 7 on better explaining and quantifying the Maurer’s clefts phenotypes). For this we now also generated parasites that episomally express mCherry tagged SBP1 in the TGD parasites with the reduced binding phenotype. This resulted in amendments to Fig. S7, addition of a Fig. S8 and updated results to better explain the phenotypes. 

This is a great paper - I just wish I'd had this system before.

Thank you!

Reviewer #2 (Recommendations for the authors):

Major Comments

(1) Does the RNAseq analysis of 3D7var0425800 and 3D7MEEDvar0425800 (Figure 1G, H) reveal any differential gene expression that might suggest a basis for loss of mutually exclusive var expression in the MEED line?

We now carried out a thorough analysis of these RNASeq experiments to look for an underlying cause for the phenotype. This was added as new Figure 1J and new Table S3. This analysis again illustrated the increased transcript levels of var genes. In addition, it showed that transcripts of a number of other exported proteins, including members of other gene families, were up in the MEED line. 

One hit that might be causal of the phenotype was sip2, which was down by close to 8-fold (pAdj 0.025). While recent work in P. berghei found this ApiAP2 to be involved in the expression of merozoite genes (Nishi et al., Sci Advances 2025(PMID: 40117352)), previous work in P. falciparum showed that it binds heterochromatic telomere regions and certain var upstream regions (Flück et al., PlosPath 2010 (PMID: 20195509), now cited in the manuscript). The other notable change was an upregulation of the non-coding RNA ruf6 which had been linked with impaired mono-allelic var expression (Guizetti et al., NAR 2016 (PMID: 27466391), now also cited in the manuscript). While it would go beyond this manuscript to follow this up, it is conceivable that alterations in chromosome end biology due to sip2 downregulation or upregulation of ruf6 are causes of the observed phenotype

We now added a paragraph on the more comprehensive analysis of the RNA Seq data of the MEED vs non-MEED lines at the end of the second results section.

(2) Could the inability of the PfEMP1-mDHFR fusion to block translocation (Fig 2A) reflect unique features of PfEMP1 trafficking, such as the existence of a soluble, chaperoned trafficking state that is not fully folded? Was a PfEMP1-BPTI fusion ever tested as an alternative to mDHFR?

This is an interesting suggestion. The PfEMP1-BPTI was never tested. However, a chaperoned trafficking state would likely also affect BPTI. Given that both domains (mDHFR and BPTI) in principle do the same when folded and would block when the construct is in the PV, it is not so likely that using a different blocking domain would make a difference. Therefore, the scenario where BPTI would block when mDHFR does not, is not that probable. The opposite would be possible (mDHFR blocking while BPTI does not, because only the latter depends on the redox state). However, this would only happen if the block  occurred before the construct reaches the PV.

At present, we believe the lacking block to be due to the organization of the domains in the construct. In the PfEMP1-mDHFR construct in this manuscript the position of the blocking domain is further away from the TMD compared to all other previously tested mDHFR fusions. Increased distance to the TMD has previously been found to be a factor impairing the blocking function of mDHFR (Mesen-Ramirez et al., PlosPath 2016 (PMID: 27168322)). Hence, our suspicion that this is the reason for the lacking block with the PfEMP1-mDHFR rather than the type of blocking domain. However, the latter option can’t be fully excluded and we might test BPTI in future work.

(3) The late promoter SBP1-mDHFR is 2A fused with the KAHRP reporter. Since 2A skipping efficiency varies between fusion contexts and significant amounts of unskipped protein can be present, it would be helpful to include a WB to determine the efficiency of skipping and provide confidence that the co-blocked KAHRP in the +WR condition (Fig 2D) is not actually fused to the C-terminus of SBP1-mDHFR-GFP.

Fortunately, this T2A fusion (crt_SBP1-mDHFR-GFP-2A-KAHRP-mScarlet^epi) was used before in work that included a Western blot showing its efficient skipping (S3 A Fig in MesenRamirez et al., PlosPath 2016). In agreement with these Western blot result, fluorescence microscopy showed very limited overlap of SBP1-mDHFR-GFP and KAHRP-mCherry in absence of WR (Fig. 3B in Mesen-Ramirez et al., PlosPath 2016 and Fig. 2 in this manuscript) which would not be the case if these two constructs were fused together. Please note that KAHRP is known to transiently localize to the Maurer’s clefts before reaching the knobs (Wickham et al., EMBOJ 2001, PMID: 11598007), and therefore occasional overlap with SBP1 at the Maurer’s clefts is expected. However, we would expect much more overlap if a substantial proportion of the construct population would not be skipped and therefore the co-blocked KAHRP-mCherry in the +WR sample is unlikely to be due to inefficient skipping and attachment to SBP1-mDHFR-GFP.

(4) Does comparison of RNAseq from the various 3D7 and IT4 lines in the study provide any insight into PTP3 expression levels between strains with different binding capacities? Was the expression level of ptp3a/b in the IT4var19 panned line similar to the expression in the parent or other activated IT4 lines? Could the expanded ptp3 gene number in IT4 indicate that specialized trafficking machinery exists for some PfEMP1 proteins (ie, IT4var19 requires the divergent PTP3 paralog for efficient trafficking)?

PTP3 in the different IT4 lines that bind:

In those parasite lines that did bind, the intrinsic variation in the binding assays, the different binding properties of different PfEMP1 variants and the variation in RNA Seq experiments to compare different parasite lines precludes a correlation of binding level vs ptp3 expression. For instance, if a PfEMP1 variant has lower binding capacity, ptp3 may still be higher but binding would be lower than if comparing to a parasite line with a better binding PfEMP1 variant. Studying the effect of PTP3 levels on binding could probably be done by overexpressing PTP3 in the same PfEMP1 SLI expressor line and assessing how this affects binding, but this would go beyond this manuscript.

PTP3 in panned vs unpanned Var19:

We did some comparisons between IT4 parent, and the IT4-Var19 panned and unpanned

(see Author response table 1). This did not reveal any clear associations. While the parent had somewhat lower ptp3 transcript levels, they were still clearly higher than in the unpanned Var19 line and other lines had also ptp3 levels comparable to the panned IT4-Var19 (see Author response table 2) 

PTP3 in the TGDs and possible reason for binding phenotype:

A key point is whether PTP3 could have influenced the lack of binding in the TGD lines (see also weakness section and point 1 of public review of reviewer 3: ptp3 may be an indirect cause resulting in lacking binding in TGD parasites). We now did RNA Seq to check for ptp3 expression in the relevant TGD lines although we did not do a systematic quantitative comparison (which would require 3 replicates of RNASeq), but we reasoned that loss of expression would also be evident in one replicate. There was no indication that the TGD lines had lost PTP3 expression (see Author response table 2) and this is unlikely to explain the binding loss in a similar fashion to the Var19 parasites. Generally, the IT4 lines showed expression of both ptp3 genes and only in the Var19 parasites before panning were the transcript levels considerably lower:

Author response table 1.

Parent vs IT4-Var19 panned and unpanned

Author response table 2.

TGD lines with binding phenotype vs parent

The absence of an influence of PTP3 on the binding phenotype in the cell lines in this manuscript (besides Var19) is further supported by its role in PfEMP1 surface display. Previous work has shown that KO of ptp3 leads to a loss of VAR2CSA surface display (Maier et al., Cell 2008). The unpanned Var19 parasite also lacked PfEMP1 surface display and panning and the resulting appearance of the binding phenotype was accompanied by surface display of PfEMP1. As both, the EMPIC3 and TryThra-TGD lines had still at least some PfEMP1 on the surface, this also (in addition to the RNA Seq above) speaks against PTP3 being the cause of the binding phenotype. The same applies to 3D7 which despite the poor binding displays PfEMP1 on the host cell surface (Figure 1D). This indicating that also the binding phenotype in 3D7 is not due to PTP3 expression loss, as this would have abolished PfEMP1 surface display. 

The idea about PTP3 paralogs for specific PfEMP1s is intriguing. In the future it might be interesting to test the frequency of parasites with two PTP3 paralogs in endemic settings and correlate it with the PfEMP1 repertoire, variant expression and potentially disease severity. 

(5) The IT4var01 line shows substantially lower binding in Figure 5F compared with the data shown in Figure 4E and 6F. Does this reflect changes in the binding capacity of the line over time or is this variability inherent to the assay?

There is some inherent variability in these assays. While we did not systematically assess this, we had no indication that this was due to the parasite line changing. The Var01 line was cultured for months and was frozen down and thawed more than once without a clear gradual trend for more or less binding. While we can’t exclude some variation from the parasite side, we suspect it is more a factor of the expression of the receptor on the CHO cells the iRBCs bind to. 

Specifically, the assays in Fig. 6F and 4E mentioned by the reviewer both had an average binding to CD36 of around 1000 iE/mm2, only the experiments in Fig. 5F are different (~ 500 iE/mm2) but these were done with a different batch of CHO cells at a different time to the experiments in Fig. 6F and 4E. 

(6) In Figure S7A, TryThrA and EMPIC3 show distinct localization as circles around the PfEMP1 signal while PeMP2 appears to co-localize with PfEMP1 or as immediately adjacent spots (strong colocalization is less apparent than SBP1, and the various PfEMP1 IFAs throughout the study). Does this indicate that TryThrA and EMPIC3 are peripheral MC proteins? Does this have any implications for their function in PfEMP1 binding? Some discussion would help as these differences are not mentioned in the text. For the EMPIC3 TGD IFAs, localization of SBP1 and PfEMP1 is noted to be normal but REX1 is not mentioned (although this also appears normal).

We apologise for the lacking description of the candidate localisations and cursory description of the Maurer’s clefts phenotypes (next point). Our original intent was to not distract too much from the main flow of the manuscript as almost every part of the manuscript could be followed up with more details. However, we fully agree that this is unsatisfactory and now provided more description (this point) and more data (next point).

Localisation of TryThrA and EMPIC3 compared to PfEMP1 at the Maurer’s clefts: the circular pattern is reminiscent of the results with Maurer’s clefts proteins reported by McMillan et al using 3D-SIM in 3D7 parasites (McMillan et al., Cell Microbiology 2014 (PMID: 23421990)). In that work SBP1 and MAHRP1 (both integral TMD proteins) were found in foci but REX1 (no TMD) in circular structures around these foci similar to what we observed here for TryThrA and EMPIC3 which both also lack a TMD. The SIM data in McMillan et al indicated that also PfEMP1 is “more peripheral”, although it did only partially overlap with REX1. The conclusion from that work was that there are sub-compartments at the Maurer’s clefts. In our IFAs (Fig. S7A) PfEMP1 is also only partially overlapping with the TryThrA and EMPIC3 circles, potentially indicating similar subcompartments to those observed by 3D-SIM. We agree with the reviewer that this might be indicative of peripheral MC proteins, fitting with a lack of TMD in these candidates, but we did not further speculate on this in the manuscript.

We now added enlargements of the ring-like structures to better illustrate this observation in Fig. S7A. In addition, we now specifically mention the localization data and the ring like signal with TryThrA and EMPIC3 in the results and state that this may be similar to the observations by McMillan et al., Cell Microbiology 2014.

We also thank the reviewer for pointing out that we had forgotten to mention REX1 in the EMPIC3-TGD, this was amended.  

(7) The atypical localization in TryThrA TGD line claimed for PfEMP1 and SBP1 in Fig S7B is not obvious. While most REX1 is clustered into a few spots in the IFA staining for SBP1 and REX1, SBP1 is only partially located in these spots and appears normal in the above IFA staining for SBP1 and HA. The atypical localization of PfEMP1-HA is also not obvious to me. The authors should clarify what is meant by "atypical" localization and provide support with quantification given the difference between the two SBP1 images shown.

We apologise for the inadequate description of these IFA phenotypes. The abnormal signal for SBP1, REX1 and PfEMP1 in the TryThrA-TGD included two phenotypes found with all 3 proteins: 

(1) a dispersed signal for these proteins in the host cell in addition to foci (the control and the other TGD parasites have only dots in the host cell with no or very little detectable dispersed signal). 

(2) foci of disproportionally high intensity and size, that we assumed might be aggregation or enlargement of the Maurer’s clefts or of the detected proteins.

The reason for the difference between the REX1 (aggregation) phenotype and the PfEMP1 and SBP1 (dispersed signal, more smaller foci) phenotypes in the images in Fig. S7B is that both phenotypes were seen with all 3 proteins but we chose a REX1 stained cell to illustrate the aggregation phenotype (the SBP1 signal in the same cell is similar to the REX1 signal, illustrating that this phenotype is not REX1 specific; please note that this cell also has a dispersed pool of REX1 and SBP1). 

Based on the IFAs 66% (n = 106 cells) of the cells in the TryThrA-TGD parasites had one or both of the observed phenotypes. We did not include this into the previous version of the manuscript because a description would have required detouring from the main focus of this results section. In addition, IFAs have some limitations for accurate quantifications, particularly for soluble pools (depending on fixing efficiency and agent, more or less of a soluble pool in the host cell can leak out). 

To answer the request to better explain and quantify the phenotype and given the limitations of IFA, we now transfected the TryThrA-TGD parasites with a plasmid mediating episomal expression of SBP1-mCherry, permitting live cell imaging and a better classification of the Maurer’s clefts phenotype. Due to the two SLI modifications in these parasites (using up 4 resistance markers) we had to use a new selection marker (mutated lactate transporter PfFNT, providing resistance to BH267.meta (Walloch et al., J. Med. Chem. 2020 (PMID: 32816478))) to transfect these parasites with an additional plasmid. 

These results are now provided as Fig. S8 and detailed in the last results section. The new data shows that the majority of the TryThrA-TGD parasites contain a dispersed pool of SBP1 in the host cell. About a third of the parasites also showed disproportionally strong SBP1 foci that may be aggregates of the Maurer’s clefts. We also transfected the EMPIC3-TGD parasites with the FNT plasmid mediating episomal SBP1-mCherry expression and observed only few cells with a cytoplasmic pool or aggregates (Fig. S8). Overall these findings agree with the previous IFA results. As the IFA suggests similar results also for REX1 and PfEMP1, this defect is likely not SBP1 specific but more general (Maurer’s clefts morphology; association or transport of multiple proteins to the Maurer’s clefts). This gives a likely explanation for the cytoadherence phenotype in the TryThrA-TGD parasites. The reason for the EMPIC3-TGD phenotype remains to be determined as we did not detect obvious changes of the Maurer’s clefts morphology or in the transport of proteins to these structures in these experiments. 

Minor comments

(1) Italicized numbers in parenthesis are present in several places in the manuscript but it is not clear what these refer to (perhaps differently formatted citations from a previous version of the manuscript). Figure 1

legend: (121); Figure S3 legend: (110), (111); Figure S6 legend: (66); etc.

We thank the reviewer for pointing out this issue with the references, this was amended.

(2) Figure 5A and legend: "BSD-R: BSD-resistance gene". Blasticidin-S (BS) is the drug while Blasticidin-S deaminase (BSD) is the resistance gene.

We thank the reviewer for pointing this out, the legend and figure were changed.

(3) Figure 5E legend: µ-SBP1-N should be α-SBP1-N.

This was amended.

(4) Figure S5 legend: "(Full data in Table S1)" should be Table S3.

This was amended.

(5) Figure S1G: The pie chart shows PF3D7_0425700 accounts for 43% of rif expression in 3D7var0425800 but the text indicates 62%.

We apologize for this mistake, the text was corrected. We also improved the citations to Fig. S1G and H in this section.

(6) "most PfEMP1-trafficking proteins show a similar early expression..." The authors might consider including a table of proteins known to be required for EMP1 trafficking and a graph showing their expression timing. Are any with later expressions known?

Most exported proteins are expressed early, which is nicely shown in Marti et al 2004 (cited for the statement) in a graph of the expression timing of all PEXEL proteins (Fig. 4B in that paper). PNEPs also have a similar profile (Grüring et al 2011, also cited for that statement), further illustrated by using early expression as a criterion to find more PNEPs (Heiber et al., 2013 (PMID: 23950716)). Together this includes most if not all of the known PfEMP1 trafficking proteins. The originally co-submitted paper (Blancke-Soares & Stäcker et al., eLife preprint doi.org/10.7554/eLife.103633.1) analysed several later expressed exported proteins

(Pf332, MSRP6) but their disruption, while influencing Maurer’s clefs morphology and anchoring, did not influence PfEMP1 transport. However, there are some conflicting results for Pf332 (referenced in Blancke-Soares & Stäcker et al). This illustrates that it may not be so easy to decide which proteins are bona fide PfEMP1 trafficking proteins. We therefore did not add a table and hope it is acceptable for the reader to rely on the provided 3 references to back this statement.

(7)  Figure S1J: The predominate var in the IT4 WT parent is var66 (which appears to be syntenic with Pf3D7_0809100, the predominate var in the 3D7 WT parent). Is there something about this locus or parasite culture conditions that selects for these vars in culture? Is this observed in other labs as well?

This is a very interesting point (although we are not certain these vars are indeed syntenic, they are on different chromosomes). As far as we know at least Pf3D7_0809100 is commonly a dominant var transcribed in other labs and was found expressed also in sporozoites (Zanghì et al. Cell Rep. 2018). However, it is unclear how uniform this really is. For IT4 we do not know in full but have also here commonly observed centromeric var genes to be dominating transcripts in unselected parasite cultures. It is possible that transcription drifts to centromeric var genes in cultured parasites. However, given the anecdotal evidence, it is unknown to which extent this is related to an inherent switching and regulation regiment or a consequence of faulty regulation following prolonged culturing.

(8) Figure 4B, C: Presumably the asterisks on the DNA gels indicate non-specific bands but this is not described in the legend. Why are non-specific bands not consistent between parent and integrated lanes?

We apologize for not mentioning this in the legend, this was amended.

It is not clear why the non-specific bands differ between the lines but in part this might be due to different concentrations and quality of DNA preps. A PCR can also behave differently depending on whether the correct primer target is present or not. If present, the PCR will run efficiently and other spurious products will be outcompeted, but in absence of the correct target, they might become detectable.  

Overall, we do not think the non-specific bands are indications of anything untoward with the lines, as for instance in Fig. 4B the high band in the 5’ integration in the IT4 line (that does not occur anywhere else) can’t be due to a genomic change as this is the parental line and does not contain the plasmid for integration. In the same gel, the ori locus band of incorrect size (likely due to crossreaction of the primers to another var gene which due to the high similarity of the ATS region is not always fully avoidable), is present in both, the parent IT4 and the integrant line which therefore also is not of concern. In C there are a couple of bands of incorrect size in the Integration line. One of these is very faint and both are too large and again therefore are likely other vars that are inefficiently picked up by these primers. The reason they are not seen in the parent line is that there the correct primer binding site is present, which then efficiently produces a product that outcompetes the product derived from non-optimal matching primer products and hence appear in the Int line where the correct match is not there anymore. For these reasons we believe these bands are not of any concern.  

(9) Figure 4C: Is there a reason KAHRP was used as a co-marker for the IFA detecting IT4var19 expression instead of SBP1 which was used throughout the rest of the study?

This is a coincidence as this line was tested when other lines were tested for KAHRP. As there were foci in the host cell we were satisfied that the HA-tagged PfEMP1 is produced and the localization deemed plausible. 

(10) Figure 6: Streptavidin labeling for the IT4var01-BirA position 3 line is substantially less than the other two lines in both IFA and WB. Does the position 3 fusion reduce PfEMP1 protein levels or is this a result of the context or surface display of the fusion? Interestingly, the position 3 trypsin cleavage product appears consistently more robust compared with the other two configurations. Does this indicate that positioning BirA upstream of the TM increases RBC membrane insertion and/or makes the surface localized protein more accessible to trypsin?

It is possible that RBC membrane insertion or trypsin accessibility is increased for the position 3 construct. But there could also be other explanations:

The reason for the more robustly detected protected fragment for the position 3 construct in the WB might also be its smaller size (in contrast to the other two versions, it does not contain BirA*) which might permit more efficient transfer to the WB membrane. In that case the more robust band might not (only) be due to better membrane insertion or better trypsin accessibility.

The lower biotinylation signal with the position 3 construct might also be explained by the farther distance of BirA* to the ATS (compared to position 1 and 2), the region where interactors are expected to bind. The position 1 and 2 constructs may therefore generally be more efficient (as closer) to biotinylate ATS proximal proteins. Further, in the final destination (PfEMP1 inserted into the RBC membrane) BirA* would be on the other side of the membrane in the position 3 construct while in the position 1 and 2 constructs BirA* would be on the side of the membrane where the ATS anchors PfEMP1 in the knob structure. In that case, labelling with position 3 would come from interactions/proximities during transport or at the Maurer’s clefts (if there indeed PfEMP1 is not membrane embedded) and might therefore be less.

Hence, while alterations in trypsin accessibility and RBC membrane insertion are possible explanations, other explanations exist. At present, we do not know which of these explanations apply and therefore did not mention any of them in the manuscript. 

Reviewer #3 (Recommendations for the authors):

(1) In the abstract and on page 8, the authors mention that they generate cell lines binding to "all major endothelial receptors" and "all known major receptors". This is a pretty allencompassing statement that might not be fully accepted by others who have reported binding to other receptors not considered in this paper (e.g. VCAM, TSP, hyaluronic acid, etc). It would be better to change this statement to something like "the most common endothelial receptors" or "the dominant endothelial receptors", or something similar.

We agree with the reviewer that these statements are too all-encompassing and changed them to “the most common endothelial receptors” (introduction) and “the most common receptors” (results).

(2) The authors targeted two rif genes for activation and in each case the gene became the most highly expressed member of the family. However, unlike var genes, there were other rif genes also expressed in these lines and the activated copy did not always make up the majority of rif mRNAs. The authors might wish to highlight that this is inconsistent with mutually exclusive expression of this gene family, something that has been discussed in the past but not definitively shown.

We thank the reviewer for highlighting this, we now added the following statement to this section: “While SLI-activation of rif genes also led to the dominant expression of the targeted rif gene, other rif genes still took up a substantial proportion of all detected rif transcripts, speaking against a mutually exclusive expression in the manner seen with var genes.”

(3) In Figure 6, H-J, the authors display volcano plots showing proteins that are thought to interact with PfEMP1. These are labeled with names from the literature, however, several are named simply "1, 2, 3, 4, 5, or 6". What do these numbers stand for?

We apologize for not clarifying this and thank the reviewer for pointing this out. There is a legend for the numbered proteins in what is now Table S4 (previously Table S3). We now amended the legend of Figure 6 to explain the numbers and pointing the reader to Table S4 for the accessions.

eLife Assessment

This study resolves a cryo-EM structure of the GPCR, human GPR30, which responds to bicarbonate and regulates cellular responses to pH and ion homeostasis. Understanding the ligand and the mechanism of activation is important to the field of receptor signaling and potentially facilitates drug development targeting this receptor. Structures and functional assays provide solid evidence for a potential bicarbonate binding site.

Reviewer #1 (Public review):

Summary:

This study resolves a cryo-EM structure of the GPCR, GPR30, in the presence of bicarbonate, which the author's lab recently identified as the physiological ligand. Understanding the ligand and the mechanism of activation is of fundamental importance to the field of receptor signaling. This solid study provides important insight into the overall structure and suggests a possible bicarbonate binding site.

Strengths:

The overall structure, and proposed mechanism of G-protein coupling are solid. Based on the structure, the authors identify a binding pocket that might accommodate bicarbonate. Although assignment of the binding pocket is speculative, extensive mutagenesis of residues in this pocket identifies several that are important to G-protein signaling. The structure shows some conformational differences with a previous structure of this protein determined in the absence of bicarbonate (PMC11217264). To my knowledge, bicarbonate is the only physiological ligand that has been identified for GPR30, making this study an important contribution to the field. However, the current study provides novel and important circumstantial evidence for the bicarbonate binding site based on mutagenesis and functional assays.

Weaknesses:

Bicarbonate is a challenging ligand for structural and biochemical studies, and because of experimental limitations, this study does not elucidate the exact binding site. Higher resolution structures would be required for structural identification of bicarbonate. The functional assay monitors activation of GPR30, and thus reports on not only bicarbonate binding, but also the integrity of the allosteric network that transduces the binding signal across the membrane. However, biochemical binding assays are challenging because the binding constant is weak, in the mM range.

The authors appropriately acknowledge the limitations of these experimental approaches, and they build a solid circumstantial case for the bicarbonate binding pocket based on extensive mutagenesis and functional analysis. However, the study does fall short of establishing the bicarbonate binding site.

Reviewer #2 (Public review):

Summary:

In this manuscript, "Cryo-EM structure of the bicarbonate receptor GPR30," the authors aimed to enrich our understanding of the role of GPR30 in pH homeostasis by combining structural analysis with a receptor function assay. This work is a natural development and extension of their previous work on Nature Communications (PMID: 38413581). In the current body of work, they solved the cryo-EM structure of the human GPR30-G-protein (mini-Gsqi) complex in the presence of bicarbonate ions at 3.15 Å resolution. From the atomic model built based on this map, they observed the overall canonical architecture of class A GPCR and also identified 3 extracellular pockets created by ECLs (Pockets A-C). Based on the polarity, location, size, and charge of each pocket, the authors hypothesized that pocket A is a good candidate for the bicarbonate binding site. To identify the bicarbonate binding site, the authors performed an exhaustive mutant analysis of the hydrophilic residues in Pocket A and analyzed receptor reactivity via calcium assay. In addition, the human GPR30-G-protein complex model also enabled the authors to elucidate the G-protein coupling mechanism of this special class A GPCR, which plays a crucial role in pH homeostasis.

Strengths:

As a continuation of their recent Nature Communications publication, the authors used cryo-EM coupled with mutagenesis and functional studies to elucidate bicarbonate-GPR30 interaction. This work provided atomic-resolution structural observations for the receptor in complex with G-protein, allowing us to explore its mechanism of action, and will further facilitate drug development targeting GPR30. There were 3 extracellular pockets created by ECLs (Pockets A-C). The authors were able to filter out 2 of them and hypothesized that pocket A was a good candidate for the bicarbonate binding site based on the polarity, location, and charge of each pocket. From there, the authors identified the key residues on GPR30 for its interaction with the substrate, bicarbonate. Together with their previous work, they mapped out amino acids that are critical for receptor reactivity.

Weaknesses:

When we see a reduction of a GPCR-mediated downstream signaling, several factors could potentially contribute to this observation: 1) a reduced total expression of this receptor due to the mutation (transcription and translation issue); 2) a reduced surface expression of this receptor due to the mutation (trafficking issue); and 3) a dysfunctional receptor that doesn't signal due to the mutation. In the current revision, based on the gating strategy, the surface expression of the HA-positive WT GPR30-expressing cells is only 10.6% of the total population, while the surface expression levels of the mutants range from 1.89% (P71A) to 64.4% (D111A). Combining this information with the functional readout in Figure 3F and G, as well as their previous work, the authors concluded that mutations at P71, E115, D125, Q138, C207, D210, and H307 would decrease bicarbonate responses. Among those sites,

E115, Q138, and H307 were from their previous Nature Comm paper.

Authors claim P71 and C207 make a structural-stability contribution, as their mutations result in a significant reduction in surface expression: P71A (1.89%) and C207A (2.71%). However, compared to 10.6% of the total population in the WT, (P71A is 17.8% of the WT, and C207A is 25.6% of the WT), this doesn't rule out the possibility that the mutated receptor is also dysfunctional: at 10 mM NaHCO3, RFU of WT is ~500, RFU of P71 and C207 are ~0.

The authors also interpret "The D125ECL1A mutant has lost its activity but is located on the surface" and only mention "D125 is unlikely to be a bicarbonate binding site, and the mutational effect could be explained due to the decreased surface expression". Again, compared to 10.6% of the total population in the WT, D125A (3.94%) is 37.2% of the WT. At 10 mM NaHCO3, the RFU of the WT is ~500, the RFU of D125 is ~0. This doesn't rule out the possibility that the mutated receptor is also dysfunctional. It is not clear why D125A didn't make it to the surface.

Other mutants that the authors didn't mention much in their text: D111A (64.4%, 607.5% of WT surface expression), E121A (50.4%, 475.5% of WT surface expression), R122 (41.0%, 386.8% of WT surface expression), N276A (38.9%, 367.0% of WT surface expression) and E218A (24.6%, 232.1% of WT surface expression) all have similar RFU as WT, although the surface expression is about 2-6 times more. On the other hand, Q215A (3.18%, 30% of WT surface expression) has similar RFU as WT, with only a third of the receptor on the surface.

Altogether, the wide range of surface expression across the different cell lines, combined with the different receptor function readouts, makes the cell functional data only partially support their structural observations.

Reviewer #3 (Public review):

Summary

GPR30 responds to bicarbonate and plays a role in regulating cellular pH and ion homeostasis. However, the molecular basis of bicarbonate recognition by GPR30 remains unresolved. This study reports the cryo-EM structure of GPR30 bound to a chimeric mini-Gq in the presence of bicarbonate, revealing mechanistic insights into its G-protein coupling. Nonetheless, the study does not identify the bicarbonate-binding site within GPR30.

Strengths

The work provides strong structural evidence clarifying how GPR30 engages and couples with Gq.

Weaknesses

Several GPR30 mutants exhibited diminished responses to bicarbonate, but their expression levels were also reduced. As a result, the mechanism by which GPR30 recognizes bicarbonate remains uncertain, leaving this aspect of the study incomplete.

Author response:

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

The parts of the text that have been changed.The major changes are as follows:

We re-analyzed the dataset and improved the local resolution of the extracellular region (Author response image 1).

We re-modeled based on the improved density and canceled the bicarbonate model based on comments from all reviewers.

We performed calcium assay using cell lines stably expressing the mutants, whose surface expression levels were analyzed by fluorescence-activated cell sorting (FACS)<br /> (Figure 3F, G and Figure 3–figure supplement 1-3).

Thus, we significantly revised our discussion of the extracellular binding pocket and the result of the mutational study. In the revised manuscript, we speculate that H307 is a candidate for the bicarbonate binding site.

Author response image 1.

Figure Comparison of local resolution between re-analyzed and previous maps.A Side and top view of the re-analyzed receptor-focused map of GPR30 colored by local resolution. B Side and top view of the previous receptor-focused map of GPR30 colored by local resolution

Reviewer #1 (Public Review):

Summary:

This study resolves a cryo-EM structure of the GPCR, GPR30, which was recently identified as a bicarbonate receptor by the authors' lab. Understanding the ligand and the mechanism of activation is of fundamental importance to the field of receptor signaling. However, the main claim of the paper, the identification of the bicarbonate binding site, is only partly supported by the structural and functional data, leaving the study incomplete.

Strengths:

The overall structure, and proposed mechanism of G-protein coupling seem solid. The authors perform fairly extensive unbiased mutagenesis to identify a host of positions that are important to G-protein signaling. To my knowledge, bicarbonate is the only physiological ligand that has been identified for GPR30, making this study a particularly important contribution to the field.

Weaknesses:

Without higher resolution structures and/or additional experimental assessment of the binding pocket, the assignment of the bicarbonate remains highly speculative. The local resolution is especially poor in the ECL loop region where the ligand is proposed to bind (4.3 - 4 .8 Å range). Of course, sometimes it is difficult to achieve high structural resolution, but in these cases, the assignment of ligands should be backed up by even more rigorous experimental validation.The functional assay monitors activation of GPR30, and thus reports on not only bicarbonate binding, but also the integrity of the allosteric network that transduces the binding signal across the membrane. Thus, disruption of bicarbonate signaling by mutagenesis of the putative coordinating residues does not necessarily mean that bicarbonate binding has been disrupted. Moreover, the mutagenesis was apparently done prior to structure determination, meaning that residues proposed to directly surround bicarbonate binding, such as E218, were not experimentally validated. Targeted mutagenesis based on the structure would strengthen the story.

Moreover, the proposed bicarbonate binding site is surprising in a chemical sense, as it is located within an acidic pocket. The authors cite several other structural studies to support the surprising observation of anionic bicarbonate surrounded by glutamate residues in an acidic pocket (references 31-34). However, it should be noted that in general, these other structures also possess a metal ion (sodium or calcium) and/or a basic sidechain (arginine or lysine) in the coordination sphere, forming a tight ion pair. Thus, the assigned bicarbonate binding site in GPR30 remains an anomaly in terms of the chemical properties of the proposed binding site.

Thank you for your insightful comments. Based on the weaknesses you pointed out, we reconstructed the receptor based on the improved density and removed the bicarbonate model. We performed calcium assays using cell lines stably expressing the variant based on the structure.

Reviewer #2(Public Review):

Summary:

In this manuscript, "Cryo-EM structure of the bicarbonate receptor GPR30," the authors aimed to enrich our understanding of the role of GPR30 in pH homeostasis by combining structural analysis with a receptor function assay. This work is a natural development and extension of their previous work (PMID: 38413581). In the current body of work, they solved the first cryo-EM structure of the human GPR30-G-protein (mini-Gsqi) complex in the presence of bicarbonate ions at 3.21 Å resolution. From the atomic model built based on this map, they observed the overall canonical architecture of class A GPCR and also identified 4 extracellular pockets created by extracellular loops (ECLs) (Pockets A-D). Based on the polarity, location, and charge of each pocket, the authors hypothesized that pocket D is a good candidate for the bicarbonate binding site. To verify their structural observation, on top of the 10 mutations they generated in the previous work, the authors introduced another 11 mutations to map out the essential residues for the bicarbonate response on hGPR30. In addition, the human GPR30-G-protein complex model also allowed the authors to untangle the G-protein coupling mechanism of this special class A GPCR that plays an important role in pH homeostasis.

Strengths:

As a continuation of their recent Nature Communication publication (PMID: 38413581), this study was carefully designed, and the authors used mutagenesis and functional studies to confirm their structural observations. This work provided high-resolution structural observations for the receptor in complex with G-protein, allowing us to explore its mechanism of action, and will further facilitate drug development targeting GPR30. There were 4 extracellular pockets created by ECLs (Pockets A-D). The authors were able to filter out 3 of them and identified that pocket D was a good candidate for the bicarbonate binding site based on the polarity, location, and charge of each pocket. From there, the authors identified the key residues on GPR30 for its interaction with the substrate, bicarbonate. Together with their previous work, they carefully mapped out nine amino acids that are critical for receptor reactivity.

Weaknesses:

It is unclear how novel the aspects presented in the new paper are compared to the most recent Nature Communications publication (PMID: 38413581). Some areas of the manuscript appear to be mixed with the previous publication. The work is still impactful to the field. The new and novel aspects of this manuscript could be better highlighted.

I also have some concerns about the TGFα shedding assay the authors used to verify their structural observation. I understand that this assay was also used in the authors' previous work published in Nature Communications. However, there are still several things in the current data that raised concerns:

Thank you for your insightful comments. Based on the weaknesses you pointed out, we highlighted the new and novel aspects of this manuscript could be better highlighted.l. We performed calcium assays using cell lines stably expressing the variant based on the structure.

(1) The authors confirmed the "similar expression levels of HA-tagged hGPR30" mutants by WB in Supplemental Figure 1A and B. However, compared to the hGPR30-HA (~6.5 when normalized to the housekeeping gene, Na-K-ATPase), several mutants of the key amino acids had much lower surface expression: S134A, D210A, C207A had ~50% reduction, D125A had ~30% reduction, and Q215A and P71A had ~20% reduction. This weakens the receptor reactivity measured by the TGFα shedding assay.

Since the calcium assay data is included in the main figure, the TGFα shedding assay and WB expression quantification data are Figure 3. –– supplement figure 1-4, but we included an explanation of the expression levels in the figure caption.

(2) In the previous work, the authors demonstrated that hGPR30 signals through the Gq signaling pathway and can trigger calcium mobilization. Given that calcium mobilization is a more direct measurement for the downstream signaling of hGPR30 than the TGFα shedding assay, pairing the mutagenesis study with the calcium assay will be a better functional validation to confirm the disruption of bicarbonate signaling.

According to the suggestion, we performed calcium assay using cell lines stably expressing the mutants (Figure 3F, G and Figure 3–figure supplement 1-3).

(3) It was quite confusing for Figure 4B that all statistical analyses were done by comparing to the mock group. It would be clearer to compare the activity of the mutants to the wild-type cell line.

Thank you for your comment. As you mentioned, the comparisons are made between wild-type GPR30 and mutants in the revised manuscript (Figure 3G, Figure 3.—figure supplement 4B)

Additional concerns about the structural data include

(1) E218 was in close contact with bicarbonate in Figure 4D. However, there is no functional validation for this observation. Including the mutagenesis study of this site in the cell-based functional assay will strengthen this structural observation.

We cancelled the bicarbonate model, and we performed mutation analysis targeting all residues facing the binding pocket using cell lines that stably express variants including E218A.

(2) For the flow chart of the cryo-EM data processing in Supplemental data 2, the authors started with 10,148,422 particles after template picking, then had 441,348 Particles left after 2D classification/heterogenous refinement, and finally ended with 148,600 particles for the local refinement for the final map. There seems to be a lot of heterogeneity in this purified sample. GPCRs usually have flexible and dynamic loop regions, which explains the poor resolution of the ECLs in this case. Thus, a solid cell-based functional validation is a must to assign the bicarbonate binding pocket to support their hypothesis.

We re-analyzed the dataset and improved the local resolution of the extracellular region (Author response image 1) and cancelled the bicarbonate model. Yet, as suggested by the reviewer, solid cell-based functional validation is efficient to analyze the receptor function response to bicarbonate. Thus, we performed mutation analysis targeting all residues facing the binding pocket using cell lines stably expressing the mutants, whose surface expression levels were analyzed by FACS (Figure 3F, G and Figure 3.––figure supplement 1-3).

Reviewer #3 (Public Review):

Summary:

GPR30 responds to bicarbonate and regulates cellular responses to pH and ion homeostasis. However, it remains unclear how GPR30 recognizes bicarbonate ions. This paper presents the cryo-EM structure of GPR30 bound to a chimeric mini-Gq in the presence of bicarbonate. The structure together with functional studies aims to provide mechanistic insights into bicarbonate recognition and G protein coupling.

Strengths:

The authors performed comprehensive mutagenesis studies to map the possible binding site of bicarbonate.

Weaknesses:

Owing to the poor resolution of the structure, some structural findings may be overclaimed.

Based on EM maps shown in Figure 1a and Figure Supplement 2, densities for side chains in the receptor particularly in ECLs (around 4 Å) are poorly defined. At this resolution, it is unlikely to observe a disulfide bond (C130ECL1-C207ECl2) and bicarbonate ions. Moreover, the disulfide between ECL1 and ECL2 has not been observed in other GPCRs and the published structure of GPR30 (PMID: 38744981). The density of this disulfide bond could be noise.

The authors observed a weak density in pocket D, which is accounted for by the bicarbonate ions. This ion is mainly coordinated by Q215 and Q138. However, the Q215A mutation only reduced but not completely abolished bicarbonate response, and the author did not present the data of Q138A mutation. Therefore, Q215 and Q138 could not be bicarbonate binding sites. While H307A completely abolished bicarbonate response, the authors proposed that this residue plays a structural role. Nevertheless, based on the structure, H307 is exposed and may be involved in binding bicarbonate. The assignment of bicarbonate in the structure is not supported by the data.

Thank you for your insightful comments. Based on the weaknesses you pointed out, we reconstructed the receptor based on the improved density and removed the bicarbonate model. We performed calcium assays using cell lines stably expressing the variant based on the structure.

Reviewer #1 (Recommendations For The Authors):

(1) The experimental validation of the bicarbonate binding could be strengthened by developing an assay that directly monitors bicarbonate binding (rather than GPCR signaling)

We agree that a direct binding assay for bicarbonate would be highly attractive (i.e. Filter binding assay using 14C-HCO₃⁻). However, the weak affinity of bicarbonate ions (in the mM range) would make reliable radioisotope-based detection impossible due to minimal specific receptor occupancy and high non-specific background and thus it is highly challenging and there are limitations to what can be done in this structural paper.

and determining a structure at comparable resolution in the absence of bicarbonate. In addition, all residues that are proposed to be located adjacent to the bicarbonate should be mutated and functionally validated.

We re-modeled the receptor based on the improved density and canceled the bicarbonate model. We performed calcium assay using cell lines stably expressing the mutants (Figure 3F, G and Figure 3.–figure supplement 1-3).

(2) What are the maps contoured in Figure 4D? The legend should describe this. Is 218 within the map region shown, or is there no density for its sidechain?

We removed the corresponding figure and cancelled the bicarbonate model.

(3) The contour level of the maps in Figure 1 - Figure Supplement 2 should also be indicated. Are these all contoured at the same level?

Thank you for your comment. We re-analyzed the same data set and obtained new density maps and models. We reworked Figure 1 and Figure 1. figure supplement 2; the contour level of the map for Figure 1 and composite map for the Figure 1. figure supplement 2 is the same, 7.65. 

(4) Regarding the cited structures of bicarbonate-binding proteins, for three of the four cited structures, the bicarbonate is actually coordinated by positive ligands, with the Asp/Glu playing a more peripheral role:

Capper et al: Overall basic cavity with tight bidentate coordination by Arg. The Glu is 5-6 Å away.

Koropatkin et al: Two structures. The first, solved at pH 5, is proposed to have carbonic acid bound. The second, solved at pH 8, shows carbonate in a complex with calcium, with the calcium coordinated by carboxylates.

Wang et al: The bicarbonate is coordinated by a lysine and a sodium ion. The sodium is coordinated by carboxylates.

The authors should more thoughtfully discuss the unusual properties of this binding site with regard to the previous literature. Is it possible that bicarbonate binds in complex with a metal ion? Could this possibility be experimentally tested?

We cancelled the bicarbonate model.

(5) As a structure of GPR30 has been recently published by another group (PMID: 38744981), it would be valuable to discuss structural similarities and differences and discuss how bicarbonate activation and activation by the chloroquine ligand identified by the other group might both be accommodated by this structure.

Thank you for your valuable comment. We compared the structure presented by another group and added our discussion, as “During the revision of this manuscript, the structures of apo-GPR30-G_q (PDB 8XOG) and the exogenous ligand Lys05-bound GPR30-G_q (PDB 8XOF) were reported [42]. We compared our structure of GPR30 in the presence of bicarbonate with these structures. In the extracellular region, the position of TM5 in GPR30 in the presence of bicarbonate is similar to that in apo-GPR30. In contrast, the position of TM6 is shifted outward relative to that of apo-GPR30, resembling the conformation observed in Lys05-bound GPR30 (Figure 6A, B). Additionally, the position of ECL1 is also shifted outward compared to that of apo-GPR30 (Figure 6B). In the GPR30 structure in the presence of bicarbonate, ECL2 was modeled, suggesting differences in structural flexibility. These findings indicate that the structure of GPR30 in the presence of bicarbonate is different from both the apo structure and the Lys05-bound structure, demonstrating that the structure and the flexibility of the extracellular domain of GPR30 change depending on the type of ligand. Furthermore, focusing on the interaction with G_q, the αN helix of G_q is not rotated in the structure bound to Lys05, in contrast to the characteristic bending of the αN helix in our structure (Figure 6C, D). Although it is necessary to consider variations in experimental conditions, such as salt concentration, the differences in the G_q binding modes suggest that the downstream signals may change in a ligand-dependent manner.” (lines 249-266).

Reviewer #2 (Recommendations For The Authors):

(1) It is highly recommended that the authors carefully go through the "insights into bicarbonate binding" section. The results of the new findings in this paper were blended in with the results from the previous work: the importance of E115, Q138, and H307 in the receptor-bicarbonate interaction was shown in the Nature Communication paper but the authors didn't make it clear, which added a little confusion.

We emphasized this fact in the main text (lines 130-132).

(2) It would be nice for the authors to add some content about the physiological concentration of HCO3 or refer more to their previous work about the rationale for selecting the bicarbonate dose in their functional assay.

Thank you for your comment. The physiological concentration of bicarbonate is 22-26 mM in the extracellular fluid, including interstitial fluid and blood, and 10-12 mM in the intracellular fluid. The bicarbonate concentration alters in various physiological and pathological conditions – metabolic acidosis in chronic kidney disease causes a drop to 2-3 mM, and metabolic alkalosis induced by severe vomiting increases HCO₃^- concentrations more than 30 mM. Thus, our present and previous works clearly show that GPR30 is activated by physiological concentrations of bicarbonate, whether it is localized intracellularly or on the membrane, and that GPR30 can be deactivated or reactivated in various pathophysiological conditions. We added this in the discussion section (lines 267-278).

(3) In Figure 3A, in the legend, the authors mentioned: "black dashed lines indicate hydrogen bonds". No hydrogen bond was noted in the figure.

We totally corrected Figure 3.

(4) Figure 3B, it would be helpful for the authors to denote the meaning of the blue-white-red color coding in the legend.

We removed the figure.

(5) Supplemental Figure 3: since AF3 was released on May 3rd, it would be awesome in the revision version if the authors would update this to the AF3 model.

The AF2 model has been replaced with the AF3. (Figure 2–figure supplement 2A-C). The AF2 and AF3 models are almost identical, and they form incorrect disulfide bonds. This confirms the usefulness of the experimental structural determination in this study.

(6) Supplemental Figure 4: it wasn't clear to me if the expression experiments were repeated multiple times or if there was any statistical analysis for the expression level was done in this study.

We performed the expression experiment by western blotting once and did not perform statistical analyses. We performed repeated FACS analyses of HEK cells stably expressing N-terminally HA-tagged wild-type or mutant GPR30s to analyze their membrane and whole-cell expressions during revision (Figure 3.–figure supplement 1-3). Using these stable cells, we performed calcium assays using cell lines stably expressing the mutants (Figure 3F, G and Figure 3–figure supplement 1-3).

(7) Supplemental Figure 4: Also, is there a reason for the authors to compare the expression level of hGPR30 to the housekeeping gene NA-K-ATPase rather than the total loaded protein? Traditionally housekeeping genes have been used as loading controls to semiquantitatively compare the expression of target proteins in western blots. However, numerous recent studies show that housekeeping proteins can be altered due to experimental conditions, biological variability across tissues, or pathologies. A consensus has developed for using total protein as the internal control for loading. An editorial from the Journal of Biological Chemistry reporting on "Principles and Guidelines for Reporting Preclinical Research" from the workshop held in June 2014 by the NIH Director's Office, Nature Publishing Group, and Science stated, "It is typically better to normalize Western blots using total protein loading as the denominator".

Thank you for your instructive comment. We evaluated western blotting with the same amount of total protein loaded 20 µg for whole-cell lysate and 1.5 µg for cell surface protein (Figure 3.–figure supplement 3C-F).

Reviewer #3 (Recommendations For The Authors):

The claim about this disulfide should be removed unless the authors can provide mass spec evidence.

Thank you for your crucial comments. Firstly, C130 is a residue of TM3, not ECL1, so our misprint has been corrected to C130^3.25. C207^ECL2, located at position 45.50, is the most conserved residue in ECL2, and it forms a disulfide bond with cysteine at position 3.25 (PMID: 35113559). The paper was additionally cited regarding the preservation of the bond of C130^3.25-C207^ECL2 (line 103). Indeed, disruption of this disulfide bond by the C207^ECL2 A mutation resulted in a marked reduction in receptor activity. In addition, the data set was re-analyzed to improve the local resolution of the extracellular region, and it was shown that the density of ECL2 is not noise (Figure 2. ––figure supplement 2). We are confident about the presence of the disulfide bond, based on the structural analysis data and the conservation.

The highly flexible extracellular region is greatly affected by experimental conditions and ligands, so we speculate that the ECL2 and the disulfide bond was not observed in other reported structures of GPR30. Then, we have added the following content to the discussion, as “In the GPR30 in the presence of bicarbonate, ECL2 was modelled, suggesting differences in structural flexibility.” (lines 256-257).

The authors should remove the assignment of bicarbonate in the structure, and tone down the binding site of bicarbonate.

We cancelled the bicarbonate model.

Minor:

(1) The potency of bicarbonate for GPR30 is in the mM range. Although the concentration of bicarbonate in the serum can reach mM range, how about its concentration in the tissues? Given its low potency, it may be not appropriate to claim GPR30 is a bicarbonate receptor at this point, but the authors can claim that GPR30 can be activated by or responds to bicarbonate.

The physiological concentration of bicarbonate is 22-26 mM in the extracellular fluid, including interstitial fluid and blood, and 10-12 mM in the intracellular fluid. Therefore, GPR30 is activated by physiological concentrations of bicarbonate in the tissues. Also, the bicarbonate concentration alters in various physiological and pathological conditions – metabolic acidosis in chronic kidney disease causes a drop to 2-3 mM, and metabolic alkalosis induced by severe vomiting increases HCO3- concentrations more than 30 mM. Thus, our work clearly shows that GPR30 is activated by physiological concentrations of bicarbonate, whether it is localized intracellularly or on the membrane, and that GPR30 can be deactivated or reactivated in various pathophysiological conditions. According to the reasons above, we claim GPR30 is a bicarbonate receptor (lines 267-278).

(2) The description that there is no consensus on a drug that targets GPR30 is not accurate, since lys05 has been reported as an agonist of GPR30 and their structure is published (PMID: 38744981). The published structures of GPR30 should be introduced in the paper.

We added the discussion about the structural comparison with the Lys05-bound structure (Figure 6, lines 249-266)

(3) BW numbers in Figure 4A should be shown.

We added BW numbers in the figures of the mutational studies.

eLife Assessment

The authors present an important approach to identify imported P. falciparum malaria cases, combining genetic and epidemiological/travel data. This tool has the potential to be expanded to other contexts. The data was analyzed using convincing methods, including a novel statistical model. This study may be of interest to researchers in public health and infectious diseases beyond malaria.

Reviewer #1 (Public review):

Summary:

This study presents a new Bayesian approach to estimate importation probabilities of malaria combining epidemiological data, travel history, and genetic data through pairwise IBD estimates. Importation is an important factor challenging malaria elimination, especially in low transmission settings. This paper focus on Magude and Matutuine, two districts in south Mozambique with very low malaria transmission. The results show isolation-by-distance in Mozambique, with genetic relatedness decreasing with distances larger than 100 km, and no spatial correlation for distances between 10 and 100 km. But again strong spatial correlation in distances smaller than 10 km. They report high genetic relatedness between Matutuine and Inhambane, higher than between Matutuine and Magude. Inhambane is the main source of importation in Matutuine, accounting for 63.5% of imported cases. Magude, on the other hand, shows smaller importation and travel rates than Matutuine, as it is a rural area with less mobility. Additionally, they report higher levels of importation and travel in the dry season, when transmission is lower. Also, no association with importation was found for occupation, sex and other factors. These data have practical implications for public health strategies aiming malaria elimination, for example, testing and treating travelers from Matutuine in the dry season.

Strengths:

The strength of this study relies in the combination of different sources of data - epidemiological, travel and genetic data - to estimate importation probabilities, the statistical analyses.

Weaknesses:

The authors recognize the limitations related to sample size and the biases of travel reports.

Reviewer #2 (Public review):

Summary:

Based on a detailed dataset, the authors present a novel Bayesian approach to classify malaria cases as either imported or locally acquired.

Strengths:

The proposed Bayesian approach for case classification is simple, well justified, and allows the integration of parasite genomics, travel history, and epidemiological data.

Weakness:

While the authors aim to classify cases as imported or locally acquired, the work lacks a quantification of the contribution of each case type to overall transmission.

Comments on revisions:

All my questions and concerns were satisfactorily addressed.

Reviewer #3 (Public review):

This work provides a novel statistical model to identify imported malaria cases, which are an important challenge for elimination, particularly in low-transmission areas. This tool was applied in Plasmodium falciparum populations in Mozambique and determined differences in importation rates in 2 low-transmission districts in the South.

Strengths:

The study has several strengths, mainly the development of a novel Bayesian model that integrates genomic, epidemiological, and travel data to estimate importation probabilities. The results showed insights into malaria transmission dynamics, particularly identifying importation sources and differences in importation rates in Mozambique. Finally, the relevance of the findings is to suggest interventions focusing on the traveler population to support efforts for malaria elimination.

Weaknesses:

The study also has some limitations, although the authors have plans to address them. The sample collection was not representative of some provinces, and not all samples had sufficient metadata for the risk factor analysis. Additionally, the authors used a proxy for transmission intensity and assumed some other conditions to calculate the importation probability for specific scenarios. They plan to conduct a new sample collection and include monthly malaria incidence estimates in the future.

Comments on revisions:

- Delete "We added this text to the discussion" in line 302 (Discussion)<br /> - I recommend adding the plans to address limitations indicated in the Response to Reviewers document in the Discussion. This would really strengthen the limitation section.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study presents a new Bayesian approach to estimate importation probabilities of malaria, combining epidemiological data, travel history, and genetic data through pairwise IBD estimates. Importation is an important factor challenging malaria elimination, especially in low-transmission settings. This paper focuses on Magude and Matutuine, two districts in southern Mozambique with very low malaria transmission. The results show isolation-by-distance in Mozambique, with genetic relatedness decreasing with distances larger than 100 km, and no spatial correlation for distances between 10 and 100 km. But again, strong spatial correlation in distances smaller than 10 km. They report high genetic relatedness between Matutuine and Inhambane, higher than between Matutuine and Magude. Inhambane is the main source of importation in Matutuine, accounting for 63.5% of imported cases. Magude, on the other hand, shows smaller importation and travel rates than Matutuine, as it is a rural area with less mobility. Additionally, they report higher levels of importation and travel in the dry season, when transmission is lower. Also, no association with importation was found for occupation, sex, and other factors. These data have practical implications for public health strategies aiming for malaria elimination, for example, testing and treating travelers from Matutuine in the dry season.

Strengths:

The strength of this study lies in the combination of different sources of data - epidemiological, travel, and genetic data - to estimate importation probabilities, and the statistical analyses.

Weaknesses:

The authors recognize the limitations related to sample size and the biases of travel reports.

We appreciate the review and comment about the manuscript.

Reviewer #2 (Public review):

Summary:

Based on a detailed dataset, the authors present a novel Bayesian approach to classify malaria cases as either imported or locally acquired.

Strengths:

The proposed Bayesian approach for case classification is simple, well justified, and allows the integration of parasite genomics, travel history, and epidemiological data. The work is well-written, very organized, and brings important contributions both to malaria control efforts in Mozambique and to the scientific community. Understanding the origin of cases is essential for designing more effective control measures and elimination strategies.

Weakness:

While the authors aim to classify cases as imported or locally acquired, the work lacks a quantification of the contribution of each case type to overall transmission.

The method presented here allows for classifying individual cases according to whether the infection occurred locally or was imported during a trip. By definition, it does not look to secondary infections after an importation event. Our next step is to conduct outbreak investigation to quantify the impact of importation events on the overall transmission, but this activity goes beyond the scope of this manuscript. We clarify this in the discussion section.

The Bayesian rationale is sound and well justified; however, the formulation appears to present an inconsistency that is replicated in both the main text and the Supplementary Material.

Thank you for pointing out the inconsistency in the final formula. In fact, the final formula corresponds to P(IA | G), instead of P(IA), so:

instead of

We have now corrected this error in the new version of the manuscript.

Reviewer #3 (Public review):

The authors present an important approach to identify imported P. falciparum malaria cases, combining genetic and epidemiological/travel data. This tool has the potential to be expanded to other contexts. The data was analyzed using convincing methods, including a novel statistical model; although some recognized limitations can be improved. This study will be of interest to researchers in public health and infectious diseases.

Strengths:

The study has several strengths, mainly the development of a novel Bayesian model that integrates genomic, epidemiological, and travel data to estimate importation probabilities. The results showed insights into malaria transmission dynamics, particularly identifying importation sources and differences in importation rates in Mozambique. Finally, the relevance of the findings is to suggest interventions focusing on the traveler population to help efforts for malaria elimination.

Weaknesses:

The study also has some limitations. The sample collection was not representative of some provinces, and not all samples had sufficient metadata for risk factor analysis, which can also be affected by travel recall bias. Additionally, the authors used a proxy for transmission intensity and assumed some conditions for the genetic variable when calculating the importation probability for specific scenarios. The weaknesses were assessed by the authors.

We acknowledge the limitations commented by the reviewer. We have the following plans to address the limitations. We will repeat the study for our data collected in 2023, which this time contains a good representation of all the provinces of Mozambique, and completeness of the metadata collection was ensured by implementing a new protocol in January 2023. Regarding the proxy for transmission intensity, we will refine the model by integrating monthly estimates of malaria incidence (previously calibrated to address testing and reporting rates) from the DHIS2 data, taking also into account the date of the reported cases in the analysis.

Reviewing Editor Comments:

The reviewers have made specific suggestions that could improve the clarity and accuracy of this report.

Reviewer #1 (Recommendations for the authors):

(1) Abstract, lines 36, 37 and 38: "Spatial genetic structure and connectivity were assessed using microhaplotype-based genetic relatedness (identity-by-descent) from 1605 P. falciparum samples collected (...)", but only 540 samples were successfully sequenced, therefore used in spatial genetic structure and connectivity analysis.

The 540 samples refer to those from Maputo province and are described in Fig. 1. The Spatial and connectivity analyses also included the samples from the rest of the provinces from the multi-cluster sampling scheme. Sample sizes from these provinces are described in Suppl. Table 2, and the total between them and the 540 samples from Maputo are the 1605 samples mentioned in the abstract. We specify this number in the caption of Sup. Fig. 4, and add it now into Fig. 3

(2) In the Introduction, some epidemiological context about Magude and Matutuine could be added. It is only mentioned in the Discussion section (lines 265-269).

We have added some context about both districts in the introduction now.

(3) In the Discussion, lines 241-244, could the lack of structure mean no barriers for gene flow due to high mobility in short distances? Maybe it could only be resolved with a large number of samples.

This could be an explanation (we mention it in the new version), although it is not something we can prove, or at least in this study.

Reviewer #2 (Recommendations for the authors):

The work is well written, very organized, and brings important contributions both to malaria control efforts in Mozambique and to the scientific community. Based on detailed datasets from Mozambique, the authors present a novel Bayesian approach to classify malaria cases as either imported or locally acquired. Understanding the origin of cases is essential for designing more effective control measures and elimination strategies. My review focuses on the Bayesian approach as well as on a few aspects of the presentation of results.

The authors combine travel history, parasite genetic relatedness, and transmission intensity from different areas to compute the probability of infection occurring in the study area, given the P. falciparum genome. The Bayesian rationale is sound and well justified; however, the formulation appears to present an inconsistency that is replicated in both the main text and the Supplementary Material. According to Bayes' Rule:

P(I_A |G) = (P(I_A) ∙ P(G|I_A)) / (P(G)),

with

P(I_A) = K ∙ T_A ∙ PR_A,

P(G│I_A) = R'_A,

and assuming

P(I_A│G) + P(I_B│G) = 1,

the expression,

(T_A ∙ PR_A ∙ R'_A) / (T_A ∙ PR_A ∙ R'_A + T_B ∙ PR_B ∙ R'_B)

appears to refer to P(I_A│G), not to P(I_A) (as indicated in the main text and Supplementary Material).

P(I_A│G) + P(I_B│G) = (P(I_A) ∙ P(G|I_A) + P(I_B) ∙ P(G|I_B)) / P(G) = 1

⇒P(G) = P(I_A) ∙ P(G|I_A) + P(I_B) ∙ P(G|I_B)

⇒P(G) = K ∙ T_A ∙ PR_A ∙ R'_A + K ∙ T_B ∙ PR_B ∙ R'_B

⇒P(I_A│G) = (T_A ∙ PR_A ∙ R'_A) / (T_A ∙ PR_A ∙ R'_A + T_B ∙ PR_B ∙ R'_B)

Please clarify this.

As mentioned in a previous comment, we acknowledge this point from the reviewer.  In fact, the final formula corresponds to P(IA | G), instead of P(IA), so:

instead of

We have now corrected this error in the new version of the manuscript and in the supplementary information.

Additional comments:

(1) Figure 3A has a scale that includes negative values, which is not reasonable for R.

We agree that R estimates are not compatible with negative values. The intention of this scale was to show the overall mean R in the centre, in white, so that blue colours represented values below the average and red values above the average. However, we proceeded to update the figures according to your recommendations.

(2) I suggest using a common scale from 0 to 0.12 (maximum values among panels) across panels A, C, and D, as well as in Sup Fig 3, to facilitate comparison.

We updated the figures according to the recommendations.

(3) The x-axis labels in Figure 3A and Supplementary Figure 2A are not aligned with the x-axis ticks.

We updated the figures so that the alignment in the x-axis is clear.

(4) Supplementary Figure 5 would be better presented if the data were divided into four separate panels.

We have divided the figure into four separate panels.

(6) Figure 5D is not referenced in the main text.

We missed the mention, which is now fixed in the new version.

(7) The authors state: "No significant differences in R were found comparing parasite samples from Magude and the rest of the districts." However, Supplementary Figure 3 shows statistically significant relatedness between parasites from Magude and Matutuine. Please clarify this.

Answer: we added clarity to this sentence which was indeed confusing.

Reviewer #3 (Recommendations for the authors):

(1) Introduction: More background info about malaria in Mozambique would be appreciated.

We included some contextualisation about malaria in Mozambique and our study districts.

(2) Why were most of the samples collected from children? Is malaria most prevalent in that group? Information could be added in the introduction.

Children are usually considered an appropriate sentinel group for malaria surveillance for several reasons. First, most malaria cases reported from symptomatic outpatient visits are children, especially in areas with moderate to high burden. Second (and probably the cause for the first reason), their lower immunity levels, due to lower time of exposure, and their immature system, provides a cleaner scenario of the effects of malaria, since the body response is less adapted from past exposures. Finally, as a vulnerable population, they deserve a stronger focus in surveillance systems. We added a comment in the introduction referring to them as a common sentinel group for surveillance.

(3) Minor: Check spaces in the text (for example, line 333 and the start of the Discussion).

Thank you for noticing, we fixed in in the new version

(4) Minor: In my case, the micro (u) symbol can be observed in Word, but not in PDF.

One of the symbols produced an error, we hope that the new version is correct now.

(5) Were COI calculations with MOIRE performed across provinces and regions, or taking all samples as one population?

Wwe took all samples as one population. However, we validated that the same results (reaching equivalent numbers and the same conclusions) were obtained when run across different populations (regions or provinces). We mention this in the manuscript now.

(6) Have you tested lower values than 0.04 for PR in Maputo?

This would not have had any impact in the classification. Only two individuals reported a trip to Maputo city (where we assumed PR=0.04), and none of them were classified as imported. If lower values of PR were assumed, their probabilities of importation would have reduced, so that we would still obtain no imported cases.

(7) Map (Supplementary Figure 1): Please, improve the resolution (like in the zoom in) and add a scale and a compass rose.

We improved the resolution of the map. We did not add a scale and a compass rose, but labelled the coordinates as longitude and latitude to clarify the scale and orientation of the map. We added this in the rest of the maps of the manuscript as well.

(8) In this work, Pimp values were bimodal to 0 or 1, making the classification easy. I wonder in other scenarios, where Pimp values are more intermediate (0.4-0.6), is the threshold at 0.5 still useful? Is there another way, like having a confidence interval of Pimp, to ensure the final classification? A discussion on this topic may be appreciated.

In this case, we would recommend doing probabilistic analyses, keeping the probability of being imported as the final outcome, and quantifying the importation rates from the weighted sum of probabilities across individuals. We added this clarification in the Methods section: “ In case of obtaining a higher fraction of intermediate values (0.4-0.6), weighted sums of individual probabilities would be more appropriate to better quantify importation rates.”

(9) Results: More details per panel, not as the whole figure (Figure 2B, Figure 3A, etc) in the manuscript would be appreciated.

We appreciate the comment and added more details

(10) Figure 3: Please, add a color legend in panel B (not only in the caption, but in the panel, such as in A, C, D).

We added a color legend in panel B.

(11) Do the authors recommend routine surveillance to detect importation in Mozambique, or are these results solid enough to propose strategies? How possible is it that importation rates vary in the future in the south? If so, how feasible is it to implement all this process (including the amplicon sequencing) routinely?

We added the following text in the discussion: “While these results propose programmatic strategies for the two study districts, routine surveillance to detect importation in Mozambique would allow for identifying new strategies in other districts aiming for elimination, as well as monitoring changes in importation rates in Magude and Matutuine in the future. If scaling molecular surveillance is not feasible, travel reports could be integrated in the routing surveillance to extrapolate the case classification based on the results of this study. “

(12) Which other proxies of transmission intensity could have been used?

Better proxies of transmission intensity could be malaria incidence at the monthly level from national surveillance systems, or estimates of force of infection, for example from the use of molecular longitudinal data if available. We added this text in the discussion.

(13) Can this strategy be applied to P. vivax-endemic areas outside Africa?

This new method can also be applied to P. vivax-endemic areas outside Africa. Symptomatic P. vivax cases are not necessarily reflecting recent infections, so that travel reports might need to cover longer time periods, which does not require any essential adaptation to the method. We added this text to the discussion.

eLife Assessment

This study presents an important finding that has identified 27 differentially methylated regions as a signature for non-invasive early cancer detection and predicting prognosis for colorectal cancer. The findings demonstrate promising clinical potential, particularly for improving cancer screening and patient monitoring. In general, the evidence supporting the claims of the authors is solid. A larger sample size will be key to further improving this work in the future. The work will be of interest to researchers interested in cancer diagnosis or colorectal cancer monitoring.

Reviewer #1 (Public review):

Summary:

Colorectal cancer (CRC) is the third most common cancer globally and the second leading cause of cancer-related deaths. Colonoscopy and fecal immunohistochemical testing are among the early diagnostic tools that have significantly enhanced patient survival rates in CRC. Methylation dysregulation has been identified in the earliest stages of CRC, offering a promising avenue for screening, prediction, and diagnosis. The manuscript entitled "Early Diagnosis and Prognostic Prediction of Colorectal Cancer through Plasma Methylation Regions" by Zhu et al. presents that a panel of genes with methylation pattern derived from cfDNA (27 DMRs), serving as a noninvasive detection method for CRC early diagnosis and prognosis.

Strengths:

The authors provided evidence that the 27 DMRs pattern worked well in predicting CRC distant metastasis, and the methylation score remarkably increased in stages III-IV. Additionally, compared with the traditional tumor marker CEA, 27 DMRs prediction showed a superior sensitivity, highlighting the potential clinical application.

Weaknesses:

The major concerns are the design of DMRs screening, the relatively low sensitivity of this DMRs' pattern in detecting early-stage of CRC, the limited size of the cohorts, and the lack of comparison with the traditional diagnosis test.

Comments on revisions:

All my concerns have been cleared, and I have no further questions.

Reviewer #2 (Public review):

In this study, the authors aimed to develop cfDNA markers for comprehensive diagnosis, metastatic assessment, and prognostic prediction of colorectal cancer (CRC). Through integrative analysis of public 450K DNA methylation datasets and in-house targeted bisulfite sequencing (BS-seq) data from CRC and paired normal tissues, as well as plasma samples, they identified a signature comprising 27 differentially methylated regions (DMRs). This signature was subsequently validated for three clinical applications: cancer detection, metastasis prediction, and prognosis assessment.

Strengths:

The 27-DMR signature demonstrates value for both diagnosis and prognosis of CRC. Additionally, the datasets generated in this study serve as a valuable resource for the research community.

Weaknesses:

The validation cohorts for cancer detection and metastasis prediction were relatively small, which may limit the generalizability of the findings. The cancer detection model's performance does not surpass some published methods or commercial products.

Author response:

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

Reviewer #1 (Public review):

Summary:

Colorectal cancer (CRC) is the third most common cancer globally and the second leading cause of cancer-related deaths. Colonoscopy and fecal immunohistochemical testing are among the early diagnostic tools that have significantly enhanced patient survival rates in CRC. Methylation dysregulation has been identified in the earliest stages of CRC, offering a promising avenue for screening, prediction, and diagnosis. The manuscript entitled "Early Diagnosis and Prognostic Prediction of Colorectal Cancer through Plasma Methylation Regions" by Zhu et al. presents that a panel of genes with methylation pattern derived from cfDNA (27 DMRs), serving as a noninvasive detection method for CRC early diagnosis and prognosis.

Strengths:

The authors provided evidence that the 27 DMRs pattern worked well in predicting CRC distant metastasis, and the methylation score remarkably increased in stage III-IV.

Weaknesses:

The major concerns are the design of DMR screening, the relatively low sensitivity of this DMR pattern in detecting early-stage CRC, the limited size of the cohorts, and the lack of comparison with the traditional diagnosis test.

We sincerely thank the reviewer for their thorough evaluation and constructive feedback on our manuscript. We are encouraged that the reviewer found our 27-DMR panel promising for predicting distant metastasis and for its performance in late-stage CRC. We have carefully considered the weaknesses pointed out and have made revisions to address these concerns, which we believe have significantly strengthened our paper.

We agree with the reviewer that achieving high sensitivity for early-stage disease is the ultimate goal for any noninvasive screening test. Detecting the minute quantities of cfDNA shed from early-stage tumors is a well-recognized challenge in the field. Although the sensitivity of our current panel for early-stage CRC is modest, its core strengths, lie in its capability to also detect advanced adenomas and its excellent performance in assessing CRC metastasis and prognosis. Furthermore, we have now added a direct comparative analysis of our 27-DMR panel against the most widely used clinical serum biomarker for CRC, carcinoembryonic antigen (CEA), using samples from the same patient cohorts. Our results demonstrate that 27-DMR methylation score significantly outperforms CEA in diagnostic accuracy for early-stage CRC (64% vs. 18%) (Table s7). And in the Discussion section, we have also acknowledged our limitations and suggest that future studies are warranted to combine the cfDNA methylation model with commonly used clinical markers, such as CEA and CA19-9, with the aim of improving the sensitivity for early diagnosis.

We acknowledge the reviewer's concern regarding the cohort size and validation in larger, prospective, multi-center cohorts is essential before this panel can be considered for clinical application. We have explicitly stated this as a limitation of our study in the Discussion section and have highlighted the need for future large-scale validation studies (Page 18, Lines 367-373). We once again thank the reviewer for their insightful comments, which have allowed us to substantially improve our manuscript. We hope that the revised version is now suitable for publication.

Reviewer #2 (Public review):

This work presents a 27-region DMR model for early diagnosis and prognostic prediction of colorectal cancer using plasma methylation markers. While this non-invasive diagnostic and prognostic tool could interest a broad readership, several critical issues require attention.

Major Concerns:

(1) Inconsistencies and clarity issues in data presentation

(a) Sample size discrepancies

The abstract mentions screening 119 CRC tissue samples, while Figure 1 shows 136 tissues. Please clarify if this represents 119 CRC and 17 normal samples.

We sincerely thank the reviewer for this careful observation and for pointing out the inconsistency. We apologize for the error and the confusion it caused. Regarding Figure 1: The reviewer is correct. The number 136 in the original Figure 1 was an error. This was due to an inadvertent double-counting of the tumor samples that were used in the differential analysis against adjacent normal tissues. The actual number of tissue samples used in this analysis is 89. We have now corrected this value in the revised Figure 1.

Regarding the Abstract: The 119 CRC tissue samples mentioned in the abstract represents the total number of unique tumor samples analyzed across all stages of our study. This number is composed of two cohorts: the initial 15 pairs of tissues used for preliminary screening, and the subsequent 89 tissue samples used for validation, totaling 119 samples. We have ensured all sample numbers are now consistent throughout the revised manuscript.

The plasma sample numbers vary across sections: the abstract cites 161 samples, Figure 1 shows 116 samples, and the Supplementary Methods mentions 77 samples (13 Normal, 15 NAA, 12 AA, 37 CRC).

We sincerely thank the reviewer for their meticulous review and for identifying these inconsistencies in the plasma sample numbers. We apologize for this oversight and the lack of clarity.

Figure 1 & Supplementary Methods (77 samples): The number 116 in the original Figure 1 was a clerical error. The correct number is 77, which is the cohort used for our differential methylation analysis. This number is now consistent with the Supplementary Methods. This cohort is composed of 13 Normal, 15 NAA, 12 AA, and 37 CRC samples. The figure has been revised accordingly.

Abstract (161 samples): The total of 161 plasma samples mentioned in the abstract is the sum of two distinct sample sets used for different stages of our analysis: The 77 samples (13 Normal, 15 NAA, 12 AA, 37 CRC) used for the differential analysis.  An additional 84 samples (33 Normal, 51 CRC) which served as the training set for the LASSO regression model. We have now clarified these distinctions in the text and ensured consistency across the abstract, figures, and methods sections.

(b) Methodological inconsistencies

The Supplementary Material reports 477 hypermethylated sites from TCGA data analysis (Δβ>0.20, FDR<0.05), but Figure 1 indicates 499 sites.

The manuscript states that analyzing TCGA data across six cancer types identified 499 CRC-specific methylation sites, yet Figure 1 shows 477. Please also explain the rationale for selecting these specific cancer types from TCGA.

We sincerely thank the reviewer for their sharp observation and for highlighting these inconsistencies. We apologize for this clerical error, which occurred when labeling the figure. The numbers 477 and 499 in Figure 1 were inadvertently swapped and the text in Supplementary Material is correct. We have now corrected this error throughout the manuscript to ensure clarity and consistency. We deeply regret the confusion this has caused.

Regarding the rationale for selecting the cancer types:

The selection of colorectal, esophageal, gastric, lung, liver, and breast cancers was based on the following strategic criteria to ensure the stringent identification of CRC-specific markers. Firstly, esophageal, gastric, liver, and colorectal cancers all originate from the gastrointestinal tract and share developmental and functional similarities. Comparing CRC against these closely related cancers allowed us to filter out general GI-tract-related methylation patterns and isolate those that are truly unique to colorectal tissue. Secondly, we included lung and breast cancer as they are two of the most common non-GI malignancies worldwide with distinct tissue origins. This helps ensure our identified markers are not just pan-cancer methylation events but are specific to CRC, even when compared against highly prevalent cancers from different lineages. Finally, these six cancer types have some of the largest and most complete datasets available in the TCGA database, including high-quality methylation data. This provided a robust statistical foundation for a reliable cross-cancer comparison. We hope this explanation clarifies our methodology. Thank you again for your valuable feedback.

"404 CRC-specific DMRs" mentioned in the main text while "404 MCBs" in Figure 1, the authors need to clarify if these terms are interchangeable or how MCBs are defined.

We sincerely thank the reviewer for pointing out this important inconsistency in terminology. We apologize for the confusion this has caused and for the error in Figure 1. The two terms are closely related in our study. The final 404 markers are technically DMRs that were identified through an analysis of MCBs. To avoid confusion, we have decided to unify the terminology. The manuscript has now been revised to consistently use "DMRs", which is the most accurate final descriptor. The label in Figure 1 has been corrected accordingly.

(2) Methodological documentation

The Results section requires a more detailed description of marker identification procedures and justification of methodological choices.

Figure 3 panels need reordering for sequential citation.

We thank the reviewer for this valuable suggestion. We agree that the original Results section lacked sufficient detail regarding the marker identification procedures and the justification for our methodological choices. To address this, we have substantially rewritten the "Methylation markers selection" subsection. This revised section provides a clear, step-by-step narrative of our marker discovery. The revised text now integrates the specific methodological details and statistical criteria. For instance, we now explicitly describe the three-pronged approach for the initial TCGA data mining and the specific criteria (Δβ, FDR, log2FC) for each, and the analysis methodology such as Wilcoxon test and LASSO regression analysis. We believe this detailed narrative now provides the necessary description and justification for our methodological choices directly within the results, significantly improving the clarity and logical flow of our manuscript. This revision can be found on (Page 9-11, Lines 180-195, 202-213). We hope these changes fully address the reviewer's concerns.

We thank the reviewer for pointing out the citation order of the panels in Figure 3. This was a helpful suggestion for improving the clarity of our manuscript. We have now reordered the panels in Figure 3 to ensure they are cited sequentially within the text. These adjustments have been made in the "Development and validation of the CRC diagnosis model" subsection of the Results (Page 11, lines 224-230). We appreciate the reviewer's attention to detail.

(3) Quality control and data transparency

No quality control metrics are presented for the in-house sequencing data (e.g., sequencing quality, alignment rate, BS conversion rate, coverage, PCA plots for each cohort).

The analysis code should be publicly available through GitHub or Zenodo.

At a minimum, processed data should be made publicly accessible to ensure reproducibility.

We sincerely thank the reviewer for their valuable and constructive feedback regarding quality control and data transparency. We fully agree that these elements are crucial for ensuring the robustness and reproducibility of our research. As the reviewer suggested, we have made all processed data and the key quality control metrics for each sample including sequencing quality scores, bisulfite (BS) conversion rates, and sequencing coverage publicly available to ensure the reproducibility of our findings. The analysis was performed using standard algorithms as detailed in the Methods section. While we are unable to host the code in a public repository at this time, all analysis scripts are available from the corresponding author upon reasonable request. The data has been deposited in the National Genomics Data Center (NGDC) and is accessible under the accession number OMIX009128. This information is now clearly stated in the "Data and Code Availability" section of the manuscript. We thank the reviewer again for pushing us to improve our manuscript in this critical aspect.

Reviewer #3 (Public review):

Summary:

This article provides a model for early diagnosis and prognostic prediction of Colorectal Cancer and demonstrates its accuracy and usability. However, there are still some minor issues that need to be revised and paid attention to.

Strengths:

A large amount of external datasets were used for verification, thus demonstrating robustness and accuracy. Meanwhile, various influencing factors of multiple samples were taken into account, providing usability.

Weaknesses:

There are notable language issues that hinder readability, as well as a lack of some key conclusions provided.

We are very grateful to the reviewer for their positive assessment of our study and for the constructive feedback provided. We are particularly encouraged that the reviewer recognized the strengths of our work, especially the robustness demonstrated through extensive external validation and the practical usability of our model. Regarding the weaknesses, we have taken the comments very seriously and have thoroughly revised the manuscript. We sincerely apologize for the language issues that hindered readability in our initial submission. To address this, the entire manuscript has undergone a comprehensive round of professional language polishing and editing. We have carefully reviewed and revised the text to improve clarity, flow, and grammatical accuracy. Besides, we agree that the conclusions could be stated more explicitly. To rectify this, we have substantially revised the final paragraph of the Discussion and the Conclusion section (Page 14-18, lines 279-305, 319-334, 346-348, 358-360, 367-379). We now more clearly summarize the main findings of our study, emphasize the clinical significance and potential applications of our model, and provide clear take-home messages. We thank you again for your time and insightful comments, which have been invaluable in improving the quality of our paper. We hope the revised manuscript now meets the standards for publication.

Reviewer #1 (Recommendations for the authors):

Detail comments are outlined below:

(1) In this study, the authors have highlighted methylated cfDNA as a noninvasive approach for CRC early diagnosis. However, the small size of cohorts for plasma screening, particularly the sample number of NAA and AA , may cause bias in the selection of DMRs. This bias may lead to inappropriate DMRs for early diagnosis. Furthermore, the similar issues for the training set with a high percentage of late-stage CRC, no AA or NAA samples were included. This absence may be the key factor in screening changed methylated cfDNA that can predict the early stages of CRC.

We are very grateful to the reviewer for this insightful methodological critique. We agree that cohort composition and sample size are critical factors in the development of robust biomarkers, and we appreciate the opportunity to clarify our study design and the interpretation of our results.

We agree with the reviewer that the number of precancerous lesion samples (NAA and AA) in our initial plasma screening cohort was limited. This is a valid point. However, it is important to contextualize the role of this step within our overall multi-stage marker selection funnel. The markers evaluated in this plasma cohort were not discovered from this small sample set alone. They were the result of a rigorous pre-selection process based on large-scale public TCGA data and our own tissue-level sequencing. This robust, tissue-based validation ensured that only the most promising CRC-specific markers were advanced for plasma testing. Therefore, while the plasma cohort was modest in size, its purpose was to confirm the circulatory detectability of markers already known to have a strong tissue-of-origin signal, thereby mitigating the potential bias from a smaller discovery set.

Our primary aim was to first build a model that could robustly and accurately identify a definitive cancer-specific methylation signal. By training the model on clear-cut invasive cancer cases versus healthy controls, we could isolate the most powerful and specific markers for established malignancy. Our working hypothesis was that these strong cancer-specific methylation patterns are initiated during the precursor stages and would therefore be detectable, albeit at lower levels, in precancerous lesions.  Unfortunately, the panel could only identify a limited proportion of precancerous lesions (48.4% in the NAA group and 52.2% in the AA group). We fully agree with the reviewer's sentiment that including a larger and more balanced set of precancerous lesions in future training cohorts could potentially optimize a model specifically for adenoma detection. We have now explicitly added this point to our Discussion section, highlighting it as an important direction for future research (Page 18, lines 367-373).

(2) The sensitivity of 27 DMRs in the external validation set (for NAA, AA and CRC 0-Ⅱare 48.4%. 52.2% and 66.7%, respectively) were much lower compared with previously published studies, like ColonES assay (DOI: 10.1016/j.eclinm.2022.101717) and ColonSecure test (DOI: 10.1186/s12943-023-01866-z). The 27 DMRs from the layered screening process did not show superior performance in a small population of an external validation cohort. Therefore, it is unlikely that this DMR pattern will be applicable to the general population in the future.

We sincerely thank the reviewer for their insightful comments and for providing a thorough comparison with the highly relevant ColonES and ColonSecure assays. This has given us an important opportunity to clarify the unique contributions and specific clinical applications of our 27-DMR panel.

We acknowledge the reviewer's point that the sensitivities of our panel for precancerous lesions (NAA: 48.4%, AA: 52.2%), while substantial, are numerically lower than those reported by the excellent ColonES assay (AA: 79.0%). However, it is important to clarify that while the ColonES and ColonSecure tests are outstanding benchmarks designed primarily for early detection and screening, the primary objective and contribution of our study were slightly different. Our model demonstrated an exceptional ability to predict distant metastasis with an AUC of 0.955 and a strong capacity for predicting overall prognosis with an AUC of 0.867. Our goal was to develop a multi-functional, biologically-rooted biomarker panel that not only contributes to early detection but, more importantly, provides crucial information for post-diagnosis patient management, including staging, risk stratification, and prognostication, from a single preoperative sample. We believe this ability to preoperatively identify high-risk patients who may require more aggressive treatment or intensive surveillance is the key contribution of our work. It provides a distinct clinical utility that complements, rather than directly competes with, pure screening assays.

We agree with the reviewer that our external validation was performed on a limited cohort, and we have acknowledged this as a limitation in our Discussion section. However, the purpose of this validation was to provide a proof-of-concept for the panel's performance across its multiple functions. The promising and exceptionally high-performing results in the prognostic domain strongly warrant further validation in larger, prospective, multi-center cohorts.

(3) The 27 DMRs pattern worked well in predicting CRC distant metastasis, and the methylation score remarkably increased in stage III-IV. In contrast, the increase of AA and 0-II groups was very mild in the validation cohort. This observation raises concerns regarding the study design, particularly in the context of the layered screening process and sample assigning.

We sincerely thank the reviewer for this insightful and critical comment. We agree with the reviewer's observation that the methylation score increased more remarkably in late-stage (III-IV) CRC compared to the milder increase in adenoma (AA) and early-stage (0-II) CRC in the validation cohort. However, the observed pattern is biologically plausible and consistent with the nature of colorectal cancer progression. Carcinogenesis is a multi-step process involving the gradual accumulation of genetic and epigenetic alterations. The methylation changes we identified are likely associated with tumor progression and metastasis. Therefore, it is expected that advanced, metastatic cancers (Stage III-IV), which have undergone significant biological changes, would exhibit a much stronger and more robust methylation signal compared to pre-cancerous lesions (adenomas) or early-stage, non-metastatic cancers (Stage 0-II). The "mild" increase in early stages reflects the initial, more subtle epigenetic alterations, while the "remarkable" increase in late stages reflects the extensive changes required for invasion and metastasis. We believe this graduated increase actually strengthens the validity of our methylation signature, as it mirrors the underlying biological progression of the disease. We hope this response and the corresponding revisions address the reviewer's comments.

(4) The authors did not provide the 27 DMRs prediction efficacy comparison with other noninvasive CRC assays, like a CEA and a FIT test.

Thank you for this valuable suggestion. We agree that comparing our model with established non-invasive assays is crucial for demonstrating its clinical potential. Following your advice, we have now included a direct comparison of the diagnostic performance between our model and the traditional tumor marker, carcinoembryonic antigen (CEA), using the external validation cohort. The results show that our model has a significantly higher sensitivity for detecting early-stage colorectal cancer and adenomas compared to CEA. This detailed comparison has been added as Table s7 in the supplementary materials, and the corresponding description has been incorporated into the Results section of our manuscript (Page 12, lines 234-236). Regarding the Fecal Immunochemical Test (FIT), we unfortunately could not perform a direct statistical comparison because very few individuals in our cohort had undergone FIT. A comparison based on such a small sample size would lack statistical power and might not yield meaningful conclusions. We have acknowledged this as a limitation of our study in the Discussion section.We believe these additions and clarifications have substantially strengthened our manuscript. Thank you again for your constructive feedback.

(5) The authors did not explicitly describe how they assigned the plasma samples to the distinct sets, nor did they specify the criteria for the plasma screen set, training set, and validation set. The detailed information for the patient grouping should be listed.

Responce: Thank you for this essential feedback. We agree that a transparent and detailed description of the sample allocation process is crucial for the manuscript. We apologize for the previous lack of clarity and have now revised the Methods section to address this. Our patient cohorts were assigned to the screening, training, and validation sets based on a chronological splitting strategy. Specifically, samples were allocated based on the date of collection in a consecutive manner. This approach was chosen to minimize selection bias and to provide a more realistic, forward-looking assessment of the model's performance, simulating a prospective validation scenario. The screening set comprised 89 tissue samples and 77 plasma samples collected between June to December 2020. The primary purpose of this set was for the initial discovery and screening of potential methylation markers. The training set and validation set included 165 plasma samples collected from December 2020 to July 2022. The external validation cohort comprised 166 plasma samples collected from from July 2022 to December 2022. The subsection titled "Study design and samples" within the Methods section of the revised manuscript, which now contains all of this detailed information (Page 6, lines 116-133). We believe this detailed explanation now makes our study design clear and transparent. Thank you again for helping us improve our manuscript.

Reviewer #2 (Recommendations for the authors):

The manuscript requires significant language editing to improve clarity and readability. We recommend that the authors seek professional editing services for revision.

Thank you for your constructive comments on the language of our manuscript. We apologize for any lack of clarity in the previous version. To address this, we have performed a thorough revision of the manuscript. The text has been carefully reviewed and edited by a native English-speaking colleague who is an expert in our research field. We have focused on correcting all grammatical errors, improving sentence structure, and refining the phrasing throughout the document to enhance readability. We are confident that these extensive revisions have significantly improved the clarity of the manuscript. We hope you will find the current version much easier to read and understand.

Reviewer #3 (Recommendations for the authors):

(1) However, I think the abstract part of the article is too detailed and should be more concise and shortened. It is not necessary to show detailed values but to summarize the results.

Thank you for this valuable suggestion. We agree that the previous version of the abstract was overly detailed and that a more concise summary would be more effective for the reader. Following your advice, we have substantially revised the abstract. We have removed the specific numerical values (such as detailed statistics) and have instead focused on summarizing the key findings and their broader implications (Page 3, lines 54-60, 64-66, 70-72). The revised abstract is now shorter and provides a clearer, high-level overview of our study's background, methods, main results, and conclusions. We believe these changes have significantly improved its readability and impact. We hope you will find the current version more appropriate.

(2) Figure 4, the color in the legend and plot are not the same, and should be revised.

Thank you for your careful attention to detail and for pointing out the color inconsistency in Figure 4. We apologize for this oversight. We have now corrected the figure as you suggested, ensuring that the colors in the legend perfectly match those in the plot. The revised Figure 4 has been updated in the manuscript. We appreciate your help in improving the quality of our figures.

(3) Please pay attention to the article format, such as the consistency of fonts and punctuation marks. (For example, Lines 75 and Line 230).

Thank you for your meticulous review and for pointing out the inconsistencies in our manuscript's formatting. We sincerely apologize for these oversights and any inconvenience they may have caused. Following your feedback, we have carefully corrected the specific issues you highlighted. Furthermore, we have conducted a thorough proofread of the entire manuscript to ensure consistency in all fonts, punctuation marks, and overall adherence to the journal's formatting guidelines. We appreciate your help in improving the presentation and professionalism of our paper.

eLife Assessment

This study identifies the uncharacterised protein FAM53C as a novel, potential regulator of the G1/S cell cycle transition, linking its function to the DYRK1A kinase and the RB/p53 pathways. The work is valuable and of interest to the cell cycle field, leveraging a strong computational screen to identify a new candidate. The findings are solid, although confidence in the siRNA depletion phenotypes would have been higher with rescue experiments using an siRNA-resistant cDNA and more robust quantification of some immunoassay data.

[Editors' note: this paper was reviewed by Review Commons.]

Reviewer #1 (Public review):

Summary:

Taylar Hammond and colleagues identified new regulators of the G1/S transition of the cell cycle. They did so by screening publicly available data from the Cancer Dependency Map and identified FAM53C as a positive regulator of the G1/S transition. Using biochemical assays they then show that FAM53 interacts with the DYRK1A kinase to inhibit its function. They show in RPE1 cells that loss of FAMC53 leads to a DYRK1A + P53-dependent cell cycle arrest. Combined inactivation of FAM53C and DYRK1A in a TP53-null background caused S-phase entry with subsequent apoptosis. Finally the authors assess the effect of FAM53C deletion in a cortical organoid model, and in Fam53c knockout mice. Whereas proliferation of the organoids is indeed inhibited, mice show virtually no phenotype.

The authors have revised the manuscript, and I respond here point-by-point to indicate which parts of the revision I found compelling, and which parts were less convincing. So the numbering is consistent with the numbering in my first review report.

(1) The p21 knockdowns are a valuable addition, and the claim that other p53 targets than p21 are involved in the FAMC53 RNAi-mediated arrest is now much more solid. Minor detail: if S4D is a quantification of S4C, it is hard to believe that the quantification was done properly (at least the DYRK1Ai conditions). Perhaps S4C is not the best representative example, or some error was made?

(2a) I appreciate the decision to remove the cyclin D1 phosphorylation data. A more nuanced model now emerges. It is not clear to me however why the Protein Simple immunoassay was used for experiments with RPE cells, and not the cortical organoids. Even though no direct claims are made based on the phospho-cyclin D data in Figure 5E+G, showing these data suggests that FAM53C deletion increases DYRK1A-mediated cyclin D1 phosphorylation. I find it tricky to show these data, while knowing now that this effect could not be shown in the RPE1 cells.<br /> (2b) The quantifications of the immunoassays are not convincing. In multiple experiments, the HSP90 levels vary wildly, which indicates big differences in protein loading if HSP90 is a proper loading control. This is for example problematic for the interpretation of figure 3F and S3I. The cyclin D1 "bands" look extremely similar between siCtrl and siFAM53C (Fig S3I), in fact the two series of 6 samples with different dosages of DYRK1Ai look seem an identical repetition of each other. I did not have to option to overlay them, but it would be important to check if a mistake was made here. The cyclin D1 signals aside, the change in cycD1/HSP90 ratios seems to be entirely caused by differences in HSP90 levels. Careful re-analysis of the raw data and more equal loading seem necessary. The same goes (to a lesser extent) for S3J+K.<br /> (2c) the new model in Fig S4L: what do the arrows at the right FAM53C and p53 that merge a point straight towards S-phase mean? They suggest that p53 (and FAM53C) directly promote S-phase progression, but most likely this is not what the authors intended with it.

(3) Clear; nicely addressed.

(4) Thank you for correcting.

(5) I appreciate that the authors are now more careful to call the IMPC analysis data preliminary. This is acceptable to me, but nevertheless, I suggest the authors to seriously consider taking this part entirely out. The risk of chance finding and the extremely skewed group sizes (as reviewer #2 had pointed out) hamper the credibility of this statistical analysis.

Reviewer #2 (Public review):

The authors sought to identify new regulators of the G1/S transition by mining the Cancer Dependency Map (DepMap) co-dependency dataset. This analysis successfully identified FAM53C, a poorly characterized protein, as a candidate. The strength of the paper lies in this initial discovery and the subsequent biochemical work convincingly showing that FAM53C can directly interact with the kinase DYRK1A, a known cell cycle regulator.

The authors then present evidence, primarily from acute siRNA knockdown in RPE-1 cells, that loss of FAM53C induces a strong G1 cell cycle arrest. Their follow-up investigation proposes a model where FAM53C normally inhibits DYRK1A, thereby protecting Cyclin D from degradation and preventing p53 activation, to allow for G1/S progression. The authors have commendably addressed some concerns from the initial review: they have now demonstrated the G1 arrest using two independent siRNAs (an improvement over the initial pool), shown the effect in several additional cancer cell lines (U2OS, A549, HCT-116), and developed a more nuanced model that incorporates p53 activation, which helps to explain some of the complex data.

However, a central and critical weakness persists. The entire functional model is built upon the very strong G1 arrest phenotype observed in vitro following acute knockdown. This finding is in stark contrast to data from other contexts. As the authors note, the knockout of Fam53c in mice results in minimal phenotypes, and the DepMap data itself suggests the gene is largely non-essential in most cancer cell lines.

This major discrepancy creates two competing interpretations:

As the authors suggest, FAM53C has a critical role in the cell cycle, but its loss is rapidly masked by compensatory mechanisms in long-term knockout models (like iPSCs and mice) or in established cancer cell lines.

The strong acute G1 arrest is an experimental artifact of the siRNA-mediated knockdown, and not a true reflection of FAM53C's primary function.

The authors' new controls (using two individual siRNAs and showing the arrest is RB-dependent) make an off-target effect less likely, but they do not definitively rule it out. The gold-standard experiment to distinguish between these two possibilities-a rescue of the phenotype using an siRNA-resistant cDNA-has not been performed.

Because this key control is missing, the foundation of the paper's functional claims is not as solid as it needs to be. While the study provides an interesting and valuable new candidate for the cell cycle field to investigate, readers should be cautious in accepting the strength of FAM53C's role in the G1/S transition until this central discrepancy is definitively resolved.

Reviewer #3 (Public review):

Summary:

In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53C-depleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off-target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Weather and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism. Although some of the data might be of interest, in current form the data is too preliminary to justify publication.

Major comments:

(1) Whole study is based on one siRNA to Fam53C and its specificity was not validated. Level of the knock down was shown only in the first figure and not in the other experiments. The observed phenotypes in the cell cycle progression may be affected by variable knock-down efficiency and/or potential off target effects.

(2) Experiments focusing on the cell cycle progression were done in a single cell line RPE1 that showed a strong sensitivity to FAM53C depletion. In contrast, phenotypes in IPSCs and in mice were only mild suggesting that there might be large differences across various cell types in the expression and function of FAM53C. Therefore, it is important to reproduce the observations in other cell types.

(3) Authors state that FAM53C is a direct inhibitor of DYRK1A kinase activity (Line 203), however this model is not supported by the data in Fig 4A. FAM53C seems to be a good substrate of DYRK1 even at high concentrations when phosphorylations of cyclin D is reduced. It rather suggests that DYRK1 is not inhibited by FAM53C but perhaps FAM53C competes with cyclin D. Further, authors should address if the phosphorylation of cyclin D is responsible for the observed cell cycle phenotype. Is this Cyclin D-Thr286 phosphorylation, or are there other sites involved?

(4) At many places, information on statistical tests is missing and SDs are not shown in the plots. For instance, what statistics was used in Fig 4C? Impact of FAM53C on cyclin D phosphorylation does not seem to be significant. IN the same experiment, does DYRK1 inhibitor prevent modification of cyclin D?

(5) Validation of SM13797 compound in terms of specificity to DYRK1 was not performed.

(6) A fraction of cells in G1 is a very easy readout but it does not measure progression through the G1 phase. Extension of the S phase or G2 delay would indirectly also result in reduction of the G1 fraction. Instead, authors could measure the dynamics of entry to S phase in cells released from a G1 block or from mitotic shake off.

Comments to the revised manuscript:

In the revised version of the manuscript, authors addressed most of the critical points. They now include new data with depletion of FAM53C using single siRNAs that show small but significant enrichment of population of the G1 cells. This G1 arrest is likely caused by a combined effects on induction of p21 expression and decreased levels of cyclin D1. Authors observed that inhibition of DYRK1 rescued cyclin D1 levels in FAM53 depleted cells suggesting that FAM53C may inhibit DYRK1. This possibility is also supported by in vitro experiments. On the other hand, inhibition of DYRK1 did not rescue the G1 arrest upon depletion of FAM53C, suggesting that FAM53C may have also DYRK1-independent role in G1. Functional rescue experiments with cyclin D1 mutants and detection of DYRK1 activity in cells would be necessary to conclusively explain the function of FAM53C in progression through G1 phase but unfortunately these experiments were technically not possible. Knock out of FAM53C in iPSCs and in mice suggest that FAM53C may have additional functions besides the cell cycle control and/or that adaptation may have occurred in these model systems. Overall, the study implicated FAM53C in fine tuning DYRK1 activity in cells that may to some extent influence the progression through G1 phase. In addition, FAM53C may also have DYRK1 and cell cycle independent functions that remain to be addressed by future studies.

Author response:

(1) General Statements

We thank the Reviewers for a fair review of our work and helpful suggestions. We have significantly revised the manuscript in response to these suggestions. We provide a point-by-point response to the Reviewers below but wanted to highlight in our response a recurring concern related to the strong cell cycle arrest observed upon the acute FAM53C knock-down being different than the limited phenotypes in other contexts, including the knockout mice and DepMap data.

First, we now show that we can recapitulate the strong G1 arrest resulting from the FAM53C knock-down using two independent siRNAs in RPE-1 cells, supporting the specificity of the effects.

Second, the G1 arrest that results from the FAM53C knock-down is also observed in cells with inactive p53, suggesting it is not due to a non-specific stress response due to “toxic” siRNAs. In addition, the arrest is dependent on RB, which fits with the genetic and biochemical data placing FAM53C upstream of RB, further supporting a specific phenotype.

Third, we have performed experiments in other human cells, including cancer cell lines. As would be expected for cancer cells, the G1 arrest is less pronounced but is still significant, indicating that the G1 arrest is not unique to RPE-1 cells.

Fourth, it is not unexpected that compensatory mechanisms would be activated upon loss of FAM53C during development or in cancer – which may explain the lack of phenotypes in vivo or upon long-term knockout. This has been true for many cell cycle regulators, either because of compensation by other family members that have overlapping functions, or by a larger scale rewiring of signaling pathways. 

(2) Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity): 

Summary: 

Taylar Hammond and colleagues identified new regulators of the G1/S transition of the cell cycle.

They did so by screening public available data from the Cancer Dependency Map, and identified FAM53C as a positive regulator of the G1/S transition. Using biochemical assays they then show that FAM53 interacts with the DYRK1A kinase to inhibit its function. DYRK1A in its is known to induce degradation of cyclin D, leading the authors to propose a model in which DYRK1Adependent cyclin D degradation is inhibited by FAM53C to permit S-phase entry. Finally the authors assess the effect of FAM53C deletion in a cortical organoid model, and in Fam53c knockout mice. Whereas proliferation of the organoids is indeed inhibited, mice show virtually no phenotype.  

Major comments: 

The authors show convincing evidence that FAM53C loss can reduce S-phase entry in cell cultures, and that it can bind to DYRK1A. However, FAM53 has multiple other binding partners and I am not entirely convinced that negative regulation of DYRK1A is the predominant mechanism to explain its effects on S-phase entry. Some of the claims that are made based on the biochemical assays, and on the physiological effects of FAM53C are overstated. In addition, some choices made methodology and data representation need further attention. 

(1) The authors do note that P21 levels increase upon FAM53C. They show convincing evidence that this is not a P53-dependent response. But the claim that " p21 upregulation alone cannot explain the G1 arrest in FAM53C-deficient cells (line 138-139) is misleading. A p53-independent p21 response could still be highly relevant. The authors could test if FAM53C knockdown inhibits proliferation after p21 knockdown or p21 deletion in RPE1 cells. 

The Reviewer raises a great point. Our initial statement needed to be clarified and also need more experimental support. We have performed experiments where we knocked down FAM53C and p21 individually, as well as in combination, in RPE-1 cells. These experiment show that p21 knock-down is not sufficient to negate the cell cycle arrest resulting from the FAM53C knockdown in RPE-1 cells (Figure 4B,C and Figure S4C,D).

We now extended these experiments to conditions where we inhibited DYRK1A, and we also compared these data to experiments in p53-null RPE-1 cells. Altogether, these experiments point to activation of p53 downstream of DYRK1A activation upon FAM53C knock-down, and indicate that p21 is not the only critical p53 target in the cell cycle arrest observed in FAM53C knock-down cells (Figure 4 and Figure S4).

(2) The authors do not convincingly show that FAM53C acts as a DYRK1A inhibitor in cells. Figures 4B+C and S4B+C show extremely faint P-CycD1 bands, and tiny differences in ratios. The P values are hovering around the 0.05, so n=3 is clearly underpowered here. Total CycD1 levels also correlate with FAM53C levels, which seems to affect the ratios more than the tiny pCycD1 bands. Why is there still a pCycD1 band visible in 4B in the GFP + BTZ + DYRK1Ai condition? And if I look at the data points I honestly don't understand how the authors can conclude from S4C that knockdown of siFAM53C increases (DYRK1A dependent) increases in pCycD1 (relative to total CycD1). In figure 5C, no blot scans are even shown, and again the differences look tiny. So the authors should either find a way to make these assays more robust, or alter their claims appropriately. 

We appreciate these comments from the Reviewer and have significantly revised the manuscript to address them.

The analysis of Cyclin D phosphorylation and stability are complicated by the upregulation of p21 upon FAM53C knock-down, in particular because p21 can be part of Cyclin D complexes, which may affect its protein levels in cells (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). Instead of focusing on Cyclin D levels and stability, we refocused the manuscript on RB and p53 downstream of FAM53C loss.

We removed previous panel 4B from the revised manuscript. For panels 4E and S4B (now panels S3J and S3K)), we used a true “immunoassay” (as indicated in the legend – not an immunoblot), which is much more quantitative and avoids error-prone steps in standard immunoblots (“Western blots”). Briefly, this system was developed by ProteinSimple. It uses capillary transfer of proteins and ELISA-like quantification with up to 6 logs of dynamic range (see their web site https://www.proteinsimple.com/wes.html). The “bands” we show are just a representation of the luminescence signals in capillaries. We made sure to further clarify the figure legends in the revised manuscript.

The representative Western blot images for 5C-D (now 5F-G) in the original submission are shown in Figure 5E, we apologize if this was not clear. The differences are small, which we acknowledge in the revised manuscript. Note that several factors can affect Cyclin D levels in cells, including the growth rate and the stage of the cell cycle. Our FACS analysis shows that normal organoids have ~63% of cells in G1 and ~13% in S phase; the overall lower proportion of S-phase cells in organoids may make the immunoblot difference appear smaller, with fewer cycling cells resulting in decreased Cyclin D phosphorylation.

Nevertheless, the Reviewer brings up a good point and comments from this Reviewer and the others made us re-think how to best interpret our results. As discussed above, we re-read carefully the Meyer paper and think that FAM53C’s role and DYRK1A activity in cells may be understood when considering levels of both CycD and p21 at the same time in a continuum. While our genetic and biochemical data support a role for FAM53C in DYRK1A inhibition, it is likely that the regulation of cell cycle progression by FAM53C is not exclusively due to this inhibition. As discussed above and below, we noted an upregulation of p21 upon FAM53C knock-down, and activation of p53 and its targets likely contributes significantly to the phenotypes observed. We added new experiments to support this more complex model (Figure 4 and Figure S4, with new model in S4L).

(3) The experiments to test if DYRK1A inhibition could rescue the G1 arrest observed upon FAM53C knockdown are not entirely convincing either. It would be much more convincing if they also perform cell counting experiments as they have done in Figures 1F and 1G, to complement the flow cytometry assays. I suggest that the authors do these cell counting experiments in RPE1 +/- P53 cells as well as HCT116 cells. In addition, did the authors test if P21 is induced by DYRK1Ai in HCT116 cells? 

We repeated the experiments with the DYRK1A inhibitor and counted the cells. In p53-null RPE1 cells, we found that cell numbers do not increase in these conditions where we had observed a cell cycle re-entry (Fig. 4E), which was accompanied by apoptotic cell death (Fig. S4I). Thus, cells re-enter the cell cycle but die as they progress through S-phase and G2/M. We note that inhibition of DYRK1A has been shown to decrease expression of G2/M regulators (PMID: 38839871), which may contribute to the inability of cells treated to DYRK1Ai to divide. Because our data in RPE-1 cells showed that p21 knock-down was not sufficient to allow the FAM53C knock-down cells to re-enter the cell cycle, we did not further analyze p21 in HCT-116 cells.

(4) The data in Figure 5C and 5D are identical, although they are supposed to represent either pCycD1 ratios or p21 levels. This is a problem because at least one of the two cannot be true. Please provide the proper data and show (representative) images of both data types.

We apologize for these duplicated panels in the original submission. We now replaced the wrong panel with the correct data (Fig. 5F,G). 

(5) Line 246: "Fam53c knockout mice display developmental and behavioral defects." I don't agree with this claim. The mutant mice are born at almost the expected Mendelian ratios, the body weight development is not consistently altered. But more importantly, no differences in adult survival or microscopic pathology were seen. The authors put strong emphasis on the IMPC behavioral analysis, but they should be more cautious. The IMPC mouse cohorts are tested for many other phenotypes related to behavior and neurological symptoms and apparently none of these other traits were changed in the IMPC Famc53c-/- cohort. Thus, the decreased exploration in a new environment could very well be a chance finding. The authors need to take away claims about developmental and behavioral defects from the abstract, results and discussion sections; the data are just too weak to justify this. 

We agree with the Reviewer that, although we observed significant p-values, this original statement may not be appropriate in the biological sense. We made sure in the revised manuscript to carefully present these data.

Minor comments: 

(6) Can the authors provide a rationale for each of the proteins they chose to generate the list of the 38 proteins in the DepMap analysis? I looked at the list and it seems to me that they do not all have described functions in the G1/S transition. The analysis may thus be biased. 

To address this point, we updated Table S1 (2nd tab) to provide a better rationale for the 38 factors chosen. Our focus was on the canonical RB pathway and we included RB binding proteins whose function had suggested they may also be playing a role in the G1/S transition. We do agree that there is some bias in this selection (e.g., there are more RB binding factors described) but we hope the Reviewer will agree with us that this list and the subsequent analysis identified expected factors, including FAM53C. Future studies using this approach and others will certainly identify new regulators of cell cycle progression.

(7) Figure 1B is confusing to me. Are these just some (arbitrarily) chosen examples? Consider leaving this heatmap out altogether, of explain in more detail. 

We agree with the Reviewer that this panel was not necessarily useful and possibly in the wrong place, and we removed it from the manuscript. We replaced it with a cartoon of top hits in the screen.

(8) The y-axes in Figures 2C, 2D, 2E, and 4D are misleading because they do not start at 0. Please let the axis start at 0, or make axis breaks. 

We re-graphed these panels.

(9) Line 229: " Consequences ... brain development." This subheader is misleading, because the in vitro cortical organoid system is a rather simplistic model for brain development, and far away from physiological brain development. Please alter the header. 

We changed the header to “Consequences of FAM53C inactivation in human cortical organoids in culture”.

(10) Figure S5F: the gating strategy is not clear to me. In particular, how do the authors know the difference between subG1 and G1 DAPI signals? Do they interpret the subG1 as apoptotic cells? If yes, why are there so many? Are the culturing or harvesting conditions of these organoids suboptimal? Perhaps the authors could consider doing IF stainings on EdU or BrdU on paraffin sections of organoids to obtain cleaner data?

Thank you for your feedback. The subG1 population in the original Figure S5F represents cells that died during the dissociation step of the organoids for FACS analysis. To address this point, we performed live & dead staining to exclude dead cells and provide clearer data. We refined gating strategy for better clarity in the new S5F panel.

(11) Figure S6A; the labeling seems incorrect. I would think that red is heterozygous here, and grey mutant. 

We fixed this mistake, thank you. 

Reviewer #1 (Significance): 

The finding that the poorly studied gene FAM53C controls the G1/S transition in cell lines is novel and interesting for the cell cycle field. However, the lack of phenotypes in Famc53-/- mice makes this finding less interesting for a broader audience. Furthermore, the mechanisms are incompletely dissected. The importance of a p53-indepent induction of p21 is not ruled out. And while the direct inhibitory interaction between FAM53C and DYRK1A is convincing (and also reported by others; PMID: 37802655), the authors do not (yet) convincingly show that DYRK1A inhibition can rescue a cell proliferation defect in FAM53C-deficient cells. 

Altogether, this study can be of interest to basic researchers in the cell cycle field. 

I am a cell biologist studying cell cycle fate decisions, and adaptation of cancer cells & stem cells to (drug-induced) stress. My technical expertise aligns well with the work presented throughout this paper, although I am not familiar with biolayer interferometry. 

Reviewer #2 (Evidence, reproducibility and clarity): 

Summary 

In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53Cdepleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Weather and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism. Although some of the data might be of interest, in current form the data is too preliminary to justify publication.

Major points 

(1) Whole study is based on one siRNA to Fam53C and its specificity was not validated. Level of the knock down was shown only in the first figure and not in the other experiments. The observed phenotypes in the cell cycle progression may be affected by variable knock-down efficiency and/or potential off target effects. 

We thank the Reviewer for raising this important point. First, we need to clarify that our experiments were performed with a pool of siRNAs (not one siRNA). Second, commercial antibodies against FAM53C are not of the best quality and it has been challenging to detect FAM53C using these antibodies in our hands – the results are often variable. In addition, to better address the Reviewer’s point and control for the phenotypes we have observed, we performed two additional series of experiments: first, we have confirmed G1 arrest in RPE-1 cells with individual siRNAs, providing more confidence for the specificity of this arrest (Fig. S1B); second, we have new data indicating that other cell lines arrest in G1 upon FAM53C knock-down (Fig. S1E,F and Fig. 4F).

(2) Experiments focusing on the cell cycle progression were done in a single cell line RPE1 that showed a strong sensitivity to FAM53C depletion. In contrast, phenotypes in IPSCs and in mice were only mild suggesting that there might be large differences across various cell types in the expression and function of FAM53C. Therefore, it is important to reproduce the observations in other cell types. 

As mentioned above, we have new data indicating that other cell lines arrest in G1 upon FAM53C knock-down (three cancer cell lines) (Fig. S1E,F and Fig. 4F).

(3) Authors state that FAM53C is a direct inhibitor of DYRK1A kinase activity (Line 203), however this model is not supported by the data in Fig 4A. FAM53C seems to be a good substrate of DYRK1 even at high concentrations when phosphorylations of cyclin D is reduced. It rather suggests that DYRK1 is not inhibited by FAM53C but perhaps FAM53C competes with cyclin D. Further, authors should address if the phosphorylation of cyclin D is responsible for the observed cell cycle phenotype. Is this Cyclin D-Thr286 phosphorylation, or are there other sites involved? 

We revised the text of the manuscript to include the possibility that FAM53C could act as a competitive substrate and/or an inhibitor.

We removed most of the Cyclin D phosphorylation/stability data from the revised manuscript. As the Reviewers pointed out, some of these data were statistically significant but the biological effects were small. As discussed above in our response to Reviewer #1, the analysis of Cyclin D phosphorylation and stability are complicated by the upregulation of p21 upon FAM53C knockdown, in particular because p21 can be part of Cyclin D complexes, which may affect its protein levels in cells (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). Instead of focusing on Cyclin D levels and stability, we refocused the manuscript on RB and p53 downstream of FAM53C loss.

We note, however, that we used specific Thr286 phospho-antibodies, which have been used extensively in the field. Our data in Figure 1 with palbociclib place FAM53C upstream of Cyclin D/CDK4,6. We performed Cyclin D overexpression experiments but RPE-1 cells did not tolerate high expression of Cyclin D1 (T286A mutant) and we have not been able to conduct more ‘genetic’ studies. 

(4) At many places, information on statistical tests is missing and SDs are not shown in the plots. For instance, what statistics was used in Fig 4C? Impact of FAM53C on cyclin D phosphorylation does not seem to be significant. In the same experiment, does DYRK1 inhibitor prevent modification of cyclin D? 

As discussed above, we removed some of these data and re-focused the manuscript on p53-p21 as a second pathway activated by loss of FAM53C.

(5) Validation of SM13797 compound in terms of specificity to DYRK1 was not performed. 

This is an important point. We had cited an abstract from the company (Biosplice) but we agree that providing data is critical. We have now revised the manuscript with a new analysis of the compound’s specificity using kinase assays. These data are shown in Fig. S3F-H.

(6) A fraction of cells in G1 is a very easy readout but it does not measure progression through the G1 phase. Extension of the S phase or G2 delay would indirectly also result in reduction of the G1 fraction. Instead, authors could measure the dynamics of entry to S phase in cells released from a G1 block or from mitotic shake off. 

The Reviewer made a good point. As discussed in our response to Reviewer #1, with p53-null RPE-1 cells, we found that cell numbers do not increase in these conditions where we had observed a cell cycle re-entry (Fig. 4E), which was accompanied by apoptotic cell death (Fig. S4I). Thus, cells re-enter the cell cycle but die as they progress through S-phase and G2/M. We note that inhibition of DYRK1A has been shown to decrease expression of G2/M regulators (PMID: 38839871), which may contribute to the inability of cells treated to DYRK1Ai to divide.

Because our data in RPE-1 cells showed that p21 knock-down was not sufficient to allow the FAM53C knock-down cells to re-enter the cell cycle, we did not further analyze p21 in HCT-116 cells. These data indicate that G1 entry by flow cytometry will not always translate into proliferation.

Other points:

(7) Fig. 2C, 2D, 2E graphs should begin with 0 

We remade these graphs.

(8) Fig. 5D shows that the difference in p21 levels is not significant in FAM53C-KO cells but difference is mentioned in the text. 

We replaced the panel by the correct panel; we apologize for this error.

(9) Fig. 6D comparison of datasets of extremely different sizes does not seem to be appropriate

We agree and revised the text. We hope that the Reviewer will agree with us that it is worth showing these data, which are clearly preliminary but provide evidence of a possible role for FAM53C in the brain.

(10) Could there be alternative splicing in mice generating a partially functional protein without exon 4? Did authors confirm that the animal model does not express FAM53C? 

We performed RNA sequencing of mouse embryonic fibroblasts derived from control and mutant mice. We clearly identified fewer reads in exon 4 in the knockout cells, and no other obvious change in the transcript (data not shown). However, immunoblot with mouse cells for FAM53C never worked well in our hands. We made sure to add this caveat to the revised manuscript.

Reviewer #2 (Significance): 

Main problem of this study is that the advanced experimental models in IPSCs and mice did not confirm the observations in the cell lines and thus the whole manuscript does not hold together. Although I acknowledge the effort the authors invested in these experiments, the data do not contribute to the main conclusion of the paper that FAM53C/DYRK1 regulates G1/S transition. 

Reviewer #3 (Evidence, reproducibility and clarity: 

This paper identifies FAM53C as a novel regulator of cell cycle progression, particularly at the G1/S transition, by inhibiting DYRK1A. Using data from the Cancer Dependency Map, the authors suggest that FAM53C acts upstream of the Cyclin D-CDK4/6-RB axis by inhibiting DYRK1A.  Specifically, their experiments suggest that FAM53C Knockdown induces G1 arrest in cells, reducing proliferation without triggering apoptosis. DYRK1A Inhibition rescues G1 arrest in P53KO cells, suggesting FAM53C normally suppresses DYRK1A activity. Mass Spectrometry and biochemical assays confirm that FAM53C directly interacts with and inhibits DYRK1A. FAM53C Knockout in Human Cortical Organoids and Mice leads to cell cycle defects, growth impairments, and behavioral changes, reinforcing its biological importance. 

Strength of the paper: 

The study introduces a novel cell cycle control signalling module upstream of CDK4/6 in G1/S regulation which could have significant impact. The identification of FAM53C using a depmap correlation analysis is a nice example of the power of this dataset. The experiments are carried out mostly in a convincing manner and support the conclusions of the manuscript. 

Critique: 

(1) The experiments rely heavily on siRNA transfections without the appropriate controls. There are so many cases of off-target effects of siRNA in the literature, and specifically for a strong phenotype on S-phase as described here, I would expect to see solid results by additional experiments. This is especially important since the ko mice do not show any significant developmental cell cycle phenotypes. Moreover, FAM53C does not show a strong fitness effect in the depmap dataset, suggesting that it is largely non-essential in most cancer cell lines. For this paper to reach publication in a high-standard journal, I would expect that the authors show a rescue of the S-phase phenotype using an siRNA-resistant cDNA, and show similar S-phase defects using an acute knock out approach with lentiviral gRNA/Cas9 delivery. 

We thank the Reviewer for this comment. Please refer to the initial response to the three Reviewers, where we discuss our use of single siRNAs and our results in multiple cell lines. Briefly, we can recapitulate the G1 arrest upon FAM53C knock-down using two independent siRNAs in RPE-1 cells. We also observe the same G1 arrest in p53 knockout cells, suggesting it is not due to a non-specific stress response. In addition, the arrest is dependent on RB, which fits with the genetic and biochemical data placing FAM53C upstream of RB, further supporting a specific phenotype. Human cancer cell lines also arrest in G1 upon FAM53C knock-down, not just RPE-1 cells. Finally, we hope the Reviewer will agree with us that compensatory mechanisms are very common in the cell cycle – which may explain the lack of phenotypes in vivo or upon long-term knockout of FAM53C.

(2) The S-phase phenotype following FAM53C should be demonstrated in a larger variety of TP53WT and mutant cell lines. Given that this paper introduces a new G1/S control element, I think this is important for credibility. Ideally, this should be done with acute gRNA/Cas9 gene deletion using a lentiviral delivery system; but if the siRNA rescue experiments work and validate an on-target effect, siRNA would be an appropriate alternative. 

We now show data with three cancer cell lines (U2OS, A549, and HCT-116 – Fig. S1E,F and Fig. 4F), in addition to our results in RPE-1 cells and in human cortical organoids. We note that the knock-down experiments are complemented by overexpression data (Fig. 1G-I), by genetic data (our original DepMap screen), and our biochemical data (showing direct binding of FAM53C to DYRK1A).

(3) The western blot images shown in the MS appear heavily over-processed and saturated (See for example S4B, 4A, B, and E). Perhaps the authors should provide the original un-processed data of the entire gels? 

For several of our panels (e.g., 4E and S4B, now panels S3J and S3K)), we used a true “immunoassay” (as indicated in the legend – not an immunoblot), which is much more quantitative and avoids error-prone steps in standard immunoblots (“Western blots”). Briefly, this system was developed by ProteinSimple. It uses capillary transfer of proteins and ELISA-like quantification with up to 6 logs of dynamic range (see their web site https://www.proteinsimple.com/wes.html). The “bands” we show are just a representation of the luminescence signals in capillaries. We made sure to further clarify the figure legends in the revised manuscript.

Data in 4A are also not a western blot but a radiograph.

For immunoblots, we will provide all the source data with uncropped blots with the final submission.

(4) A critical experiment for the proposed mechanism is the rescue of the FAM53C S-phase reduction using DYRK1A inhibition shown in Figure 4. The legend here states that the data were extracted from BrdU incorporation assays, but in Figure S4D only the PI histograms are shown, and the S-phase population is not quantified. The authors should show the BrdU scatterplot and quantify the phenotype using the S-phase population in these plots. G1 measurements from PI histograms are not precise enough to allow for conclusions. Also, why are the intensities of the PI peaks so variable in these plots? Compare, for example, the HCT116 upper and lower panels where the siRNA appears to have caused an increase in ploidy. 

We apologize for the confusion and we fixed these errors, for most of the analyses, we used PI to measure G1 and S-phase entry. We added relevant flow cytometry plots to supplemental figures (Fig. S1G, H, I, as well as Fig. S4E and S4K, and Fig. S5F).

(5) There's an apparent contradiction in how RB deletion rescues the G1 arrest (Figure 2) while p21 seems to maintain the arrest even when DYRK1A is inhibited. Is p21 not induced when FAM53C is depleted in RB ko cells? This should be measured and discussed. 

This comment and comments from the two other Reviewers made us reconsider our model. We re-read carefully the Meyer paper and think that DYRK1A activity may be understood when considering levels of both CycD and p21 at the same time in a continuum (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). While our genetic and biochemical data support a role for FAM53C in DYRK1A inhibition, it is obvious that the regulation of cell cycle progression by FAM53C is not exclusively due to this inhibition. As discussed above and below, we noted an upregulation of p21 upon FAM53C knock-down, and activation of p53 and its targets likely contributes significantly to the phenotypes observed. We added new experiments to support this more complex model (Figure 4 and Figure S4, with new model in S4L).

Reviewer #3 (Significance): 

In conclusion, I believe that this MS could potentially be important for the cell cycle field and also provide a new target pathway that could be relevant for cancer therapy. However, the paper has quite a few gaps and inconsistencies that need to be addressed with further experiments. My main worry is that the acute depletion phenotypes appear so strong, while the gene is nonessential in mice and shows only a minor fitness effect in the depmap screens. More convincing controls are necessary to rule out experimental artefacts that misguide the interpretation of the results.

We appreciate this comment and hope that the Reviewer will agree it is still important to share our data with the field, even if the phenotypes in mice are modest.

eLife Assessment

This fundamental work examines how tRNA modifications influence antibiotic tolerance, providing novel insights that may have therapeutic uses. The evidence supporting the conclusions is convincing. Strengths of the manuscript include the mechanism of tRNA modification influencing antibiotic tolerance and the precise measurement techniques used throughout. Further analysis of growth rate impacts and specific identification of the proteins responsible for the effect would further strengthen the manuscript.

Reviewer #1 (Public review):

Summary:

Cotton et al. investigated the role of tusB in antibiotic tolerance in Yersinia pseudotuberculosis. They used the IP2226 strain and introduced appropriate mutations and complementation constructs. Assays were performed to measure growth rates, antibiotic tolerance, tRNA modification, gene expression and proteomic profiles. In addition, experiments to measure ribosome pausing and bioinformatic analysis of codon usage in ribosomal proteins provided in-depth mechanistic support for the conclusions.

Strengths:

The findings are consistent with the authors having uncovered new mechanistic insights into bacterial antibiotic tolerance mediated by reducing ribosomal protein abundance.

Weaknesses:

Since the WT strain grows faster than the tusB mutant, there is a question of how growth rate, per se, impacts some of the analysis done. The authors should address this issue. In addition, it may not be essential, but would analysis of another slow-growing mutant (in some other antibiotic tolerance pathway if available) serve as a good control in this context?

Reviewer #2 (Public review):

Summary:

This study addresses a critical clinical challenge-bacterial antibiotic tolerance (a key driver of treatment failure distinct from genetic resistance)-by uncovering a novel regulatory role of the conserved s2U tRNA modification in Yersinia pseudotuberculosis. Its strengths are notable and lay a solid foundation for understanding phenotypic drug tolerance. The study is the first to link s2U tRNA modification loss to antibiotic tolerance, specifically targeting translation/transcription-inhibiting antibiotics (doxycycline, gentamicin, rifampicin). By establishing a causal chain - s2U deficiency → codon-specific ribosome pausing (at AAA/CAA/GAA) → reduced ribosomal protein translation → global translational suppression → tolerance - it expands the functional landscape of tRNA modifications beyond canonical translation fidelity, filling a gap in how RNA epigenetics shapes bacterial stress adaptation.

Strengths:

This study makes a valuable contribution to understanding tRNA modification-mediated antibiotic tolerance.

Weaknesses:

There are several limitations that weaken the robustness of the study's mechanistic conclusions. Addressing these gaps would significantly enhance its impact and translational potential.

Reviewer #3 (Public review):

Summary:

In the manuscript of Cotten et al., the authors study the 2-thiolation of tRNA in bacterial antibiotic resistance. The wildtype organism, Yersinia pseudotuberculosis, downregulates 2-thiolation as a response to antibiotics targeting the ribosome. In this manuscript, the authors show that a knockout of tusB causes slower translation. They provide evidence on the mechanisms of the slowing by determining transcription and translation, ribosome profiling and performing codon-usage analysis. They successfully determined that 2 codons are drivers of the translation slowdown, and the data is highly conclusive. Technically, I have nothing to criticize.

Strengths:

All in all, the study is very well made, and the writing is clear and concise. It covers a wide array of state-of-the-art analyses to unravel the interplay of tRNA modifications in translation.

Weaknesses:

The only question that remains to be asked is why the slowed translation leads to a better survival of the bacteria under antibiotic stress. In my opinion, the mechanism itself remains unclear. Thus, the statement that "We expect that this reduction in ribosomal proteins is globally reducing the translational capacity of the cell and is responsible for inducing tolerance to ribosome and RNA polymerase-targeting antibiotics" does not truly emphasize the remaining open question of why slowed translation favors survival. Therefore, I would recommend a minor text revision.

Author response:

Reviewer #1 (Public review): 

Summary: 

Cotton et al. investigated the role of tusB in antibiotic tolerance in Yersinia pseudotuberculosis. They used the IP2226 strain and introduced appropriate mutations and complementation constructs. Assays were performed to measure growth rates, antibiotic tolerance, tRNA modification, gene expression and proteomic profiles. In addition, experiments to measure ribosome pausing and bioinformatic analysis of codon usage in ribosomal proteins provided in-depth mechanistic support for the conclusions. 

Strengths: 

The findings are consistent with the authors having uncovered new mechanistic insights into bacterial antibiotic tolerance mediated by reducing ribosomal protein abundance. 

Weaknesses: 

Since the WT strain grows faster than the tusB mutant, there is a question of how growth rate, per se, impacts some of the analysis done. The authors should address this issue. In addition, it may not be essential, but would analysis of another slow-growing mutant (in some other antibiotic tolerance pathway if available) serve as a good control in this context? 

We would like to thank the reviewer for their time spent reviewing our manuscript and for their positive review. We plan to address their comment as to how growth rate impacts the analyses and plan to incorporate another slow-growing mutant in the revised version of the manuscript.

Reviewer #2 (Public review): 

Summary: 

This study addresses a critical clinical challenge-bacterial antibiotic tolerance (a key driver of treatment failure distinct from genetic resistance)-by uncovering a novel regulatory role of the conserved s2U tRNA modification in Yersinia pseudotuberculosis. Its strengths are notable and lay a solid foundation for understanding phenotypic drug tolerance. The study is the first to link s2U tRNA modification loss to antibiotic tolerance, specifically targeting translation/transcription-inhibiting antibiotics (doxycycline, gentamicin, rifampicin). By establishing a causal chain - s2U deficiency → codon-specific ribosome pausing (at AAA/CAA/GAA) → reduced ribosomal protein translation → global translational suppression → tolerance - it expands the functional landscape of tRNA modifications beyond canonical translation fidelity, filling a gap in how RNA epigenetics shapes bacterial stress adaptation. 

Strengths: 

This study makes a valuable contribution to understanding tRNA modification-mediated antibiotic tolerance. 

Weaknesses: 

There are several limitations that weaken the robustness of the study's mechanistic conclusions. Addressing these gaps would significantly enhance its impact and translational potential. 

We would like to thank the reviewer for their time spent reviewing our manuscript, and for both their positive comments about the significance and novelty of this work as well as their critiques. We plan to address their specific recommendations in the revised manuscript by focusing on the contribution of specific ribosomal proteins (i.e. the 30S subunit protein, S13) through overexpression, codon replacement, and stability experiments. We also plan to design experiments to assess in vivo relevance and assess possible impacts on other pathways involved in antibiotic tolerance.

Reviewer #3 (Public review): 

Summary: 

In the manuscript of Cotten et al., the authors study the 2-thiolation of tRNA in bacterial antibiotic resistance. The wildtype organism, Yersinia pseudotuberculosis, downregulates 2-thiolation as a response to antibiotics targeting the ribosome. In this manuscript, the authors show that a knockout of tusB causes slower translation. They provide evidence on the mechanisms of the slowing by determining transcription and translation, ribosome profiling and performing codon-usage analysis. They successfully determined that 2 codons are drivers of the translation slowdown, and the data is highly conclusive. Technically, I have nothing to criticize. 

Strengths: 

All in all, the study is very well made, and the writing is clear and concise. It covers a wide array of state-of-the-art analyses to unravel the interplay of tRNA modifications in translation. 

Weaknesses: 

The only question that remains to be asked is why the slowed translation leads to a better survival of the bacteria under antibiotic stress. In my opinion, the mechanism itself remains unclear. Thus, the statement that "We expect that this reduction in ribosomal proteins is globally reducing the translational capacity of the cell and is responsible for inducing tolerance to ribosome and RNA polymerase-targeting antibiotics" does not truly emphasize the remaining open question of why slowed translation favors survival. Therefore, I would recommend a minor text revision. 

We would like to thank the reviewer for their time spent reviewing our manuscript and for their positive review of the technical aspects, experimental design, and writing. We will incorporate their suggested text revision into the revised manuscript, and will add to this statement if additional planned experiments shed light on this remaining question.

eLife Assessment

This valuable study examines how mammals descend effectively and securely along vertical substrates. The conclusions from comparative analyses based on behavioral data and morphological measurements collected from 21 species across a wide range of taxa are convincing, making the work of interest to all biologists studying animal locomotion.

Reviewer #1 (Public review):

Summary:

This unique study reports original and extensive behavioral data collected by the authors on 21 living mammal taxa in zoo conditions (primates, tree shrew, rodents, carnivorans, and marsupials) on how descent along a vertical substrate can be done effectively and securely using gait variables. Ten morphological variables reflecting head size and limb proportions are examined in relationship to vertical descent strategies and then applied to reconstruct modes of vertical descent in fossil mammals.

Strengths:

This is a broad and data-rich comparative study, which requires a good understanding of the mammal groups being compared and how they are interrelated, the kinematic variables that underlie the locomotion used by the animals during vertical descent, and the morphological variables that are associated with vertical descent styles. Thankfully, the study presents data in a cogent way with clear hypotheses at the beginning, followed by results and a discussion that addresses each of those hypotheses using the relevant behavioral and morphological variables, always keeping in mind the relationships of the mammal groups under investigation. As pointed out in the study, there is a clear phylogenetic signal associated with vertical descent style. Strepsirrhine primates much prefer descending tail first, platyrrhine primates descend sideways when given a choice, whereas all other mammals (with the exception of the raccoon) descend head first. Not surprisingly, all mammals descending a vertical substrate do so in a more deliberate way, by reducing speed, and by keeping the limbs in contact for a longer period (i.e., higher duty factors).

Reviewer #2 (Public review):

Summary:

This paper contains kinematic analyses of a large comparative sample of small to medium-sized arboreal mammals (n = 21 species) traveling on near-vertical arboreal supports of varying diameter. This data is paired with morphological measures from the extant sample to reconstruct potential behaviors in a selection of fossil euarchontaglires. This research is valuable to anyone working in mammal locomotion and primate evolution.

Strengths:

The experimental data collection methods align with best research practices in this field and are presented with enough detail to allow for reproducibility of the study as well as comparison with similar datasets. The four predictions in the introduction are well aligned with the design of the study to allow for hypothesis testing. Behaviors are well described and documented, and Figure 1 does an excellent job in conveying the variety of locomotor behaviors observed in this sample. I think the authors took an interesting and unique angle by considering the influence of encephalization quotient on descent and the experience of forward pitch in animals with very large heads.

Comment from the Reviewing Editor on the revised version:

The authors responded to many comments of the reviewers, and I would be happy to see the authors make this version the Version of Record.

Author response:

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

eLife Assessment:

This valuable study examines how mammals descend effectively and securely along vertical substrates. The conclusions from comparative analyses based on behavioral data and morphological measurements collected from 21 species across a wide range of taxa are convincing, making the work of interest to all biologists studying animal locomotion.

We would like to greatly thank the two reviewers for their time in reviewing this work, and for their valuable comments and suggestions that will help to improve this manuscript.

Overall, we agree with the weaknesses raised, which are mainly areas for consideration in future studies: to study more species, and in a natural habitat context.

We will nevertheless add a few modifications to improve the manuscript, notably by making certain figures more readable, and adding definitions and bibliography in the main text concerning gait characteristics.

We also provide brief comments on each point of weakness raised by the reviewers below, in blue.

Reviewer #1 (Public review):

Summary:

This unique study reports original and extensive behavioral data collected by the authors on 21 living mammal taxa in zoo conditions (primates, tree shrew, rodents, carnivorans, and marsupials) on how descent along a vertical substrate can be done effectively and securely using gait variables. Ten morphological variables reflecting head size and limb proportions are examined in relationship to vertical descent strategies and then applied to reconstruct modes of vertical descent in fossil mammals.

Strengths:

This is a broad and data-rich comparative study, which requires a good understanding of the mammal groups being compared and how they are interrelated, the kinematic variables that underlie the locomotion used by the animals during vertical descent, and the morphological variables that are associated with vertical descent styles. Thankfully, the study presents data in a cogent way with clear hypotheses at the beginning, followed by results and a discussion that addresses each of those hypotheses using the relevant behavioral and morphological variables, always keeping in mind the relationships of the mammal groups under investigation. As pointed out in the study, there is a clear phylogenetic signal associated with vertical descent style. Strepsirrhine primates much prefer descending tail first, platyrrhine primates descend sideways when given a choice, whereas all other mammals (with the exception of the raccoon) descend head first. Not surprisingly, all mammals descending a vertical substrate do so in a more deliberate way, by reducing speed, and by keeping the limbs in contact for a longer period (i.e., higher duty factors).

Weaknesses:

The different gait patterns used by mammals during vertical descent are a bit more difficult to interpret. It is somewhat paradoxical that asymmetrical gaits such as bounds, half bounds, and gallops are more common during descent since they are associated with higher speeds and lower duty factors. Also, the arguments about the limb support polygons provided by DSDC vs. LSDC gaits apply for horizontal substrates, but perhaps not as much for vertical substrates.

We analyzed gait patterns using methods commonly found in the literature and discussed our results accordingly. However, the study of limbs support polygons was indeed developed specifically for studying locomotion on horizontal supports, and may not be applicable for studying vertical locomotion, which is in fact a type of locomotion shared by all arboreal species. In the future, it would be interesting to consider new methods for analyzing vertical gaits.

The importance of body mass cannot be overemphasized as it affects all aspects of an animal's biology. In this case, larger mammals with larger heads avoid descending head-first. Variation in trunk/tail and limb proportions also covaries with different vertical descent strategies. For example, a lower intermembral index is associated with tail-first descent. That said, the authors are quick to acknowledge that the five lemur species of their sample are driving this correlation. There is a wide range of intermembral indices among primates, and this simple measure of forelimb over hindlimb has vital functional implications for locomotion: primates with relatively long hindlimbs tend to emphasize leaping, primates with more even limb proportions are typically pronograde quadrupeds, and primates with relatively long forelimbs tend to emphasize suspensory locomotion and brachiation. Equally important is the fact that the intermembral index has been shown to increase with body mass in many primate families as a way to keep functional equivalence for (ascending) climbing behavior (see Jungers, 1985). Therefore, the manner in which a primate descends a vertical substrate may just be a by-product of limb proportions that evolved for different locomotor purposes. Clearly, more vertical descent data within a wider array of primate intermembral indices would clarify these relationships. Similarly, vertical descent data for other primate groups with longer tails, such as arboreal cercopithecoids, and particularly atelines with very long and prehensile tails, should provide more insights into the relationship between longer tail length and tail-first descent observed in the five lemurs. The relatively longer hallux of lemurs correlates with tail-first descent, whereas the more evenly grasping autopods of platyrrhines allow for all four limbs to be used for sideways descent. In that context, the pygmy loris offers a striking contrast. Here is a small primate equipped with four pincer-like, highly grasping autopods and a tail reduced to a short stub. Interestingly, this primate is unique within the sample in showing the strongest preference for head-first descent, just like other non-primate mammals. Again, a wider sample of primates should go a long way in clarifying the morphological and behavioral relationships reported in this study.

We agree with this statement. In the future, we plan to study other species, particularly large-bodied ones with varied intermembral indexes.

Reconstruction of the ancient lifestyles, including preferred locomotor behaviors, is a formidable task that requires careful documentation of strong form-function relationships from extant species that can be used as analogs to infer behavior in extinct species. The fossil record offers challenges of its own, as complete and undistorted skulls and postcranial skeletons are rare occurrences. When more complete remains are available, the entire evidence should be considered to reconstruct the adaptive profile of a fossil species rather than a single ("magic") trait.

We completely agree with this, and we would like to emphasize that our intention here was simply to conduct a modest inference test, the purpose of which is to provide food for thought for future studies, and whose results should be considered in light of a comprehensive evolutionary model.

Reviewer #2 (Public review):

Summary:

This paper contains kinematic analyses of a large comparative sample of small to medium-sized arboreal mammals (n = 21 species) traveling on near-vertical arboreal supports of varying diameter. This data is paired with morphological measures from the extant sample to reconstruct potential behaviors in a selection of fossil euarchontaglires. This research is valuable to anyone working in mammal locomotion and primate evolution.

Strengths:

The experimental data collection methods align with best research practices in this field and are presented with enough detail to allow for reproducibility of the study as well as comparison with similar datasets. The four predictions in the introduction are well aligned with the design of the study to allow for hypothesis testing. Behaviors are well described and documented, and Figure 1 does an excellent job in conveying the variety of locomotor behaviors observed in this sample. I think the authors took an interesting and unique angle by considering the influence of encephalization quotient on descent and the experience of forward pitch in animals with very large heads.

Weaknesses:

The authors acknowledge the challenges that are inherent with working with captive animals in enclosures and how that might influence observed behaviors compared to these species' wild counterparts. The number of individuals per species in this sample is low; however, this is consistent with the majority of experimental papers in this area of research because of the difficulties in attaining larger sample sizes.

Yes, that is indeed the main cost/benefit trade-off with this type of study. Working with captive animals allows for large comparative studies, but there is a risk of variations in locomotor behavior among individuals in the natural environment, as well as few individuals per species in the dataset. That is why we plan and encourage colleagues to conduct studies in the natural environment to compare with these results. However, this type of study is very time-consuming and requires focusing on a single species at a time, which limits the comparative aspect.

Figure 2 is difficult to interpret because of the large amount of information it is trying to convey.

We agree that this figure is dense. One possible solution would be to combine species by phylogenetic groups to reduce the amount of information, as we did with Fig. 3 on the dataset relating to gaits. However, we believe that this would be unfortunate in the case of speed and duty factor because we would have to provide the complete figure in SI anyway, as the species-level information is valuable. We therefore prefer to keep this comprehensive figure here and we will enlarge the data points to improve their visibility, and provide the figure with a sufficiently high resolution to allow zooming in on the details.

Reviewer #1 (Recommendations for the authors):

As indicated in the first section above, this is a strong comparative study that addresses important questions, relative to the evolution of arboreal locomotion in primates and close mammal relatives. My recommendations should be taken in the context of improving a manuscript that is already generally acceptable.

(1) The terms symmetrical and asymmetrical gaits should be briefly defined in the main text (not just in the Methods section) by citing work done by Hildebrand and other relevant studies. To that effect, the statement on lines 96-97 about the convergence of symmetrical gaits is unclear. What does "Symmetrical gaits have evolved convergently in rodents, scandentians, carnivorans, and marsupials" mean? Symmetrical gaits such as the walk, run, trot, etc., are pretty the norm in most mammals and were likely found in metatherians and basal eutherians. This needs clarification. On line 239, the term "ambling" is used in the context of related asymmetrical gaits. To be clear, the amble is a type of running gait involving no whole-body aerial phase and is therefore a symmetrical gait (see Schmitt et al., 2006).

We have added a definition of the terms symmetrical and asymmetrical gaits and added references in the introduction such as: “Symmetrical gaits are defined as locomotor patterns in which the footfalls of a girdle (a pair of fore- or hindlimbs) are evenly spaced in time, with the right and left limbs of a pair of limbs being approximately 50% out of phase with each other (Hildebrand, 1966, 1967). Symmetrical gaits can be further divided into two types: diagonal-sequence gaits, in which a hindlimb footfall is followed by that of the contralateral forelimb, and lateral-sequence gaits, in which a hindlimb footfall is followed by that of the ipsilateral forelimb (Hildebrand, 1967; Shapiro and Raichlen, 2005; Cartmill et al., 2007b). In contrast, asymmetrical gaits are characterized by unevenly spaced footfalls within a girdle, with the right and left limbs moving in near synchrony (Hildebrand, 1977).” Now found in lines 87-94.

We corrected the sentence such as “Symmetrical gaits are also common in rodents, scandentians, etc..” Now found in line 107.

Thank you for pointing this out. We indeed did not use the right term to mention related asymmetrical gaits with increased duty factors. We removed the term « ambling » and the associated reference here. Now found in line 256.

(2) Correlations are used in the paper to examine how brain mass scales with body mass. It is correct to assume that a correlation significantly different from 0 is indicative of allometry (in this case, positive). That said, lines are used in Figure S2 that go through the bivariate scatter plot. The vast majority of scaling studies rely on regression techniques to calculate and compare slopes, which are different statistically from correlations. In this case, a slope not significantly different from 1.0 would support the hypothesis of isometry based on geometric similarity (as brain mass and body mass are two volumes). The authors could refer to the work of Bob Martin and the 1985 edited book by Jungers and contributions therein. These studies should also be cited in the paper.

Thank you for recommending us this better suited method. We replaced the correlations with major axis orthogonal regressions, as recommended by Martin and Barbour 1989. We found a positive slope for all species significantly different from 1 (0.36), indicating a negative allometry (we realized we were mistaken about the allometry terminology, initially reporting a “positive allometry” instead of a positive correlation).

We corrected in the manuscript in the Results and Methods sections, and cited Martin and Barbour 1989 such as:

“To ensure that the EQs of the different species studied are comparable and meaningful, we tested the allometry between the brain and body masses in our dataset following [84] and found a significant and positive slope for all species (major axis orthogonal regression on log transformed values: slope = 0.36, r² = 0.92, p = 5.0.10^-12), indicating a negative allometry (r = 0.97, df = 19, p = 2.0.10^-13), and similar allometric coefficients when restricting the analysis to phylogenetic groups (Fig. S2).” Now found in lines 289-298.

- “To control that brain allometry is homogeneous among all phylogenetic groups, to be able to compare EQ between species, we computed major axis orthogonal regressions, following the recommendation of Martin and Barbour [84], between the Log transformed brain and body masses, over all species and by phylogenetic group using the sma package in R (Fig. S2).” Now found in lines 336-338.

We also changed Figure S2 in Supplementary Information accordingly.

(3) Trunk length is used as the denominator for many of the indices used in the study. In this way, trunk length is considered to be a proxy for body size. There should be a demonstration that trunk length scales isometrically with body mass in all of the mammals compared. If not the case, some of the indices may not be directly comparable.

We did not use trunk length as a proxy for body mass, but to compute geometric body proportions in order to test whether intrinsic body proportions could be related to vertical descent behaviors, namely the length of the tail and of the fore- and hindlimbs relative to the animal. We chose those indices to quantify the capability of limbs to act as levers or counterweights to rotate the animals for this specific question of vertical descent behavior. We therefore do not think that body mass allometry with respect to trunk length is relevant to compare these indices across species here. Also, we don’t expect that trunk length (which is a single dimension) would scale isometrically with body mass, which scales more as a volume.

(4) Given the numerous comparisons done in this study, a Bonferroni correction method should be considered to mitigate type I error (accepting a false positive).

We had already corrected all our statistical tests using the Benjamini-Hochberg method to control for false positives; see the SuppTables Excel file for the complete results of the statistical analyses. We chose this method over the Bonferroni correction because the more modern and balanced Benjamini-Hochberg procedure is better suited for analyses involving a large number of hypotheses.

(5) The terms "arm" and "leg" used in the main text and Table 1 are anatomically incorrect. Instead, the terms "forelimb" and hindlimb" should be used as they include the length sum of the stylopod, zeugopod, and autopod.

Indeed, thank you for pointing that out. We have corrected this error within the manuscript as well as in the figures 4 and S3.

(6) On p. 14, the authors make the statement that the postcranial anatomy of Adapis and Notharctus remains undescribed. The authors should consult the work of Dagosto, Covert, Godinot and others.

We did not state that the postcranial remains of Adapis and Notharctus have not been described. However, we were unfortunately unable to find published illustrations of the known postcranial elements that could be reliably used in this study. To avoid any misunderstanding, we removed the sentence such as: “However, we could not find suitable illustrations of the known postcranial elements of these species in the literature that could be reliably incorporated into this study. Thus, we only included their reconstructed body mass and EQ,..”. Now found in lines 393-397.

Reviewer #2 (Recommendations for the authors):

(1) Line 65/69 - Perchalski et al. 2021 is a single-author publication, so no et al. or w/ colleagues.

Indeed. This has been corrected in the manuscript, now found in lines 65 and 70.

(2) Lines 96-98 - Is it appropriate to say that the use of symmetrical gaits are examples of convergent evolution? There's less burden of evidence to state that these are shared behaviors, rather than suggesting they independently evolved across all those groups.

We agree with this and corrected the sentence such as “Symmetrical gaits are also common in rodents, scandentians, etc..” Now found in line 107.

(3) Line 198 - I am confused by how to interpret (-16,36 %) compared to how other numbers are presented in the rest of the paragraph.

To avoid confusion, we rephrased this sentence such as: “In contrast, primates did not significantly reduce their speed compared to ascents when descending sideways or tail-first (Fig. 2A, SuppTables B).”  Now found in lines 207-209.

eLife Assessment

This valuable study identifies asymmetric dimethylarginine (ADMA) modification of histones as a potential key determinant of the initial genomic binding of Rhino, a Drosophila-specific chromatin protein essential for piRNA cluster specification. The authors provide correlative genomic and imaging data to support their model, although functional validation of the proposed mechanism remains incomplete. Testing the redundancy between dART4 and dART1, which together could affect the prominent piRNA loci, in addition to the minor ones investigated in the manuscript, may change our assessment.

Reviewer #2 (Public review):

The Revision title and abstract are not updated enough to distinguish the special niche piRNA clusters from the more prominent major dual strand piRNA clusters that are widely known in the field for Drosophila, like 42AB and 38C. This revision mainly adds the term "piRNA source loci (piSL)" that is too vague and not a well-accepted name that would distinguish just these particularly niche piRNA clusters from major dual strand piRNA clusters like 42AB and 38C. This piSL term is problematic because it seems to imply these piSL's are connected to or would eventually become major dual strand piRNA clusters, but there is zero evidence in this study for any genetic or evolutionary connection between these two distinct types of piRNA sources. This revision still lacks the necessary changes needed to point out like in the abstract that major dual strand piRNA clusters like 42AB, 38C, 80F, and 102F in Drosophila that make up the bulk of piRNAs cannot be shown to be impacted by changes aimed at depleting ADMA-histones from these loci, and the authors' current evidence is still only limited to showing in these few 'niche' piRNA clusters that ADMA-histones may exhibit a direct interaction with Rhino as supported only by the knockdown of Drosophila Art4.

The author's rebuttal letter argues that 42AB and 38C are just conserved piRNA clusters that may no longer be regulated by ADMA. This is still a weak claim for dismissing the potential genetic redundancy problem when this study can only report strong knockdown of Art4. First, the dual strand 42AB piRNA cluster's conservation as a Drosophilid piRNA cluster is actually still a relatively recent evolutionary innovation in just D.simulans and D.melanogaster that are less than 3MYA diverged. This 42AB cluster is no longer conserved in D.sechelia and is also younger than the uni-strand Flamenco piRNA cluster that is conserve to 7MYA. The evolutionary arguments by the authors are not well-grounded. Second, the 42AB and 38C are the largest major dual strand piRNA clusters with very significant localization of Rhino and impact from Rhino loss of function, and if this paper's central thesis is that ADMA-histones directed by Art1 or Art4 is critical for the expression of dual-strand piRNA cluster loci by impacting Rhino, the current data still remain weak with no new experiments to help bolster their claims.

The author's rebuttal letter argues that the challenges they faced in trying to knock down Art1 in the fly was thwarted by reagent issues, and the explanations are unsatisfactory. They claim they only tested two RNAi cross lines to try to knock down Art1: the strain BDSC #36891, y[1] sc[*] v[1] sev[21]; P{y[+t7.7], v[+t1.8]=TRiP.GL01072}attP2/TM3, Sb[1] that they said they could not obtain this strain to be alive from the stock center? And then testing an alternative line VDRC #v110391P{KK101196}VIE-260B that displayed mediocre knockdown, the authors seemed to suggest they have given up trying to make this very important experiment work? They should have tried to figure out with the BDSC, a venerable stock center for Drosophila genetic tools, why they could not receive that fly strain alive (shipping flies at the economy rate internationally may be cheaper but often is too strenuous for flies to survive), and the authors have not acknowledged testing two other available knockdown lines for Art1: BDSC #31348, y[1] v[1]; P{y[+t7.7] v[+t1.8]=TRiP.JF01306}attP2 dsRNA and VDRC #w1118 P{GD11959}v40388. Trying to get good knockdown of Art1 would be a critical must-have experiment to address whether this arginine methyltransferase has an in vivo impact on ADMA-histones in the Drosophila ovary and showing an impact on 42AB and 38C. The revision does not address this major deficiency in impact on these two major dual strand piRNA clusters, only the very few niche piRNA clusters that are responsive to Art4 knockdown.

The rebuttal letter argues that "Therefore, conserved clusters such as 42AB and 38C may no longer be regulated by ADMA." but then the revision discussion is still speculating much too wildly that the piRNA source loci are then precursors for the eventual large piRNA clusters of 42AB and 38C. This renaming of the term piRNA source loci and the model in Fig. 7C is still misleading because 42AB and 38C are the main largest dual-strand piRNA clusters, and the pictures depict the ADMA-histones as recruiting Rhino and then Kipferl at a piRNA cluster. The term "piRNA source loci" does not sound distinct enough to separate it from the main piRNA clusters of 42AB and 38C, and I had suggested calling them 'niche piRNA clusters' to denote they are very special and distinct to only be responsive to Drosophila Art4 knockdown.

In regards to the revision's changing of gene names, the convention for gene names is to use the previous name designation. Rather than calling the gene DART1, the conventional name of this gene in Flybase is Art1 (CG6554). There is the same problem with using the new name DART4 when in Flybase the gene is called Art4 (CG5358). Alternatively, the authors should clarify the re-naming up front and make it consistent with Drosophila genetics nomenclature, perhaps dArt1 or dArt4 would be more appropriate.

Reviewer #3 (Public review):

Summary:

This study investigates how Rhino, a chromatin-associated HP1-family protein essential for germline piRNA biogenesis in Drosophila, is initially recruited to specific genomic loci. Although canonical dual-strand piRNA clusters such as 42AB, 38C, 80F, and 102F produce the majority of germline piRNAs, the mechanisms guiding Rhino to these regions remain poorly understood. To explore the earliest steps of Rhino loading, the authors use a doxycycline-inducible Rhino transgene in OSC cells, a system that expresses only the primary Piwi pathway and therefore provides an experimentally accessible, epigenetically naïve context distinct from the endogenous germline environment. Through a combination of inducible Rhino expression, knockdown of selected Drosophila PRMTs (DARTs), ChIP-seq, small RNA sequencing, and imaging, the authors propose that asymmetric arginine-methylated histones, particularly those deposited by DART4, contribute to defining initial sites of Rhino association. They identify a subset of Rhino-bound loci, termed DART4-dependent piRNA source loci (piSL), which lose Rhino, Kipferl, and piRNA production upon DART4 depletion and may represent nascent or transitional piRNA clusters. Overall, the study provides intriguing evidence for a link between ADMA histone marks and de novo Rhino recruitment, particularly in the simplified OSC context, and offers new candidate loci for further exploration of early piRNA-cluster chromatin dynamics.

Strengths:

This study offers important insights into how asymmetric dimethylarginine (ADMA) histone marks contribute to the initial recruitment of Rhino, a Drosophila HP1-family protein essential for dual-strand piRNA cluster specification. Using an integrative approach that includes ectopic expression of a Rhino transgene in OSC cells, germline knockdown of DART4 in Drosophila ovaries, ChIP-seq, small RNA-seq, and imaging, the authors show that ADMA marks particularly H3R17me2a and H4R3me2acorrelate with Rhino binding at the boundaries of canonical piRNA clusters and at DART4-dependent piRNA source loci (piSL). These piSL may represent nascent or transitional piRNA-generating regions. Overall, the dataset presented here provides a valuable resource for understanding the chromatin features associated with the emergence and maturation of piRNA clusters.

Weaknesses:

Despite the strengths of the study, several important limitations remain. Although Rhino binding correlates with ADMA-enriched boundaries, the data do not directly demonstrate that these histone marks are required for Rhino spreading, leaving the mechanistic relationship correlative rather than causal. The DART4-dependent piRNA source loci identified here produce only low levels of piRNAs, and their functional contribution remains uncertain. In addition, redundancy among DART family methyltransferases remains unresolved: only DART4 was tested in the germline, and effective knockdown of DART1 or other DARTs could not be achieved, limiting the ability to evaluate whether ADMA-histones more broadly regulate Rhino recruitment at canonical clusters. Consequently, the current dataset primarily supports DART4-dependent effects at a small subset of evolutionarily young loci, and both the model and the title may overstate the generality of this mechanism across the full repertoire of dual-strand piRNA clusters.

In conclusion, this study is carefully executed and puts forward compelling hypotheses regarding the early chromatin environment that may underlie piRNA cluster formation. The findings will be relevant to researchers interested in genome regulation, small RNA biology, and chromatin-mediated transposon control.

Author response:

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

Reviewer #1(Public review):

Summary:

In this study, the authors aim to understand how Rhino, a chromatin protein essential for small RNA production in fruit flies, is initially recruited to specific regions of the genome. They propose that asymmetric arginine methylation of histones, particularly mediated by the enzyme DART4, plays a key role in defining the first genomic sites of Rhino localization. Using a combination of inducible expression systems, chromatin immunoprecipitation, and genetic knockdowns, the authors identify a new class of Rhinobound loci, termed DART4 clusters, that may represent nascent or transitional piRNA clusters.

Strengths:

One of the main strengths of this work lies in its comprehensive use of genomic data to reveal a correlation between ADMA histones and Rhino enrichment at the border of known piRNA clusters. The use of both cultured cells and ovaries adds robustness to this observation. The knockdown of DART4 supports a role for H3R17me2a in shaping Rhino binding at a subset of genomic regions.

Weaknesses:

However, Rhino binding at, and piRNA production from, canonical piRNA clusters appears largely unaffected by DART4 depletion, and spreading of Rhino from ADMArich boundaries was not directly demonstrated. Therefore, while the correlation is clearly documented, further investigation would be needed to determine the functional requirement of these histone marks in piRNA cluster specification.

The study identify piRNA cluster-like regions called DART4 clusters. While the model proposes that DART4 clusters represent evolutionary precursors of mature piRNA clusters, the functional output of these clusters remains limited. Additional experiments could help clarify whether low-level piRNA production from these loci is sufficient to guide Piwi-dependent silencing.

In summary, the authors present a well-executed study that raises intriguing hypotheses about the early chromatin context of piRNA cluster formation. The work will be of interest to researchers studying genome regulation, small RNA pathways, and the chromatin mechanisms of transposon control. It provides useful resources and new candidate loci for follow-up studies, while also highlighting the need for further functional validation to fully support the proposed model.

We sincerely thank Reviewer #1 for the thoughtful and constructive summary of our work. We appreciate the reviewer’s recognition that our study provides a comprehensive analysis of the relationship between ADMA-histones and Rhino localization, and that it raises intriguing hypotheses about the early chromatin context of piRNA cluster formation.

We fully agree with the reviewer that our data primarily demonstrate correlation between ADMA-histones and Rhino localization, rather than direct causation. In response, we have carefully revised the text throughout the manuscript to avoid overstatements implying causality (details provided below).

We also acknowledge the reviewer’s important point that the functional requirement of ADMA-histones for piRNA clusters specification remains to be further established. We have now added the discussion about our experimental limitations (page 18).

Overall, we have revised the manuscript to present our findings more cautiously and transparently, emphasizing that our data reveal a correlation between ADMA-histone marks and the initial localization of Rhino, rather than proving a direct mechanistic requirement. We thank the reviewer again for highlighting these important distinctions.

Reviewer #2 (Public review):

This study seeks to understand how the Rhino factor knows how to localize to specific transposon loci and to specific piRNA clusters to direct the correct formation of specialized heterochromatin that promotes piRNA biogenesis in the fly germline. In particular, these dual-strand piRNA clusters with names like 42AB, 38C, 80F, and 102F generate the bulk of ovarian piRNAs in the nurse cells of the fly ovary, but the evolutionary significance of these dual-strand piRNA clusters remains mysterious since triple null mutants of these dual-strand piRNA clusters still allows fly ovaries to develop and remain fertile. Nevertheless, mutants of Rhino and its interactors Deadlock, Cutoff, Kipferl and Moonshiner, etc, causes more piRNA loss beyond these dual-strand clusters and exhibit the phenotype of major female infertility, so the impact of proper assembly of Rhino, the RDC, Kipferl etc onto proper piRNA chromatin is an important and interesting biological question that is not fully understood.

This study tries to first test ectopic expression of Rhino via engineering a Dox-inducible Rhino transgene in the OSC line that only expresses the primary Piwi pathway that reflects the natural single pathway expression the follicle cells and is quite distinct from the nurse cell germline piRNA pathway that is promoted by Rhino, Moonshiner, etc. The authors present some compelling evidence that this ectopic Rhino expression in OSCs may reveal how Rhino can initiate de novo binding via ADMA histone marks, a feat that would be much more challenging to demonstrate in the germline where this epigenetic naïve state cannot be modeled since germ cell collapse would likely ensue. In the OSC, the authors have tested the knockdown of four of the 11 known Drosophila PRMTs (DARTs), and comparing to ectopic Rhino foci that they observe in HP1a knockdown (KD), they conclude DART1 and DART4 are the prime factors to study further in looking for disruption of ADMA histone marks. The authors also test KD of DART8 and CG17726 in OSCs, but in the fly, the authors only test Germ Line KD of DART4 only, they do not explain why these other DARTs are not tested in GLKD, the UAS-RNAi resources in Drosophila strain repositories should be very complete and have reagents for these knockdowns to be accessible.

The authors only characterize some particular ADMA marks of H3R17me2a as showing strong decrease after DART4 GLKD, and then they see some small subset of piRNA clusters go down in piRNA production as shown in Figure 6B and Figure 6F and Supplementary Figure 7. This small subset of DART4-dependent piRNA clusters does lose Rhino and Kipferl recruitment, which is an interesting result.

However, the biggest issue with this study is the mystery that the set of the most prominent dual-strand piRNA clusters. 42AB, 38C, 80F, and 102F, are the prime genomic loci subjected to Rhino regulation, and they do not show any change in piRNA production in the GLKD of DART4. The authors bury this surprising negative result in Supplementary Figure 5E, but this is also evident in no decrease (actually an n.s. increase) in Rhino association in Figure 5D. Since these main piRNA clusters involve the RDC, Kipferl, Moonshiner, etc, and it does not change in ADMA status and piRNA loss after DART4 GLKD, this poses a problem with the model in Figure 7C. In this study, there is only a GLKD of DART4 and no GLKD of the other DARTs in fly ovaries.

One way the authors rationalize this peculiar exception is the argument that DART4 is only acting on evolutionarily "young" piRNA clusters like the bx, CG14629, and CG31612, but the lack of any change on the majority of other piRNA clusters in Figure 6F leaves upon the unsatisfying concern that there is much functional redundancy remaining with other DARTs not being tested by GLKD in the fly that would have a bigger impact on the other main dual-strand piRNA clusters being regulated by Rhino and ADMA-histone marks.

Also, the current data does not provide convincing enough support for the model Figure 7C and the paper title of ADMA-histones being the key determinant in the fly ovary for Rhino recognition of the dual-strand piRNA clusters. Although much of this study's data is well constructed and presented, there remains a large gap that no other DARTs were tested in GLKD that would show a big loss of piRNAs from the main dual-strand piRNA clusters of 42AB, 38C, 80F, and 102F, where Rhino has prominent spreading in these regions.

As the manuscript currently stands, I do not think the authors present enough data to conclude that "ADMA-histones [As a Major new histone mark class] does play a crucial role in the initial recognition of dual-strand piRNA cluster regions by Rhino" because the data here mainly just show a small subset of evolutionarily young piRNA clusters have a strong effect from GLKD of DART4. The authors could extensively revise the study to be much more specific in the title and conclusion that they have uncovered this very unique niche of a small subset of DART4-dependent piRNA clusters, but this niche finding may dampen the impact and significance of this study since other major dual-strand piRNA clusters do not change during DART4 GLKD, and the authors do not show data GLKD of any other DARTs. The niche finding of just a small subset of DART-4-dependent piRNA clusters might make another specialized genetics forum a more appropriate venue.

We are deeply grateful to Reviewer #2 for the detailed and insightful review that carefully situates our study in the broader context of Rhino-mediated piRNA cluster regulation. We appreciate the reviewer’s recognition that our inducible Rhino expression system in OSCs provides a valuable model to explore de novo Rhino recruitment under a simplified chromatin environment.

At the same time, we agree that the current data mainly support a role for DART4 in regulating a subset of evolutionarily young piRNA clusters, and do not demonstrate a requirement for ADMA-histones at the major dual-strand piRNA clusters such as 42AB or 38C. We have therefore revised the title and main conclusions to more accurately reflect the scope of our findings.

We agree with the reviewer that functional redundancy among DARTs may explain why major dual-strand piRNA clusters are unaffected by DART4 GLKD. Indeed, we have tried DART1 GLKD in the germline, which shows collapse of Rhino foci in OSCs.For DART1 GLKD, two approaches were possible:

(1) Crossing the BDSC UAS-RNAi line (ID: 36891) with nos-GAL4.

(2) Crossing the VDRC UAS-RNAi line (ID: 110391) with nos-GAL4 and UAS-Dcr2.

The first approach was not feasible because the UAS-RNAi line always arrived as dead on arrival (DOA) and could not be maintained in our laboratory. The second approach did not yield effective and stable knockdown (as follows).

DART8 and CG17726 did not alter Rhino foci in OSC knockdown experiments; therefore, we did not attempt germline knockdown (GLKD) of these DARTs in the ovary.  We agree with the reviewer’s opinion that there are piRNA source loci where Rhino localization depends on DART1, and that simultaneous depletion of multiple DARTs may indeed reveal additional positive results because ADMA-histones such as H3R8me2a may be completely eliminated by the knockdown of multiple DARTs. At the same time, we note that many evolutionarily conserved piRNA clusters show a loss of ADMA accumulation compared with evolutionarily young piRNA clusters, with levels that are comparable to the background input in ChIP-seq reads. Therefore, conserved clusters such as 42AB and 38C may no longer be regulated by ADMA. Even if multiple DARTs function redundantly to regulate ADMA, it may be difficult to disrupt Rhino localization at such conserved piRNA clusters by depletion of DARTs. While disruption of Rhino localization at conserved clusters like 42AB and 38C may be challenging, we cannot exclude the possibility that DART depletion affects Rhino binding at less conserved piRNA clusters, where ADMA modification remains detectable. We added clarifications in the Discussion to acknowledge the potential redundancy with other DARTs and to note that further knockdown experiments in the germline will be necessary to test this model comprehensively (page 18).

We appreciate the reviewer’s critical feedback, which has helped us refine the message and strengthen the interpretative balance of the paper.

Reviewer #1 (Recommendations for the authors):

In multiple places, the link between ADMA histones and Rhino recruitment is presented in terms that imply causality. Please revise these statements to reflect that, in most cases, the evidence supports correlation rather than direct functional necessity. Similarly, statements suggesting that ADMA histones promote Rhino spreading should be revised unless supported by direct evidence.

We sincerely thank the reviewer for the insightful comments. We recognize that these suggestions are crucial for improving the manuscript, and we have revised it accordingly to address the concerns. The specific revisions we made are detailed below.

(1) Page 1, line 14: The original sentence “in establishing the sites” was changed to “may establish the potential sites.”

(2) Page 4, lines 11-12: The original sentence “genomic regions where Rhino binds at the ends and propagates in the areas in a DART4-dependent manner, but not stably anchored” was changed to “genomic regions that have ADMA-histones at their ends and exhibit broad Rhino spreading across their internal regions in a DART4dependent manner”

(3) Page4, lines 12-15: The original sentence “Kipferl is present at the regions but not sufficient to stabilize Rhino-genomic binding after Rhino propagates.” was changed to “In contrast to authentic piRNA clusters, Kipferl was lost together with Rhino upon DART4 depletion in these regions, suggesting that Kipferl by itself is not sufficient to stabilize Rhino binding; rather, their localization depends on DART4.”

(4) Page4, lines17-18: The original sentence “are considered to be primitive clusters” was changed to “might be nascent dual-strand piRNA source loci”.

(5) Page 8, line 7: The original sentence “Involvement of ADMA-histones in the genomic localization of Rhino was implicated.” was changed to “Correlation of ADMA-histones in the genomic localization of Rhino was implicated.”

(6) Page 8, lines 19-21: The original sentence “These results suggest that ADMAhistones, together with H3K9me3, contribute significantly and specifically to the recruitment of Rhino to the ends of dual-strand clusters in OSCs.” was changed to “These results raise the possibility that ADMA-histones, together with H3K9me3, may contribute specifically to the recruitment of Rhino to the ends of dual-strand clusters in OSCs.”

(7) Page 10, lines 11-13: The original sentence “These results suggest that DART1 and DART4 are involved in Rhino recruitment at distinct genomic sites through the decreases in ADMA-histones in each of their KD conditions (H4R3me2a and H3R17me2a, respectively).” was changed to ”These results suggest that DART1 and DART4 could contribute to Rhino recruitment at distinct genomic sites through the decreases in ADMA-histones in each of their KD conditions (H4R3me2a and H3R17me2a, respectively).”

(8) Page 13, line 2: The original sentence “Genomic regions where Rhino spreads in a DART4-dependent manner, but not stably anchored, produce some piRNAs“ was changed to “Genomic regions where Rhino binds broadly in a DART4-dependent manner, but not stably anchored, produce some piRNAs”

(9) Page 13, lines 21-22: The original sentence “These results support the hypothesis that ADMA-histones are involved in the genomic binding of Rhino both before and after Rhino spreading, resulting in stable genome binding.” was changed to “These results raise the possibility that a subset of Rhino localized to genomic regions correlating with ADMA-histones may serve as origins of spreading.”

(10) Page 16, lines 6-8: The original sentence “In this study, we took advantage of cultured OSCs for our analysis and found that chromatin marks (i.e., ADMA-histones) play a crucial role in the loading of Rhino onto the genome.” was changed to “In this study, we took advantage of cultured OSCs for our analysis and found that chromatin marks (i.e., bivalent nucleosomes containing H3K9me3 and ADMA-histones) appear to contribute to the initial loading of Rhino onto the genome.”

(11) Page16, line 12: The original sentence “We propose that the process of piRNA cluster formation begins with the initial loading of Rhino onto bivalent nucleosomes containing H3K9me3 and ADMA-histones (Fig. 7C). In OSCs, the absence of Kipferl and other necessary factors means that Rhino loading into the genome does not proceed to the next step.” was removed.

Major points

(1)  Clarify the limited colocalization between Rhino and H3K9me3 in OSCs. The observation that FLAG-Rhino foci show minimal overlap with H3K9me3 in OSCs appears inconsistent with the proposed model by the authors in the discussion, in which Rhino is initially recruited to bivalent nucleosomes bearing both H3K9me3 and ADMA marks. This discrepancy should be addressed. 

We thank the reviewer’s insightful comments. Indeed, ChIP-seq shows that Rhino partially overlaps with H3K9me3 (Fig. 1F), but immunofluorescence did not reveal any detectable overlap (Fig. 1A). We interpret this discrepancy as arising from the fact that immunofluorescence primarily visualizes H3K9me3 foci that are localized as broad domains in the genome, such as those at centromeres, pericentromeres, or telomeres (named chromocenters), whereas the sharp and interspersed H3K9me3 signals along chromosome arms are difficult to detect by immunofluorescence. We now have these explanations in the revised text (page 6).

(2)  Please indicate whether the FLAG-Rhino used in OSCs has been tested for functionality in vivo-for example, by rescuing Rhino mutant phenotypes. This is particularly relevant given that no spreading is observed with this construct.

We thank the reviewer for raising this important point. We have not directly tested the functionality of FLAG-Rhino construct used in OSCs in living Drosophila fly; i.e., it has not been used to rescue Rhino mutant phenotypes in flies. We acknowledge that FLAGRhino has not previously been expressed in OSCs, and that its localization pattern in OSCs differs from that observed in ovaries, where Rhino is endogenously expressed. However, several lines of evidence suggest that the addition of the N-terminal FLAG tag is unlikely to compromise Rhino function

(1) In previous studies, N-terminally tagged Rhino (e.g., 3xFLAG-V5-Precision-GFPRhino) was expressed in a living Drosophila ovary and was shown to localize properly to piRNA clusters, indicating that the tag does not prevent Rhino from binding its genomic targets (Baumgartner et al., 2022; eLife. Fig. 3 supplement 1G).

(2) In Drosophila S2 cells, FLAG-tagged tandem Rhino chromodomains construct was shown to bind H3K9me3/H3K27me3 bivalent chromatin, demonstrating that the FLAG tag does not impair this fundamental chromatin interaction (Akkouche et al., 2025; Nat Struct Mol Biol. Fig. 4b).

(3) GFP-tagged Rhino has been demonstrated to rescue the transposon derepression phenotype of Rhino mutant flies, further supporting that the addition of tags does not abolish its in vivo function. (Parhad et al., 2017; Dev Cell. Fig.1D).

Therefore, we interpret the partial localization of FLAG-Rhino in OSCs as reflecting the specific chromatin environment and regulatory context of OSCs rather than functional impairment due to the FLAG tag.

(3) Given the low levels of piRNA production and the absence of measurable effects on transposon expression or fertility upon DART4 knockdown, the rationale for classifying these regions as piRNA clusters should be clearly stated. Additional experiments could help clarify whether low-level piRNA production from these loci is sufficient to guide Piwidependent silencing. The authors should also consider and discuss the possibility that some of these differences may reflect background-specific genomic variation rather than DART4-dependent regulation per see.

We thank the reviewer for the insightful comments. As noted, DART4 knockdown did not measurably affect transposon expression or fertility. piRNAs generated from DART4associated clusters associate with Piwi but are insufficient for target repression. Although loss of DART4 largely eliminated piRNAs from these clusters, the cluster-derived transcripts themselves were unchanged. To clarify this point, we now refer to these regions as DART4-dependent piRNA-source loci (DART4 piSLs) in the revised text. We also acknowledge that some observed differences may reflect strain-specific genomic variation and have added this caveat on page 16.

(4)  The authors should describe the genomic context of DART4 clusters in more detail. Specifically, it would be helpful to indicate whether these regions overlap with known transposable elements, gene bodies, or intergenic regions, and to report the typical size range of the clusters. Are any of the piRNAs produced from these clusters predicted to target known transcripts? 

We thank the reviewer’s insightful comments. The overlap of DART4 piSL with transposable elements, gene bodies, and intergenic regions is shown in the right panel of Supplementary Fig. 6E (denoted as “Rhino reduced regions in DART4 GLKD” in the figure). The typical size range of these clusters is presented in Supplementary Fig. 6G. The annotation of piRNA reads derived from these piSL is shown in the right panel of Supplementary Fig. 6F, indicating that most of them appear to target host genes. The specific genes and transposons matched by the piRNAs produced from DART4 piSL are listed in Supplementary Table 8.

(5)  While correlations between Rhino and ADMA histone marks (especially H3R8me2a,H3R17me2a, H4R3me2a) are robust, many ADMA-enriched regions do not recruit Rhino. Please discuss this observation and consider the possible involvement of additional factors.

We thank the reviewer’s insightful comments. As pointed out, not all ADMA-enriched regions recruit Rhino; rather, Rhino is recruited only at sites where ADMAs overlap with H3K9me3. Furthermore, the combination of H3K9me3 and ADMAs alone does not fully account for the specificity of Rhino recruitment, suggesting the involvement of additional co-factors (for example, other ADMA marks such as H3R42me2a, or chromatininteracting proteins). In addition, since histone modifications—including arginine methylation—have the possibility that they are secondary consequences of modifications on other proteins rather than primary regulatory events, it is possible that DART1/4 contribute to Rhino recruitment not only through histone methylation but also via arginine methylation of non-histone chromatin-interacting factors. However, methylation of HP1a does not appear to be involved (Supplementary Fig. 3G). We have added new sentences about these points in the Discussion section (page 18).

(6) The manuscript states that Kipferl is present at DART4 clusters but does not stabilize Rhino binding. Please specify which experimental results support this conclusion and explain.

We apologize for the lack of clarity regarding Kipferl data. Supplementary Fig. 7A and 7B show that Kipferl localizes at major DART4 piSL. This Kipferl localization is lost together with Rhino upon DART4 GLKD, indicating that Rhino localization at DART4 piSL depends on DART4 rather than on Kipferl. From these results, we infer that, unlike at authentic piRNA clusters, Kipferl may not be sufficient to stabilize the association of Rhino with the genome at DART4 piSL. We have added this interpretation on page 14.

Minor points

(1) Figure 1D: Please specify which piRNA clusters are included in the metaplot - all clusters, or only the major producers? 

We thank the reviewer for the question. The metaplot was not generated from a predefined list of “all” piRNA clusters or only the “major producers.” Instead, it was constructed from Rhino ChIP–seq peaks (“Rhino domains”) that are ≥1.5 kb in length.These Rhino domains mainly correspond to the subregions within major dual-strand clusters (e.g., 42AB, 38C) as well as additional clusters such as 80F, 102F, and eyeless, among others. We have provided the full list of domains and their corresponding piRNA clusters (with genomic coordinates) in Supplementary Table 9 and added the additional explanation in Fig. 1d legend.

(2) Supplemental Figure 5E is referred to as 5D in the main text.

We corrected the figure citations on pages 11-12: the reference to Supplementary Fig. 5E has been changed to 5D, and the reference to Supplementary Fig. 5F has been changed to 5E.

(3) Supplemental Figure 7C: The color legend does not match the pie chart, which may confuse readers.

We thank the reviewer for the helpful comment. We are afraid we were not entirely sure what specific aspect of the legend was confusing, but to avoid any possible misunderstanding, we revised Supplemental Fig. 7C so that the color boxes in the legend now exactly match the corresponding colors in the pie chart. We hope this modification improves clarity.

(4) Since the manuscript focuses on the roles of DART1 and DART4, including their expression profiles in OSCs and ovaries would help contextualize the observed phenotypes. Please consider adding this information if available.

We thank the reviewer for the suggestion. We have now included a scatter plot comparing RNA-seq expression in OSCs and ovaries (Supplementary Fig. 3H). In these datasets, DART1 is strongly expressed in both tissues, whereas DART4 shows no detectable reads. Notably, ref. 28 reports strong expression of both DART1 and DART4 in ovaries by western blot and northern blot. In our own qPCR analysis in OSCs, DART4 expression is about 3% of DART1, which, although low, may still be sufficient for functional roles such as modification of H3R17me2a (Fig. 3C, Supplementary Fig. 3F and 3I). We have added these new data and additional explanation in the revised manuscript (page 11).

(5) Several of the genome browser snapshots, particularly scale and genome coordinates, are difficult to read. 

We apologize for the difficulty in reading several of the genome browser snapshots in the original submission. We have re-generated the relevant figures using IGV, which provides clearer visualization of scale and genome coordinates. The previous images have been replaced with the improved versions in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

(1) The authors need to elaborate on what this sentence means, as it is very unclear what they are describing about Rhino residency: "The results show that Rhino in OSCs tends to reside in the genome where Rhino binds locally in the ovary (Fig. 1C)." 

We apologize for the lack of clarity in the original sentence. The text has been revised as follows:

”Rhino expressed in OSCs bound predominantly to genomic sites exhibiting sharp and interspersed Rhino localization patterns in the ovary, while showing little localization within broad Rhino domains, including major piRNA clusters.”

In addition, to clarify the behavior of Rhino at broad domains, we have added the phrase “the terminal regions of broad domains, such as major piRNA clusters” to the subsequent sentence.

(2) The red correlation line is very confusing in Figure 5F. What sort of line does this mean in this scatter plot? 

We apologize for the lack of clarity regarding the red line in Fig. 5F. The red line represents the least-squares linear regression fit to the data points, calculated using the lm() function in R, and was added with abline() to illustrate the correlation between ctrl GLKD and DART4 GLKD values. In the revised figure, we have clarified this in the legend by specifying that it is a regression line.

(3) There is no confirmation of the successful knockdown of the various DARTs in the OSCs.

We thank the reviewer for the comment. The knockdown efficiency of the various DARTs in OSCs was confirmed by RT–qPCR. The data are now shown in Supplementary Fig. 3J. 

(4) What is the purpose of an unnumbered "Method Figure" in the supplementary data file? Why not just give it a number and mention it properly in the text? 

We thank the reviewer for the suggestion. We have now assigned a number to the previously unnumbered "Method Figure" and have included it as Supplementary Fig. 9.

The figure is now properly cited in the Methods section.

(5) For Figure 5A, those fly strain numbers in the labels are better reserved in the Methods, and a more appropriate label is to describe the GAL4 driver and the UAS-RNAi construct by their conventional names.

We thank the reviewer for the suggestion. The labels in Fig. 5A have been updated to use the conventional names of the GAL4 drivers and UAS-RNAi constructs. Specifically, they now read Ctrl GLKD (nos-GAL4 > UAS-emp) and DART4 GLKD (nos-GAL4 > UASDART4). The original fly strain numbers are listed in the Methods section.

eLife Assessment

This useful study presents the potentially interesting idea that LRRK2 regulates cellular BMP levels and their release via extracellular vesicles, with GCase activity further modulating this process in mutant LRRK2-expressing cells. However, some of the evidence supporting these conclusions remains incomplete, and additional work is suggested under certain conditions. Overall, the study will be of interest to cell biologists working on Parkinson's disease.

Reviewer #1 (Public review):

Summary:

Even though mutations in LRRK2 and GBA1 (which encodes the protein GCase) increase the risk of developing Parkinson's disease (PD), the specific mechanisms driving neurodegeneration remain unclear. Given their known roles in lysosomal function, the authors investigate how LRRK2 and GCase activity influence the exocytosis of the lysosomal lipid BMP via extracellular vesicles (EVs). They use fibroblasts carrying the PD-associated LRRK2-R1441G mutation and pharmacologically modulate LRRK2 and GCase activity.

Strengths:

The authors examine both proteins at endogenous levels, using MEFs instead of cancer cells. The study's scope is potentially interesting and could yield relevant insights into PD disease mechanisms.

Weaknesses:

Many of the authors' conclusions are overstated and not sufficiently supported by the data. Several statistical errors undermine their claims. Pharmacological treatment is very long, leading to potential off target effects. Additionally, the authors should be more rigorous when using EV markers.

Comments on revisions:

The authors have not addressed most of my concerns. For example, instead of trying with a 1-2 hour MLi2 treatment, they cited all the papers that use extremely long time points for LRRK2 inhibition; the fact that other groups do it does not mean it is biologically correct. They also refused to quantify their western blots in a proper manner, without the "hyper-normalization" claiming that it is an accepted way to quantify western blots. Again, it is statistically incorrect and biologically impossible. They also do not have a satisfactory explanation as to why the R1441G cells (which increase LRRK2 kinase activity) have no effect on EV release, but they still claim it is LRRK2 kinase activity dependent.

Overall, I am very confused by the model proposed by the authors. They only see increased EV release in the G2019S expressing cells, but not the R1441G cells, yet they claim that the increase of EV release is LRRK2 kinase activity dependent. Then, they claim that the presence of BMP (unchanged in R1441G vs CTL) in EVs is also LRRK2 kinase activity dependent. Finally, they perform TIRF with pHluorin-CD63 construct and observed an increase in G2019S cells vs CTL "further confirming that BMP release is associated with EV secretion". First, I could not see the increase in BMP release in G2019S cells (if I missed it, I apologize). And second, why didn't they do this experiment in R1441G cells? As, the R1441G cells have not displayed an increase in EV release compared to CTL cells, it could also be possible that the BMP release might be more abundant through lysosomal exocytosis (which could explain the pHluorin results) than EVs. Overall, the authors nicely demonstrate that the R1441G cells have more BMP species, likely due to increase CLN5 expression, but the release of the BMP is still not clear to this reviewer.

Reviewer #2 (Public review):

Summary:

In this paper, authors used MEFs expressing the R1441G mutant of leucine-rich repeat kinase 2 (LRRK2), a mutant associated with the early onset of Parkinson's disease. They report that in these cells LAMP2 fluorescence is higher but BMP fluorescence is lower, MVE size is reduced and that MVEs contain less ILVs. They also report that LAMP2-positive EVs are increased in mutant cells in a process sensitive to LRRK2 kinase inhibition but are further increased by glucocerebrosidase (GCase) inhibition, and that total di-22:6-BMP and total di-18:1-BMP are increased in mutant LRRK2 MEFs compared to WT cells by mass spectrometry. They also report that LRRK2 kinase inhibition partially restores cellular BMP levels, and that GCase inhibition further increased BMP levels, and that in EVs from the LRRK2 mutant, LRRK2 inhibition decreases BMP while GCase inhibition has the opposite effect. Moreover, they report that BMP increase is not due to increased BMP synthesis, although authors observe that CLN5 is increased in LRRK2 mutant cells. Finally, they report that GW4869 decreases EV release and exosomal BMP, while bafilomycin A1 increases EV release. They conclude that LRRK2 regulates BMP levels (in cells) and release (via EVs). They also conclude that the process is modulated by GCase in LRRK2 mutant cells, and that these studies may contribute to the use of BMP-positive EVs as a biomarker for Parkinson's disease and associated treatments.

Strengths:

This is a potentially interesting paper,. However, I had comments that authors needed to address to clarify some aspects of their study.

Weaknesses:

(1) The authors seem to have missed the point in their reply to my first comment. They mention the paper by Stuffers et al., who reports that endosome biogenesis continues without ESCRT. This is a nice paper, but it is irrelevant to the subject at hand. In my initial comment, I drew the author's attention to an apparent contradiction: higher LAMP2 staining in R1441G LRRK2 knock-in MEFs and yet smaller MVEs with a reduced surface area. LAMP2 being one of the major glycoproteins of MVE's limiting membrane, one would have expected lower LAMP2 staining if cells contain fewer and smaller MVEs. Authors now state that elevated LAMP2 expression in cells expressing R1441G reflects a cell type-specific effect (differential penetrance of LRRK2 signaling on lysosomal biogenesis), because amounts of LAMP1 and CD63 are similar in cells from LRRK2 G2019S PD patients and control cells (new Fig 7A-F). However, authors still conclude that LRRK2 modulates the lysosomal network, including LAMP2 and CLN5. Does it?

Similarly, the mass spec analysis of BMP (Fig S1H) does not support the data in Fig 1. Does this Table include all major isoforms found in these cells? If so, the dominant isoform is by far the di-18:1 isoform in wt and R1441G cells (at least 10X more abundant than other isoforms). Now, di-18:1-BMP is roughly 4X more abundant in R1441G cells when compared to wt cells, while BMP is reduced by half in R1441G cells (light microscopy in Fig 1). Authors argue that light microscopy may only detects a so-called antibody accessible pool. What is this? And why would this pool decrease in R1441G cells when LAMP2 is higher? Alternatively, they argue that the anti-BMP antibody may be less specific and detect other analytes. As I had already mentioned, this makes no sense, since the observed signal is lower and not higher. If authors do not trust their light microscopy analysis, why show the data?

(2) Cells contain 3 LAMP2 isoforms. Which one is upregulated and/or secreted in exosomes?

(3) The new Fig S4A is far from convincing. How were cells fractionated and what are the gradients (not described in Methods)? CD63 (presumably endolysosomes) is spread over fractions 8 - 13. LRRK2 (fractions 8-9) does not copurify with CD63. The bulk of LRRK2 is at the bottom (presumably cytosol if this is a floatation gradient), and a minor fraction moves into the gradient. CLN5 is even less clear since the bulk is also at the bottom with a tiny fraction only between LRRK2 and CD63. Also, why do authors conclude that a considerable pool of newly synthesized CLN5 did not reach its final destination at the endolysosome and may instead be retained in the ER? Where is the ER on the gradient?

(4) Fig S4B shows blots of whole cell lysates from CTRL and LRRK2 mutant-derived fibroblasts: 6 lanes are shown but without captions, containing varying amounts of calnexin and CD63. In addition, the blots look very dirty. Where is CD63? Is it the minor band at ≈37 kD (as in Fig S4A)? Or the major band below the 50kD marker? What are the other bands on these blots? As a result, the quantification shown in the bar graph does not mean much.

(5) The cell content of 18.1-BMP is increased approx. 5X by BafA1 (Fig 6C) but amounts of 18.1-BMP secreted in EVs hardly changes (Fig 6E). Since BMP is mostly present as 18.1 isoform (22:6-BMP being only a minor species, Fig S1H), does it mean that BafA1 does not increase BMP secretion and/or only a minor fraction of total cellular BMP is secreted in exosomes?

Comments on revisions:

How come 0.2 mmol/L of 22:6 and 18:1 fatty acid both correspond to 65 µg/mL (Fig 4A)?

It is stated in the Legend of Fig4 that long (B-C) and short (D) chase time points are shown as fold change. There is no panel D in the figure.

Author response:

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

eLife Assessment

This useful study presents the potentially interesting concept that LRRK2 regulates cellular BMP levels and their release via extracellular vesicles, with GCase activity further modulating this process in mutant LRRK2-expressing cells. However, the evidence supporting the conclusions remains incomplete, and certain statistical analyses are inadequate. This work would be of interest to cell biologists working on Parkinson's disease.

Reviewer #1 (Public review):

Summary:

Even though mutations in LRRK2 and GBA1 (which encodes the protein GCase) increase the risk of developing Parkinson's disease (PD), the specific mechanisms driving neurodegeneration remain unclear. Given their known roles in lysosomal function, the authors investigate how LRRK2 and GCase activity influence the exocytosis of the lysosomal lipid BMP via extracellular vesicles (EVs). They use fibroblasts carrying the PDassociated LRRK2-R1441G mutation and pharmacologically modulate LRRK2 and GCase activity.

Strengths:

The authors examine both proteins at endogenous levels, using MEFs instead of cancer cells. The study's scope is potentially interesting and could yield relevant insights into PD disease mechanisms.

Weaknesses:

Many of the authors' conclusions are overstated and not sufficiently supported by the data. Several statistical errors undermine their claims. Pharmacological treatment is very long, leading to potential off-target effects. Additionally, the authors should be more rigorous when using EV markers.

We thank the reviewer for these valuable observations. In the revised manuscript, we have addressed each of these points as follows:

(1) Conclusions and data support – We carefully revised our text throughout the manuscript to ensure that all conclusions are better supported by the presented data. For instance, we now explicitly state that while pharmacological modulation supports the regulatory role of LRRK2 activity in EV-mediated BMP release, we have softened our conclusions concerning the contribution of GCase in this model (see revised Results and Discussion sections).

(2) Statistical analyses – We reanalyzed experiments involving more than two groups and replaced simple t-tests with non-parametric Kruskal-Wallis tests followed by Dunn’s post hoc comparisons. This approach, described in the updated figure legends (e.g., Figure 2D-F and H-J), provides a more rigorous statistical framework that accounts for small sample sizes and variability typical of EV quantifications.

(3) Pharmacological treatment duration – Prolonged MLi-2 treatments have been extensively used in the field without evidence of significant off-target effects. Several studies, including Fell et al. (2015, J Pharmacol Exp Ther 355:397-409), De Wit et al. (2019, Mol Neurobiol 56:5273-5286), Ho et al. (2022, NPJ Parkinson’s Dis 8:115),Tengberg et al. (2024, Neurobiol Dis 202:106728), and Jaimon et al. (2025, Sci Signal 18:eads5761), have applied long-term (24-48 h) MLi-2 treatments at comparable concentrations without detecting toxicity or off-target alterations, including in MEFs (Ho et al., 2022; Dhekne et al., 2018, eLife 7:e40202).  In our study, 48-hour incubations were necessary to sustain full LRRK2 inhibition throughout the extracellular vesicle (EV) collection period. EV biogenesis, BMP biosynthesis, and packaging into EVs are timedependent processes; therefore, extended incubation and collection periods (48 h) were required to allow downstream effects of LRRK2 inhibition on BMP production and release to manifest, and to obtain sufficient EV material for biochemical and lipidomic analyses. This experimental design also reflects our and others’ previous observations in humans and non-human primates, where urinary BMP changes are associated with chronic or subchronic LRRK2 inhibitor treatment (Baptista MAS, Merchant K, et al. Sci Transl Med. 2020, 12:eaav0820; Jennings D, et al. Sci Transl Med. 2022, 14:eabj2658; Maloney MT, et al. Mol Neurodegener. 2025, 20:89). Importantly, under these conditions, we did not observe significant changes in cell viability or morphology, supporting that the treatment was well tolerated.  We have clarified this rationale in the revised Methods section to emphasize that the prolonged incubation reflects the experimental design for EV isolation rather than a requirement for achieving LRRK2 inhibition.

(4) EV markers – We and others have reported enrichment of Flotillin-1 and LAMP proteins in isolated small EV fractions (Kowal et al., 2016; Lu et al., 2018; Mathieu et al., 2021; Ferreira et al., 2022). Moreover, LAMP proteins have been reported to be more enriched in EVs of endolysosomal origin (Mathieu et al., 2021). To further strengthen this point, we performed new experiments using a CD63-pHluorin sensor combined with TIRF microscopy, which allowed real-time visualization of CD63-positive exosome release. These new data (now presented in Figure 7, Panels G-I; Videos 1 and 2) confirm increased CD63-positive EV release in LRRK2 mutant fibroblasts, which was reversed by LRRK2 inhibition with MLi-2. The CD63-positive compartment was also largely BMPpositive (new Figure 7D, F, G), reinforcing our conclusions and providing additional rigor in EV marker validation.

Reviewer #2 (Public review):

Summary:

In this paper, the authors used MEFs expressing the R1441G mutant of leucine-rich repeat kinase 2 (LRRK2), a mutant associated with the early onset of Parkinson's disease. They report that in these cells LAMP2 fluorescence is higher but BMP fluorescence is lower, MVE size is reduced, and that MVEs contain less ILVs. They also report that LAMP2-positive EVs are increased in mutant cells in a process sensitive to LRRK2 kinase inhibition but are further increased by glucocerebrosidase (GCase) inhibition, and that total di-22:6-BMP and total di-18:1-BMP are increased in mutant LRRK2 MEFs compared to WT cells by mass spectrometry. They also report that LRRK2 kinase inhibition partially restores cellular BMP levels, and that GCase inhibition further increases BMP levels, and that in EVs from the LRRK2 mutant, LRRK2 inhibition decreases BMP while GCase inhibition has the opposite effect. Moreover, they report that the BMP increase is not due to increased BMP synthesis, although the authors observe that CLN5 is increased in LRRK2 mutant cells. Finally, they report that GW4869 decreases EV release and exosomal BMP, while bafilomycin A1 increases EV release. They conclude that LRRK2 regulates BMP levels (in cells) and release (via EVs). They also conclude that the process is modulated by GCase in LRRK2 mutant cells, and that these studies may contribute to the use of BMP-positive EVs as a biomarker for Parkinson's disease and associated treatments.

Strengths:

This is an interesting paper, which provides novel insights into the biogenesis of exosomes with exciting biomedical potential. However, I have comments that authors need to address to clarify some aspects of their study.

Weaknesses:

(1) The intensity of LAMP2 staining is increased significantly in cells expressing the R1441G mutant of LRRK2 when compared to WT cells (Figure 1C). Yet mutant cells contain significantly smaller MVEs with fewer ILVs, and the MVE surface area is reduced (Figure 1D-F). This is quite surprising since LAMP2 is a major component of the limiting membrane of late endosomes. Are other proteins of endo-lysosomes (eg, LAMP1, CD63, RAB7) or markers (lysotracker) also decreased (see also below)?

As referenced in our original manuscript, several previous studies have reported endolysosomal morphological and homeostatic defects in cells harboring pathogenic LRRK2 mutations. LAMP2 can be upregulated as part of a lysosomal biogenesis or stress response (e.g., via MiT/TFE transcription factors such as TFEB; Sardiello et al., Science 2009, 325:473-477), whereas ILV biogenesis is primarily controlled by ESCRT- and SMPD3-dependent pathways that are regulated independently of MiT/TFE-driven transcriptional programs. Indeed, Stuffers et al. (Traffic 2009, 10:925-937) demonstrated that depletion of key ESCRT subunits markedly inhibited ILV formation while concomitantly increasing LAMP2 expression, highlighting the mechanistic dissociation between LAMP2 abundance and ILV number. In our study, we observed a similar pattern in R1441G LRRK2 MEFs, in which elevated LAMP2 staining and protein levels occurred despite a reduction in MVE size and ILV number. We interpret this as a compensatory lysosomal biogenesis response.

Our revised manuscript now includes new immunofluorescence data for BMP, LAMP1 and CD63 (New Figure 7, Panels A-F) together with biochemical analysis of CD63 protein levels (New Supplemental Figure 4, Panel B) in human skin fibroblasts derived from healthy donors and LRRK2 G2019S PD patients. Quantitative analysis of these experiments revealed no statistically significant differences in total cellular levels of either LAMP1 or CD63 between groups. However, we observed a consistent decrease in BMP immunostaining intensity (New Figure 7, Panel A and B), in agreement with our findings in mouse fibroblasts. We therefore propose that the elevated LAMP2 expression observed in the engineered MEF clone expressing R1441G may reflect a cell type-specific effect, potentially linked to differential penetrance of LRRK2 signaling on the lysosomal biogenesis response. We have updated the Results and Discussion section of the manuscript to incorporate and clarify these findings.

(2) LRRK2 has been reported to interact with endolysosomal membranes. Does the R1441G mutant bind LAMP2- and/or BMP-positive membranes? 

We agree that LRRK2 has been reported to associate dynamically with endolysosomal membranes, particularly under conditions of endolysosomal stress or damage (Eguchi T, et al. PNAS 2018, 115:E9115-E9124; Bonet-Ponce L, et al. Sci Adv. 2020, 6:eabb2454; Wang X, et al. Elife. 2023, 12:e87255).

Nevertheless, to explore whether LRRK2 associates with BMP-positive endolysosomes, we performed subcellular fractionation followed by biochemical analysis of endolysosomal fractions, since our available LRRK2 antibodies did not provide reliable immunofluorescence signals. These experiments were carried out using human skin fibroblasts derived from both healthy controls and Parkinson’s disease patients carrying the LRRK2-G2019S mutation. In both control and mutant fibroblasts, a pool of LRRK2 was detected in fractions positive for the BMP synthase CLN5 and the endolysosomal marker CD63 (New Supplementary Figure 4, Panel A), supporting the localization of LRRK2 to endolysosomal membranes that are likely BMP-enriched. Our manuscript’s Results and Methods sections have been updated accordingly.

Does the mutant affect endolysosomes?

As referenced in our original manuscript, several studies have reported that pathogenic LRRK2 mutations can lead to endolysosomal defects. Consistent with these reports, we also observed morphological alterations in endolysosomes of cells expressing mutant LRRK2, including reduced MVE size and fewer ILVs, as shown in Figure 1D–F. These observations are in agreement with previously described phenotypes associated with pathogenic LRRK2 variants. Furthermore, in mutant LRRK2 MEFs, and now in humanderived fibroblasts (see new Figure 7, Panel A and B), we observed a decrease in BMP immunostaining signal.

(3) Immunofluorescence data indicate that BMP is decreased in mutant LRRK2expressing cells compared to WT (Figure 1A-B), but mass spec data indicate that di-22:6BMP and di-18:1-BMP are increased (Figure 3). Authors conclude that the BMP pool detected by mass spec in mutant cells is less antibody-accessible than that present in wt cells, or that the anti-BMP antibody is less specific and that it detects other analytes. This is an awkward conclusion, since the IF signal with the antibody is lower (not higher): why would the antibody be less specific? Could it be that the antibody does not see all BMP isoforms equally well? Moreover, the observations that mutant cells contain smaller MVEs (Figure 1D-F) with fewer ILVs are consistent with the IF data and reduced BMP amounts. This needs to be clarified.

As previously reported by us (Lu et al., J Cell Biol 2022;221:e202105060) and others (Berg AL, et al. Cancer Lett. 2023, 557:216090), discrepancies can occur between BMP levels detected by immunofluorescence and those quantified by mass spectrometry. This is because immunostaining reflects the pool of antibody-accessible BMP, whereas lipidomics measures the total cellular content of all BMP molecular species, irrespective of their distribution or accessibility.

We agree that the anti-BMP antibody may not detect all BMP isoforms equally well. Differences in acyl chain composition (such as the degree of saturation or chain length) can alter the stereochemistry of BMP and, consequently, epitope accessibility to antibody binding.

In addition, in a personal communication with Monther Abu-Remaileh (Stanford University), we were informed that the antibody may also cross-react with other lipid species in endolysosomes. Nevertheless, since there is no formal evidence supporting this, we have removed the sentence in the Discussion section stating “Alternatively, the antibody may also detect non-BMP analytes” to avoid any potential misinterpretations. In its place, we have added a short statement noting that “not all BMP isoforms may be detected equally well”.

Mass spectrometry data are only shown for two BMP species (di-22:6, di-18:1). What are the major BMP isoforms in WT cells? The authors should show the complete analysis for all BMP species if they wish to draw quantitative conclusions about the amounts of BMP in wt and mutant cells. Finally, BMP and PG are isobaric lipids. Fragmentation of BMPs or PGs results in characteristic fingerprints, but the presence of each daughter ion is not absolutely specific for either lipid. This should be clarified, e.g., were BMP and PG separated before mass spec analysis? Was PG affected? The authors should also compare the BMP data with mass spec data obtained with a control lipid, e.g., PC.

Regarding BMP isoforms, our targeted UPLC-MS/MS analyses revealed that 2,2′-di-22:6-BMP (sn2/sn2′) and 2,2′-di-18:1-BMP (sn2/sn2′) are the predominant BMP isoforms in MEF cells, consistent with previous reports showing docosahexaenoyl (22:6; DHA) and oleoyl (18:1) BMP as the most abundant isoforms. Across diverse mammalian cells and tissues, BMP typically exhibits a fatty acid composition dominated by oleoyl, with polyunsaturated fatty acids (particularly DHA) also contributing substantially. Enrichment of DHA-containing BMP species has been observed in multiple systems, including rat uterine stromal cells, PC12 cells, THP-1 and RAW macrophages, as well as in rat and human liver. This consistent presence of oleoyl- and docosahexaenoyl-containing BMP species across tissues indicates that these acyl chains are conserved features influencing the lipid’s structural and functional characteristics (Kobayashi et al. J Biol Chem, 2002; Hullin-Matsuda et al. Prostaglandins Leukotriens Essent Fatty Acids, 2009; Thompson et al. Int J Toxicol. 2012; Delton-Vandenbroucke et al. J Lipid Res, 2019).

Nevertheless, we have included a Table (Panel H in updated Supplemental Figure 1) showing other BMP species that were also detected in our lipidomics analysis. Overall, dioleoyl (18:1)- and di-docosahexaenoyl (22:6)-BMP species were the most abundant in MEF cells, whereas di-arachidonoyl (20:4)- and di-linoleoyl (18:2)-BMP isoforms were present at lower levels. Consistently, R1441G LRRK2 MEFs displayed higher levels of dioleoyl- and di-docosahexaenoyl-BMP compared with WT cells, and these elevations were reduced following LRRK2 kinase inhibition with MLi-2. Data from three independent representative experiments are shown, and the manuscript has been revised accordingly to include these results.

Regarding the separation of BMP and PG species, we confirm that BMP and PG were chromatographically resolved prior to MS/MS detection using a validated UPLC-MS/MS method developed by Nextcea, Inc. PG exhibits a substantially longer LC retention time than BMP, ensuring complete baseline separation. This approach (established by Nextcea nearly two decades ago and later validated through a multi-year collaboration with the U.S. FDA to clinically qualify di-22:6-BMP as a biomarker) prevents any ambiguity arising from the isobaric nature of BMP and PG species. No changes in PG levels were detected under any experimental conditions.

Finally, we employed isotope-labeled BMP as an internal standard to ensure robust normalization across samples. These additional details and references cited above have been included in the revised Methods and References sections to further clarify the analytical rigor of our lipidomics workflow.

(4) It is quite surprising that the amounts of labeled BMP continue to increase for up to 24h after a short 25min pulse with heavy BMP precursors (Figure 4B).

In these isotope-labeling experiments, it is important to note (as described in our original manuscript) that two distinct pools of metabolically labeled BMP species were detected: semi-labeled BMP (with only one heavy isotope-labeled fatty acyl chain) and fully-labeled BMP (with both fatty acyl chains labeled). We consider the fully-labeled BMP pool to provide the most reliable readout for BMP turnover, as it showed a rapid decline after a 1h chase (decreasing by more than 50% within 8 h in all conditions), reaching its lowest levels at the end of the 48-h chase period.

The apparent increase in semi-labeled BMP species over time may be explained by continued incorporation of labeled precursors following the initial pulse. Specifically, once existing semi-labeled and fully-labeled BMP molecules are degraded by PLA2G15 (Nyame K, et al. Nature 2025, 642:474-483), the resulting isotope-labeled lysophosphatidylglycerol (LPG) and fatty acids could be recycled and re-enter a new round of BMP biosynthesis, leading to a gradual accumulation of semi-labeled BMP such as di-18:1-BMP. Why would this reasoning not also apply to the fully-labeled species? Once the pulse is completed, newly incorporated non-labeled fatty acyl chains present in the cellular pool can compete with labeled ones during subsequent rounds of lipid remodeling or synthesis. As a result, the probability of generating semi-labeled BMP molecules becomes higher than that of forming fully-labeled species. Consistent with this, our data show an increase in only semi-labeled BMP species (but not in fully-labeled ones) up to 24 hours after the pulse. We have added a clarification regarding this point in the revised manuscript.

(5) It is argued that upregulation of CLN5 may be due to an overall upregulation of lysosomal enzymes, as LAMP2 levels were also increased (Figure 2A, C, E). Again, this is not consistent with the observed decrease in MVE size and number (Figure 1D-F). As mentioned above, other independent markers of endo-lysosomes should be analyzed (eg, LAMP1, CD63, RAB7), and/or other lysosomal enzymes (e.g. cathepsin. D).

Our revised manuscript now includes new immunofluorescence data for BMP, LAMP1 and CD63 (New Figure 7, Panels A-F) together with biochemical analysis of CD63 protein levels (New Supplemental Figure 4, Panel B) in human skin fibroblasts derived from healthy controls and LRRK2 G2019S PD patients. Quantitative analysis of these experiments revealed no statistically significant differences in total cellular levels of either LAMP1 or CD63 between groups. However, our results consistently show increased CLN5 protein levels in both mouse and human fibroblast cell lines harboring pathogenic LRRK2 mutations. Upregulation of CLN5 may reflect a compensatory effect from loss of BMP via EV exocytosis. As discussed above, the elevated LAMP2 signal observed in the engineered MEF clone expressing R1441G could represent a cell type-specific effect, potentially linked to differential penetrance of LRRK2 signaling on the lysosomal biogenesis response. Our Results and Discussion sections have been updated accordingly.

(6) The authors report that the increase in BMP is not due to an increase in BMP synthesis (Figure 4), although they observe a significant increase in CLN5 (Figure 5A) in LRRK2 mutant cells. Some clarification is needed.

In our original manuscript, we proposed that although CLN5 protein levels are increased in R1441G LRRK2 MEFs, the absence of significant changes in BMP synthesis rates (Figure 4B, C) may reflect either limited substrate availability or that CLN5 is already operating near its maximal enzymatic capacity. Our new subcellular fractionation data (new Figure 7, Panel A) further indicate that, despite a relative increase in total CLN5 levels in G2019S LRRK2 human fibroblasts, the amount of CLN5 associated with endolysosomes remains comparable between mutant LRRK2 and control cells. This suggests that a considerable fraction of upregulated CLN5 may not localize to endolysosomes, potentially accumulating in the endoplasmic reticulum due to enhanced translation or impaired trafficking. Unfortunately, the available anti-CLN5 antibody did not yield reliable immunofluorescence signals, preventing us from directly confirming this possibility. Nevertheless, in light of our new data (new Supplemental Figure 4A), we have included a clarification in the revised manuscript discussing this possibility as well.

(7) Authors observe that both LAMP2 and BMP are decreased in EVs by GW4869 and increased by bafilomycin (Figure 6). Given my comments above on Figure 1, it would also be nice to illustrate/quantify the effects of these compounds on cells by immunofluorescence.

We appreciate the reviewer’s suggestion. We have previously published immunofluorescence data showing increased BMP accumulation in endolysosomes following treatment with bafilomycin A1 Lu A, et al. J Cell Biol. 2009, 184:863-879). However, in the present study, our lipidomics analyses revealed a decrease in both di22:6-BMP and di-18:1-BMP species in cells treated with this compound. As discussed above, this apparent discrepancy likely reflects methodological differences between immunofluorescence, which detects only antibody-accessible BMP pools, and lipidomics, which quantifies total cellular BMP content. 

Moreover, in a recent study (Andreu Z, et al. Nanotheranostics 2023, 7:1-21), BMP levels were analyzed by immunofluorescence in cells treated with spiroepoxide, a potent and selective irreversible inhibitor of nSMase (different from GW4869) known to block EV release. Spiroepoxide-treated cells showed decreased BMP immunostaining; a result that, again, does not align with mass spectrometry data revealing increased cellular BMP levels upon GW4869 treatment. Notably, in that study, spiroepoxide was used instead of GW4869 because the intrinsic autofluorescence of GW4869 could potentially interfere with the immunofluorescence BMP signal.

We therefore consider lipidomics measurements to provide a more reliable and quantitative representation of BMP dynamics under these conditions.

Reviewer #1 (Recommendations for the authors):

Major concerns:

(1) 48 h for MLi2 treatment seems too long. LRRK2 kinase activity is inhibited with much shorter incubation times. The longer the incubation, the more likely off-target effects are. The authors should repeat these experiments with 1-2 h of MLi2.

We thank the reviewer for this valuable comment. We acknowledge that MLi-2 is a potent and selective LRRK2 kinase inhibitor that achieves near-complete target engagement within a few hours of treatment. However, prolonged exposure has been widely used in the field without evidence of significant off-target effects. Several studies, including Fell et al. (2015, J Pharmacol Exp Ther 355:397-409), De Wit et al. (2019, Mol Neurobiol 56:5273-5286), Ho et al. (2022, NPJ Parkinson’s Dis 8:115), Tengberg et al. (2024, Neurobiol Dis 202:106728), and Jaimon et al. (2025, Sci Signal 18:eads5761), have employed long-term (24-48 h) MLi-2 treatments at comparable concentrations without detecting toxicity or off-target alterations, including in MEFs (Ho et al., 2022; Dhekne et al., 2018, eLife 7:e40202).

In our study, 48-hour incubations were necessary to sustain full LRRK2 inhibition throughout the extracellular vesicle (EV) collection period. EV biogenesis, BMP biosynthesis, and packaging into EVs are time-dependent processes; therefore, extended incubation and collection periods (48 h) were required to allow downstream effects of LRRK2 inhibition on BMP production and release to manifest, and to obtain sufficient EV material for biochemical and lipidomic analyses. This experimental design also reflects our and others’ previous observations in humans and non-human primates, where urinary BMP changes are associated with chronic or subchronic LRRK2 inhibitor treatment (Baptista MAS, Merchant K, et al. Sci Transl Med. 2020, 12:eaav0820; Jennings D, et al. Sci Transl Med. 2022, 14:eabj2658; Maloney MT, et al. Mol Neurodegener. 2025, 20:89). Importantly, under these conditions, we did not observe significant changes in cell viability or morphology, supporting that the treatment was well tolerated.

We have clarified this rationale in the revised Methods section to emphasize that the prolonged incubation reflects the experimental design for EV isolation rather than a requirement for achieving LRRK2 inhibition.

(2) Is there a reason why the authors don't include CD81, CD63, and Syntenin-1 in their study as an EV marker? Using solely Flotilin-1 does not seem to be enough to justify their claims.

We actually used not only Flotillin-1 but also LAMP2 as EV markers in our study. While both Flotillin-1 and LAMP2 detection on EVs may vary depending on the cell type, we and others have reported enrichment of Flotillin-1 and LAMP proteins in isolated small EV fractions (Kowal et al., 2016; Lu et al., 2018; Mathieu et al., 2021; Ferreira et al., 2022). In particular, one of these studies reported that “LAMP1-positive subpopulations of EVs represent MVB/lysosome-derived exosomes, which also contain syntenin-1.” Therefore, our choice of EV markers (LAMP2 and Flotillin-1) is consistent with those previously and reliably used to characterize small EVs.

Nevertheless, to further address the reviewer’s concern, we performed additional experiments using a CD63-based fluorescence sensor (CD63-pHluorin), which, combined with TIRF microscopy, enables real-time visualization of CD63-positive exosome release. These experiments were conducted in control and LRRK2-mutant fibroblasts, and the data are presented in new Figure 7 (Panels G-I; Videos 1 and 2). We have also included all relevant references and clarified this point in the revised manuscript.

(3) Indeed, to quantify the amount of certain proteins in EVs, the authors should normalize them by CD63 or CD81.

Protein normalization in isolated EV fractions is indeed challenging. Although tetraspanins such as CD63 and CD81 are commonly enriched in EVs, their abundance can vary considerably across EV subpopulations, cell types, and experimental conditions, making them unreliable as universal normalization markers (Théry et al., J Extracell Vesicles, 2018; Margolis & Sadovsky, Nat Rev Mol Cell Biol, 2019).  Current guidelines from the International Society for Extracellular Vesicles (ISEV), as described in the Minimal Information for Studies of Extracellular Vesicles 2018 (MISEV2018; Théry C, et al. JExtracell Vesicles. 2018, 7:1535750) and updated in MISEV2024 (Welsh JA, et al. J Extracell Vesicles. 2024, 13:e12404), recommend reporting multiple EV markers rather than relying on a single protein for normalization. They also suggest ensuring comparable experimental conditions by using the same number of cells at the start of the experiment and normalizing EV data to cell number or whole-cell lysate protein content at the end of the experiment, among other approaches.

In our study, we normalized EV data to whole-cell lysate (WCL) protein content, as this approach accounts for differences in EV production due to variations in cell number or treatment conditions and is commonly used in the field (Kowal et al., PNAS, 2016; Mathieu et al., Nat Commun, 2021). We also included Flotillin-1 and LAMP2 as EV markers, both of which have been validated as molecular markers of small EV subpopulations.

(4) Hyper normalization in WB quantification in Figure 2E-G is statistically incorrect, as it assumes that one group (in this case, R1441G ctrl) has no variability at all, which is not biologically possible. The authors should repeat the quantification without hypernormalizing one of their groups. This issue is prevalent across the whole manuscript.

We understand the concern regarding “hyper-normalization” (i.e., expressing all values relative to one condition set to 1), which may mask variability in the reference group. However, it is standard practice in immunoblotting analysis to express data relative to a control condition for comparison, as variations in membrane transfer, exposure time, and signal development can differ across blots. In our case, the data are expressed as relative levels (arbitrary units) rather than absolute quantitative values. To facilitate comparison between datasets and account for inter-experimental variation, we continued to express values relative to the mutant LRRK2 MEF condition.

On the other hand, in lipidomics experiments, despite using the same number of seeded cells and identical extraction and analysis protocols, minor biological and technical variability was observed across independent replicates. This variability is inherent to the experimental system and is now explicitly represented in the new table included in Supplemental Figure 1F, which compiles three independent representative lipidomics experiments showing quantitative BMP levels across different conditions.

(5) The authors perform a t-test in Figure 2E-G when comparing more than 2 groups, which is wrong. The authors should use a two-way ANOVA as they are comparing genotype and treatment.

We appreciate the reviewer’s comment and agree with this observation. The MLi-2 and CBE experiments were performed independently and in separate experimental runs; therefore, we have reanalyzed these datasets separately rather than combining them in a two-way ANOVA. To properly compare more than two groups within each dataset, we have now applied a Kruskal-Wallis test followed by an uncorrected Dunn’s post hoc test (Figure 2 D-F and H-J). This non-parametric approach is more appropriate for our data structure, as EV experiments are usually subject to high variability and immunoblot quantifications involving small sample sizes (n≈6) do not always meet the assumptions of normality or equal variance. The Kruskal-Wallis test does not assume normality or equal variances, making it more robust for small, variable biological datasets. The statistical analyses and figure legend have been updated in the revised manuscript accordingly.

In addition, since our CBE treatments yielded statistically non-significant data, we have softened our conclusions throughout the manuscript concerning the contribution of GCase activity to EV-mediated BMP release modulation.

(6) There is a very strong reduction in flotillin-1 in R1441G cells vs WT (Figure 2G) in the EV fraction. That reduction is further exacerbated with MLi2, which likely means it is not kinase activity dependent. Can the authors comment on that?

We agree with the reviewer that Flotillin-1 showed a different behavior compared with LAMP2 in these experiments. As recommended by the MISEV guidelines (Théry C, et al. J Extracell Vesicles. 2018;  7:1535750; Welsh JA, et al. J Extracell Vesicles. 2024, 13:e12404), it is important to analyze more than one EV-associated protein marker. We examined LAMP2, which, together with LAMP1, has been reported to be specifically enriched in EVs of endolysosomal origin (exosomes; Mathieu et al., Nat Commun. 2021, 12:4389 ). In contrast, Flotillin-1 is also associated with small EVs but may represent a distinct EV subpopulation from those positive for LAMP proteins (Kowal J, et al. PNAS 2016, 113:E968-E977).

Nevertheless, the biochemical analysis of isolated EV fractions was complemented by our lipidomics data and, in the revised version, by TIRF microscopy analysis of exosome release in control and G2019S LRRK2 human fibroblasts (new Figure 7, Panels G-I; Videos 1 and 2). In this analysis, we confirmed increased exocytosis of CD63-pHluorin– positive endolysosomes in G2019S LRRK2 human fibroblasts compared to controls, an effect that was reversed by MLi-2 treatment. The CD63-pHluorin–positive compartment of these cells was also largely positive for BMP (new Figure 7G). Collectively, these findings further support the regulatory role of LRRK2 activity in EV-mediated BMP secretion.

(7) In Figure 2C, the authors should express that the LAMP2-EV and flotillin-1 EV fractions from the WB are highly exposed. As presently presented, it is slightly misleading.

We thank the reviewer for this comment. In EV preparations, the amount of protein recovered is typically very low. Therefore, although we loaded all the EV protein obtained from each sample, the immunoblots for LAMP2 and Flotillin-1 in EV fractions required longer exposure times to visualize clear signals across all conditions. We have now indicated in the corresponding figure legend that these EV blots are long-exposure blots to facilitate signal detection and avoid any potential misunderstanding.

(8) If Figure 2C and D are from two different experiments, they should not be plotted together in Figure 2E-G. You cannot compare the effect of MLi2 vs CBE if done in completely different experiments.

We appreciate the reviewer’s comment and agree with this observation. The MLi-2 and CBE experiments were performed independently and in separate experimental runs; therefore, we have reanalyzed these datasets separately rather than combining them in a two-way ANOVA. To properly compare more than two groups within each dataset, we have now applied a Kruskal-Wallis test followed by an uncorrected Dunn’s post hoc test (Figure 2 D-F and H-J). This non-parametric approach is more appropriate for our data structure, as EV experiments are usually subject to high variability and immunoblot quantifications involving small sample sizes (n≈6) do not always meet the assumptions of normality or equal variance. The Kruskal-Wallis test does not assume normality or equal variances, making it more robust for small, variable biological datasets. The revised statistical analyses and figure legends have been updated accordingly in the manuscript.

(9) The authors state that "For the R1441G MEF cells, MLi-2 decreased EV concentration while CBE increased EV particles per ml, in agreement with the effects observed in our biochemical analysis." As Figure S1D shows no statistical significance, the authors don't have sufficient evidence to make this claim.

We apologize for this overstatement. We have revised the text to clarify that, although the differences did not reach statistical significance, a consistent trend toward decreased EV concentration upon MLi-2 treatment and increased EV release following CBE treatment was observed in R1441G MEF cells.

(10) "Altogether, given that BMP is specifically enriched in ILVs (which become exosomes upon release), the data presented above support our biochemical analysis (Figure 2C, D, F) and suggest a role for LRRK2 and GCase in modulating BMP release in association with LAMP2-positive exosomes from MEF cells." As Figure 3E shows no statistical difference of BMP on EVs upon CBE treatment, this sentence is not accurate and should be reframed. Furthermore, the authors claim an increase in EV-LAMP2 in R1441G cells compared to WT, however, the amount of BMP in EVs of R1441G cells vs WT is unchanged with a non-significant reduction. This contradiction does not support the authors' conclusions and really puts into question their whole model.

We thank the reviewer for this observation. After reanalyzing our biochemical data from isolated EV fractions (see new Panels D-F and H-J) using an improved statistical approach, we found that although EV-associated LAMP2 levels were consistently elevated in untreated R1441G LRRK2 MEFs compared to WT cells, CBE treatment only produced a non-significant trend toward increased EV-associated LAMP2 compared to untreated R1441G LRRK2 cells. Accordingly, we have revised the sentence to read as follows:

“Altogether, given that BMP is specifically enriched in ILVs (which become exosomes upon release), the data presented above support our biochemical analysis (Figure 2C, E, G, I) and suggest that LRRK2 activity regulates BMP release in association with LAMP2positive exosomes, whereas GCase activity appears to have a more variable effect under the tested conditions.”

We also agree with the reviewer that, in our MEF model, the amount of BMP in EVs of R1441G cells vs WT is unchanged with a non-significant reduction. However, pharmacological modulation supports our conclusion that BMP release is modulated by LRRK2 activity. Specifically, treatment with the LRRK2 inhibitor MLi-2 decreased EVassociated BMP and LAMP2 levels in R1441G LRRK2 MEFs, and our new data (new Figure 7, Panel G-I; Videos 1 and 2) show increased exocytosis of CD63-pHluorin– positive endolysosomes in G2019S LRRK2 human fibroblasts compared to controls, an effect that was reversed by MLi-2 treatment. The CD63-pHluorin–positive compartment of these cells was also largely positive for BMP (new Figure 7G).

In light of the reviewer’s comment about CBE treatment, we have softened our conclusions throughout the manuscript concerning the contribution of GCase activity in this model.

(11) In Figure 5, 16 h of MLi2 treatment is too long and can lead to off-target effects. I would advise reducing it to 1-4 h.

Prolonged MLi-2 treatments have been extensively used in the field without evidence of significant off-target effects. Several studies, including Fell et al. (2015, J Pharmacol Exp Ther 355:397-409), De Wit et al. (2019, Mol Neurobiol 56:5273-5286), Ho et al. (2022, NPJ Parkinson’s Dis 8:115), Tengberg et al. (2024, Neurobiol Dis 202:106728), and Jaimon et al. (2025, Sci Signal 18:eads5761), have applied long-term (24-48 h) MLi-2 treatments at comparable concentrations without detecting toxicity or off-target alterations, including in MEFs (Ho et al., 2022; Dhekne et al., 2018, eLife 7:e40202). Moreover, the data presented in Figure 5 demonstrate a reduction in CLN5 protein levels in both MEFs and human fibroblasts following MLi-2 treatment, confirming the specificity of the observed effects in LRRK2 mutant cells.

(12) "Our data suggest that BMP is exocytosed in association with EVs and that LRRK2 and GCase activities modulate BMP secretion." Again, cells carrying the R1441G mutation have the same amount of BMP in EVs than WT. This sentence is not factually accurate. Accordingly, CBE did not change the amount of BMP in EVs.

We thank the reviewer for this observation and agree that, in our MEF model, the amount of BMP in EVs from R1441G LRRK2 cells is comparable to that observed in WT cells. However, pharmacological modulation supports our conclusion that BMP release is modulated by LRRK2 activity. Specifically, treatment with the LRRK2 inhibitor MLi-2 decreased EV-associated BMP levels in R1441G LRRK2 MEFs, and our new data (new Figure 7G-I; Videos 1 and 2) show increased exocytosis of CD63-pHluorin–positive endolysosomes in G2019S LRRK2 human fibroblasts compared to controls, an effect that was reversed by MLi-2 treatment. The CD63-pHluorin–positive compartment of these cells was also largely positive for BMP (new Figure 7G). These findings further support the regulatory role of LRRK2 activity in EV-mediated BMP secretion. In addition, in light of the reviewer’s comment about CBE treatment, we have softened our conclusions throughout the paper concerning the contribution of GCase activity in this model.

(13) Figure 6; EV release should have been monitored by more accurate markers such as CD63 and CD81.

We thank the reviewer for this comment. We and others (Kowal et al., 2016; Lu et al., 2018; Mathieu et al., 2021; Ferreira et al., 2022) have reported enrichment of Flotillin-1 and LAMP proteins in isolated small EV fractions. In particular, one of these studies (Mathieu et al., Nat Commun. 2021), in which bafilomycin A1 was also used (to boost exosome release), reported that “LAMP1-positive subpopulations of EVs represent MVB/lysosome-derived exosomes, which also contain syntenin-1.” Altogether, our choice of EV markers (LAMP2 and Flotillin-1) is consistent with those previously and accurately used to characterize EVs. We have now included all relevant references in the revised manuscript to further clarify this point.

(14) Figure 6 suggests that exosomal BMP is controlled by EV release. I would think that is rather obvious.

We agree that the finding that exosomal BMP release is influenced by EV secretion may appear “obvious.” However, our intention in Figure 6 was to provide direct experimental evidence confirming this relationship using pharmacological modulators of EV release. Specifically, inhibition of EV secretion with GW4869 reduced exosomal BMP levels, whereas stimulation with bafilomycin A1 increased them. These data were important to establish a causal link between EV trafficking and BMP export, thereby validating our model and supporting the interpretation that LRRK2 regulates BMP homeostasis through EV-mediated exocytosis, which is further modulated, to some extent, by GCase activity. 

Minor concerns:

(1) Figure 1: Change colors to be color blind friendly.

We thank the reviewer for this helpful suggestion. We have adjusted the colors in Figure 1 to be color-blind friendly. In addition, we have applied the same color-blind friendly palette to the new immunofluorescence data presented in new Figure 7, Panel A and D.

(2) More consistency on "Xmin" vs "X min" would be appreciated.

We thank the reviewer for this observation. We have revised the manuscript to ensure consistent formatting of time indications throughout the text and figures, using the standardized format “X min.”

Reviewer #2 (Recommendations for the authors):

(1)  Figure 2C-D. Were equal amounts of protein loaded in each lane?

Equal protein amounts were loaded in lanes corresponding to whole-cell lysate (WCL) fractions and normalized based on α-Tubulin levels.

For the extracellular vesicle (EV) fractions, all protein recovered from EV pellets after isolation was loaded. In all EV-related experiments, we seeded the same number of EVproducing cells per condition, and the resulting EV-derived data (from both immunoblotting and lipidomics analyses) were normalized to the corresponding whole cell lysate (WCL) protein content to ensure comparability across conditions.

All these technical details have been included in the Materials section of our revised manuscript.

(2) The authors refer to the papers of Medoh et al (ref 43) and Singh et al. (44) for the key role of CLN5 in the BMP biosynthetic pathway. However, Medoh et al reported that CLN5 is the lysosomal BMP synthase. In contrast, Singh et al. reported that PLD3 and PLD4 mediate the synthesis of SS-BMP, and did not find any role for CLN5. 

To avoid any confusion or misinterpretation of our findings regarding CLN5 and given that we do not analyze PLD3 or PLD4 in our study, we have decided to replace the reference to Singh et al. with Bulfon D. et al. (Nat. Commun. 2024, 15:9937) instead. This last work, conducted by an independent group distinct from the one that originally described CLN5, also validated CLN5 as the sole BMP synthase in cells.

Also, authors mention that bafilomycin A1 (B-A1) dramatically boosts EV exocytosis, referring to Kowal et al., 2016 (ref 35) and Lu et al., 2018 (ref 45). However, this is not shown in Kowal et al.

We thank the reviewer for pointing out this mistake. We apologize for the incorrect citation and have now corrected the reference. The statement regarding the effect of bafilomycin A1 on EV exocytosis now appropriately refers to Mathieu et al., 2021 and Lu et al., 2018.

(3) Page 7, it is stated that "No statistically significant differences in intracellular BMP levels were observed in WT LRRK2 MEFs upon LRRK2 or GCase inhibition(Supplemental Figure 1D, E)". The authors probably mean "Supplemental Figure 1F, G"

We thank the reviewer for noting this error. We have corrected the text to refer to panels F and G of Supplemental Figure 1, which correspond to the relevant data. We have also revised the reference to panel I of Supplemental Figure 1 accordingly.

eLife Assessment

This study presents important findings describing the early assembly of vascular basement membrane and how vascular cells switch from responding to cues provided by the external environment to those provided by self-assembled basement membrane. The evidence supporting the claims of the authors is convincing, with state-of-the-art microscopy and several different culture conditions examined. The work will be of interest to cell biologists studying the ECM, vascular development, as well as medical scientists focused on diseases that depend on vascular growth.

Reviewer #1 (Public review):

Summary:

Marchand et al. seek to understand how basement membrane (BM) is initially assembled around developing vasculature (and by extension basement membrane assembly generally progresses). To do this, they use an established cell culture system that is amenable to advanced microscopy techniques, including high-resolution fluorescence imaging and atomic force microscopy. This allows them to produce very high-quality imaging data that includes both protein localization and matrix topography in 3D. They show that fibronectin (FN) is remodeled as collagen IV (Col IV) assembles. Lysyl oxidase-like-2 (LOXL2) is needed for this process, and without it, BM does not form correctly, cells cannot adhere to BM, and cells also don't correctly form junctions with other cells.

Detailed Review:

The authors provide quantitative measures of BM fibril assembly at the earliest timepoints. They show two phases of growth - initial deposition, elongation, and interconnection of short fibers; the second is a significant thickening. As the BM forms, FN is immediately associated with filaments, but laminin and Col IV are not associated with fibers as detected by AFM. LOXL2 is associated with fibers, similar to FN. At a later time point, Col IV becomes associated with fibers, but laminin never does. Likely FN templates LOXL2, which crosslink Col IV into fibrils over time. Could the authors comment on how this data fits with in vivo data from model organisms? Also, I would like to know if they can uncouple LOXL2 from the FN matrix? Could you express a mutated form of LOXL2 that cannot interact with FN but still is able to crosslink Col IV?)

Depletion of LOXL2 supports this mechanism. Without it, Col IV and FN are uncoupled and accumulate as large aggregates rather than a complex fibrous network. Further, long-term thickening/growth of the fibronectin network is inhibited, indicating LOXL2 and/or the Col IV network positively reinforces fibronectin assembly. (Does LOXL2 directly act on FN, or is this effect dependent on Col IV? The nature of the molecular interactions between COL IV, LOXL2, and FN will be an important future research area.)

Next, Marchand et al. ask if failure to produce mature BM (induced by LOXL2 depletion) has consequences for underlying cells. They demonstrate a clear shift in the orientation of actin towards a linear alignment, and similarly, cells change shape from round to very elongated. Cell junctions also shifted to a linear arrangement in LOXL2 depletion. This fits with the known balance between cell-ECM and cell-cell adhesion. The changes in actin network and cell shape/adhesion correlate with a change in B1 integrin localization upon LOXL2 depletion. B1 integrin colocalized with sparse early FN fibers, but was absent from large FN aggregates that occur if LOXL2 is depleted. Similar reorganization of integrin adhesion components (FAK, Vinc, Pax). Clearly, there is feedback between BM assembly and cell junction organization. But I think the authors might emphasize to the reader that this normally reinforces the epithelial fate of these cells. It's less a balance and more like a tipping point. (Related to this section, I could not read Figure 4C graphs unless I enlarged them to 300%.)

Finally, they culture cells on micro groove plates, with or without LOXL2. The grooved substrate can orient the cells, and they show this is superseded by BM once it assembles. Without LOXL2 cells on micro-grooved substrates become disorganized, similar to their observation on flat surfaces (elongated cells, linear actin, etc.). This demonstrates a switch from external topographical cues to self-generated BM. This is consistent with the idea of reorganizing junctions to produce a stable epithelial tube. I was very interested in their 3D culture. The effect of BM assembly on tube diameter makes sense. But how does BM assembly support complex capillary functions like branching? (Can they force branching with targeted mutations that decouple integrin from the BM?) Is this a question of change to cell fate? (Are other remodeling enzymes activated after initial BM assembly that could support growth and/or branching?)

Reviewer #2 (Public review):

Summary:

The manuscript entitled "Adaptation of endothelial cells to microenvironment 1 topographical cues through lysyl oxidase like-2-mediated basement membrane scaffolding" by Marchand et al., aims to determine the impact of LOXL2 on the dynamic formation of vascular basement membranes (BMs).

Strengths:

This manuscript includes a nice combination of different methods and presents the results in an appropriate manner.

Furthermore, the results clearly demonstrate an impact of LOXL2 on collagen IV-fibronectin organization and topography. Finally, the proper arrangement of collagen IV-fibronectin impacts cell alignment.

Weaknesses:

An open question for this reviewer is what the real take-home message of the present study is? Can the authors deliver novel insight into BM formation transferable to the in vivo situation? Why do the authors not see a "real" BM? Could it be that in vivo endothelial cells do not build the vascular BM alone? Thus, are additional cell types needed? And what will happen then if LOXL2 expression is altered?

Major comments:

(1) Can the authors show that LOXL2 cross-links fibronectin and collagen IV?

(2) The authors stated that LOXL2 depletion affects cytoskeleton arrangements and cell shape. Could it be that this is simply a secondary effect mediated primarily through the altered cross-linking of fibronectin and collagen IV?

(3) Can the authors perform cell adhesion studies on CDMs derived from wild-type versus LOXL2-deficient cells?

(4) Line 226-230: Can the authors provide the proliferation data of wildtype and LOXL2-depleted cells supporting their Src and Akt activity findings?

(5) Line 298-299: The authors made a statement about laminin. Can the authors think of a co-culture of wild-type versus LOXL2-depleted endothelial cells in combination with pericytes or fibroblasts, as these cells contribute to the efficient assembly of a functional vascular basement membrane (10.1182/blood-2009-05-222364). Here, you can determine the impact of altered fibronectin-collagen IV cross-linking on laminin network formation. This will affect their conclusion in lines 299-304, as these facts are solely based on endothelial cells.

(6) Suggestion: can the authors supplement recombinant LOXL2 protein in its active version to the LOXL2-depleted endothelial cells to rescue the observed changes? And further compare LOXL2 enzymatic function with LOXL2 protein harbouring Zn instead of Cu, making it enzymatic inactive. Here, the authors might be able to strengthen their hypothesis that LOXL2 might bridge fibronectin and collagen IV or link both proteins.

(7) There are grammatical errors in the manuscript that the authors should work on.

Reviewer #3 (Public review):

This important study shows that basement membrane (BM) generation is a key event mediating cell 3D organization in response to microenvironmental cues. Such a mechanism participates in the endothelial cell capacity to organize into a capillary vessel segment through the shift of interactions with the interstitial ECM to interactions with vascular BM. This is particularly important for the developing, sprouting vasculature. The authors conclusively show, using TIRF and atomic force microscopy substantiated by 3D sprouting assays, that the lysyl oxidase Loxl2 plays a key role herein. With respect to translation into clinical practice, the dysregulation of adherens junctions and barrier properties associated with Loxl2 dysfunction mediated defects in BM supports its involvement in the progression of long-term microvascular diseases.

An outstanding question not answered in the current MS is how Loxl2 integrates into the Dll4-Notch mediated control of tip-stalk-phalanx cell differentiation in the developing (embryonic) vasculature. The authors focused a lot on Loxl2 loss of function; however, in a (patho)physiological context, Loxl2 gain of function would be relevant. Loxl2 is a hypoxia target and Loxl2 accumulates in the ECM upon hypoxic stress (as occurs during ischemic CVD, stroke/heart infarct). It would be interesting to know how Loxl2 gain-of-function impacts BM assembly, endothelial behavior, mechanosensing, and vessel angiogenic remodeling.

eLife Assessment

Amyotrophic lateral sclerosis (ALS) affects nerve cells in the brain and spinal cord. The authors' approach to use genetic code expansion to tag two ALS proteins associated with stress granules has value and should be useful in the ALS field. Parts of the work are well done, but there are concerns that the evidence is incomplete overall, and additional controls would strengthen the study.

Reviewer #1 (Public review):

Summary:

The authors utilize genetic code expansion to tag TDP-43 and G3BP1, and evaluate this protein tagging system (ANAP) compared to antibodies, and evaluate protein trafficking and stress granule formation in response to stress with sodium arsenite treatment. They find similar staining to antibodies in HeLa cells, mouse embryonic stem cells, and primary mouse cortical neurons. This is a useful study that demonstrates the utility of ANAP tagging to evaluate ALS proteins.

Strengths:

Rescue of cell survival by ANAP-tagged TDP-43 is compelling

Weaknesses:

While the ANAP-tagged proteins had similar distributions to antibody staining, there were some discrepancies that may be more explained by the technique than by novel findings, as the authors suggested. The inclusion of additional controls to evaluate this would be helpful.

Reviewer #2 (Public review):

Summary:

In this manuscript, Chen and colleagues describe a novel means of labeling two RNA-binding proteins, G3BP1 and TDP-43, using genetic code expansion. Overexpressed constructs that incorporate the intrinsically-fluorescent non-canonical amino acid Anap redistribute to cytoplasmic granules upon application of external stressors such as sodium arsenite. Similar labeling and redistribution of overexpressed G3BP1 and TDP-43 were observed in cultures of mouse primary neurons.

Strengths:

Genetic code expansion and non-canonical amino acid labeling have quite a few advantages over traditional fusion proteins for tracking protein redistribution in living cells. The authors show that they are able to label exogenous G3BP1 and TDP-43 with the non-canonical amino acid Anap and follow labeled proteins in living cells with and without stress.

Weaknesses:

The authors do not convincingly leverage the advantages of genetic code expansion in the current study. There is no specific question posed by the authors that can be or is answered using this approach, and several of the experiments lack critical controls. This is also not the first example of TDP-43 labeling by genetic code expansion (see PMID: 38290242). As a result, the study as a whole adds little to our understanding of protein trafficking and behavior under stress.

eLife Assessment

This important study uses the delay line axon model in the chick brainstem auditory circuit to examine the interactions between oligodendrocytes and axons in the formation of internodal distances. This is a significant and actively studied topic, and the authors have used this preparation to support the hypothesis that regional heterogeneity in oligodendrocytes underlies the observed variation in internodal length. In a solid series of experiments, the authors have used enhanced tetanus neurotoxin light chains, a genetically encoded silencing tool, to inhibit vesicular release from axons and support the hypothesis that regional heterogeneity among oligodendrocytes may underlie the biased nodal spacing pattern in the sound localization circuit.

[Editors' note: this paper was reviewed by Review Commons.]

Reviewer #2 (Public review):

Summary:

Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

Significance:

In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

Reviewer #3 (Public review):

Summary:

The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing.

Significance:

The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

Editors' note: The authors have written an effective rebuttal to the previous round of reviews.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #3:

Comments on revised version:

This revised version is in large improved and the responses to reviewers' comments are generally relevant. However, the response regarding pre-nodes is not satisfactory. I understand that the authors prefer to avoid further experimentations, but I think this is an important point that needs to be clarified. Exploring stages between E12 and E15 are therefore of importance. When carefully examining some of the figures (Fig. 1E or 2D) I think that at E15 they may well be pre-nodes formation prior to myelin deposition, on structure the authors considered to be heminodes. To be convincing they should use double or triple labeling with, in addition to the nodal proteins (ankG and/or Nav pan), a good myelin marker such as antiPLP. The rat monoclonal developed by late Pr Ikenaka would give a sharper staining than the anti MAG they used. (I assume the clone must still be available in Okazaki ).

We appreciate your insightful comment regarding the possible presence of pre-nodal clusters along NM axons and your kind suggestion to use the PLP antibody (clone AA3; Yamamura et al., J Neurochem, 1991). We have obtained this monoclonal antibody from Dr. Kenji Tanaka previously in Okazaki and confirmed that it works well in chicken tissues. However, since this clone recognizes both PLP and DM-20 isoforms, it labels not only myelin-forming oligodendrocytes (MFOLs) but also newly formed oligodendrocytes (NFOLs) (Yokoyama et al., J Neurochem, 2025). Therefore, it is not ideal for determining whether nodal protein clusters are formed before myelin deposition.

Instead, we performed double immunostaining for MAG and AnkG between E12 and E15 to clarify the temporal relationship between myelin maturation and node formation. The results showed that detectable AnkG clusters along NM axons began to appear very sparsely around E13, coinciding with the emergence of MAG signals, and became more prominent with development. This temporal pattern does not match the definition of pre-nodal clusters, which are formed prior to myelination.

Although we cannot completely rule out the possibility of undetectable pre-nodal clusters or those composed of molecules other than AnkG, our results support the view that pre-nodal clusters are unlikely to play a major role in determining the regional difference in nodal spacing along NM axons. These new data have been added as Figure 2—figure supplement 1, and the relevant sections in the Results, Discussion, and Figure legend have been revised accordingly (page 5, line 4; page 10, line 7; page 29, line 1).

eLife Assessment

This is a valuable study that combines biophysical and evolutionary approaches to understand why particular mutations in the SARS-CoV-2 protein N arose during the COVID-19 pandemic. The evidence is solid and supports the conclusions.

Reviewer #1 (Public review):

Summary:

The authors attempted to clarify the impact of N protein mutations on ribonucleoprotein (RNP) assembly and stability using analytical ultracentrifugation (AUC) and mass photometry (MP). These complementary approaches provide a more comprehensive understanding of the underlying processes. Both SV-AUC and MP results consistently showed enhanced RNP assembly and stability due to N protein mutations.<br /> The overall research design appears well planned, and the experiments were carefully executed.

Strengths:

SV-AUC, performed at higher concentrations (3 µM), captured the hydrodynamic properties of bulk assembled complexes, while MP provided crucial information on dissociation rates and complex lifetimes at nanomolar concentrations. Together, the methods offered detailed insights into association states and dissociation kinetics across a broad concentration range. This represents a thorough application of solution physicochemistry.

Weaknesses:

Unlike AUC, MP observes only a part of solution. In MP, bound molecules are accumulated on the glass surface (not dissociated) thus concentration in solution should change as time develops. How does such concentration change impact the result shown here?

Comments on revisions:

The response from the authors is appropriate and reasonable.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors apply a variety of biophysical and computational techniques to characterize the effects of mutations in the SARS-CoV-2 N protein on the formation of ribonucleoprotein particles (RNPs). They find convergent evolution in multiple repeated independent mutations strengthening binding interfaces, compensating for other mutations that reduce RNP stability but which enhance viral replication.

Strengths:

The authors assay the effects of a variety of mutations found in SARS-CoV-2 variants of concern using a variety of approaches, including biophysical characterization of assembly properties of RNPs, combined with computational prediction of the effects of mutations on molecular structures and interactions. The findings of the paper contribute to our increasing understanding of the principles driving viral self-assembly, and increases the foundation for potential future design of therapeutics such as assembly inhibitors.

Weaknesses:

For the most part, the paper is well-written, the data presented support the claims made, and the arguments made easy to follow. However, I believe that parts of the presentation could be substantially improved. I found portions of the text to be overly long and verbose and likely could be substantially edited; the use of acronyms and initialisms is pervasive, making parts of the exposition laborious to follow; and portions of the figures are too small and difficult to read/understand.

Comments on revisions:

The authors have adequately addressed all of my concerns.

Reviewer #3 (Public review):

Summary:

This manuscript investigates how mutations in the SARS-CoV-2 nucleocapsid protein (N) alter ribonucleoprotein (RNP) assembly, stability, and viral fitness. The authors focus on mutations such as P13L, G214C, G215C combining biophysical assays (SV-AUC, mass photometry, CD spectroscopy, EM), VLP formation, and reverse genetics. They propose that SARS-CoV-2 exploits "fuzzy complex" principles, where distributed weak interfaces in disordered regions allow both stability and plasticity, with measurable consequences for viral replication.

Strengths:

* The paper demonstrates a comprehensive integration of structural biophysics, peptide/protein assays, VLP systems, and reverse genetics.

* Identification of both de novo (P13L) and stabilizing (G214C/G215C) interfaces provides a mechanistic insight into RNP formation.

* Strong application of the "fuzzy complex" framework to viral assembly, showing how weak/disordered interactions support evolvability, is a significant conceptual advance in viral capsid assembly.

* Overall, the study provides a mechanistic context for mutations that have arisen in major SARS-CoV-2 variants (Omicron, Delta, Lambda) and a mechanistic basis for how mutations influence phenotype via altered biomolecular interactions.

Weaknesses:

The weaknesses are shared via detailed comments to follow.

Comments on revisions:

The authors have addressed the criticisms of the original manuscript satisfactorily.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The authors attempted to clarify the impact of N protein mutations on ribonucleoprotein (RNP) assembly and stability using analytical ultracentrifugation (AUC) and mass photometry (MP). These complementary approaches provide a more comprehensive understanding of the underlying processes. Both SV-AUC and MP results consistently showed enhanced RNP assembly and stability due to N protein mutations.

The overall research design appears well planned, and the experiments were carefully executed.

Strengths:

SV-AUC, performed at higher concentrations (3 µM), captured the hydrodynamic properties of bulk assembled complexes, while MP provided crucial information on dissociation rates and complex lifetimes at nanomolar concentrations. Together, the methods offered detailed insights into association states and dissociation kinetics across a broad concentration range. This represents a thorough application of solution physicochemistry.

We thank the Reviewer for this positive assessment. 

Weaknesses:

Unlike AUC, MP observes only a part of the solution. In MP, bound molecules are accumulated on the glass surface (not dissociated), thus the concentration in solution should change as time develops. How does such concentration change impact the result shown here?

We agree with the Reviewer that the concentration in solution above the surface will change with time; however, the impact of surface adsorption turns out to be negligible. To show this we have added a calculation as Supplementary Methods that is based on the number of imaged adsorption events, the fraction of imaged area to total surface area, and the initial sample volume and concentration. Under our experimental conditions the reduction is less than 1%, which is well within the range of experimental concentration errors.

This is in line with the observation that surface adsorption of proteins to glass is critical and needs to be prevented when working at picomolar concentrations (Zhao H, Mayer ML, Schuck P. 2014. Analysis of protein interactions with picomolar binding affinity by fluorescence-detected sedimentation velocity. Anal Chem 86:3181–3187. doi:10.1021/ac500093m), but is ordinarily negligible when working at the mid nanomolar concentration range. The difference in the MP experiments is that where usually the surface adsorption to glass and plastic is invisible, it is being imaged and quantified in MP. The negligible impact of surface adsorption on solution concentration in typical MP experiments is also in line with the results of several studies that have successfully measured dissociation constants of binding equilibria by MP (Young G et al., Science 360 (2018) 432; Wu & Piszczeck, Anal Biochem 592 (2020) 113575; Solterman et al. Angewandte Chemie 59 (2020) 10774) with samples in the 5-50 nM range and similar experimental setup. It should be noted that in the MP experiments no surface functionalization is employed, in contrast to optical biosensors that utilize surface-immobilized ligands and polymeric matrices and thereby enhance the surface binding capacity.

Even though this depletion effect is negligible under ordinary MP conditions, the Reviewer raises a good point and readers may have a similar question with this novel technique. For this reason, we have added in the MP section of the Methods the sentence “In either configuration, the impact of surface binding on the sample concentration is < 1% and negligible, as described in the Supplementary Methods S1.” and added the detailed calculations in the Supplement accordingly. The use of SV as a traditional, orthogonal technique and the observation of consistent results with those of MP should further dispel readers’ methodological concerns in this point.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors apply a variety of biophysical and computational techniques to characterize the effects of mutations in the SARS-CoV-2 N protein on the formation of ribonucleoprotein particles (RNPs). They find convergent evolution in multiple repeated independent mutations strengthening binding interfaces, compensating for other mutations that reduce RNP stability but which enhance viral replication.

Strengths:

The authors assay the effects of a variety of mutations found in SARS-CoV-2 variants of concern using a variety of approaches, including biophysical characterization of assembly properties of RNPs, combined with computational prediction of the effects of mutations on molecular structures and interactions. The findings of the paper contribute to our increasing understanding of the principles driving viral self-assembly, and increase the foundation for potential future design of therapeutics such as assembly inhibitors.

Thank you for highlighting the strengths of our paper and the potential impact on future design of therapeutics.

Weaknesses:

For the most part, the paper is well-written, the data presented support the claims made, and the arguments are easy to follow. However, I believe that parts of the presentation could be substantially improved. I found portions of the text to be overly long and verbose and likely could be substantially edited; the use of acronyms and initialisms is pervasive, making parts of the exposition laborious to follow; and portions of the figures are too small and difficult to read/understand.

We are glad the Reviewer concurs the data support our conclusions, and finds the arguments easy to follow.  We appreciate the comment that the work was not optimally presented. To address this point, we have identified multiple opportunities to streamline the text without jeopardizing the clarity. We have also rewritten the end of the Introduction.

As recommended, we have reduced and harmonized the use of acronyms and abbreviations throughout the text to improve readability. Specifically, we have now spelled out nucleic acid (NA), intrinsically disordered regions (IDR), full-length (FL), AlphaFold (AF3), and variants of concern (VOC).

Finally, we have improved the presentation of most figures, adding labels and new panels, and increased the label font sizes to facilitate more detailed inspections of the data.

Reviewer #3 (Public Review):

This manuscript investigates how mutations in the SARS-CoV-2 nucleocapsid protein (N) alter ribonucleoprotein (RNP) assembly, stability, and viral fitness. The authors focus on mutations such as P13L, G214C, and G215C, combining biophysical assays (SV-AUC, mass photometry, CD spectroscopy, EM), VLP formation, and reverse genetics. They propose that SARS-CoV-2 exploits "fuzzy complex" principles, where distributed weak interfaces in disordered regions allow both stability and plasticity, with measurable consequences for viral replication.

Strengths:

(1) The paper demonstrates a comprehensive integration of structural biophysics, peptide/protein assays, VLP systems, and reverse genetics.

(2) Identification of both de novo (P13L) and stabilizing (G214C/G215C) interfaces provides a mechanistic insight into RNP formation.

(3) Strong application of the "fuzzy complex" framework to viral assembly, showing how weak/disordered interactions support evolvability, is a significant conceptual advance in viral capsid assembly.

(4) Overall, the study provides a mechanistic context for mutations that have arisen in major SARS-CoV-2 variants (Omicron, Delta, Lambda) and a mechanistic basis for how mutations influence phenotype via altered biomolecular interactions.

We are grateful for these comments highlighting this work as a significant conceptual advance.

Weaknesses:

(1) The arrangement of N dimers around LRS helices is presented in Figure 1C, but the text concedes that "the arrangement sketched in Figure 1C is not unique" (lines 144-146) and that AF3 modeling attempts yielded "only inconsistent results" (line 149).

The authors should therefore present the models more cautiously as hypotheses instead. Additional alternative arrangements should be included in the Supplementary Information, so the readers do not over-interpret a single schematic model.

We agree that in the absence of high-resolution structures the RNP models are hypothetical, and have now emphasized this in the Results, following the Reviewer’s recommendation. To present alternative arrangements that satisfy the biophysical constraints upfront, we have promoted the previous Supplementary Figure 11 showing different models to the first Supplementary Figure, and expanded it with examples of different oligomers. In this way it is referenced early on in the Results and in the legend to Figure 1C. We agree this strengthens the manuscript, as one of the take-home messages is the inherent polydispersity of the RNPs.

The fact that AF3 can only provide inconsistent results will not come as a surprise, given the substantial disordered regions of the complex, and is a drawback of AF3 rather than our structural model. We slightly emphasized this point so as to clarify that the presentation of the AF3-based RNP structure serves solely as supporting evidence that our hypothetical model is sterically reasonable.

The new Results paragraph reads:

“As suggested in the cartoon of Figure 1C, this supports the hypothesis of a three-dimensional arrangement with a central LRS oligomer with symmetry properties and dimensions similar to low resolution EM images of model RNPs (Carlson et al., 2022, 2020) and cryo-ET of RNPs in virions (Klein et al., 2020; Yao et al., 2020).  It should be noted, however, that the arrangement sketched in Figure 1C is not unique and other subunit orientations could be envisioned that satisfy all constraints from experimentally observed binding interfaces, including different oligomers and anti-parallel subunits as illustrated in Supplementary Figure S1. Extending previous ColabFold structural predictions that show multiple N-protein dimers self-assembled via the LRS coiled-coils (Zhao et al., 2023), we attempted the AlphaFold modeling of RNPs combining multiple N dimers with SL7 RNA ligands, mimicking our biophysical assembly model. Current AlphaFold restrictions limit the prediction to pentamers of N-protein dimers with 10 copies of SL7 RNA. While only inconsistent results were obtained – which is not surprising given the large intrinsically disordered regions exceed the predictive power of AlphaFold – some models did produce an overall RNP organization similar to Figure 1C, suggesting such an arrangement is at least sterically reasonable with regard to possible N-protein subunit orientations in an RNP (Supplementary Figure S2)”

(2) Negative-stained EM fibrils (Figure 2A) and CD spectra (Figure 2B) are presented to argue that P13L promotes β-sheet self-association. However, the claim could benefit from more orthogonal validation of β-sheet self-association. Additional confirmation via FTIR spectra or ThT fluorescence could be used to further distinguish structured β-sheets from amorphous aggregation.

We completely agree that the application of multiple orthogonal biophysical methods can strengthen the conclusions. In addition to EM fibrils and CD spectra (a classical gold standard technique for protein secondary structure in solution), we already have support from ColabFold modeling, as well as NMR results from the Zweckstetter lab showing the potential for for β-sheet-like conformations.

Furthermore, we believe the evidence for the absence of ‘amorphous aggregates’ is very strong, as this would be inconsistent with the long-range order required to create the visibly fibrillar morphology in EM, and amorphous aggregates would be inconsistent with the increased solution viscosity. In this context, it is also highly relevant that the β-sheet-like secondary structure recorded by CD is concentration-dependent and reversible upon dilution. The long-range spatial order of fibrils is consistent with the formation of secondary structure in solution.

In addition, it must be kept in mind that what we see is specific to N-arm peptides carrying the P13L mutation (in EM, CD, and structural prediction) and does not occur in the other two N-arm peptides (ancestral N-arm and N-arm with deletion of 31-33), linker peptides, or C-arm peptides.

Most importantly, as elaborated in more detail below, we do not claim that fibril formation is physiologically relevant. At the heart of this – in the context of the evolution of fuzzy complexes – is that the P13L mutation creates additional weak protein-protein interactions. Indeed, the assembly of fibrils geometrically requires at least two interfaces for each subunit. These weak interactions are at play physiologically in the context of the disordered RNP particles, and in macromolecular condensates, but not in the formation of fibrils. Therefore, while we appreciate the suggestion for FTIR spectra ThT staining, we are afraid further emphasis on the fibril structure might confuse the reader, and therefore we would rather clarify upfront that these fibrillar assemblies are not thought to form in vivo from full-length protein, but merely demonstrate the presence of N-arm self-association interfaces in the model of truncated peptides.

Accordingly, we have amended the Results paragraph reporting the fibrils:

“Thus, the N-arm mutation P13L is responsible for the formation of fibrils in N-arm peptides after prolonged storage. Some of these N-arm fibrils exhibit a twisted morphology with width of »5 nm (Figure 2A), in some instances exhibiting patterns of strand breaks. Such fibrils are frequently encountered in proteins that can stack β-sheets, such as in amyloids (Paravastu et al., 2008). While we have not observed fibril formation in the context of full-length N, and have no evidence such fibrils are physiologically relevant, their occurrence in solutions of truncated N-arm peptide nonetheless demonstrates the introduction of ordered N-arm self-association interfaces in conformations of P13L mutants.”

And more completely summarized experimental evidence prior to describing the ColabFold prediction results (which previously did not include mention of the NMR):

“Finally, confirming the interpretation of the EM images and the CD data, as well as the b-structure propensity reported from NMR data (Zachrdla et al., 2022), the structural prediction of N[10-20]:P13L in ColabFold displayed oligomers with stacking b-sheets …”

(3) In the main text, the authors alternate between emphasizing non-covalent effects ("a major effect of the cysteines already arises in reduced conditions without any covalent bonds," line 576) and highlighting "oxidized tetrameric N-proteins of N:G214C and N:G215C can be incorporated into RNPs". Therefore, the biological relevance of disulfide redox chemistry in viral assembly in vivo remains unclear. Discussing cellular redox plausibility and whether the authors' oxidizing conditions are meant as a mechanistic stress test rather than physiological mimicry could improve the interpretation of these results.

The paper could benefit if the authors provide a summary figure or table contrasting reduced vs. oxidized conditions for G214C/G215C mutants (self-association, oligomerization state, RNP stability). Explicitly discuss whether disulfides are likely to form in infected cells.

We thank the Reviewer for raising this most interesting point.  The reason why the biological relevance of N dilsulfides remains unclear is simply that this is still unknown, unfortunately. Recently, Kubinski et al. have strongly argued for the formation of disulfides in infected cells, but in our view the evidence remains weak since the majority of disulfide bonds in that work presented as post-lysis artifacts, and it appears the non-covalent effects alone could explain the physiological observations. We aimed for a balanced presentation and wrote in the relevant Results section:

“Covalent disulfide bonds in the LRS in non-reducing conditions were found to further promote LRS oligomerization. However, there is no conclusive data yet whether covalent bonds in the LRS occur in vivo, or any G215C effect is entirely non-covalent due to the significant strengthening of LRS helix oligomerization (see Discussion).”

Despite the uncertainty regarding physiological disulfide bond formation, we believe it is useful to ask whether covalently crosslinked N dimers would aid or constrain RNP assembly in our biophysical model. We have now better explained this motivation in the Results section describing the RNP experiments:

“Even though it is still unclear whether disulfide bonds of N cysteine mutants form in vivo, we were curious about the impact of disulfide-linked oligomers of the cysteine mutants on their RNP structure and stability in our biophysical assembly model.”

The referenced paragraph from the Discussion reads:

“Regarding the cysteine mutations that have been repeatedly introduced in the LRS prior to the rise of the Omicron VOCs, it is an open question whether they lead to covalent bonds in vivo or in the VLP assay. While examples of disulfide-linked viral nucleocapsid proteins have been reported (Kubinski et al., 2024; Prokudina et al., 2004; Wootton and Yoo, 2003), a methodological difficulty in their detection is artifactual disulfide bond formation post-lysis of infected cells (Kubinski et al., 2024; Wootton and Yoo, 2003).  However, our results clearly show that a major effect of the cysteines already arises in reduced conditions without any covalent bonds, through extension of the LRS helices, and concomitant redirection of the disordered N-terminal sequence. While oxidized tetrameric N-proteins of N:G214C and N:G215C can be incorporated into RNPs, the covalent bonds provided only marginally improved RNP stability.  Interestingly, the introduction of cysteines imposes preferences of RNP oligomeric states dependent on oxidation state, consistent with our MD simulations highlighting the impact of cysteine orientation of 214C versus 215C relative to the hydrophobic surface of the LRS helices. Overall, considering potentially detrimental structural constraints from covalent bonds on LRS clusters seeding RNPs, energetic penalties on RNP disassembly, as well as the required monomeric state of the LRS helix for interaction with the NSP3 Ubl domain (Bessa et al., 2022), at present it is unclear to what extent the formation of disulfide linkages between LRS helices would be beneficial or detrimental in the viral life cycle.”

We feel that this text addresses the Reviewer’s comment, and that expanding the existing discussion further would conflict with other recommendations to shorten and focus the text.

Finally, we have addressed the valuable suggestion of a new table summarizing the oligomeric state and self-association of the different cysteine mutants by inserting a new column in the existing Table 1 reporting all species’ oligomeric state at low micromolar concentrations. In this way they can be compared at a glance with the other mutants as well. A more detailed comparison of the concentration-dependent size-distribution is provided in Figure 4.

(4) VLP assays (Figure 7) show little enhancement for P13L or G215C alone, whereas Figure 8 shows that P13L provides clear fitness advantages. This discrepancy is acknowledged but not reconciled with any mechanistic or systematic rationale. The authors should consider emphasizing the limitations of VLP assays and the sources of the discrepancy with respect to Figure 8.

We thank the Reviewer for this comment, which highlights a very important point. 

For clarification and to improve the cohesion of the manuscript we have inserted a reference to the Discussion after the presentation of the VLP results, which provides a natural transition to the following description of the reverse genetics experiments:

“As expanded on in the Discussion, the failure to observe enhancement by P13L alone may be related to limitations of the VLP assay in sensitivity, including the restriction to a single round of infection, and protein expression levels.”

This references a paragraph in the Discussion about the limitations of the VLP assay in general and the reasons we believe the enhancement by P13L alone was not picked up:

“…While this assay has been widely used for rapid assessment of spike protein and N variants (Syed et al., 2021), it has limitations due to the addition of non-genomic RNA and the lack of double membrane vesicles from which gRNA emerges through the NSP3/NSP4 pore complex potentially poised for packaging (Bessa et al., 2022; Ke et al., 2024; Ni et al., 2023). It should also be recognized that the results do not directly reflect the relative efficiency of RNP assembly only, since protein expression levels, their localization, and their posttranslational modifications are not controlled for. Susceptibility for such factors might be exacerbated with mutations that modulate weak protein interactions. For example, as shown previously (Syed et al., 2024; Zhao et al., 2024), a GSK3 inhibitor inhibiting N-protein phosphorylation significantly enhances VLP formation and eliminates the advantage provided for by the N:G215C mutation relative to the ancestral N – presumably due to an increase in assembly-competent, non-phosphorylated N-protein erasing an affinity advantage. A similar process may be underlying the absent or marginal improvement in VLP readout from the cysteine LRS mutants and P13L at the achieved transfection level in the present work, and the enhanced signal from R203K/G204R and R203M (the latter being consistent with previous reports (Li et al., 2025; Syed et al., 2021)) modulating protein phosphorylation. Nonetheless, mirroring the results of the biophysical in vitro experiments, the addition of RNP-stabilizing P13L and G214C mutations on top of R203K/G204R led to a significantly larger VLP signal.

The VLP assay may be limited in sensitivity to mutation effects due to its restriction to a single round of infection. To avoid this and other potential limitations of the VLP assay for the study of viral packaging, for the key mutation N:P13L we carried out reverse genetics experiments. These showed the sole N:P13L mutation significantly increases viral fitness (Figure 8).”

(5) Figures 5 and 6 are dense, and the several overlays make it hard to read. The authors should consider picking the most extreme results to make a point in the main Figure 5 and move the other overlays to the Supplementary. Additionally, annotating MP peaks directly with "2×, 4×, 6× subunits" can help non-experts.

We completely agree with the Reviewer – these figures were very dense.  To mitigate this problem without having the reader to switch back-and-forth to the supplement, we subdivided the panels of Figure 5 and showed only a subset of curves in each.  In this way the data are easier to read while still readily compared. It is a large figure, but it contains the key data for the present work and is therefore worthwhile to have in one place. For the MP histogram data we also have inserted the suggested peak labels. Similarly, we have split Figure 6A into two panels for clarity.

(6) The paper has several names and shorthand notations for the mutants, making it hard to keep up. The authors could include a table that contains mutation keys, with each shorthand (Ancestral, Nο/No, Nλ, etc.) mapped onto exact N mutations (P13L, Δ31-33, R203K/G204R, G214C/G215C, etc.). They could then use the same glyphs (Latin vs Greek) consistently in text and figure labels.

Yes, we agree this is a problem and we apologize for the confusion. However, it is not possible to refer exclusively to either Latin or Greek terminology, which we feel would be even more detrimental to readability (the former being exhaustively lengthy and the latter being imprecise). But we have used a rational system: If the complete set of mutations of a variant are present, then its Greek letter will be used as an abbreviation, and otherwise we use Latin amino acid/position indicators for individual mutations or combinations thereof. Unfortunately, previously we inadvertently failed to explicitly mention this, and we are most grateful for the Reviewer to point this out.

We have now rectified this by including upfront the sentence:

“We will adopt a nomenclature where the complete set of defining mutations of a variant will be referred to by its Greek letter, i.e., N:P13L/R203K/G204R/G214C is N_­­λ, and analogously the set of Omicron mutations N:P13L/Δ31-33/R203K/G204R are referred to as N_ο; see Table 1”

This will define the two shorthands N_λ and N_ο used. Furthermore, as suggested and pointed to in the text, Table 1 does provide the keys to mutation and variants, including the information in which variant any of the other mutations studied here occur.

(7) The EM fibrils (Figure 2A) and CD spectra (Figure 2B) were collected at mM peptide concentrations. These are far above physiological levels and may encourage non-specific aggregation. Similarly, the authors mention" ultra-weak binding energies that require mM concentrations to significantly populate oligomers". On the other hand, the experiments with full-length protein were performed at concentrations closer to biologically relevant concentrations in the micromolar range. While I appreciate the need to work at high concentrations to detect weak interactions, this raises questions about physiological relevance.

This is indeed an important point to clarify. We agree that much lower nucleocapsid protein concentrations are present in the cytosol on average, and these were used in our RNP assembly experiments. However, there are at least two important physiologically relevant cases where high local N concentrations do occur:

(1) Once assembled in RNPs, the disordered N-terminal extensions are locally at a very high concentration within the volume they can explore while tethered to the NTD. A back-of-the-envelope calculation assuming 12 N-protein subunits confining 12 N-terminal extensions to the volume of a single RNP (≈14x14x14 nm³ by cryoEM; Klein et al 2020) leads to an effective concentration of 7.4 mM. Obviously the N-arm peptides are not completely free and there will be constraints that would hinder or promote encounter complex probability, but interfaces with mM Kd are clearly strong enough to populate Narm-Narm contacts extending from N-protein in the RNP.

Additionally, any interaction where N-proteins are brought in close proximity could allow weak N-arm interactions to provide additional stability. Besides the RNP, we demonstrate this in our Results for nucleic-acid liganded N tetramers (Figure 4B), but this might similarly occur in complexes with NSP3 or host proteins. Generally, it is quite common that small additional binding energies play important roles in the modulation of multivalent protein complexes.

(2) Within the macromolecular condensate the local concentration will be substantially higher than on average within the infected cell.  While we do not know its precise concentration, it is well-established that the sum of many ultra-weak interactions is driving the formation of this dense liquid phase. In our previous eLife paper (Nguyen et al., 2024) we have shown LLPS is suppressed with the R203K/G204R mutation, but it is ‘rescued’ with the additional P13L/del31-33 mutation of the Omicron variant showing strong LLPS. Similarly, LLPS is suppressed by the LRS mutant L222P, but rescued in conjunction with P13L. This is another biologically relevant scenario where weak interactions are critical.

We have emphasized these points in the revised manuscript as described below.

Specifically:

(a) Could some of the fibril/β-sheet features attributed to P13L (Figure 2A-C) reflect non-specific aggregation at high concentrations rather than bona fide self-association motifs that could play out in biologically relevant scenarios?

We understand this concern from the experience with proteins that often have limited solubility and tendencies to aggregate, sometimes accompanied by unfolding and driven by hydrophobic interactions, or clustering on the path to LLPS. However, we are struggling to reconcile the picture of non-specific aggregation with the context of our P13L N-arm peptides. The term ‘non-specific aggregation’ implies the idea of amorphous aggregates, which we would contend is inconsistent with the observed geometry of fibrils, which exhibit long-range order. In addition, non-specific aggregation does not lead to increased solution viscosity, which we describe, but fibril formation does. Another connotation of ‘aggregates’ is irreversibility.  However, we find the beta-sheet-like conformation seen at 1 mM becomes significantly more disordered when the same sample is diluted to 0.4 mM peptide. This is consistent with a reversible self-association driven by a conformational change toward ordered secondary structure.

To highlight the reversibility, we have clarified the description: “Interestingly, diluting the 1 mM sample (solid) to a concentration of 0.4 mM (dashed) reveals a large shift in the far-UV spectra … both indicative of a significant increase of disorder upon dilution. This is consistent with the stabilization of b-sheets in a reversible, strongly cooperative self-association process with an effective K_D in the high mM to low mM range.”

We have also inserted a concentration conversion to mg/ml units, which shows even 1 mM of peptides is only ~5 mg/ml, i.e. not excessively high. “While the ancestral N-arm at »1 mM (» 4.6 mg/ml) concentrations exhibits CD spectra with a minimum at »200 nm typical of disordered conformations (black)”

With regard to the question of specificity, we have studied similar N-arm peptides without P13L mutations and with the 31-33 deletion under equivalent conditions. But we observe the reversible self-association, conformational change, and fibril formation only for those containing the P13L mutation, consistent with ColabFold predictions. Neither did we observe fibrils with disordered C-arm peptides.

How these weak self-association motifs in the N-arm can be physiologically relevant in the context of full-length protein modulating the stability of multi-molecular complexes and enhancing LLPS was outlined above, and further clarified in the manuscript as detailed below.

(b) How do the authors justify extrapolating from the mM-range peptide behaviors to the crowded but far lower effective concentrations in cells?

As pointed out above, the key to this question is the local preconcentration as the N-arm peptides are tethered to the rest of protein in the context of flexible multi-molecular assemblies. Another mechanism to consider is the formation of condensates. The response to the next comment will expand on this.

The authors should consider adding a dedicated section (either in Methods or Discussion) justifying the use of high concentrations, with estimation of local concentrations in RNPs and how they compare to the in vitro ranges used here. For concentration-dependent phenomena discussed here, it is vital to ensure that the findings are not artefacts of non-physiological peptide aggregation..

The use of high concentration in biophysical experiments is quite common, for example, in NMR or crystallography, insofar as they elucidate molecular properties. We believe this is obvious; the Reviewer will certainly agree with us, and this does not require further elaboration. The property observed in this case is the existence of specific, weak protein self-association interfaces in the N-arm.

Our response to the Reviewer’s point 7(a) addresses the distinction between artefactual aggregation and self-association of N-arm peptides. The relevance of these weak protein self-association interfaces in the context of the full-length protein is the second underlying question.

As we have previously stated in a dedicated Results paragraph:

“In contrast to the modulation of the coiled-coil LRS interfaces, the de novo creation of the N-arm self-association interface through beta-sheet interactions enabled by P13L cannot be readily observed in full-length N-protein at low M concentrations. Similar to the ancestral LRS interface, it provides only ultra-weak binding energies that require mM concentrations to significantly populate oligomers. This is fully consistent with the previous observation by SV-AUC that neither N:P13L,31-33 nor N_o with the full set of Omicron mutations show any significant higher-order self-association at low M concentrations, whereas at high local concentrations – as observed in phase-separated droplets – they can modulate and cooperatively enhance self-association processes (Nguyen et al., 2024). (If fact, P13L can substitute for the LRS promoting LLPS, as observed in the rescue of LLPS by N:P13L,31-33/L222P mutants whereas N:L222P LRS-abrogating mutants are deficient in LLPS.) Another process that increases the local concentration of N-arm chains is the tetramerization of full-length N-protein. As described earlier, occupancy of the NA-binding site in the NTD allosterically promotes self-assembly of the LRS into higher oligomers (Zhao et al., 2021). We hypothesized that these oligomers may be cooperatively stabilized by additional N-arm interactions in P13L mutants.”

To state completely unambiguously why weak interfaces are important, we have followed the Reviewer’s suggestion and added an additional clarification already earlier, at the end of the P13L Results section:

“While this self-association interface in the P13L N-arm is weak and its direct observation in biophysical experiments requires mM concentrations, which far exceed average intracellular concentration of N, such  weak interactions can become highly relevant physiologically when high local concentrations are prevailing, for example, when the disordered extension is preconcentrated while tethered within macromolecular assemblies as in the RNP, or in macromolecular condensates.”

Furthermore, we have added early in the Discussion:

“Even though the solution affinity of the N-arm P13L interface is ultra-weak, the average local concentration of N-arm chains across the RNP volume (in a back-of-the-envelope calculation assuming a ≈14 nm cube (Klein et al., 2020) with a dodecameric N cluster) is ≈7.4 mM, such that disordered N-arm peptides could well create populations of N-arm clusters stabilizing RNPs through this interface.  However, besides the RNP-stabilizing mutants we have also observed unexpected RNP destabilization by the ubiquitous R203K/G204R double mutation, which may be caused by the introduction of additional charges close to the self-association interface in the LRS. In our experiments, this destabilization is more than compensated for by the P13L mutation. (Another scenario where ultra-weak interactions can have a critical impact is in molecular condensates. We previously reported the suppression of LLPS by the R203K/G204R mutation, which is rescued by the additional P13L/Δ31-33 mutation (Nguyen et al., 2024). This is consistent with compensatory weak stabilizing and destabilizing impacts of weak interactions on the RNP observed here.)”

Reviewer #1 (Recommendations for the Authors):

In Figure 1B, it is unclear what the orange lines connecting polypeptides represent, as well as the zig-zag orange lines in the N-arm.

We thank the Reviewer for this comment. We intended this to represent regions of self-association but recognize the patterned background is confusing. We have changed this now to solid-colored backgrounds, and indicated this in the figure legend:

“Regions of self-association are indicated by shaded backgrounds.”

Regarding presentation, in Figure 5 (MP), the relationship between mass and oligomer size should be shown more clearly.

We agree. To this end we have labeled the peaks in the MP histograms in Figure 5 with the oligomeric state of the 2N/2SL7 subunits.

Reviewer #2 (Recommendations for the Authors):

I find the science of the paper to be convincing and compellingly supported.

Thank you for this positive statement.

My primary complaints are with presentation or minor technical questions that, honestly, primarily arise due to my own ignorance and unfamiliarity with some of the techniques employed.

My primary issue is with the figures. I find, generally, the text in axes labels, ticks, and legends to be too small to comfortably read. This is particularly true in the CD spectra and

other data presented in Figures 1D, 2B, 4, 5, 6, and 8.

We agree and have increased the font size of all text and labels of the plots in Figure 1, 2, 4, 5, 6, and 8.

I also found the use of initialisms to be a bit overbearing and inconsistent. For example, the authors repeatedly switch between spelling out "nucleic acid" and the initialism "NA" (which is also never explicitly spelled out in the text). With the already substantial length of the text, my own personal opinion would be to suggest spelling out all initialisms in the interest of making the reading easier.

This is a valid criticism. To improve the readability, we have followed this advice and systematically spelled out “nucleic acid” instead of using “NA”.  Similarly, we have now written out full-length instead of the abbreviation FL, and omitted the abbreviation IDR for intrinsically disordered regions, as well as VOC for variant of concern, and AF3 for AlphaFold.

Regarding the reference to mutants, we have now explained upfront the system of Latin and Greek nomenclature we consistently applied.

“We will adopt a nomenclature where the complete set of defining mutations of a variant will be referred to by its Greek letter, i.e., N:P13L/R203K/G204R/G214C is N­­_l, and analogously the set of Omicron mutations N:P13L/Δ31-33/R203K/G204R are referred to as N_ο; see Table 1”

I found the text to be verbose, bordering on overly so; the Introduction is more than two pages long. The section "Enhanced oligomerization of the leucine-rich sequence through cysteine mutations" has two long paragraphs of introduction before the present results are discussed, et cetera. An (admittedly, very rough) estimation of the length of the paper places it at ~9,000 -10,000 words long, and I think that the presentation might benefit from significant editing and

shortening.

We agree the manuscript is longer than would be desirable, and we generally prefer not to insert mini-introductions into Results sections. On the other hand, in order to make a solid contribution to understanding the big picture of fuzzy complexes in molecular evolution of RNA virus proteins it is indispensable to go into the details of RNP assembly and several of the interfaces. Therefore, we feel the length is in the range that it needs to be without losing clarity. In addition, other Reviewer suggestions to extend the discussion, for example, of limitations of VLP assays and the in vivo state of cysteines, conflict with significant shortening.

In the particular case of the cysteine mutations, cited by the Reviewer, we believe it is important to add detailed background on G215C, because the Results proceed in a comparison of the self-association mode between G215C and G214C. This is of significant interest in the present context not only for the independent introduction of interface-enhancing mutations highlighting the evolution of fuzzy complexes, but also because it illustrates the pleomorphic ability of RNPs.

Nonetheless, we have slightly shortened this text and merged the background into a single paragraph. More generally, we have critically reread the text to remove tangential sentences where possible and to make it more concise.

I have a few more specific comments.

In Figure 1A, I suggest explicitly labeling the location of the LRS, as it comes up repeatedly.

Yes, we thank the Reviewer for this suggestion and have introduced this label in Figure 1A.

In Figure 1B, the legend indicates that the red lines indicate "new inter-dimer interactions." However, these red lines are overlayed on a vertical stripe of red squiggles; it is unclear to me and not explicitly described in the legend what these squiggles are meant to illustrate.

We agree this background was confusing. As mentioned in our Response to Reviewer #1 we have replaced the structured background with a solid background and explained in the figure legend that these areas depict regions of self-association.

On lines 44-45, the authors state, "The IDRs amount to 45%, ..." 45% of what?

Thank you, this was unclear.  We have now clarified “The IDRs amount to ≈45% of total residues”

In lines 244 - 246, the authors compare the sizes of complexes in reducing versus non- reducing conditions as measured by dynamic light scattering, stating, "However, dynamic light scattering (DLS) revealed the presence of N210-246:G214C complexes with hydrodynamic radii 244 ranging from 6 to 40 nm (in comparison to 1-2 nm for N210- 246:G215C(Zhao et al., 2022)) in reducing conditions, and slightly larger in non-reducing conditions (Supplementary Figure S4)." Using this single statistic seems to me to be a less-than-ideal way of characterizing what seems to me to be happening here. In Supplementary Figure 4, it appears to me that what is happening is that in non-reduced conditions, the sample is monodisperse, whereas in reducing conditions, the distribution becomes polydisperse/bimodal, with two clearly separate populations. I feel that this could use a more

thorough description rather than just stating the overall range of particle sizes.

Yes, the Reviewer is correct – it is indeed a good idea to be more precise here. To this end we have carried out cumulant analyses on the autocorrelation functions, as a time-honored method to quantify the polydispersity.  Both samples are polydisperse, but more so in reducing conditions. We have now added “For N210-246:G214C a cumulant analysis results in radii of 8.8 nm and 10.6 nm and polydispersity indices of 0.40 and 0.35 for reducing and non-reducing conditions, respectively”

Finally, I have one remaining comment that is a result of my own inexperience with circular dichroism and interpreting the spectra. For me personally, I would appreciate a more thoroughdescription/illustration of the statistics involved in the CD spectra, but perhaps this is not necessary for people who are more familiar with interpreting these kinds of data. For example, in Figure 1D, it is not clear to me what the error bars/confidence intervals for the CD data look like. I see many squiggles, some of which the authors claim are significant (e.g., the differences between ~215 - 230 nm), and others are not worthy of comment. Let's say, for example, that I fit a smoothed spline through these data and then measure the magnitude of the fluctuations from that spline to define/quantify confidence intervals. What does that distribution look like? Or maybe the confidence intervals are so small that all squiggles are significant?

Thank you, this is a good question. As mentioned in the methods section, the CD spectra shown are averages of triplicate scans. Therefore, it is straightforward to extract the standard deviation at each wavelength from the three measurements (although a spline would probably work just as well). The values are what one would expect for the squiggles to be random noise. In the region 215 – 220 nm characteristic for helical secondary structure the standard deviations are small relative to the separation between curves, which indicates that the differences are highly significant. Naturally, the curves do overlap in other spectral regions, which would make a plot including the wavelength-dependent error bars or confidence bands too crowded. Therefore, we have kept the plot of the averaged triplicate scans, but have now provided the average standard deviations for all species in the figure legend and mentioned their significant separation:

“Triplicate scans yield average standard deviations of 0.13 (N), 0.17 (N+SL7), 0.16 (N_l), and 0.21 (N_l +SL7) 10³ deg cm²/dmol, respectively, with non-overlapping confidence bands for the different species, for example, between 215-220 nm.”

Reviewer #3 (Recommendations for the Authors):

(1) The Discussion reiterates much of the background (mutational tolerance, fuzziness, SLiMs) already covered in the Introduction, diluting focus on the key new findings. The authors should consider shortening and refocusing the discussion on the main contributions in light of existing knowledge of viral assembly.

In the Introduction we have provided background on intrinsically disordered proteins in general and their mutational tolerance, as well as the concept of fuzzy complexes. The first several paragraphs of the Discussion have a different focus, which is protein binding interfaces between viral proteins (obviously key in fuzzy complexes), specifically their modulation and the remarkable de novo introduction of binding interfaces. We believe this deserves emphasis, since this highlights a novel aspect of fuzziness, for the mutant spectrum of RNA viruses to encode a range and of assembly stabilities and architectures. 

To reduce redundancy between the end of the Introduction and the beginning of the Discussion, we have shortened the last paragraph of the Introduction and removed its preview of the conclusions, as described in the response to the next comment of the Reviewer (see below).

Unfortunately, the length of the Discussion is dictated in part also by the need to discuss methodological aspects, among them the limitations of VLP assays, and the redox state of the cysteine in the LRS mutants, which were important points recommended by other suggestions of the Reviewers. Similarly, we believe the discussion of other potential functions of Omicron N-arm mutations is warranted, as well as the background of the R203K/G204R double mutation that has attracted significant attention in the field due to its effects on phosphorylation and expression of truncated N species that also form RNPs. Our goal was to integrate the results by us and other laboratories regarding specific mutation effects into a comprehensive picture of molecular evolution of N, which we believe the framework of fuzzy complexes can provide.

(2) The Abstract and early Introduction set a broad stage (IDPs, fuzziness), but don't explicitly state the concrete hypotheses that the experiments test. Please add 2-3 sentences in the Introduction that enumerate testable hypotheses, e.g.:

(a) P13L creates a new N-arm interface that increases RNP stability.

(b) G214C/G215C strengthens LRS oligomerization to stabilize higher-order N assemblies.

We agree the introduction can be improved.  However, it seems to us that it cannot be neatly framed in the hypothesis – answer dichotomy, without losing a lot of nuances and without requiring an even longer and more detailed introduction.

One of the main questions is to test whether the framework of fuzzy complexes can be applied to understand molecular evolution of N, and we feel the introduction is already flowing well towards this:

“ … In fuzzy complexes the total binding energy is distributed into multiple distinct ultra-weak interaction sites (Olsen et al., 2017). Similar to individual RNA virus proteins with loose or absent structure, maintaining disorder and a spatial distribution of low-energy interactions in the protein complexes may increase the tolerance for mutations and improve evolvability of protein complexes.\

The unprecedented worldwide sequencing effort of SARS-CoV-2 genomes during its rapid evolution in humans provides a unique opportunity to examine these concepts. ...”

To bring this to a more concrete set of questions in the end, we have shortened and rewritten the last paragraph in the Introduction:

“To examine how architecture and energetics of RNP assemblies can be impacted by N-protein mutations we study a panel of N-proteins derived from ancestral Wuhan-Hu-1 and different VOCs, including Alpha, Delta, Lambda, and Omicron (see Table 1), in biophysical experiments, VLP assays, and mutant virus. Specifically, we ask how the RNP size distribution and life-time is modulated by: (1) the novel binding interface created by the P13L mutation of Omicron; (2) enhancements of other weak self-association interfaces through G215C of Delta and G214C of Lambda; (3) the ubiquitous R203K/G204R double mutation of Alpha, Lambda, and Omicron.  We also test whether the P13L mutation improves viral fitness, similar to G215C and R203K/G204R. The results are discussed in the framework of fuzzy complexes and molecular evolution of N in the course of viral adaptation to the human host. Understanding the salient features of the binding interfaces in viral assembly and their evolution expands our foundation for the design of therapeutics such as assembly inhibitors.”

eLife Assessment

Glioblastoma is among the most aggressive cancers without a cure, and its cells are characterized by high mitochondrial membrane potential. This manuscript provides convincing evidence that glioblastoma tumorigenesis is closely linked to mitochondrial stress. The study makes a valuable contribution to the field by advancing our understanding of the metabolic mechanisms driving glioblastoma and highlighting potential therapeutic targets.

Reviewer #1 (Public review):

Summary:

Cai et al have investigated the role of msiCAT-tailed mitochondrial proteins that frequently exist in glioblastoma stem cells. Overexpression of msiCAT-tailed mitochondrial ATP synthase F1 subunit alpha (ATP5) protein increases the mitochondrial membrane potential and blocks mitochondrial permeability transition pore formation/opening. These changes in mitochondrial properties provide resistance to staurosporine (STS)-induced apoptosis in GBM cells. Therefore, msiCAT-tailing can promote cell survival and migration, while genetic and pharmacological inhibition of msiCAT-tailing can prevent the overgrowth of GBM cells.

Strengths:

The CATailing concept has not been explored in cancer settings. Therefore, the present provides new insights for widening the therapeutic avenue.

Reviewer #2 (Public Review):

This work explores the connection between glioblastoma, mito-RQC, and msiCAT-tailing. They build upon previous work concluding that ATP5alpha is CAT-tailed and explore how CAT-tailing may affect cell physiology and sensitivity to chemotherapy. The authors conclude that when ATP5alpha is CAT-tailed, it either incorporates into the proton pump or aggregates and that these events dysregulate MPTP opening and mitochondrial membrane potential and that this regulates drug sensitivity. This work includes several intriguing and novel observations connecting cell physiology, RQC, and drug sensitivity. This is also the first time this reviewer has seen an investigation of how a CAT tail may specifically affect the function of a protein.

Comment from the Reviewing Editor:

The revisions made the work more valuable and convincing. The authors adequately made point-by-point response to the reviewers comments by providing new data. Image acquisition and data analysis were further clarified. NEMF knockdown experiments and additional control data for ATP5α featuring a poly-glycine-serine (GS) tail support their conclusion.

Author response:

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

eLife Assessment:

Glioblastoma is one of the most aggressive cancers without a cure. Glioblastoma cells are known to have high mitochondrial potential. This useful study demonstrates the critical role of the ribosome-associated quality control (RQC) pathway in regulating mitochondrial membrane potential and glioblastoma growth. Some assays are incomplete; further revision will improve the significance of this study.

For clarity, we propose revising the second sentence to: "It is well-established that certain cancer cells, such as glioblastoma cells, exhibit elevated mitochondrial membrane potential."

Reviewer #1 (Public Review):

Summary:

Cai et al have investigated the role of msiCAT-tailed mitochondrial proteins that frequently exist in glioblastoma stem cells. Overexpression of msiCAT-tailed mitochondrial ATP synthase F1 subunit alpha (ATP5) protein increases the mitochondrial membrane potential and blocks mitochondrial permeability transition pore formation/opening. These changes in mitochondrial properties provide resistance to staurosporine (STS)-induced apoptosis in GBM cells. Therefore, msiCAT-tailing can promote cell survival and migration, while genetic and pharmacological inhibition of msiCAT-tailing can prevent the overgrowth of GBM cells.

Strengths:

The CAT-tailing concept has not been explored in cancer settings. Therefore, the present provides new insights for widening the therapeutic avenue. 

Your acknowledgment of our study's pioneering elements is greatly appreciated.

Weaknesses:

Although the paper does have strengths in principle, the weaknesses of the paper are that these strengths are not directly demonstrated. The conclusions of this paper are mostly well-supported by data, but some aspects of image acquisition and data analysis need to be clarified and extended.

We are grateful for your acknowledgment of our study’s innovative approach and its possible influence on cancer therapy. We sincerely appreciate your valuable feedback. In response, this updated manuscript presents substantial new findings that reinforce our central argument. Moreover, we have broadened our data analysis and interpretation, as well as refined our methodological descriptions.

Reviewer #2 (Public Review):

This work explores the connection between glioblastoma, mito-RQC, and msiCAT-tailing. They build upon previous work concluding that ATP5alpha is CAT-tailed and explore how CAT-tailing may affect cell physiology and sensitivity to chemotherapy. The authors conclude that when ATP5alpha is CAT-tailed, it either incorporates into the proton pump or aggregates and that these events dysregulate MPTP opening and mitochondrial membrane potential and that this regulates drug sensitivity. This work includes several intriguing and novel observations connecting cell physiology, RQC, and drug sensitivity. This is also the first time this reviewer has seen an investigation of how a CAT tail may specifically affect the function of a protein. However, some of the conclusions in this work are not well supported. This significantly weakens the work but can be addressed through further experiments or by weakening the text.

We appreciate the recognition of our study's novelty. To address your concerns about our conclusions, we have revised the manuscript. This revision includes new data and corrections of identified issues. Our detailed responses to your specific points are outlined below.

Reviewer #1 (Recommendations For The Authors):

(1) In Figure 1B, please replace the high-exposure blots of ATP5 and COX with representative results. The current results are difficult to interpret clearly. Additionally, it would be helpful if the author could explain the nature of the two different bands in NEMF and ANKZF1. Did the authors also examine other RQC factors and mitochondrial ETC proteins? I'm also curious to understand why CAT-tailing is specific to C-I30, ATP5, and COX-V, and why the authors did not show the significance of COX-V.

We appreciate your inquiry regarding the data.  Additional attempts were made using new patient-derived samples; however, these results did not improve upon the existing ATP5⍺, (NDUS3)C-I30, and COX4 signals presented in the figure.  This is possibly due to the fact that CAT-tail modified mitochondrial proteins represent only a small fraction of the total proteins in these cells.  It is acknowledged that the small tails visible above the prominent main bands are not particularly distinct. To address this, the revised version includes updated images to better illustrate the differences. We believe the assertion that GBM/GSCs possess CAT-tailed proteins is substantiated by a combination of subsequent experimental findings. The figure (refer to new Fig. 1B) serves primarily as an introduction. It is important to note that the CAT-tailed ATP5⍺ plays a vital role in modulating mitochondrial potential and glioma phenotypes, a function which has been demonstrated through subsequent experiments.

It is acknowledged that the CAT-tail modification is not exclusive to the ATP5⍺protein.  ATP5⍺ was selected as the primary focus of this study due to its prevalence in mitochondria and its specific involvement in cancer development, as noted by Chang YW et al.  Future research will explore the possibility of CAT tails on other mitochondrial ETC proteins. Currently, NDUS3 (C-I30), ATP5⍺, and COX4 serve as examples confirming the existence of these modifications. It remains challenging to detect endogenous CAT-tailing, and bulk proteomics is not yet feasible for this purpose. COX4 is considered significant.  We hypothesize that CAT-tailed COX4 may function similarly to the previously studied C-I30 (Wu Z, et al), potentially causing substantial mitochondrial proteostasis stress.  

Concerning RQC proteins, our blotting analysis of GBM cell lines now includes additional RQC-related factors. The primary, more prominent bands (indicated by arrowheads) are, in our assessment, the intended bands for NEMF and ANKZF1.  Subsequent blotting analyses showed only single bands for both ANKZF1 and NEMF, respectively. The additional, larger molecular weight band of NEMF, which was initially considered for property analysis (phosphorylation, ubiquitination, etc.), was not examined further as it did not appear in subsequent experiments (refer to new Fig. S1C).

References:

Chang YW, et al. Spatial and temporal dynamics of ATP synthase from mitochondria toward the cell surface. Communications biology. 2023;6(1).

Wu Z, et al. MISTERMINATE Mechanistically Links Mitochondrial Dysfunction With Proteostasis Failure. Molecular cell. 2019;75(4).

(2) In addition to Figure 1B, it would be interesting to explore CAT-tailed mETC proteins in cancer tissue samples.

This is an excellent point, and we appreciate the question. We conducted staining for ATP5⍺ and key RQC proteins in both tumor and normal mouse tissues. Notably, ATP5⍺ in GBM exhibited a greater tendency to form clustered punctate patterns compared to normal brain tissue, and not all of it co-localized with the mitochondrial marker TOM20 (refer to new Fig. S3C-E). Crucially, we observed a significant increase in NEMF expression within mouse xenograft tumor tissues, alongside a decrease in ANKZF1 expression (refer to new Fig. S1A, B). These findings align with our observations in human samples.

(3) Please knock down ATP5 in the patient's cells and check whether both the upper band and lower band of ATP5 have disappeared or not.

This control was essential and has been executed now. To validate the antibody's specificity, siRNA knockdown was performed. The simultaneous elimination of both upper and lower bands upon siRNA treatment (refer to new Fig. S2A) confirms they represent genuine signals recognized by the antibody.

(4) In Figure 1C and ID, add long exposure to spot aggregation and oligomer. Figure 1D, please add the blots where control and ATP5 are also shown in NHA and SF (similar to SVG and GSC827).

New data are included in the revised manuscript to address the queries. Specifically, the new Fig 1D now displays the full queue as requested, featuring blots for Control, ATP5α, AT3, and AT20. Our analysis reveals that AT20 aggregates exhibit higher expression and accumulation rates in GSC and SF cells.

Fig. 1C has been updated to include experimental groups treated with cycloheximide and sgNEMF. Our results show that sgNEMF effectively inhibits CAT-tailing in GBM cell lines, whereas cycloheximide has no impact. After consulting with the Reporter's original creator and optimizing expression conditions, we observed no significant aggregates with β-globin-non-stop protein, potentially due to the length of endogenous CAT-tail formation (as noted by Inada, 2020, in Cell Reports). Our analysis focused on the ratio of CAT-tailed (red box blots) and non-CAT-tailed proteins (green box blots). Comparing these ratios revealed that both anisomycin treatment and sgNEMF effectively hinder the CAT-tailing process, while cycloheximide has no effect.

(5) In Figure 1E, please double-check the results with the figure legend. ATP5A aggregated should be shown endogenously. The number of aggregates shown in the bar graph is not represented in micrographs. Please replace the images. For Figure 1E, to confirm the ATP5-specific aggregates, it would be better if the authors would show endogenous immunostaining of C-130 and Cox-IV.

Labels in Fig. 1E were corrected to reflect that the bar graph in Fig. 1F indicates the number of cells with aggregates, not the quantity of aggregates per cell. The presence

(6) Figure 3A. Please add representative images in the anisomycin sections. It is difficult to address the difference.

We appreciate your feedback. Upon re-examining the Calcein fluorescence intensity data in Fig. 3A, we believe the images accurately represent the statistical variations presented in Fig. 3B. To address your concerns more effectively, please specify which signals in Fig. 3A you find potentially misleading. We are prepared to revise or substitute those images accordingly.

(7) Figure 3D. If NEMF is overexpressed, is the CAT-tailing of ATP 5 reversed?

Thank you. Your prediction aligns with our findings. We've added data to the revised Fig. S6A, B, which demonstrates that both NEMF overexpression and ANKZF1 knockdown lead to elevated levels of CRC. This increase, however, was not statistically significant in GSC cells. A plausible explanation for this discrepancy is that the MPTP of GSC cells is already closed, thus any additional increase in CAT-tailing activity does not result in further amplification.

(8) Figure 3G. Why on the BN page are AT20 aggregates not the same as shown in Figure 2E?

We appreciate your inquiry regarding the ATP5⍺ blots, specifically those in the original Fig. 3G (left) and 2E (right). Careful observation of the ATP5⍺ band placement in these figures reveals a high degree of similarity. Notably, there are aggregates present at the top, and the diffuse signals extend downwards. Given that this is a gradient polyacrylamide native PAGE, the concentration diminishes towards the top. Consequently, the non-rigid nature of the Blue Native PAGE gel may lead to slight variations in the aggregate signals; however, the overall patterns are very much alike. To mitigate potential misinterpretations, we have rearranged the blot order in the new Fig. 3M.

(9) Figure 4D. The amount of aggregation mediated by AT20 is more compared to AT3. Why are there no such drastic effects observed between AT3 and AT20 in the Tunnel assay?

The previous Figure 4D presents the quantification of cell migration from the experiment depicted in Figure 4C. But this is a good point. TUNEL staining results are directly influenced by mitochondrial membrane potential and the state of mitochondrial permeability transition pores

(MPTP), not by the degree of protein aggregation. Our previous experiments showed comparable effects of AT3 and AT20 on mitochondria (Fig. 2E, 3K), which aligns with the expected similar outcomes on TUNEL staining. As for its biological nature, this could be very complicated. We hope to explore it in future studies.

(10) Figure 5C: The role of NEMF and ANKZF1 can be further clarified by conducting Annexin-PI assays using FACS. The inclusion of these additional data points will provide more robust evidence for CAT-tailing's role in cancer cells.

In response to your suggestion, we have incorporated additional data into the revised version.Using the Annexin-PI kit, we labeled apoptotic cells and detected them using flow cytometry (FACS). Our findings indicate that anisomycin pretreatment, NEMF knockdown (sgNEMF), and ANZKF1 upregulation (oeANKZF1) significantly increase the rate of STS-induced apoptosis compared to the control group (refer to new Fig. S9D-G).

(11) Figure 5F: STS is a known apoptosis inhibitor. Why it is not showing PARP cleavage? Also, cell death analysis would be more pronounced, if it could be shown at a later time point. What is the STS and Anisomycin at 24h or 48h time-point? Since PARP is cleaved, it would also be better if the authors could include caspase blots.

I guess what you meant to say here is "Staurosporine is a protein kinase inhibitor that can induce apoptosis in multiple mammalian cell lines." Our study observed PARP cleavage even in GSCs, which are typically more resistant to staurosporine-induced apoptosis (C-PARP in Fig. S9B). The ratio of C-PARP to total PARP increased. We selected a 180-minute treatment duration because longer treatments with STS + anisomycin led to a late stage of apoptosis and non-specific protein degradation (e.g., at 24 or 48 hours), making PARP comparisons less meaningful. Following your suggestion, we also examined caspase 3/7 activity in GSC cells treated with DMSO, CHX, and anisomycin. We found that anisomycin treatment also activated caspases (Fig. S9A).

(12) In Figure 5, the addition of an explanation, how CAT-tailing can induce cell death, would add more information such as BAX-BCL2 ratio, and cytochrome-c release from the mitochondria.

Thank you for your suggestion. In this study, we state that specific CAT-tails inhibit GSC cell death/apoptosis rather than inducing it. Therefore, we do not expect that examining BAX-BCL2 and mitochondrial cytochrome c release would offer additional insights.

(13) To confirm the STS resistance, it would be better if the author could do the experiments in the STS-resistant cell line and then perform the Anisomycin experiments.

Thank you. We should emphasize that our data primarily originates from GSC cells. These cells already exhibit STS-resistance when compared to the control cells (Fig. S8A-C).

(14) It would be more advantageous if the author could show ATP5 CATailed status under standard chemotherapy conditions in either cell lines or in vivo conditions.

This is an interesting question. It's worth exploring this question; however, GSC cells exhibit strong resistance to standard chemotherapy treatments like temozolomide (TMZ).

Additionally, we couldn't detect changes in CAT-tailed ATP5⍺ and thus did not include that data.

(15) In vivo (cancer mouse model or cancer fly model) data will add more weight to the story.

We appreciate your intriguing question. An effective approach would be to test the RQC pathway's function using the Drosophila Notch overexpression-induced brain tumor model. However, Khaket et al. have conducted similar studies, stating, "The RNAi of Clbn, VCP, and Listerin (Ltn), homologs of key components of the yeast RQC machinery, all attenuated NSC over-proliferation induced by Notch OE (Figs. 5A and S5A–D, G)." This data supports our theory, and we have incorporated it into the Discussion. While the mouse model more closely resembles the clinical setting, it is not covered by our current IACUC proposal. We intend to verify this hypothesis in a future study.

Reference:

Khaket TP, Rimal S, Wang X, Bhurtel S, Wu YC, Lu B. Ribosome stalling during c-myc translation presents actionable cancer cell vulnerability. PNAS Nexus. 2024 Aug 13;3(8):pgae321.

Reviewer #2 (Recommendations For The Authors):

Figure 1B, C: To demonstrate that Globin, ATP5alpha, and C-130 are CAT-tailed, it is necessary to show that the high mobility band disappears after NEMF deletion or mutagenesis of the NFACT domain of NEMF. This can be done in a cell line. The anisomycin experiment is not convincing because the intensity of the bands drops and because no control is done to show that the effects are not due to translation inhibition (e.g. cycloheximide, which inhibits translation but not CAT tailing). Establishing ATP5alpha as a bonafide RQC substrate and CAT-tailed protein is critical to the relevance of the rest of the paper.

Thank you for suggesting this crucial control experiment. To confirm the observed signal is indeed a bona fide CAT-tail, it's essential to demonstrate that NEMF is necessary for the CAT-tailing process. We have incorporated data from NEMF knockdown (sgNEMF) and cycloheximide treatment into the revised manuscript. Our findings show that both sgNEMF and anisomycin treatment effectively inhibit the formation of CAT-tailing signals on the reporter protein (Fig. 1C). Similarly, NEMF knockdown in a GSC cell line also effectively eliminated CAT-tails on overexpressed ATP5⍺ (Fig. S2B).

In general, the text should be weakened to reflect that conclusions were largely gleaned from artificial CAT tails made of AT repeats rather than endogenously CAT-tailed ATP5alpha. CAT tails could have other sequences or be made of pure alanine, as has been suggested by some studies.

Thank you for your reminder. We have reviewed the recent studies by Khan et al. and Chang et al., and we found their analysis of CAT tail components to be highly insightful. We concur with your suggestion regarding the design of the CAT tail sequence. We aimed to design a tail that maintained stability and resisted rapid degradation, regardless of its length. In the revised version, we clarify that our conclusions are based on artificial CAT tails, specifically those composed of AT repeat sequences (p. 9). We acknowledge that the presence of other sequence components may lead to different outcomes (p. 19).

Reference:

Khan D, Vinayak AA, Sitron CS, Brandman O. Mechanochemical forces regulate the composition and fate of stalled nascent chains. bioRxiv [Preprint]. 2024 Oct 14:2024.08.02.606406. Chang WD, Yoon MJ, Yeo KH, Choe YJ. Threonine-rich carboxyl-terminal extension drives aggregation of stalled polypeptides. Mol Cell. 2024 Nov 21;84(22):4334-4349.e7. 

Throughout the work (e.g. 3B, C), anisomycin effects should be compared to those with cycloheximide to observe if the effects are specific to a CAT tail inhibitor rather than a translation inhibitor.

We agree that including cycloheximide control experiments is crucial. The revised version now incorporates new data, as depicted in Fig. S5A, B, illustrating alterations in the on/off state of MPTP following cycloheximide treatment. Furthermore, Fig. S6A, B present changes in Calcium Retention Capacity (CRC) under cycloheximide treatment. The consistency of results across these experiments, despite cycloheximide treatment, suggests that anisomycin's role is specifically as a CAT tail inhibitor, rather than a translation inhibitor.

Line 110, it is unclear what "short-tailed ATP5" is. Do you mean ATP5alpha-AT3? If so this needs to be introduced properly. Line 132: should say "may indicate accumulation of CAT-tailed protein" rather than "imply".

We acknowledge your points. We have clarified that the "short-tailed ATP5α" refers to ATP5α-AT3 and incorporated the requested changes into the revised manuscript.

Figure 1C: how big are those potential CAT-tails (need to be verified as mentioned earlier)?They look gigantic. Include a ladder.

In the revised Fig. 1D, molecular weight markers have been included to denote signal sizes. The aggregates in the previous Fig. 1C, also present in the control plasmid, are likely a result of signal overexposure. The CAT-tailed protein is observed just above the intended band in these blots. These aggregates have been re-presented in the updated figures, and their signal intensities quantified.

Line 170: "indicating that GBM cells have more capability to deal with protein aggregation". This logic is unclear. Please explain.

We appreciate your question and have thoroughly re-evaluated our conclusion. We offer several potential explanations for the data presented in Fig. 1D: (1) ATP5α-AT20 may demonstrate superior stability. (2) GSC (GBM) cells might lack adequate mechanisms to monitor protein accumulation. (3) GSC (GBM) cells could possess an increased adaptive capacity to the toxicity arising from protein accumulation. This discussion has been incorporated into the revised manuscript (lines 166-169).

Line 177: how do you know the endogenous ATP5alpha forms aggregates due to CAT-tailing? Need to measure in a NEMF hypomorph.

We understand your concern and have addressed it. Revised Fig. 3G, H demonstrates that a reduction in NEMF levels, achieved through sgNEMF in GSC cells, significantly diminishes ATP5α aggregation. This, in conjunction with the Anisomycin treatment data presented in revised Fig. 3E, F, confirms the substantial impact of the CAT-tailing process on this aggregation.

Line 218: really need a cycloheximide or NEMF hypomorph control to show this specific to CAT-tailing.

We have revised the manuscript to include data from sgNEMF and cycloheximide treatments, specifically Fig. 3G, H, and Fig. S5C, D, as detailed in our response above.

Lines 249,266, Figure 5A: The mentioned experiments would benefit from controls including an extension of ATP5alpha that was not alanine and threonine, perhaps a gly-ser linker, as well as an NEMF hypomorph.

We sincerely appreciate your insightful comments. In response, the revised manuscript now incorporates control data for ATP5α featuring a poly-glycine-serine (GS) tail. This data is specifically presented in Figs. S2E-G, S4E, S7A, D, E, and S8F, G. Our experimental findings consistently demonstrate that the overexpression of ATP5α, when modified with GS tails, had no discernible impact on protein aggregation, mitochondrial membrane potential, GSC cell mobility, or any other indicators assessed in our study.

Figure S5A should be part of the main figures and not in the supplement.

This has been moved to the main figure (Fig. 5C).

eLife Assessment

Weindel et al examine behavioural and EEG data in an innovative contrast comparison paradigm where they vary mean contrast widely while keeping contrast difference constant. As intended, this allowed an elegant decomposition of processing stages: while sensory encoding shortened with increasing contrast in keeping with Pieron's law, the period of decision formation lengthened, in keeping with Fechner's law, which was applied to drift rates in a diffusion model of that period. This is an important demonstration of how these two laws apply in concert, to two distinct processing levels, and the multivariate topography parsing, mixed effect models and diffusion models are convincing.

Reviewer #1 (Public review):

This study uses a new 'hidden multivariate pattern method' to parse in time and space the neural events intervening between stimulus and response in an immediately-reported perceptual decision, and use the resultant neural event timing information to show quite convincingly that Pieron's and Fechner's laws can apply in concert at distinct processing levels.

They designed a clever contrast comparison paradigm in which the contrast difference is kept constant while widely manipulating mean contrast, so that sensory encoding of the overall stimulus would be boosted with increasing mean contrast, whereas decision difficulty and hence duration would increase. With this, they found that the time intervening between early sensory-evoked components, up to an 'N200'-type component associated with launching the decision process, varies inversely with contrast according to Pieron's law. Meanwhile, the time intervals running up to neural events peaking near the time of response, consistent with decision termination, increases with contrast, fitting Fechner's law. Further, a diffusion model whose drift rates are scaled by Fechner's law, fit to RT, predicts the observed proportion of correct responses very well.

In the process of review and revision it was highlighted that presumably the full sequence of neural events intervening between stimulus and response is massively task dependent, but;

(1) The method is intended to capture all key components that specifically covary with RT, as opposed to each and every component in general, and

(2) The main conclusions of the study mentioned above do not change whether the method is set up to track three neural events, or five, as was done in the final analysis.

The propensity for topographic parsing algorithms to potentially lump-together distinct processes that partially co-evolve was acknowledged, but a key clarification in review was that even though the method entails a specification of neural event duration - which was changed from 50 to 25 ms - the success of the method is not strongly contingent on the actual underlying neural events in question having that very duration - indeed, the components extracted using that short template duration can be observed to evolve over a longer time frame associated with the Fechner diffusion process.

Notably, standard average event-related potential analysis was able to show expected amplitude effects - where sensory signals increased with contrast but decision signals decreased - but assessment of the by-trial distribution of their timings was grealy aided by the HMP method.

One of the stages of processing implicated in the parsing analysis was linked to attention orientation, and the authors speculate on whether this might reflect a spatially-selective deployment of attention or a resource allocation, but sensibly refrain from speculating too far since the focus here was on the sensory and decision process durations and their respective adherence to Pieron and Fechner's laws.

Reviewer #2 (Public review):

Summary:

The authors decomposed response times into component processes and manipulated the duration of these processes in opposing directions by varying contrast, and overall by manipulating speed-accuracy tradeoffs. They identify different processes and their durations by identifying neural states in time and validate their functional significance by showing that their properties vary selectively as expected with predicted effects of the contrast manipulation. They identify 4 processes: stimulus encoding, attention orienting, decision and motor execution. These map onto 5 classical event related potentials. The decision-making component matched the CPP and its properties varied with contrast and predicted decision-accuracy.

Strengths:

The design of the experiment is remarkable and offers crucial insights. The analyses techniques are beyond-state-of-the art and the analyses are well motivated and offer clear insights.

Weaknesses:

The number of identified events depends on the parameter setting of the analysis. While the authors discuss weaknesses of the approach this needs to be made explicit as well. It is also unclear to what extent topographies map onto processes since e.g., different combinations of sources can lead to the same scalp topography.

Reviewer #3 (Public review):

Summary:

In this manuscript the authors examine the processing stages involved in perceptual decision-making using a new approach to analysing EEG data, combined with a critical stimulus manipulation. This new EEG analysis method enables single-trial estimates of the timing and amplitude of transient changes in EEG time-series recurrent across trials in a behavioural task. The authors find evidence for five events between stimulus onset and the response in a two-spatial-interval visual discrimination task. By analysing the timing and amplitude of these events in relation to behaviour and the stimulus manipulation, the authors interpret these events as related to separable processing stages for stimulus encoding (first two events), attention orientation (second event), motor planning (fourth event) and decision (deliberation, final event). This is largely consistent with previous findings from both event-related potentials (across trials) and single-trial estimates using decoding techniques and neural network approaches. However, by taking a data-driven approach (as opposed to theory-driven decoding analyses) a more nuanced picture emerges: there are several stimulus encoding steps which may contribute differently to behaviour, and decision processes extend beyond the planning of the motor response.

Strengths:

This work is not only important for the conceptual advance, but also in promoting this new analysis technique, which will likely prove useful in future research. For the broader picture, this work is an excellent example of the utility of neural measures for mental chronometry.

Weaknesses:

Though beyond the scope of this manuscript, these results should be considered within the broader decision-making literature, where task or domain-specific processes may not generalise (for example, in value-based decision-making).

Author response:

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

Reviewer #1 (Public review):  

From my reading, this study aimed to achieve two things:  

(1) A neurally-informed account of how Pieron's and Fechner's laws can apply in concert at distinct processing levels.  

(2) A comprehensive map in time and space of all neural events intervening between stimulus and response in an immediately-reported perceptual decision.  

I believe that the authors achieved the first point, mainly owing to a clever contrast comparison paradigm, but with good help also from a new topographic parsing algorithm they created. With this, they found that the time intervening between an early initial sensory evoked potential and an "N2" type process associated with launching the decision process varies inversely with contrast according to Pieron's law. Meanwhile, the interval from that second event up to a neural event peaking just before response increases with contrast, fitting Fechner's law, and a very nice finding is that a diffusion model whose drift rates are scaled by Fechner's law, fit to RT, predicts the observed proportion of correct responses very well. These are all strengths of the study.   

We thank the reviewer for their comments that added context to the events we detected in relation to previous findings. We also believe that the change in the HMP algorithm suggested by the reviewer improved the precision of our analyses and the manuscript. We respond to the reviewer’s specific comments below.

(1) The second, generally stated aim above is, in the opinion of this reviewer, unconvincing and ill-defined. Presumably, the full sequence of neural events is massively task-dependent, and surely it is more in number than just three. Even the sensory evoked potential typically observed for average ERPs, even for passive viewing, would include a series of 3 or more components - C1, P1, N1, etc. So are some events being missed? Perhaps the authors are identifying key events that impressively demarcate Pieron- and Fechner-adherent sections of the RT, but they might want to temper the claim that they are finding ALL events. In addition, the propensity for topographic parsing algorithms to potentially lump together distinct processes that partially co-evolve should be acknowledged.  

We agree with the reviewer that the topographical solutions found by HMP will be dependent on the task and the quality and type of data. We address this point in the last section of the discussion (see also response to R3.5). We would also like to add that the events detected by HMP are, by construction, those that contribute to the RT and not necessarily all ERPs elicited by a stimulus.

In addition to the new last section of the discussion we also make these points clear in the revised manuscript at the discussion start: 

“By modeling the recorded single-trial EEG signal between stimulus onset and response as a sequence of multivariate events with varying by-trial peak times, we  aimed to detect recurrent events that contribute to the duration of the reaction time in the present perceptual decision-making task”.

Regarding the typical visual ERPs, in response to this comment but also comments R1.2, R1.3 and R2.1, we aimed for a more precise description of the topographies and thus reduced the width of the HMP expected events to 25ms. This ensures that we do not miss events shorter than the initial expectations of 50ms (see Appendix B of Weindel et al., 2024 and also response to  R1.3). This new estimation provides evidence for at least two of the visual ERPs that, based on their timings and topographies (in relation with the spatial frequency of the stimulus), we interpret as the N40 and the P100 (see response to R1.5 for the justification of this categorization). We provide a description and justification of the interpretations in the result section “Five trial-recurrent sequential events occur in the EEG during decisions” and the discussion section “Visual encoding time”.

(2) To take a salient example, the last neural event seems to blend the centroparietal positivity with a more frontal midline negativity, some of which would capture the CNV and some motor-execution related components that are more tightly time-locked to, of course, the response. If the authors plotted the traditional single-electrode ERP at the frontal focus and centroparietal focus separately, they are likely to see very different dynamics and contrast- and SAT-dependency. What does this mean for the validity of the multivariate method? If two or more components are being lumped into one neural event, wouldn't it mean that properties of one (e.g., frontal burstiness at response) are being misattributed to the other (centroparietal signal that also peaks but less sharply at response)?

Using the new HMP parameterization described above we show that the reviewer's intuition was correct. Using an expected pattern duration of 25ms the last event in the original manuscript splits in two events. The before-last event, now referred to the lateralized readiness potential (LRP) presents a strong lateralization (Figure 3) with an increased negativity over the motor cortex contralateral to the right hand. The effect of contrast is mostly on the last event that we interpret as the CPP (Figure 5). Despite the improved precision of the topographies of the identified events, it is however to be noted that some components will overlap. If the LRP is generated when a certain amount of evidence is accumulated (e.g. that the CPP crosses a certain value) then a time-based topography will necessarily include that CPP activity in addition to the lateralized potential. We discuss this in the section “Motor execution” of the discussion:

“Adding the abrupt onset of this potential, we believe that this event is the start of motor execution, engaged after a certain amount of evidence. The evidence for this interpretation is manifest in the fact that the event's topography shares some activity with the CPP event that follows, an expected result if the LRP is triggered at a certain amount of evidence, indexed by the CPP”.

(3) Also related to the method, why must the neural events all be 50 ms wide, and what happens if that is changed? Is it realistic that these neural events would be the same duration on every trial, even if their duration was a free parameter? This might be reasonable for sensory and motor components, but unlikely for cognitive.  

The HMP method is sensitive to the event's duration as shown in the manuscript about the method (Appendix B of Weindel et al., 2024). Nevertheless as long as the topography in the real data is longer than the expected one it shouldn't be missed (i.e. same goes for by-trial variations in the event width). For this reason we halved the expected event width of 50ms (introduced by the original HsMM-MVPA paper by Anderson and colleagues) in the revision. This new estimation with 25ms thus is much less likely to miss events as evidenced by the new visual and motor events. In the revised manuscript this is addressed at the start of the Results section:

“Contrary to previous applications (Anderson et al.,2016; Berberyan et al., 2021; Zhang et al., 2018; Krause et al., 2024) we assumed that the multivariate pattern was represented by a 25ms half-sine as our previous research showed that a shorter expected pattern width increases the likelihood of detecting cognitive events (see Appendix B of Weindel et al., 2024)”.

Regarding the event width as a free parameter this is both technically and statistically difficult to implement as the amount of computing capacity, flexibility and trade-offs among the HMP parameters would, given the current implementation, render the model unfit for most computers and statistically unidentifiable.

(4) In general, I wonder about the analytic advantage of the parsing method - the paradigm itself is so well-designed that the story may be clear from standard average event-related potential analysis, and this might sidestep the doubts around whether the algorithm is correctly parsing all neural events.  

Average ERP analysis suffers from an impossibility to differentiate between an effect of an experimental factor on the amplitude vs. on the timing of the underlying components (Luck, 2005). Furthermore the overlap of components across trials bluries the distinction between them. For both reasons we would not be able to reach the same level of certainty and precision using ERP analyses. Furthermore the relatively low number of trials per experimental cell (contrast level X SAT X participant = 6 trials) makes the analyses hard to perform on ERP which typically require more trials per modality. From the reviewer’s comment we understand that this point was not clear. We therefore discuss this in the revision, Section “Functional interpretation of the events” of the results:

“Nevertheless identifying neural dynamics on these ERPs centered on stimulus is complicated by the time variation of the underlying single-trial events (see probabilities displayed in Figure 3 for an illustration and Burle et al., 2008, for a discussion). The likely impact of contrast on both amplitude and time on the underlying single-trial event does not allow one to interpret the average ERP traces as showing an effect in one or the other dimension without strong assumptions (Luck, 2005)”.

(5) In particular, would the authors consider plotting CPP waveforms in the traditional way, across contrast levels? The elegant design is such that the C1 component (which has similar topography) will show up negative and early, giving way to the CPP, and these two components will show opposite amplitude variations (not just temporal intervals as is this paper's main focus), because the brighter the two gratings, the stronger the aggregate early sensory response but the weaker the decision evidence due to Fechner. I believe this would provide a simple, helpful corroborating analysis to back up the main functional interpretation in the paper.  

We agree with the suggestion and have introduced the representation on top of Figure 5 for sets of three electrodes in the occipital, posterior and frontal regions. The new panels clearly show an inversion of the contrast effect dependent on the time and locus of the electrodes. We discuss this in Section “Functional interpretation of the events” of the results:

“This representation shows that there is an inversion of the contrast effect with higher contrasts having a higher amplitude on the electrodes associated with visual potentials in the first couple of deciseconds (left panel of Figure 5A) while parietal and frontal electrodes shows a higher amplitude for lower contrasts in later portions of the ERPs (middle and right panel of Figure 5A)”.

To us, this crucially shows that we cannot achieve the same decomposition using traditional ERP analyses. In these plots it appears that while, as described by the reviewer, there is an inversion, the timing and amplitude of the changes due to contrast can hardly be interpreted.

(6) The first component is picking up on the C1 component (which is negative for these stimulus locations), not a "P100". Please consult any visual evoked potential study (e.g., Luck, Hillyard, etc). It is unexpected that this does not vary in latency with contrast - see, for example. Gebodh et al (2017, Brain Topography) - and there is little discussion of this. Could it be that nonlinear trends were not correctly tested for?  

We disagree with the reviewer on the interpretation of the ERP. The timing of the detected component is later than the one usually associated with a C1. Furthermore the central display does not create optimal conditions to detect a C1

We do agree that the topography raises the confusion but we believe that this is due to the spatial frequency of the stimulus that generates a high posterior positivity (see references in the following extract). The new HMP solution also now happens to show an effect of contrast on the P100 latencies, we believe this is due to the increased precision in the time location of the component. We discuss this in the “Visual encoding time” section of the discussion:

“The following event, the P100, is expressed around 70ms after the N40, its topography is congruent with reports for stimuli with low spatial frequencies as used in the current study (Kenemans et al., 2002, 2000; Proverbio et al., 1996). The timing of this P100 component is changed by the contrast of the stimulus in the direction expected by the Piéron law (Figure 4A)”. 

(7) There is very little analysis or discussion of the second stage linked to attention orientation - what would the role of attention orientation be in this task? Is it spatial attention directed to the higher contrast grating (and if so, should it lateralise accordingly?), or is it more of an alerting function the authors have in mind here?  

We agree that we were not specific enough on the interpretation of this attention stage. We now discuss our hypothesis in the section “Attention orientation” of the discussion:  

“We do however observe an asymmetry in the topographical map Figure 3. This asymmetry might point to an attentional bias with participants (or at least some participants) allocating attention to one side over the other in the same way as the N2pc component (Luck and Hillyard, 1994, Luck et al., 1997). Based on this collection of observations, we conclude that this third event represents an attention orientation process. In line with the finding of Philiastides et al. (2006), this attention orientation event might also relate to the allocation of resources. Other designs varying the expected cognitive load or spatial attention could help in further interpreting the functional role of this third event”.

We would like to add that it is unlikely that the asymmetry we mention in the discussion cannot stem from the redirection towards higher contrast as the experimental design balanced the side of presentation. We therefore believe that this is a behavioral bias rather than a bias toward the highest contrast stimulus as suggested by the reviewer. We hope that, while more could be tested and discussed, this discussion is sufficient given the current manuscript's goal.

Reviewer #2 (Public review):  

Summary:  

The authors decomposed response times into component processes and manipulated the duration of these processes in opposing directions by varying contrast, and overall by manipulating speed-accuracy tradeoffs. They identify different processes and their durations by identifying neural states in time and validate their functional significance by showing that their properties vary selectively as expected with the predicted effects of the contrast manipulation. They identify 3 processes: stimulus encoding, attention orienting, and decision. These map onto classical event-related potentials. The decision-making component matched the CPP, and its properties varied with contrast and predicted decision-accuracy, while also exhibiting a burst not characteristic of evidence accumulation.  

Strengths:  

The design of the experiment is remarkable and offers crucial insights. The analysis techniques are beyond state-of-the-art, and the analyses are well motivated and offer clear insights.  

Weaknesses:  

It is not clear to me that the results confirm that there are only 3 processes, since e.g., motor preparation and execution were not captured. While the authors discuss this, this is a clear weakness of the approach, as other components may also have been missed. It is also unclear to what extent topographies map onto processes, since, e.g., different combinations of sources can lead to the same scalp topography.  

We thank the reviewer for their kind words and for the attention they brought on the question of the missing motor preparation event. In light of this comment (and also R1.1, R3.3) the revised manuscript uses a finer grained approach for the multivariate event detection. This preciser estimation comes from the use of a shorter expected pattern in which the initial expectation of a 50ms half-sine was halved, therefore ensuring that we do not miss events shorter than the initial expectations (see Appendix B of Weindel et al., 2024 and also response to  R1.3). In the new solution the motor component that the reviewer expected is found as evidenced by the topography of the event, its lateralization and a time-to-response congruent with a response execution event. This is now described in the section “Motor execution” of the revised manuscript: 

“The before last event, identified as the LRP, shows a strong hemispheric asymmetry congruent with a right hand response. The peak of this event is approximately 100 ms before the response which is congruent with reports that the LRP peaks at the onset of electromyographical activity in the effector muscle (Burle et al., 2004), typically happening 100ms before the response in such decision-making tasks (Weindel et al., 2021). Furthermore, while its peak time is dependent on contrast, its expression in the EEG is less clearly related to the contrast manipulation than the following CPP event”.

Reviewer #3 (Public review):  

Summary:  

In this manuscript, the authors examine the processing stages involved in perceptual decision-making using a new approach to analysing EEG data, combined with a critical stimulus manipulation. This new EEG analysis method enables single-trial estimates of the timing and amplitude of transient changes in EEG time-series, recurrent across trials in a behavioural task. The authors find evidence for three events between stimulus onset and the response in a two-spatial-interval visual discrimination task. By analysing the timing and amplitude of these events in relation to behaviour and the stimulus manipulation, the authors interpret these events as related to separable processing stages for stimulus encoding, attention orientation, and decision (deliberation). This is largely consistent with previous findings from both event-related potentials (across trials) and single-trial estimates using decoding techniques and neural network approaches.  

Strengths:  

This work is not only important for the conceptual advance, but also in promoting this new analysis technique, which will likely prove useful in future research. For the broader picture, this work is an excellent example of the utility of neural measures for mental chronometry.  

We appreciate the very positive review and thank the reviewer for pointing out important weaknesses in our original manuscript and also providing resources to address them in the recommendations to authors. Below we comment on each identified weakness and how we addressed them.   

Weaknesses:  

(1) The manuscript would benefit from some conceptual clarifications, which are important for readers to understand this manuscript as a stand-alone work. This includes clearer definitions of Piéron's and Fechner's laws, and a fuller description of the EEG analysis technique.

We agree that the description of both laws were insufficient, we therefore added the following text in the last paragraph of the introduction:

“Piéron’s law predicts that the time to perceive the two stimuli (and thus the choice situation) should follow a negative power law with the stimulus intensity (Figure 1, green curve). In contradistinction, Fechner’s law states that the perceived difference between the two patches follows the logarithm of the absolute contrast of the two patches (Figure 1, yellow curve). As the task of our participants is to judge the contrast difference, Piéron’s law should predict the time at which the comparison starts (i.e. the stimuli become perceptible), while Fechner’s law should implement the comparison, and thus decision, difficulty”.

Regarding the EEG analysis technique we added a few elements at the start of the result:

“The hidden multivariate pattern model (HMP) implemented assumed that a task-related multivariate pattern event is represented by a half-sine whose timing varies from trial to trial based on a gamma distribution with a shape parameter of 2 and a scale, controlling the average latency of the event, free-to-vary per event (Weindel et al., 2024)”.

We also made the technique clearer at the start of the discussion:

“By modeling the recorded single-trial EEG signal between stimulus onset and response as a sequence of multivariate events with varying by-trial peak times, we aimed to detect recurrent events that contribute to the duration of the reaction time in the present perceptual decision-making task. In addition to the number of events, using this hidden multivariate pattern approach (Weindel et al., 2024) we estimated the trial-by-trial probability of each event’s peak, therefore accessing at which time sample each event was the most likely to occur”.

Additionally, we added a proper description in the method section (see the new first paragraph of the “Hidden multivariate pattern” subsection). 

(2) The manuscript, broadly, but the introduction especially, may be improved by clearly delineating the multiple aims of this project: examining the processes for decision-making, obtaining single-trial estimates of meaningful EEG-events, and whether central parietal positivity reflects ramping activity or steps averaged across trials.

For the sake of clarity we removed the question of the ramping activity vs steps in the introduction and focused on the processes in decision-making and their single-trial measurement as this is the main topic of the paper. Furthermore the references provided by the reviewer allowed us to write a more comprehensive review of previous studies and how the current study is in line with those. These changes are mainly manifested in these new sentences:

“As an example Philiastides et al. (2006) used a classifier on the EEG activity of several conditions to show that the strength of an early EEG component was proportional to the strength of the stimulus while a later component was related to decision difficulty and behavioral performance (see also Salvador et al., 2022; Philiastides and Sajda, 2006). Furthermore the authors interpreted that a third EEG component was indicative of the resource allocated to the upcoming decision given the perceived decision difficulty. In their study, they showed that it is possible to use single-trial information to separate cognitive processes within decision-making. Nevertheless, their method requires a decoding approach, which requires separate classifiers for each component of interest and restrains the detection of the components to those with decodable discriminating features (e.g. stimuli with strong neural generators such as face stimuli, see Philiastides et al., 2006)”.

(3) A fuller discussion of the limitations of the work, in particular, the absence of motor contributions to reaction time, would also be appreciated. 

As laid out in responses to comments R1.1 and R2 the new estimates now include evidence for a motor preparation component. We discuss this in the new “motor execution” paragraph in the discussion section. Additionally we discuss the limitation of the study and the method in the two last paragraphs of the discussion (in the new Section “Generalization and limitation”).

(4) At times, the novelty of the work is perhaps overstated. Rather, readers may appreciate a more comprehensive discussion of the distinctions between the current work and previous techniques to gauge single-trial estimates of decision-related activity, as well as previous findings concerning distinct processing stages in decision-making. Moreover, a discussion of how the events described in this study might generalise to different decision-making tasks in different contexts (for example, in auditory perception, or even value-based decision-making) would also be appreciated.  

We agree that the original text could be read as overstating. In addition to the changes linked to R3.2 we also now discuss the link with the previous studies in the before-last paragraph of the discussion before the conclusion in the new “Generalization and limitations” section:

“The present study showed what cognitive processes are contributing to the reaction time and estimated single-trial times of these processes for this specific perceptual decision-making task. The identified processes and topographies ought to be dependent on the task and even the stimuli (e.g. sensory events will change with the sensory modality). More complex designs might generate a higher number of cognitive processes (e.g. memory retrieval from a cue, Anderson et al., 2016) and so could more natural stimuli which might trigger other processes in the EEG (e.g. appraisal vs. choice as shown by Frömer et al., 2024). Nevertheless, the observation of early sensory vs. late decision EEG components is likely to generalize across many stimuli and tasks as it has been observed in other designs and methods (Philiastides et al., 2006; Salvador et al., 2022). To these studies we add that we can evaluate the trial-level contribution, as already done for specific processes (e.g. Si et al., 2020; Sturm et al., 2016), for the collection of events detected in the current study”.

Reviewing Editor Comments:  

As you will see, all three reviewers agree that the paper makes a valuable contribution and has many strengths. You will also see that they have provided a range of constructive comments highlighting potential issues with the interpretation of the outcomes of your signal decomposition method. In particular, all three reviewers point out that your results do not identify separate motor preparation signals, which we know must be operating on this type of task. The reviewers suggest further discussion of this issue and the potential limitations of your analysis approach, as well as suggesting some additional analyses that could be run to explore this further. While making these changes would undoubtedly enhance the paper and the final public reviews, I should note that my sense is that they are unlikely to change the reviewers' ratings of the significance of the findings and the strength of evidence in the final eLife assessment  

Reviewer #1 (Recommendations for the authors):  

(1) Abstract: "choice onset" is ill-defined and not the label most would give the start of the RT interval. Do you mean stimulus onset?  

We replaced with "choice onset" with "stimulus onset" in the abstract

(2) Similarly "choice elements" in the introduction seem to refer to sensory attributes/objects being decided about?  

We replaced "choice-elements" with "choice-relevant features of the stimuli"

(3) "how the RT emerges from these putative components" - it would be helpful to specify more what level of answer you're looking for, as one could simply answer "when they're done."  

We replaced with "how the variability in RTs emerges from these putative components"

(4) Line 61-62: I'm not sure this is a fully correct characterisation of Frömer et al. It was not similar in invoking a step function - it did not invoke any particular mechanism or function, and in that respect does not compare well to Latimer et al. Also, I believe it was the overlap of stimulus-locked components, not response-locked, that they argued could falsely generate accumulator-like buildup in the response-locked ERP.  

We indeed wrongly described Frömer et al. The sentence is now "In human EEG data, the classical observation of a slowly evolving centro-parietal positivity, scaling with evidence accumulation, was suggested to result from the overlap of time-varying stimulus-related activity in the response-locked event related potential"

(5) Line 78: Should this be single-trial *latency*?  

This referred to location in time but we agree that the term is confusing and thus replaced it with latencies.

(6) The caption of Figure 1 should state what is meant by the y-axis "time"  

We added the sentence "The y-axis refers the time predicted by each law given a contrast value (x-axis) and the chosen set of parameters." in the caption of Figure 1

(7) Line 107: Is this the correct description of Fechner's law? If the perceived difference follows the log of the physical difference, then a constant physical difference should mean a constant perceived difference. Perhaps a typo here.  

This was indeed a typo we replaced the corresponding part of the sentence with "the perceived difference between the two patches follows the logarithm of the absolute contrast of the two patches"

(8) Line 128: By scale, do you mean magnitude/amplitude?  

No, this refers to the parameter of a gamma distribution. To clarify we edited the sentence:  "based on a gamma distribution with a shape parameter of 2 and a scale parameter, controlling the average latency of the event, free-to-vary per event"

(9) The caption of Figure 3 is insufficient to make sense of the top panel. What does the inter-event interval mean, and why is it important to show? What is the "response" event?  

We agree that the top panel was insufficiently described. To keep the length of the paper short and because of the relatively low amount of information provided by these panels we replaced them for a figure only showing the average topographies as well as the asymmetry tests for each event.

(10) Figure 4: caption should say what the top vs bottom row represents (presumably, accuracy vs speed emphasis?), and what the individual dots represent, given the caption says these are "trial and participant averaged". A legend should be provided for the rightmost panels.  

We agree and therefore edited Figure 4. The beginning of the caption mentioned by the reviewer now reads: “A) The panels represent the average duration between events for each contrast level, averaged across participants and trials (stimulus and response respectively as first and last events) for accuracy (top) and speed instructions (bottom).”. Additionally we added legends for the SAT instructions and the model fits.

(11) Line 189: argued for a decision-making role of what?  

Stafford and Gurney (2004) proposed that Pieron’s law could reflect a non-linear transformation from sensory input to action outcomes, which they argued reflected a response mechanism. We (Van Maanen et al., 2012) specified this result by showing that a Bayesian Observer Model in which evidence for two alternative options was accumulated following Bayes Rule indeed predicted a power relation between the difference in sensory input of the two alternatives, and mean RT. However, the current data suggest that such an explanation cannot be the full story, as also noted by R3. To clarify this point we replaced the comment by the following sentence:

“Note that this observation is not necessarily incongruent with theoretical work that argued that Piéron’s law could also be a result of a response selection mechanism (Stafford and Gurney, 2004; Van Maanen et al., 2012; Palmer et al., 2005). It could be that differences in stimulus intensity between the two options also contribute to a Piéron-like relationship in the later intervals, that is convoluted with Fechner’s law (see Donkin and Van Maanen, 2014 for a similar argument). Unfortunately, our data do not allow us to discriminate between a pure logarithmic growth function and one that is mediated by a decreasing power function”.

(12) Table 2: There is an SAT effect even on the first interval, which is quite remarkable and could be discussed more - does this mean that the C1 component occurs earlier under speed pressure? This would be the first such finding.  

The original event we qualified as a P100 was sensitive to SAT but the earliest event is now the N40 and isn’t statistically sensitive to speed pressure in this data. We believe that the fact that the P100 is still sensitive to SAT is not a surprise and therefore do not outline it.

(13) Line 221: "decrease of activation when contrast (and thus difficulty) increases" - is this shown somewhere in the paper?  

The whole section for this analysis was rewritten (see comment below)

(14) I find the analysis of Figure 5 interesting, but the interpretation odd. What is found is that the peak of the decision signal aligns with the response, consistent with previous work, but the authors choose to interpret this as the decision signal "occurring as a short-lived burst." Where is the quantitative analysis of its duration across trials? It can at least be visually appraised in the surface plot, and this shows that the signal has a stimulus-locked onset and, apart from the slowest RTs, remains present and for the most part building, until response. What about this is burst-like? A peak is not a burst.  

This was the residue of a previous version of the paper where an analysis reported that no evidence accumulation trace was found. But after proper simulations this analysis turned out to be false because of a poor statistical test. Thus we removed this paragraph in the revised manuscript and Figure 5 has now been extended to include surface plots for all the events.

Reviewer #2 (Recommendations for the authors):  

Overall, I really enjoyed reading this paper. However, in some places the approach is a bit opaque or the results are difficult to follow. As I read the paper, I noted:  

Did you do a simple DDM, or did you do a collapsing bound for speed?  

The fitted DDM was an adaptation of the proportional rate diffusion model. We make this clearer at the end of the introduction: "Given that Fechner’s law is expected to capture decision difficulty we connected this law to the classical diffusion decision models by replacing the rate of accumulation with Fechner’s law in the proportional rate diffusion model of Palmer et al.(2005).”

It is confusing that the order of intervals in the text doesn't match the order in the table. It might be better to say what events the interval is between rather than assuming that the reader reconstructs.  

We agree and adapted the order in both the text and the table. The table is now also more explicit (e.g. RT instead of S-R)

Otherwise, I do wonder to what extent the method is able to differentiate processes that yield similar scalp topographies and find it a bit concerning that no motor component was identified.  

We believe that the new version with the LRP/CPP is a demonstration that the method can handle similar topographies. The method can handle events with close topographies as long as they are separate in time, however if they are not sequential to one another the method cannot capture both events. We now discuss this, in relation with the C1/P100 overlap, in the discussion section “Visual encoding time”:

“Nevertheless this event, seemingly overlapping with the P100 even at the trial level (Figure 5C), cannot be recovered by the method we applied. The fact that the P100 was recovered instead of the C1 could indicate that only the timing of the P100 contributes to the RT (see Section 3 of Weindel et al., 2024)”.

And we more generally address the question of overlap in the new section “Generalization and limitation”.

Reviewer #3 (Recommendations for the authors):  

Major Comments:  

(1) If we agree on one thing, it is that motor processes contribute to response time. Line 364: "In the case of decision-making, these discrete neural events are visual encoding, attention-orientation, and decision commitment, and their latency make up the reaction time." Does the third event, "decision commitment", capture both central parietal positivity (decision deliberation) and motor components? If so, how can the authors attribute the effects to decision deliberation as opposed to motor preparation?  

Thanks to the suggestions also in the public part. This main problem is now addressed as we do capture both a motor component and a decision commitment.

Line 351 suggests that the third event may contain two components.  

This was indeed our initial, badly written, hypothesis. Nevertheless the new solution again addresses this problem.

The time series in Figure 6 shows an additional peak that is not evident in the simulated ramp of Appendix 1.  

This was probably due to the overlap of both the CPP and the LRP. It is now much clearer that the CPP looks mostly like a ramp while the LRP looks much more like a burst-like/peaked activity. We make this clear in the “Decision event” paragraph of the discussion section:

“Regarding the build-up of this component, the CPP is seen as originating from single-trial ramping EEG activities but other work (Latimer et al., 2015; Zoltowski et al., 2019) have found support for a discrete event at the trial-level. The ERPs on the trial-by-trial centered event in Figure 5 show support for both accounts. As outlined above, the LRP is indeed a short burst-like activity but the build-up of the CPP between high vs low contrast diverges much earlier than its peak”.

Previous analyses (Weindel et al., 2024) found motor-related activity from central parietal topographies close to the response by comparing the difference in single-trial events on left- vs right-hand response trials. The authors suggest at line 315 that the use of only the right hand for responding prevented them from identifying a motor event.  

The use of only the right hand should have made the event more identifiable because the topography would be consistent across trials (rather than inverting on left vs right hand response trials).  

The reviewer is correct, in the original manuscript we didn’t test for lateralization, but the comment of the reviewer gave us the idea to explicitly test for the asymmetry (Figure 3). This test now clearly shows what would be expected for a motor event with a strong negativity over the left motor cortex.

The authors state on line 422 that the EEG data were truncated at the time of the response.  

Could this have prevented the authors from identifying a motor event that might overlap with the timing of the response?  

We thank the reviewer for this suggestion. This would have been a possibility but the problem is that adding samples after the response also adds the post-response processes (error monitoring, button release, stimulus disappearance, etc.). While increasing the samples after the response is definitely something that we need to inspect, we think that the separation we achieved in this revision doesn’t call for this supplementary analysis.

The largest effects of contrast on the third event amplitude appear around the peak as opposed to the ramp. If the peak is caused by the motor component, how does this affect the conclusions that this third event shows a decision-deliberation parietal processes as opposed to a motor process (a number of studies suggest a causal role for motor processes in decision-making e.g. Purcell et al., 2010 Psych Rev; Jun et al., 2021 Nat Neuro; Donner et al., 2009 Curr Bio).  

This result now changed and it does look like the peak capturing most of the effect is no longer true. We do however think that there might be some link to theories of motor-related accumulation. We therefore added this to the discussion in the Motor execution section:

“Based on all these observations, it is therefore very likely that this LRP event signs the first passage of a two-step decision process as suggested by recent decision-making models (Servant et al., 2021; Verdonck et al., 2021; Balsdon et al., 2023)”.

I would suggest further investigation into the motor component (perhaps by extending the time window of analysed EEG to a few hundred ms after the response) and at least some discussion of the potential contribution of motor processes, in relation to the previous literature.  

We believe that the absence of a motor component is sufficiently addressed in the revised manuscript and in the responses to the other comments.    

(2) What do we learn from this work? Readers would appreciate more attention to previous findings and a clearer outline of how this work differs. Two points stand out, outlined below. I believe the authors can address these potential complaints in the introduction and discussion, and perhaps provide some clarification in the presentation of the results.  

In the introduction, the authors state that "... to date, no study has been able to provide single-trial evidence of multiple EEG components involved in decision-making..." (line 64). Many readers would disagree with this. For example, Philiastides, Ratcliff, & Sadja (2006) use a single-trial analysis to unravel early and late EEG components relating to decision difficulty and accuracy (across different perceptual decisions), which could be related to the components in the current work. Other, network-based single-trial EEG analyses (e.g., Si et al., 2020, NeuroImage, Sturn et al., 2016 J Neurosci Methods) could also be related to the current component approach. Yet other approaches have used inverse encoding models to examine EEG components related to separable decision processes within trials (e.g., Salvador et al., 2022, Nat Comms). The results of the current work are consistent with this previous work - the two components from Philiastides et al., 2006 can be mapped onto the components in the current work, and Salvador et al., 2022 also uncover stimulus- and decision-deliberation related components.  

We completely agree with the reviewer that the link to previous work was insufficient. We now include all references that the reviewer points out both in the introduction (see response R3.2) and in the discussion (see response R3.4). We wish to thank the reviewer for bringing these papers to our attention as they are important for the manuscript.

The authors relate their components to ERPs. This prompts the question of whether we would get the same results with ERP analyses (and, on the whole, the results of the current work are consistent with conclusions based on ERP analyses, with the exception of the missing motor component). It's nice that this analysis is single-trial, but many of the follow-up analyses are based on grouping by condition anyway. Even the single-trial analysis presented in Figure 4 could be obtained by median splits (given the hypotheses propose opposite directions of effects, except for the linear model). 

We do not agree with the reviewer in the sense that classical ERP analyses would require much more data-points. The performance of the method is here to use the information shared across all contrast levels to be able to model the processing time of a single contrast level (6 trials per participant). Furthermore, as stated in the response to R1.4 and R1.5, the aim of the paper is to have the time of information processing components which cannot be achieved with classical ERPs without strong, and likely false, assumptions.

Medium Comments:  

(1) The presentation of Piéron's law for the behavioural analysis is confusing. First, both laws should be clearly defined for readers who may be unfamiliar with this work. I found the proposal that Piéron's law predicts decreasing RT for increasing pedestal contrast in a contrast discrimination paradigm task surprising, especially given the last author's previous work. For example, Donkin and van Maanen (2014) write "However, the commonality ofPiéron's Law across so many paradigms has lead researchers (e.g., Stafford & Gurney, 2004; Van Maanen et al., 2012) to propose that Piéron's Law is unrelated to stimulus scaling, but is a result of the architecture of the response selection (or decision making) process." The pedestal contrast is unrelated to the difficulty of the contrast discrimination task (except for the consideration of Fechner's law). Instead, Piéron's law would apply to the subjective difference in contrast in this task, as opposed to the pedestal contrast. The EEG results are consistent with these intuitions about Piéron's law (or more generally, that contrast is accumulated over time, so a later EEG component for lower pedestal contrast makes sense): pedestal contrast should lead to faster detection, but not necessarily faster discrimination. Perhaps, given the complexity of the manuscript as a whole, the predictions for the behavioural results could be simplified?  

We agree that the initial version was confusing. We now clarified the presentation of Piéron's law at the end of the introduction (see also response to R2).

Once Fechner's law is applied, decision difficulty increases with increasing contrast, so Piéron's law on the decision-relevant intensity (perceived difference in contrast) would also predict increasing RT with increasing pedestal contrast. It is unlikely that the data are of sufficient resolution to distinguish a log function from a power of a log function, but perhaps the claim on line 189 could be weakened (the EEG results demonstrate Piéron's law for detection, but do not provide evidence against Piéron's law in discrimination decisions).  

This is an excellent observation, thank you for bringing it to our attention. Indeed, the data support the notion that Pieron’s law is related to detection, but do not rule out that it is also related to decision or discrimination. In earlier work, we (Donkin & Van Maanen, 2014) addressed this question as well, and reached a similar conclusion. After fitting evidence accumulation models to data, we found no linear relationship between drift rates and stimulus difficulty, as would have been the case if Pieron's law could be fully explained by the decision process (as -indirectly- argued by Stafford & Gurney, 2004; Van Maanen et al., 2012). The fact that we observed evidence for a non-linear relationship between drift rates and stimulus difficulty led us to the same conclusion, that Pieron’s law could be reflected in both discrimination and decision processes. We added the following comment to the discussion about the functional locus of Pieron's law to clarify this point:

“Note that this observation is not necessarily incongruent with theoretical work that argued that Piéron’s law could also be a result of a response selection mechanism (Stafford and Gurney, 2004; Van Maanen et al., 2012; Palmer et al., 2005). It could be that differences in stimulus intensity between the two options also contribute to a Piéron like relationship in the later intervals, that is convoluted with Fechner’s law (see Donkin and Van Maanen, 2014, for a similar argument). Unfortunately, our data do not allow us to discriminate between a pure logarithmic growth function and one that is mediated by a decreasing power function”.

(2) Appendix 1 shows that the event detection of the HMP method will also pick up on ramping activity. The description of the problem in the introduction is that event-like activity could look like ramping when averaged across trials. To address this problem, the authors should simulate events (with some reasonable dispersion in timing such that they look like ramping when averaged) and show that the HMP method would not pull out something that looked like ramping. In other words, the evidence for ramping in this work is not affected by the previously identified confounds.  

We agree that this demonstration was necessary and thus added the suggested simulation to Appendix 1. As can be seen in the Figure 1 of the appendix, when we simulate a half-sine the average ERP based on the timing of the event looks like a half-sine.

(3) Some readers may be interested in a fuller discussion of the failure of the Fechner diffusion model in the speed condition.  

We are unsure which failure the reviewer refers to but assumed it was in relation to the behavioral results and thus added: 

It is unlikely that neither Piéron nor Fechner law impact the RT in the speed condition. Instead this result is likely due to the composite nature of the RT where both laws co-exist in the RT but cancel each other out due to their opposite prediction.

Minor Comments:  

(1) "By-trial" is used throughout. Normally, it is "trial-by-trial" or "single-trial" or "trial-wise".

We replaced all occurrences of “by-trial”  with the three terms suggested were appropriate.

(2) Line 22: "The sum of the times required for the completion of each of these precessing steps is the reaction time (RT)." The total time required. Processing.  

Corrected for both.

(3) Line 26/27: "Despite being an almost two century old problem (von Helmholtz, 2021)." Perhaps the citation with the original year would make this point clearer.  

We agree and replaced the citation.

(4) Line 73: "accounted by estimating". Accounted for by estimating.  

Corrected.

(5) Line 77 "provides an estimation on the." Of the.  

Corrected.

(6) Line 86: "The task of the participants was to answer which of two sinusoidal gratings." The picture looks like Gabor's? Is there a 2d Gaussian filter on top of the grating? Clarify in the methods, too.  

We incorrectly described the stimuli as those were indeed just Gabor’s. This is now corrected both in the main text and the method section.

(7) Figure 1 legend: "The Fechner diffusion law" Fechner's law or your Fechner diffusion model?  

Law was incorrect so we changed to model as suggested.

(8) Line 115: "further allows to connects the..." Allows connecting the.  

Corrected.

(9) Line 123: "lower than 100 ms or higher than..." Faster/slower.  

Corrected.

(10) Line 131: "To test what law." Which law.?  

Corrected to model.

(11) Figure 2 legend: "Left: Mean RT (dot) and average fit (line) over trials and participants for each contrast level used." The fit is over trials and participants? Each dot is? Average trials for each contrast level in each participant?  

This sentence was corrected to “Mean RT (dot) for each contrast level and averaged predictions of the individual fits (line) with Accuracy (Top) and Speed (Bottom) instructions.”.

(12) Line 231: "A comprehensive analysis of contrast effect on". The effect of contrast on.  

This title was changed to “functional interpretation of the events”.

(13) Line 23: "the three HMP event with". Three HMP events.

The sentence no longer exists in the revised manuscript.

(14) Line 270: "Secondly, we computed the Pearson correlation coefficient between the contrast averaged proportion of correct." Pearson is for continuous variables. Proportion correct is not continuous. Use Spearman, Kendall, or compute d'.  

The reviewer rightly pointed out our error, we corrected this by computing Spearman correlation.

(15)  Line 377: "trial 𝑛 + 1 was randomly sampled from a uniform distribution between 0.5 and 1.25 seconds." It's just confusing why post-response activity in Figure 5 does look so consistent. Throughout methods: "model was fitted" should be "was fit", and line 448, "were split".  

We do not have a specific hypothesis of why the post-response activity in the previous Figure 5 was so consistent. Maybe the Gaussian window (same as in other manuscripts with a similar figure, e.g. O’Connell et al. 2012) generated this consistency. We also corrected the errors mentioned in the methods.

(16) The linear mixed models paragraph is a bit confusing. Can it clearly state which data/ table is being referred to and then explain the model? "The general linear mixed model on proportion of correct responses was performed using a logit link. The linear mixed models were performed on the raw milliseconds scale for the interval durations and on the standardized values for the electrode match." We go directly from proportion correct to raw milliseconds...  

The confusion was indeed due to the initial inclusion of a general linear mixed model on proportion correct which was removed as it was not very informative. The new revision should be clearer on the linear mixed models (see first sentence of subsection ‘linear mixed models' in the method section).

(17) A fuller description of the HMP model would be appreciated.  

We agree that this was necessary and added the description of the HMP model in the corresponding method section “Hidden multivariate pattern” in addition to a more comprehensive presentation of HMP in the first paragraph of the Result and Discussion sections.

(18) Line 458: "Fechner's law (Fechner, 1860) states that the perceived difference (𝑝) between the two patches follows the logarithm of the difference in physical intensity between..." ratio of physical intensity.  

Corrected.

(19) P is defined in equations 2 and 4. I would include the beta in equation 4, like in equation 2, then remove the beta from equations 3 and 5 (makes it more readable). I would also just include the delta in equation 2, state that in this case, c1 = c+delta/2 or whatever.  

This indeed makes the equation more readable so we applied the suggestions for equations 2, 3, 4 and 5. The delta was not added in equation 2 but instead in the text that follows:

“Where 𝐶1 = 𝐶0 + 𝛿, again with a modality and individual specific adjustment slope (𝛽).” 

(20) The appendix suggests comparing the amplitudes with those in Figure 3, but the colour bar legend is missing, so the reader can only assume the same scale is used?  

We added the color bar as it was indeed missing. Note though that the previous version displayed the estimation for the simulated data while this plot in the revised manuscript shows the solution on real data obtained after downsampling the data (and therefore look for a larger pattern as in the main text). We believe that this representation is more useful given that the solution for the downsampled data is no longer the same as the one in the main text (due to the difference in pattern width).

eLife Assessment

This Review Article provides a thorough overview of whole-brain activity changes induced by brain stimulation and summarizes the current state of the field. However, it lacks integration across spatial and mechanistic scales, which limits the reader's ability to understand how the different findings relate to one another. In addition, several key concepts are not explained in sufficient depth for non-expert readers. The manuscript would benefit from the development of a cohesive conceptual framework to more clearly synthesize the existing literature.

Reviewer #1 (Public review):

Summary:

This paper is a comprehensive review of perturbation studies and the state-dependence of the brain's response to perturbation at the circuit, mesoscale, and macroscale levels.

Strengths:

The strengths of the paper are the thorough description of many perturbation studies at different levels of organization, and the integration of both experimental and modeling studies. The review clearly communicates the need to consider (1) brain or local-population state, and (2) multiple levels of organization, in order to understand perturbation responses. Another major strength is the ability for the reader to reproduce figures using the EBRAINS platform.

Weaknesses:

Two major points of improvement should be resolved with the review, in order to make it useful for a broad audience.

The first is that the review does not include a significant integration across scales, and as a result, reads like three separate (though comprehensive) reviews. Currently, the only integration across the scales is in the brief conclusion paragraph. I would recommend adding an additional section, in which the overarching picture is discussed. (i.e. a unifying view of state dependence, and what is learned by considering across scales). This need not be too long, but it should be longer than a single conclusion paragraph.

The second major weakness is that there is a lack of clarity on many points throughout, which is needed for the reader to fully understand the results described.

Reviewer #2 (Public review):

Summary:

In this review article, the authors discuss the whole-brain activity changes induced by brain stimulation. They review the literature on how these activity changes depend on the cognitive state of the brain and divide the results by the scale of the change being induced, from microscale changes across small groups of neurons, up to macroscale changes across the entire brain. Finally, they describe attempts to model these changes using computational models.

Strengths:

The review provides an overview of the results within this subfield of neuroscience, and the authors are able to discuss a lot of prior results. The framing of the changes in neuronal activity in terms of computational changes is also a helpful approach.

Weaknesses:

However, the authors are not able to contextualize these results within a single framework, i.e. explaining from first principles how different aspects of stimulus-induced changes interact to generate functional changes in the brain, and how different changes - at distinct spatiotemporal scales - combine to form larger effects. This is a significant weakness in generating a review of the literature, since the authors do not provide a cohesive conceptual framework on which to frame the results. Similarly, the authors do not explain how their different computational models fit together, and how one can get a singular computational understanding of the distinct mechanisms of brain activity changes due to stimulation under different brain states, by combining the results derived from each separate model.

Major Comments:

(1) The authors have written this review as if it were intended for an audience who is already familiar with the topics. For example, they introduce concepts like complexity, spiral vs planar waves, without much explanation.

(2) Regarding complexity, the authors present a quantification termed PCI. However, in the associated box, they state that PCI could be implemented in a number of different ways, using analogous metrics (which are, nonetheless, not identical). Yet the authors simply claim that all these metrics are sufficiently similar to be grouped together as "PCI". The authors do not provide much intuition about this, and they also don't present any other potential quantifications. This makes any interpretation of their results strongly dependent on your understanding of the concept of PCI. It would be helpful to present some other, analogous metric to demonstrate that the results that the authors are focusing on are not somehow tied to the specific computational structure of the PCI metric.

(3) The authors divide the review into sections organized by the spatial extent of the effects that they are exploring (e.g. from microscale to macroscale). However, they don't bring together these insights into a cohesive structure - for example, by providing potential explanations of the macroscale effects by using the microscale changes.

(4) The authors completely ignore any aspect of cell-type specificity in their review, despite the known importance of specific cell types at the microcircuit scale. This makes it difficult to map their results onto the true biological system.

(5) The authors introduce several different computational models, such as the Hopf model, the AdEx model, and the MPR model. However, they do not provide the reader with a conceptual understanding of the structure of each of these models (except through potentially more complex terminology, e.g. the Hopf model is a "phenomenological Stuart-Landau nonlinear oscillator"). Additionally, though they present the results of each simulation, they don't provide the reader with intuition about how these models compare against each other, and how best to interpret results derived from each model.

(6) In several cases, the authors make statements that they appear to believe to be completely straightforward (and require no justification), but that do not appear so to the reader. For example, they mention: "In wakefulness and REM sleep, ..., the membrane potential is depolarized and close to the spike threshold, which explains why neurons respond more reliably and with less response variability compared with slow-wave sleep". However, this statement is not obvious to the reader and requires explanation (for example, in a system that is close to balance, bringing cells closer to the firing threshold can result in increased response jitter).

Author response:

We would like to thank the editors and reviewers for excellent suggestions to improve the paper. We will carefully address these suggestions in a revised version.

eLife Assessment

This potentially valuable cross-sectional longitudinal study leverages high-definition transcranial direct current stimulation to the left dorsolateral prefrontal cortex to examine its effect on procrastination behavior over an extended time span. Support for the conclusions is incomplete owing to missing information about the analyses, the nature of the procrastination tasks, and the derived dependent measures.

Reviewer #1 (Public review):

Summary:

The authors report the results of a tDCS brain stimulation study (verum vs sham stimulation of left DLPFC; between-subjects) in 46 participants, using an intense stimulation protocol over 2 weeks, combined with an experience-sampling approach, plus follow-up measures after 6 months.

Strengths:

The authors are studying a relevant and interesting research question using an intriguing design, following participants quite intensely over time and even at a follow-up time point. The use of an experience-sampling approach is another strength of the work.

Weaknesses:

There are quite a few weaknesses, some related to the actual study and some more strongly related to the reporting about the study in the manuscript. The concerns are listed roughly in the order in which they appear in the manuscript.

(1) In the introduction, the authors present procrastination nearly as if it were the most relevant and problematic issue there is in psychology. Surely, procrastination is a relevant and study-worthy topic, but that is also true if it is presented in more modest (and appropriate) terms. The manuscript mentions that procrastination is a main cause of psychopathology and bodily disease. These claims could possibly be described as 'sensationalized'. Also, the studies to support these claims seem to report associations, not causal mechanisms, as is implied in the manuscript.

(2) It is laudable that the study was pre-registered; however, the cited OSF repository cannot be accessed and therefore, the OSF materials cannot be used to (a) check the preregistration or to (b) fill in the gaps and uncertainties about the exact analyses the authors conducted (this is important because the description of the analyses is insufficiently detailed and it is often unclear how they analyzed the data).

(3) Related to the previous point: I find it impossible to check the analyses with respect to their appropriateness because too little detail and/or explanation is given. Therefore, I find it impossible to evaluate whether the conclusions are valid and warranted.

(4) Why is a medium effect size chosen for the a priori power analysis? Is it reasonable to assume a medium effect size? This should be discussed/motivated. Related: 18 participants for a medium effect size in a between-subjects design strikes me as implausibly low; even for a within-subjects design, it would appear low (but perhaps I am just not fully understanding the details of the power analysis).

(5) It remains somewhat ambiguous whether the sham group had the same number of stimulation sessions as the verum stimulation group; please clarify: Did both groups come in the same number of times into the lab? I.e., were all procedures identical except whether the stimulation was verum or sham?

(6) The TDM analysis and hyperbolic discounting approach were unclear to me; this needs to be described in more detail, otherwise it cannot be evaluated.

(7) Coming back to the point about the statistical analyses not being described in enough detail: One important example of this is the inclusion of random slopes in their mixed-effects model which is unclear. This is highly relevant as omission of random slopes has been repeatedly shown that it can lead to extremely inflated Type 1 errors (e.g., inflating Type 1 errors by a factor of then, e.g., a significant p value of .05 might be obtained when the true p value is .5). Thus, if indeed random slopes have been omitted, then it is possible that significant effects are significant only due to inflated Type 1 error. Without more information about the models, this cannot be ruled out.

(8) Related to the previous point: The authors report, for example, on the first results page, line 420, an F-test as F(1, 269). This means the test has 269 residual degrees of freedom despite a sample size of about 50 participants. This likely suggests that relevant random slopes for this test were omitted, meaning that this statistical test likely suffers from inflated Type 1 error, and the reported p-value < .001 might be severely inflated. If that is the case, each observation was treated as independent instead of accounting for the nestedness of data within participants. The authors should check this carefully for this and all other statistical tests using mixed-effects models.

(9) Many of the statistical procedures seem quite complex and hard to follow. If the results are indeed so robust as they are presented to be, would it make sense to use simpler analysis approaches (perhaps in addition to the complex ones) that are easier for the average reader to understand and comprehend?

(10) As was noted by an earlier reviewer, the paper reports nearly exclusively about the role of the left DLPFC, while there is also work that demonstrates the role of the right DLPFC in self-control. A more balanced presentation of the relevant scientific literature would be desirable.

(11) Active stimulation reduced procrastination, reduced task aversiveness, and increased the outcome value. If I am not mistaken, the authors claim based on these results that the brain stimulation effect operates via self-control, but - unless I missed it - the authors do not have any direct evidence (such as measures or specific task measures) that actually capture self-control. Thus, that self-control is involved seems speculation, but there is no empirical evidence for this; or am I mistaken about this? If that is indeed correct, I think it needs to be made explicit that it is an untested assumption (which might be very plausible, but it is still in the current study not empirically tested) that self-control plays any role in the reported results.

(12) Figures 3F and 3H show that procrastination rates in the active modulation group go to 0 in all participants by sessions 6 and 7. This seems surprising and, to be honest, rather unlikely that there is absolutely no individual variation in this group anymore. In any case, this is quite extraordinary and should be explicitly discussed, if this is indeed correct: What might be the reasons that this is such an extreme pattern? Just a random fluctuation? Are the results robust if these extreme cells are ignored? The authors remove other cells in their design due to unusual patterns, so perhaps the same should be done here, at least as a robustness check.

(13) The supplemental materials, unfortunately, do not give more information, which would be needed to understand the analyses the authors actually conducted. I had hoped I would find the missing information there, but it's not there.

In sum, the reported/cited/discussed literature gives the impression of being incomplete/selectively reported; the analyses are not reported sufficiently transparently/fully to evaluate whether they are appropriate and thus whether the results are trustworthy or not. At least some of the patterns in the results seem highly unlikely (0 procrastination in the verum group in the last 2 observation periods), and the sample size seems very small for a between-subjects design.

Reviewer #2 (Public review):

Summary:

Chen and colleagues conducted a cross-sectional longitudinal study, administering high-definition transcranial direct stimulation targeting the left DLPFC to examine the effect of HD-tDCS on real-world procrastination behavior. They find that seven sessions of active neuromodulation to the left DLPFC elicited greater modulation of procrastination measures (e.g., task-execution willingness, procrastination rates, task aversiveness, outcome value) relative to sham. They report that tDCS effects on task-execution willingness and procrastination are mediated by task outcome value and claim that this neuromodulatory intervention reduces procrastination rates quantified by their task. Although the study addresses an interesting question regarding the role of DLPFC on procrastination, concerns about the validity of the procrastination moderate enthusiasm for the study and limit the interpretability of the mechanism underlying the reported findings.

Strengths:

(1) This is a well-designed protocol with rigorous administration of high-definition transcranial direct current stimulation across multiple sessions. The approach is solid and aims to address an important question regarding the putative role of DLPFC in modulating chronic procrastination behavior.

(2) The quantification of task aversiveness through AUC metrics is a clever approach to account for the temporal dynamics of task aversiveness, which is notoriously difficult to quantify.

Weaknesses:

(1) The lack of specificity surrounding the "real-world measures" of procrastination is problematic and undermines the strength of the evidence surrounding the DLPFC effects on procrastination behavior. It would be helpful to detail what "real-world tasks" individuals reported, which would inform the efficacy of the intervention on procrastination performance across the diversity of tasks. It is also unclear when and how tasks were reported using the ESM procedure. Providing greater detail of these measures overall would enhance the paper's impact.

(2) Additionally, it is unclear whether the reported effects could be due to differential reporting of tasks (e.g., it could be that participants learned across sessions to report more achievable or less aversive task goals, rather than stimulation of DLPFC reducing procrastination per se). It would be helpful to demonstrate whether these self-reported tasks are consistent across sessions and similar in difficulty within each participant, which would strengthen the claims regarding the intervention.

(3) It would be helpful to show evidence that the procrastination measures are valid and consistent, and detail how each of these measures was quantified and differed across sessions and by intervention. For instance, while the AUC metric is an innovative way to quantify the temporal dynamics of task-aversiveness, it was unclear how the timepoints were collected relative to the task deadline. It would be helpful to include greater detail on how these self-reported tasks and deadlines were determined and collected, which would clarify how these procrastination measures were quantified and varied across time.

(4) There are strong claims about the multi-session neuromodulation alleviating chronic procrastination, which should be moderated, given the concerns regarding how procrastination was quantified. It would also be helpful to clarify whether DLPFC stimulation modulates subjective measures of procrastination, or alternatively, whether these effects could be driven by improved working memory or attention to the reported tasks. In general, more work is needed to clarify whether the targeted mechanisms are specific to procrastination and/or to rule out alternative explanations.

Reviewer #3 (Public review):

This manuscript explores whether high-definition transcranial direct current stimulation (HD-tDCS) of the left DLPFC can reduce real-world procrastination, as predicted by the Temporal Decision Model (TDM). The research question is interesting, and the topic - neuromodulation of self-regulatory behavior - is timely.

However, the study also suffers from a limited sample size, and sometimes it was difficult to follow the statistics.

The preregistration and ecological design (ESM) are commendable, but I was not able the find the preregistration, as reported in the paper.

Overall, the paper requires substantial clarification and tightening.

Author response:

Reviewer #1:

(1) We fully thank you to point out the risks of sensationalizing ramification of procrastination on psychopathology, and would rewrite the Introduction section by adding balanced evidence and overall toning down such inappropriate claims meanwhile.

(2) Thank you to raise this crucial question. We are sorry for this fundamental technical issue to preregistration. This occurs from a seriously technical hurdle. The OSF has banned my OSF account, as it claimed to detect “suspicious user’s activities” in my account. This causes no accesses to all materials that already deposited in this OSF account, including this preregistration. We have contacted OSF team, but received no valid technical solution. We reckon that this may be mistaken by my affiliation changes to Third Military Medical University of People’s Liberation Army (PLA). To tackle with this technical issue, we shall upload preregistration in a new repository soon.

(3) This is a back-to-back study to conceptually probe into whether strengthening left DLPFC can mitigate procrastination via reducing task aversiveness or weighting outcome value. Thus, the current study selected a medium effect size in aprior by following the previous one (Xu et al., 2023). This effect size is calculated by the new tool called “Power Contours” (Baker et al., 2021), which weights statistical power by increasing within-subject repeated measures. As you kindly pointed out, we shall clarify effect size calculation in the revised manuscript.

(4) Yes, both groups come in the same number of times into the lab for tDCS stimulation, except to the type (active vs sham).

(5) We shall add full details for clarifying TDM and hyperbolic discounting modeling.

(6) Thank you to raise this very crucial statistical question. We shall double-check whether multiple sessions are modeled as random slopes, and would like to reanalysis it in case which those random slopes are omitted.

(7) Thank you. We have no intentions of confusing you by adding those complicated statistics, but indeed enrich understanding of how we can interpret those findings.

(8) Yes, as mentioned above, we shall add balanced evidence to clarify both left and right DLPFC may function to self-control capability in the Introduction section.

(9) Yes, this is a conceptual hypothesis --- actively stimulating left DLPFC could improve self-control functions. Thank you for this very nuanced but crucial insight, and we could explicitly clarify the nature of our conclusions.

(10) Yes, we ensure that all the participants successfully completed their tasks before deadline at session 6 and 7, and the procrastination rates have been all decreased to 0. Personally speaking, this is somewhat surprise to us as well, but we affirmed this case. For a portion of participants included in the active group, we have received written letters of thanks from them. Thus, this is surprise but exciting finding. Furthermore, thank you for this helpful suggestion, and we would like to do this robustness check by iteratively removing each session, to obviate the statistical biases from an extreme pattern.

(11) Yep, we fully agree with you to add full details in the main text rather in Supplemental materials, and would like to do so in the first round of revision.

Reviewer #2:

(1) Thank you for this very crucial suggestion. We are sorry for this case that much details are omitted to comply with editorial requirement at Nature Human Behaviour (last submission). We do apologize to confuse you as those ambiguous descriptions, and would like to clearly clarify how we measure participants’ procrastination in the real-world tasks. In brief, we asked participant to report a real task that would really happen in the tomorrow and its deadline is also no more than tomorrow. When tomorrow comes, we used ESM to require participant reporting real task completion rate (0-100%) at five time points before the deadline. The five time points are determined by a hyperbolic discounting model (see how and why we set those five time points in the full author’s response letter later). When participant reports the real task completion rate (0-100%) at a given time point, she/he is required to provide a photo to prove its authenticity. The dependent variable --- real-world procrastination rates --- is thus calculated as 100% subtracts the task completion rate (0-100%) when the deadline meets. That is to say, if participant reports task has been fully completed before or when deadline meets, his/her real-world procrastination rate is 100% - 100% = 0%; if reporting task has been completed 60% when deadline meets, the real-world procrastination rate is determined as 100% - 60% = 40%. Do not worry for spurious reporting, we asked all the participants to provide photo verifying the real task completion rate. This is merely a short instance. We shall show the full details in the formal author response letter later.

(2) This is a very meaningful point. We agree with you for this case that participants may learn how to complete this experiment task swiftly rather benefit from neuromodulation. This speculation makes sense, but is compromised by experimental control and empirical observations. Firstly, we do not say “You must complete this task” or “The task completion is associated with bonus/rewards you may get” for participants, which indicates no motivations to do so. Then, the measures to task completion rate are not yet fully based on self-reporting, and we mandate them to provide photos for verification. Thus, this controls the marked risks of spurious reporting. Lastly, all the participants, including ones in either active or sham group, received all the same treatments, excepting “real simulation” and “sham simulation” protocol. Results demonstrated the significant amelioration in the active group rather sham one, indicating no significant “placebo” or “task learning” side effect.

(3) Thank you. As you kindly suggested, we would like to add huge details for those measures in the revised manuscript. While this is a great idea, we did not collect procrastination scores from scales after neuromodulation, and would like to warrant this point into the Limitation section.

(4) Yep, this is a conceptual hypothesis --- actively stimulating left DLPFC could improve self-control functions. We cannot rule out possibilities of amplifying working memory, attention or other cognitive components from this neuromodulation protocol. We fully agree with you for this helpful recommendation --- we would like tone down those claims regarding the roles of DLPFC on self-control, and explicitly warrant that this mechanism may be specialized to the procrastination.

Reviewer #3:

(1) Thank you for taking valuable time to review our manuscript. Yep, limited sample size should warrant cautions to draw a solid conclusion. We would like to claim it into the limitation section. Also, we have streamlined and tightened statistic section by removing complicated and redundancy statistical models.

(2) As mentioned above, we are sorry for this fundamental technical issue to preregistration. This occurs from a seriously technical hurdle. The OSF has banned my OSF account, as it claimed to detect “suspicious user’s activities” in my account. This causes no accesses to all materials that already deposited in this OSF account, including this preregistration. We have contacted OSF team, but received no valid technical solution. We reckon that this may be mistaken by my affiliation changes to Third Military Medical University of People’s Liberation Army (PLA). To tackle with this technical issue, we shall upload preregistration in a new repository soon.

(3) Yep, thank you for this very helpful suggestion. As you kindly indicated, we would like to clarify measures, analyses, methods, and protocols, as well as tighten the whole manuscript.

References

Baker, D. H., Vilidaite, G., Lygo, F. A., Smith, A. K., Flack, T. R., Gouws, A. D., & Andrews, T. J. (2021). Power contours: Optimising sample size and precision in experimental psychology and human neuroscience. Psychological methods, 26(3), 295–314. https://doi.org/10.1037/met0000337

Xu, T., Zhang, S., Zhou, F., & Feng, T. (2023). Stimulation of left dorsolateral prefrontal cortex enhances willingness for task completion by amplifying task outcome value. Journal of experimental psychology. General, 152(4), 1122-1133. https://doi.org/10.1037/xge0001312

Again, we wholeheartedly appreciate all of those very helpful and insightful comments, with each one to contribute substantially for the quality of this manuscript. Notably, those response we presented above are merely provisional and initial. We shall revise our manuscript following those suggestions, one-by-one, along with a full-length response letter.

eLife Assessment

In this Review Article, the authors survey the literature describing how correlated dynamical states relate to various cognitive states, including anesthesia and sleep. While the topic is significant and the coverage broad, the manuscript does not yet provide a synthesis that connects the many available findings or highlights converging themes across studies. Additionally, many of the disparate concepts are not introduced at the level of first principles. As a result, the Review remains difficult to access for readers outside the immediate subfield. Developing a clearer integrative perspective would help make the article informative to a wider audience.

Reviewer #1 (Public review):

Summary:

In the paper, the authors review literature on synchronous activity, its relationship to brain state, and the multi-scale mechanisms underlying it.

Strengths:

The overall strength of the paper is the wide range of information reviewed, and the diversity of perspectives/approaches it brings together.

Weaknesses:

However, this strength is also the source of its major weaknesses - namely, that the overall structure lacks clarity, and there are inconsistencies throughout. Overall, in the opinion of this reviewer, the manuscript reads as disorganized and incomplete. Major and minor points are delineated below.

Major points:

(1) Most of the text in many figures was too small to read.

(2) Terminology is inconsistent throughout the manuscript. What is the difference between slow oscillations and delta waves? Sometimes the term slow waves is used instead. For sleep state, sometimes the term SWS is used, sometimes non-REM. Similarly, "spindle activity" is not defined, but simply stated as if the reader knows. This brings up two issues: (a) the manuscript should be clearer and more consistent about its terminology, and (b) it's unclear who is the intended readership of the review - is it a pedagogical review for people outside the field of sleep and slow oscillations, or is it meant to be a consensus statement for readers who are already in the field in which a pressing concern has been addressed? It seems part way between these two, and as a result, is ineffective at either goal.

(3) I suggest the authors look again at the overall structure and flow of the review... many sections feel redundant, and it's unclear how they fit together into a single review.

(4) There are many speculative statements in the review that are not justified or explained sufficiently for the reader. For example: "While highly regular slow waves in vivo suggest a single mechanism of generation, namely local cortical circuits, irregular cycles are compatible with a larger role of subcortical nuclei, ..."; "The involvement of different cortical areas and subcortical nuclei can form the basis of these different roles in memory.". For these statements, I assume the relationship between slow wave statistics, subcortical nuclei, and memory either has been written about before, and then should be cited and summarized, or is a novel claim of the authors, which then should be explained and defended rather than stated. There are other similar examples, and I suggest the authors go through the manuscript and make sure that it's clear what is a novel claim of the authors vs a cited claim, and make sure that both are sufficiently justified for the reader.

(5) An especially notable example can be found in the section on the role of the thalamus, where the authors state that they "hold that slow oscillations are fundamentally cortical". However, this section is far too short, and very little evidence is provided to back up this claim. Please review the ways in which the thalamus modulates, and, e.g., ways in which up-down is similar/different without the thalamus.

Reviewer #2 (Public review):

Summary:

In this review article, the authors discuss the correlated dynamical states associated with distinct cognitive states, including those associated with anesthesia and sleep. They present evidence that these states are primarily cortically generated, and demonstrate the properties of these dynamical states at different levels, from the microscale dynamics in individual neurons to the macroscale dynamics across the brain.

Strengths:

Multiple groups have been adding to this field over the past decades, and therefore, a review of this literature is very helpful. This review collates a large amount of the literature within this field into a single document, which should make it a valuable resource within this area of neuroscience.

Weaknesses:

Unfortunately, this review does not seem to be a balanced viewpoint of the field in question. Although there are a lot of authors in the review, it feels as if they are from a common school of thought. The authors provide only a single perspective on these dynamical states, focusing on the perspective of wave-like electrical dynamics across the cortex. Their perspective is embedded in methods such as EEG and LFP recordings. This makes the work hard to interpret outside of the field in which the authors reside. Indeed, the review seems intended for a more specialized audience.

In addition, the article reads more like a catalog of prior studies as opposed to a true synthesis across the large volume of data in this field that highlights links across multiple sources. Hence, it does not seem to provide a novel way of understanding the dynamics involved in cognitive state transitions.

We have included more details on these general comments below:

Major Comments:

(1) The authors have written this review as if it were intended for an audience who is already familiar with these topics. They do not define many of the terms that they introduce within the review, including concepts like complexity, metastability, and oscillations that are fundamental to the concepts that the authors are introducing. Though these may seem like first principles concepts to the authors, they often introduce assumptions that may be unfamiliar to the general reader. For example, are slow wave oscillations periodic? A naïve reader may assume that oscillations - characterized by their frequency - should be somewhat periodic, but that is often not the case. For a journal with a general biological science readership, it would be particularly helpful for each of these terms to be formally defined and characterized.

(2) It would be helpful for the authors to reframe their work in different perspectives and to incorporate all the literature on the dynamics of cortical brain states, and not simply the work that is most familiar to them. As one example, the authors do not discuss cell-type-specific changes in brain state during anesthesia and in altered states of consciousness (including dissociative states and hallucinatory states). There is recent work in this vein (Suzuki and Larkum, 2020; Vesuna et al, 2020; Bharioke, Munz et al, 2023), and yet the authors do not discuss these papers.

(3) Given the authors' clear, extensive knowledge of their field, it would also be extremely helpful for the authors to reframe fundamental concepts in terms of neuronal population activity, trajectory analyses, etc. This would enable a more general audience to better understand their work.

(4) The authors have one section focused on thalamic contributions to cortical wave-like activity. This is a cursory treatment of a subject that is quite controversial in the field. It would be helpful if the authors could provide a more balanced consideration of all the evidence regarding potential thalamocortical interactions and their role in wave-like activity.

(5) The authors present many computational models and describe the results of simulations with these different models. However, this doesn't provide the reader with intuition about what each model adds or removes from the true biological picture. It would be helpful for the authors to provide some intuition about the assumptions and constraints that underlie each model.

(6) The authors state that "The main mechanism [of slow oscillatory dynamics] consists of a combination of two ingredients: the recurrent connectivity, which maintains the excitability in the network, and adaptation, an activity-dependent fatigue variable that provides inhibitory feedback". They make this statement as a fact, yet they don't provide much justification for it. Additionally, it's not clear that any other possible combination of ingredients would be able to produce slow oscillatory dynamics.

(7) The authors often define one concept in terms of other equally complex concepts. For example: "EIA (excitatory-inhibitory with adaptation) cortical circuits then display the typical slow-fast dynamics of relaxation oscillators". The reader would need an explanation of slow-fast dynamics and relaxation oscillators to understand this line, neither of which is provided in the text.

(8) When discussing sleep, the authors do not discuss REM sleep, focusing on slow-wave non-REM sleep. It would be helpful if the authors could at least frame the full sleep cycle and discuss why they are focusing on one part of it.

(9) The authors introduce the concept of sleep spindles without any explanation.

eLife Assessment

This important work combines theoretical analysis with precise experimental perturbation to demonstrate a previously unappreciated quantitative characteristic of the Wnt signaling pathway, which is anti-resonance, or a suppression of pathway output at intermediate activation frequencies. This effect is demonstrated experimentally with compelling evidence from optogenetic stimulation in multiple cell types, alongside modeling results that corroborate the phenomenon. While the demonstration of this phenomenon has yet to be extended to fully physiological situations, its clear existence within optogenetically stimulated systems shows that it is likely a significant factor that contributes to the behavior of this central signaling pathway.

Reviewer #1 (Public review):

Summary:

This report demonstrates that the gene expression output of the Wnt pathway, when controlled precisely by a synthetic light-based input, depends substantially on the frequency of stimulation. The particular frequency-dependent trend that is observed - anti-resonance, a suppression of target gene expression at intermediate frequencies given a constant duty cycle - is a novel aspect that has not been clearly shown before for this or other signaling pathways. The paper provides both clear experimental evidence of the phenomenon with engineered cellular systems and a model-based analysis of how the pairing of rate constants in pathway activation/deactivation could result in such a trend.

Strengths:

This report couples in vitro experimental data with an abstracted mathematical model. Both of these approaches appear to be technically sound and to provide consistent and strong support for the main conclusion. The experimental data are particularly clear, and the demonstration that Brachyury expression is subject to anti-resonance in ESCs is particularly compelling. The modeling approach is reasonably scaled for the system at the level of detail that is needed in this case, and the hidden variable analysis provides some insight into how the anti-resonance works.

In this revised manuscript, the authors have addressed issues in presentation and in discussing the broader relevance of their study to other pathways. Other limitations of the paper, including the fact that the anti-resonance phenomenon has not yet been demonstrated using physiological Wnt ligands and that the model has not been validated using experimental manipulations to establish that the mechanisms of the cell system and the model are the same, were deemed out of the scope of this initial demonstration by both the reviewers and authors. These questions will provide an interesting basis for further studies.

Reviewer #2 (Public review):

Summary:

By combining optogenetics with theoretical modelling the authors identify an anti-resonance behavior in the WnT signaling pathway. This behavior is manifested as a minimal response at a certain stimulation frequency. Using an abstracted hidden variable model, the authors explain their findings by a competition of timescales. Furthermore, they experimentally show that this anti-resonance influences the cell fate decision involved in human gastrulation.

Strengths:

- This interdisciplinary study combines precise optogenetic manipulation with advanced modelling.<br /> - The results are directly tested in two different systems: HEK293T cells and H9 human embryonic stem cells.<br /> - The model is implemented based on previous literature and has two levels of detail: i) a detailed biochemical model and ii) an abstract model with a hidden parameter

Weaknesses:

- While the experiments provide both single-cell data and population data, the model only considers population data.<br /> - Although the model captures the experimental data for TopFlash very well, the beta-Cat curves (Fig 2B) are only described qualitatively. This discrepancy is not discussed.

Overall Assessment:

The authors convincingly identified an anti-resonance behavior in a signaling pathway that is involved in cell fate decisions. The focus on a dynamic signal and the identification of such a behavior is important. I believe that the model approach of abstracting a complicated pathway with a hidden variable is an important tool to obtain an intuitive understanding of complicated dependencies in biology. Such a combination of precise ontogenetical manipulation with effective models will provide a new perspective on causal dependencies in signaling pathways and should not be limited only to the system that the authors study.

Comments on revisions:

I don't have any more comments for the authors and would like to congratulate them for the nice piece of work!

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This report demonstrates that the gene expression output of the Wnt pathway, when controlled precisely by a synthetic light-based input, depends substantially on the frequency of stimulation. The particular frequency-dependent trend that is observed - anti-resonance, a suppression of target gene expression at intermediate frequencies given a constant duty cycle - is a novel aspect that has not been clearly shown before for this or other signaling pathways. The paper provides both clear experimental evidence of the phenomenon with engineered cellular systems and a model-based analysis of how the pairing of rate constants in pathway activation/deactivation could result in such a trend.

Strengths:

This report couples in vitro experimental data with an abstracted mathematical model. Both of these approaches appear to be technically sound and to provide consistent and strong support for the main conclusion. The experimental data are particularly clear, and the demonstration that Brachyury expression is subject to anti-resonance in ESCs is particularly compelling. The modeling approach is reasonably scaled for the system at the level of detail that is needed in this case, and the hidden variable analysis provides some insight into how the anti-resonance works.

Weaknesses:

(1) The anti-resonance phenomenon has not been demonstrated using physiological Wnt ligands; however, I view this as only a minor weakness for an initial report of the phenomenon. The potential significance of the phenomenon for Wnt outweighs the amount of effort it would take to carry the demonstration further - testing different frequencies/duty cycles at the level of ligand stimulus using microfluidics could get quite involved, and would likely take quite some time. Adding some more discussion about how the time scales of ligand-receptor binding could play into the reduced model would further ameliorate this issue.

We thank the reviewer for this comment and the interesting suggestion to test the anti-resonance phenomenon with microfluidics. We agree that combining physiological Wnt ligands with microfluidic stimulation would go beyond the scope of this current study, though it is an interesting extension. One advantage of the optogenetic setup, as mentioned in the discussion, is that the Wnt stimulus can be turned off sharply. This allows us to test the output from perfectly square wave input profiles; in microfluidics, washing the sticky ligand off the cells might “smear” the effective input profile cells respond to.

We show in Supplement Fig. 6, that our reduced model matches the experimental data and that we would expect the antiresonance phenomenon as long as (see Fig. 4). Practically, a smeared input profile implies an effective reduction of 𝑘_off, which means that the phenomenon would be visible with microfluidics (provided the minimum is deep enough, see Fig. 4). However, this should still be considered with caution, as the antiresonance would then appear because the cells essentially receive a smeared out or continuous pulse in the high frequency limit, rather than cells responding to a square wave in a specific way.

(2) While the model is fully consistent with the data, it has not been validated using experimental manipulations to establish that the mechanisms of the cell system and the model are the same. There may be some ways to make such modifications, for example, using a proteasome inhibitor. An alternative would be to more explicitly mention the need to validate the model's mechanism with experiments.

We thank the reviewer for this valuable and constructive comment. We agree that future experimental perturbations that directly modulate pathway activation and reset kinetics—such as proteasome inhibition, targeted degradation of pathway components, or engineered changes in receptor turnover—would provide an important validation of the model’s mechanistic interpretation. In the present study, our primary goal was to establish the existence and quantitative features of anti-resonance in the Wnt pathway and to identify the minimal set of timescale relationships that can explain it. We view the proposed experimental validations as exciting next steps that extend beyond the scope of the current work, and we are grateful to the reviewer for emphasizing their importance. We now mention this explicitly in the discussion of our manuscript.

(3) I think the manuscript misses an opportunity to discuss the potential of the phenomenon in other pathways. The hedgehog pathway, for example, involves GSK3-mediated partial proteolysis of a transcription factor, which could conceivably be subject to similar behaviors, and there are certainly other examples as well.

We thank the reviewer for pointing out an opportunity to emphasize the possibility of this phenomenon in other pathways. The minimal model indicates that anti-resonance emerges whenever a rapid activating process is paired with a slower deactivating/reset process. Beyond Hedgehog/Gli processing, candidate circuits include: NF-κB (rapid IκBα phosphorylation/degradation vs slower IκBα resynthesis), ERK (fast phosphorylation bursts vs slower transcriptional negative feedback such as DUSPs), Notch (fast γ-secretase NICD release vs slower NICD turnover and feedback), BMP/TGF-β–SMAD (fast R-SMAD phosphorylation vs slower receptor trafficking/SMAD7 feedback), and Hippo/YAP (rapid cytoplasmic sequestration vs slower transcriptional feedback). Each contains the same timescale separation that should create a frequency ‘stop-band,’ predicting suppressed gene expression or fate transitions at intermediate stimulation frequencies. We have updated the manuscript’s discussion to mention the Hedgehog connection with the following added sentence in the discussion: Analogous band-stop filtering should arise in other developmental circuits that couple a fast ‘ON’ step to slower deactivation or negative feedback. In Hedgehog, for example, PKA/CK1/GSK3-mediated partial proteolysis of Gli with slower recovery of full-length Gli creates the same fast-activation/slow-reset motif our hidden-variable model predicts will yield anti-resonance, and Wnt–Hedgehog crosstalk through the shared kinase GSK3 suggests such frequency selectivity could occur in other developmental signaling pathways.

We also added an additional sentence regarding different activation and deactivation timescales in other pathways.

(4) Some aspects of the modeling and hidden variable analysis are not optimally presented in the main text, although when considered together with the Supplemental Data, there are no significant deficiencies.

We have addressed the model choices and analysis now more clearly in the main manuscript and also referred to the Supplemental Data more directly.

Reviewer #2 (Public review):

Summary:

By combining optogenetics with theoretical modelling, the authors identify an anti-resonance behavior in the WnT signaling pathway. This behavior is manifested as a minimal response at a certain stimulation frequency. Using an abstracted hidden variable model, the authors explain their findings by a competition of timescales. Furthermore, they experimentally show that this anti-resonance influences the cell fate decision involved in human gastrulation.

Strengths:

(1) This interdisciplinary study combines precise optogenetic manipulation with advanced modelling.

(2) The results are directly tested in two different systems: HEK293T cells and H9 human embryonic stem cells.

(3) The model is implemented based on previous literature and has two levels of detail: i) a detailed biochemical model and ii) an abstract model with a hidden parameter.

Weaknesses:

(1) While the experiments provide both single-cell data and population data, the model only considers population data.

We thank the reviewer for correctly pointing out that the single-cell measurements would in principle allow us to incorporate the cell-to-cell heterogeneity into the model. In this study, we sought to identify a minimal quantitative model of the Wnt pathway that could explain anti-resonance through competing time scales. We believe that, for our purposes, focusing on population data allowed us to keep the complexity of the model to a minimum to increase its explanatory value. We agree with the reviewer that considering single-cell trajectories is an interesting direction for further work.

(2) Although the model captures the experimental data for TopFlash very well, the beta-Cat curves (Figure 2B) are only described qualitatively. This discrepancy is not discussed.

Indeed, our model fits to mean β-catenin expressions are more qualitative than for TopFlash. The fit for β-catenin was tricky, as expression of β-catenin is typically low and closer to the detectable limits than TopFlash. These experimental constraints mean that the variation between individual signal trajectories is higher for β-catenin compared to the light-off condition than for TopFlash. Therefore, we strove to obtain a qualitative rather than a quantitative fit to the mean expression profile in β-catenin.  The current model fit is well within the standard deviation of variation. Given the observed heterogeneity and the fact that we take the parameters from literature (which ensures that the order of magnitude of parameters is in a sensible range), we believe that the model fits are reasonable. We now mention this explicitly in the text.

Overall Assessment:

The authors convincingly identified an anti-resonance behavior in a signaling pathway that is involved in cell fate decisions. The focus on a dynamic signal and the identification of such a behavior is important. I believe that the model approach of abstracting a complicated pathway with a hidden variable is an important tool to obtain an intuitive understanding of complicated dependencies in biology. Such a combination of precise ontogenetic manipulation with effective models will provide a new perspective on causal dependencies in signaling pathways and should not be limited only to the system that the authors study.

We thank both reviewers for the positive assessment of our manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

There are several points that deserve more discussion, as noted above in the review.

(1) It would be worthwhile to consider whether a relatively simple experiment with a proteasome inhibitor or similar pharmacological manipulation could provide useful validation data for the model.

We address this point above in the weaknesses section from reviewer 1.

(2) The figure legend for S5C should clarify whether the values plotted are at a particular fixed time point, or (more likely) at a certain time following the second pulse, which would be variable.

We have modified the figure caption to clarify that the values plotted are at a fixed time point in the simulation (t\=48 hrs). We chose this timepoint sufficiently long after the second pulse to ensure that there are no residual dynamical effects. We thank the reviewer for noting this.

(3) As noted in the Sci Score document, various aspects of the resource reporter should be improved, such as including RRIDs, etc.

We are sending out our plasmids to AddGene; versions for Python and Matlab are listed in our methods section.

Reviewer #2 (Recommendations for the authors):

I mostly have suggestions to improve the clarity of the presentation.

(1) Not all symbols in the equations given in the main text are explained. This is rather annoying, because either you present them and explain what they are or you don't show them and refer to the supplements. For example, d_0 or c_o or \bar{b} or n or K are not explained.

We have now more clearly presented the parameters in the main text and added signposts to the Methods section.

(2) Overall, it is often not clear what data in the figures are redundant, although the authors referred to them in the text. For example, in Figure 2c, a curve for 24 hours is shown and referred back to Figure 1D. However, in Figure 1D there is no curve for 24 hours. Is the data from Supplementary Figure 1 H and K also in the main text?

We thank the referee for pointing out these redundancies. We have now included the 24hr line in Figure 1D and are now only showing the unsmoothed data, also in the main text of the manuscript. To clarify supplemental figures, we have now removed S1H and S1K since all they showed was the unsmoothed version of the data. The remaining plots in Supplementary Figure 1 are normalized differently from what we show in Figure 1 to demonstrate our choice of normalization is not the reason for the observed optogenetic response.

eLife Assessment

Following retinal injury, zebrafish Müller glia reenter the cell cycle and generate replacement cells; this potentially valuable study proposes that injury induces a cxcl18b+ transitional state in Müller cells, which then express nitric oxide, inhibiting Notch signaling and allowing Müller glial cells to reenter the cell cycle. However, the evidence supporting the claims is incomplete, and the authors have made interpretations and conclusions that are not supported by the data. Questions of the temporal expression and function of cxcl18b, as well as the source of potential inflammatory cues before cxcl18b expression, remain unanswered and technical limitations and data inconsistencies raise concerns. Using larval animals complicates the analysis since the retina is still forming, and distinguishing between injury-induced regeneration and ongoing development is complex. With more rigorous testing of the signaling pathways proposed and a clear demonstration of their interdependence, the link between nitric oxide signaling and Notch activity, particularly, would interest those investigating retinal regeneration.

Reviewer #1 (Public review):

Summary:

This study presents a valuable contribution of NO signaling in zebrafish retinal regeneration in larval animals. The data on NO signaling are solid. There are multiple limitations to the study, but these are largely acknowledged by the authors in the revised text.

Strengths:

New data on NO signaling is valuable to the field but may be limited to larval "regeneration".

Weaknesses:

A weakness of the approach is testing cone ablation and regeneration in early larval animals. A near identical study was already done by Hoang et al 2020 in the adult zebrafish, a more relevant biological timepoint.

Reviewer #2 (Public review):

Summary:

In this manuscript Ye at al. examine the sequence of events that occur in the damaged zebrafish Muller glia (MG) in states between quiescence and the onset of proliferation. Using an inducible metronidazole (MTZ) and nitroreductase system to ablate red/green cones in larval zebrafish, they identify a novel transitional MG state that is characterized by the expression of cxcl18b. Using trajectory analysis from single-cell RNA-seq datasets, they find that cxcl18b is expressed before MG expression PCNA and become proliferative. They find that cxcl18b expression peaks in MG at approximately 24 hours post injury (hpi) and rapidly declines as MG proliferate following injury. In a most interesting finding, the authors find a link between nos2b-dependent nitric oxide signaling and cxcl18b-mediated proliferation. Mutagenesis of nos2b decreases MG proliferation. The mechanism linking NO signaling to proliferation was suggested to function via notch signaling as pharmacological inhibition of nitric oxide signaling resulted in elevated Notch activity, thus preventing MG proliferation. The authors suggest a model whereby cxcl18b induces autocrine NO signaling in MG to reduce activity of Notch3, thereby promoting MG proliferation.

Strengths:

The authors utilize a number of sophisticated transgenic approaches and generate novel lines that will have value to the field. The identification of a novel cxcl18b transition state is exciting and the putative link between NO signaling and Notch activity would provide new insight into the drivers of Muller glia proliferation.

Weaknesses:

While the overall model is appealing and may serve as a foundation for future studies, some information gaps remain and certain conclusions rely on correlational data. The cellular expression of nos2b remains unclear as the single-cell RNA-seq data cannot provide expression data that matches RT-PCR results. The temporal sequence of events are based on transgene expression in the Tg(cxcl18b:GFP) lines, where persistence of the GFP fluorescence may not reflect endogenous cxcl18b. The identity of putative cxcl18b receptors on MG to support an autocrine signaling pathway remains unclear. Nevertheless, this is an interesting study that should open new avenues of exploration.

Author response:

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

Reviewer #1:

(1) The authors state that more is known about glial reactivation than cell-cycle re-entry. They are confusing many points here. More gene networks that require cell-cycle re-entry are known. Some of the genes listed for "reactivation" are, in fact, required for cell cycle re-entry/proliferation. And the authors confuse gliosis vs glial reactivation.

We thank the reviewer for this important and constructive comment. We fully agree that clearly distinguishing between the concepts of glial reactivation, glial proliferation, gliosis, and neurogenesis is essential to avoid conceptual confusion in our study.

Injury-induced retinal regeneration in zebrafish:

Glial reactivation refers to the initial response of quiescent Müller glia (MG) to injury, characterized by morphological changes and upregulation of reactive markers (e.g., gfap, ascl1a, lin28a) and activation of signaling pathways such as Notch, Jak/Stat, and Wnt (Lahne et al., 2020; Pollak et al., 2013; Sifuentes et al., 2016; Yao et al., 2016).

Glial proliferation refers to the clonal expansion of these MG-derived progenitor cells, which undergo rapid cell-cycle re-entry and amplify to generate sufficient progenitors for regeneration (Iribarne and Hyde, 2022; Lee et al., 2024; Wan and Goldman, 2016)

Gliosis vs neurogenesis represents a divergent fate decision following proliferation. In zebrafish, MG-derived progenitor cells differentiate into retinal neurons that can replace those damaged or lost due to retinal injury. In contrast, mammalian MG tend to undergo an initial gliotic surge and rapidly revert to a quiescent state, exhibiting gliosis and glial scarring (Thomas et al., 2016; Yin et al., 2024). Thus, we totally agreed that gliosis cannot be confused with glial reactivation because glial reactivation is the very first step of glial injury responses, whereas gliogensis is the very last glial response to the injury.

We agree with the reviewer that many genes typically described as “reactivation markers” (e.g., ascl1a, lin28a, sox2, mycb, mych) are also essential regulators of cell-cycle re-entry (Gorsuch et al., 2017; Hamon et al., 2019; Lee et al., 2024; Lourenço et al., 2021; Pollak et al., 2013; Thomas et al., 2016). Because the glial reactivation is a leading event for glial proliferation, the regulators of glial reactivation are expected to be responsible for glial proliferation as well.

In our study, we focused on the states preceding glial proliferation to understand the mechanism underlying injury-induced glial cell-cycle re-entry. We defined these transitional states and the subsequent proliferative MG states based on single-cell RNA-seq trajectory analysis. (revised lines: 41-58)

(2) A major weakness of the approach is testing cone ablation and regeneration in early larval animals. For example, cones are ablated starting the day that they are born. MG that are responding are also very young, less than 48 hrs old. It is also unclear whether the immune response of microglia is a mature response. All of these assays would be of higher significance if they were performed in the context of a mature, fully differentiated, adult retina. All analysis in the paper is negatively affected by this biological variable.

We thank the reviewer for raising this important point regarding the developmental stage of the retina in our model system. We have carefully considered this concern and now provide additional clarification and justification, as follows:

(1) The glial responses in larval and adult retina:

Previous studies have demonstrated that injury-induced glial responses are largely conserved in larval and adult zebrafish retina, including reactive gliosis marked by gfap upregulation and proliferation(Meyers et al., 2012; Sarich et al., 2025). In our study, G/R cones were ablated beginning at 5 dpf using metronidazole (MTZ), and we observed robust induction of PCNA⁺ MG in the inner nuclear layer, consistent with injury-induced proliferation (Figure 1E). These findings align with previous studies showing that key features of MG regenerative responses are conserved across larval and adult stages.

(2) The microglial responses in larval and adult retina:

Retinal microglia functionally mature at 5 dpf in the zebrafish retina (Mazzolini et al., 2020; Svahn et al., 2013), and prior studies have demonstrated that microglia in larval and adult zebrafish exhibit similar responses to injury, including migration, morphological activation, and phagocytosis(Nagashima and Hitchcock, 2021; White et al., 2017). In our experiments using Tg(mpeg1: GFP) larvae, we observed clear microglial recruitment to the outer nuclear layer (ONL) following cone ablation (Figure 1E and Figure 1-figure supplement 1A), supporting the functional competence of larval microglia in injury-induced immune responses

(3) The contribution using larval animals to study the regeneration program:

We agree that regeneration studies in the adult retina can provide important biological insights, particularly in a fully differentiated tissue environment. Accordingly, we have acknowledged this limitation in our revised manuscript “limitations of this study” section (revised lines 534-540: “1. Our study focuses on larval zebrafish, in which the core features of MG and immune responses are conserved compared to the adult. However, we acknowledge that the adult retina—with its fully matured differentiated retina and immune response—provides irreplaceable biological insight. Nevertheless, larval models offer a powerful platform to uncover conserved regenerative mechanisms and serve as a valuable complement for identifying age-dependent differences in MG-mediated regeneration.”) and have stated our intention to extend future analyses to adult zebrafish, especially to explore age-dependent differences in redox signaling and MG proliferation. At the same time, we believe that the larval model offers unique advantages for uncovering fundamental, conserved mechanisms of regeneration and enables characterization of age-dependent regulatory differences. Thus, our study in larval animals serves as a complementary and informative platform for understanding both the conserved and developmental stage-specific features of injury-induced regeneration.

(4) Related to the above point, the clonal analysis of cxcl18b+ MG is complicated by the fact that new MG are still being born in the CMZ (as are new cones for that matter).

We thank the reviewer for raising this important point regarding potential contributions from CMZ-derived progenitors to the lineage-traced cxcl18b⁺ MG clones. To address this concern, we have implemented evidence to rule out a CMZ origin for the clones analyzed:

Spatial restriction of clones: All clones included in our analysis were located exclusively within the central and dorsal retina, as shown in Figure 2H. From the spatial distribution of reactive MG populations across the retina, we observed a patterned organization in which the vast majority of proliferating MG arose from local mature MG–derived progenitors, rather than from peripheral CMZ-derived progenitors. However, we acknowledge that we cannot entirely exclude the possibility that CMZ-derived progenitors contribute to injury-induced MG proliferation, particularly in the peripheral retina.

We have clarified this point in the revised Methods section (revised lines 756–762: “Clone analysis of cxcl18b⁺ lineage-traced MG was restricted to cells located in the central and dorsal region of the zebrafish retina after G/R cone ablation in Figure 2, Figure 6, and their figure supplement. This spatial restriction strongly suggests that the proliferative MG originate from local mature MG, although we cannot completely rule out the possibility that CMZ-derived progenitors contribute to the generation of proliferative MG in the peripheral retina.”) and updated the corresponding figure legends.

(4) A near identical study was already done by Hoang et al., 2020, in adult zebrafish, a more relevant biological timepoint. Did the authors check this published RNA-seq database for their gene(s) of interest?

We thank the reviewer for pointing out the relevance of the study by Hoang et al., 2020, which characterized the transcriptional dynamics of MG reactivation in the adult zebrafish retina. We agree that comparisons with their single-cell RNA-seq dataset are important to confirm the conservation of our findings in larval vs adult zebrafish.

To this end, we examined the adult zebrafish MG dataset reported by Hoang et al., and confirmed that cxcl18b is also present and enriched in their analysis, particularly in activated MG populations under various injury paradigms:

(1) cxcl18b is listed as a differentially expressed gene (DEG) in Supplementary Table ST2, enriched in GFP⁺ MG following injury. It is also significantly upregulated in both NMDA-induced and light damage conditions, as shown in Supplementary Table ST3.

(2) In Supplementary Table ST5, cxcl18b is identified as a classifier of activated MG, with classification power scores of 0.552 (NMDA), 0.632 (light damage), and 0.574 (TNFα + γ-secretase inhibitor treatment), indicating its consistent expression across multiple injury models.

(3). In their pseudotime analysis (Figure 4C and Supplementary Table ST8), cxcl18b is specifically expressed in Module 5, which is expressed earlier along the trajectory than ascl1a. This temporal pattern of cxcl18b preceding ascl1a expression is consistent with our trajectory analysis in larval MG (Figure 1H), further supporting its role as an early marker of the transitional state before proliferation.

These findings underscore the robustness and biological relevance of cxcl18b as a conserved marker of injury-responsive MG in both larval and adult zebrafish. Our data expand upon the prior work by specifically characterizing a cxcl18b-defined transitional MG state preceding cell-cycle re-entry, thereby offering additional insights into the temporal staging of MG activation during regeneration.

(5) KD of cxcl18b did not affect MG proliferation or any other defined outcome. And yet the authors continually state such phrases as "microglia-mediated inflammation is critical for activating the cxcl18b-defined transitional states that drive MG proliferation." This is false. Cxcl18b does not drive MG proliferation at all.

We thank the reviewer for raising this concern. We agree with the reviewer and have revised this statement as "These findings suggest that microglia-mediated inflammation may contribute to the activation of cxcl18b-defined transitional states that precede MG proliferation, although a causal relationship remains to be established." (revised lines 251-253).

(6) A technical concern is that intravitreal injections are not routinely performed in larval fish.

We appreciate the reviewer’s technical concern regarding the use of intravitreal injections in larval zebrafish. In our study, we performed intraocular injection according to previously established methods (Alvarez et al., 2009; Giannaccini et al., 2018; Rosa et al., 2023). This approach involves carefully delivering a small volume of viral suspension into the intraocular space by a glass micropipette. To address this concern, we will revise the Materials and Methods section to clearly describe the injection procedure and will cite the relevant references accordingly.

Reviewer #2:

(1) The authors note a peak of PCNA+ Muller glia at 72 hours post injury. This is somewhat surprising as the MG would be expected to generate progenitor cells that would continue proliferating and stain with PCNA. Indeed, only a handful of PCNA+ cells are seen in the INL/ONL layer in Figure 1E2 with few clusters of progenitors present. It would be helpful to stain with a Muller glia marker to confirm these PCNA+ cells are Muller glia. It's also curious that almost all the PCNA+ cells are in the dorsal retina, even though G/R cone loss extends across both dorsal and ventral retina. Is this typical for cone ablation models in larval zebrafish?

We thank the reviewer for their insightful comment regarding the spatial distribution and identity of PCNA⁺ cells following injury.

In our study, we observed that the injury-induced proliferating cells (PCNA⁺) were predominantly located in the central and dorsal regions of the retina at 72 hours post-injury (hpi) (Figure 1E). To verify the identity of these proliferating cells, we performed additional immunostaining using BLBP, and confirmed that the majority of PCNA⁺ cells also express BLBP (Figure 1–figure supplement 1B in our revised Data), these results supporting their MG origin.

The regional bias of MG proliferation towards the central and dorsal retina is consistent with previous findings. Notably, (Krylov et al., 2023) demonstrated that MG exhibit region-specific heterogeneity in their regenerative responses to photoreceptor ablation. Their study identified proliferative MG subpopulations predominantly in the central (fgf24-expressing) and dorsal (efnb2a-expressing) domains, whereas ventral MG showed limited proliferative capacity (Krylov et al., 2023). These observations provide a plausible explanation for the spatially restricted PCNA⁺ MG population observed in our model following cone ablation.

(2) In Line 148: What is meant by "most original MG states" in this context? Original meaning novel? Or original meaning the earliest state MG adopted following injury? The language here is confusing.

We thank the reviewer for pointing out the ambiguous phrasing in our original manuscript. The term “most original MG states” was imprecise and misleading, as it could be interpreted as referring to the quiescent state of MG. In our context, we intended to describe the earliest transitional states in MG respond to injury, as they begin to exit quiescence and enter reactive characteristics. These early transitional MG populations co-express quiescent markers such as cx43 and early reactive markers gfap, as shown in Figure 1H.

To avoid confusion and improve conceptual clarity, we have revised the manuscript by replacing “most original MG states” with “early transitional MG state” (revised line 154) and have added a clearer explanation in the corresponding Results section to define this population more accurately.

(3) Perhaps provide a better image in Figure 2A of the cxcl18b at 48 hpi and 72 hpi. The current images appear virtually identical, with very little cxcl18b expression observed, especially compared to the 24 hpi. This is in contrast to the Tg(cxcl18b:GFP) transgenic line shown in Figure 2D, which indicates either much higher expression in proliferating cells at 48 hpi or the stability of GFP protein. Can the authors provide guidance on the accurate temporal expression of cxcl18b? Does expression peak rapidly at 24 hpi and then rapidly decline or is there persistence of expression to 48-72 hpi?

We appreciate the reviewer’s careful observation regarding the apparent similarity of cxcl18b expression at 48 hpi and 72 hpi in the in situ hybridization (ISH) images (Figure 2A), and the differences compared to the Tg(cxcl18b: GFP) reporter line shown in Figure 2D.

(1) The similarity of ISH images at the 48 hpi and 72 hpi (Figure 2A):

The cxcl18b mRNA signal peaked at 24 hpi, suggesting a rapid transcriptional response after retina injury. By 48 hpi, cxcl18b expression had already declined substantially, and by 72 hpi, the signal was further reduced to near-background levels. This temporal expression pattern explains why the ISH images at 48 hpi and 72 hpi appear nearly identical and much weaker compared to 24 hpi.

(2) The discrepancy between ISH and GFP reporter signal (Figure 2D):

The Tg(cxcl18b: GFP) reporter line shows persistent GFP expression beyond the transcriptional window of cxcl18b mRNA. This may be due to the prolonged stay of GFP protein, which remains detectable even after the endogenous transcription of cxcl18b has diminished. This explanation is also noted in the manuscript (revised lines 198–200). As a result, GFP⁺ MG cells are still visible at 48–72 hpi, and some of them co-label with PCNA.

These findings are consistent with our Pseudotime analysis based on scRNA-seq data (Figure 1H), which shows that cxcl18b expression precedes the induction of proliferative markers such as pcna and ascl1a.

(4) Line 198: The establishment of the Tg(cxcl18b:Cre-vhmc:mcherry::ef1a:loxP-dsRed-loxP-EGFP::lws2:nfsb-mCherry) is considerable but the nomenclature doesn't properly fit. Is the mCherry fused with Cre and driven by the cxcl18b promoter? What is the vhmc component? Finally, while this may provide the ability to clonally track cxcl18b-expressing MG, it does not address the prior question of what is the actual temporal expression of cxcl18b? If anything, this only addresses whether proliferating MG expressed cxcl18b at some point in their history, but does not indicate that cxcl18b expression co-exists in proliferating cells. The most convincing evidence is in Supplemental Figure 2B.

The "vmhc" component refers to the ventricular myosin heavy chain promoter, commonly used to label atrial cardiomyocytes (Jin et al., 2009). We cloned the vmhc upstream region containing its promoter and fusing with mCherry for selection during transgenic fish line construction.

Clone analysis using the Tg(cxcl18b: Cre-vmhc: mCherry::ef1a: loxP-DsRed-loxP-EGFP::lws2: nfsb-mCherry) further indicates that cxcl18b-defined the transitional state is the essential routing for MG proliferation. We have clarified in the revised text that this lineage tracing indicates a “history of injury-induced cxcl18b expression” rather than its ongoing expression during proliferation (revised line 205).

(5) Line 203: The data shown in Figure 2F do not indicate that these MG are cxcl18b+. Rather, the data are consistent with the interpretation that these MG expressed Cre at some prior stage and now express GFP from the ef1a promoter rather than DsRed. Whether these MG continue to express cxcl18b at the time these fish were collected is not addressed by these data. It is not accurate to conclude that these cells are cxcl18b+.

We thank the reviewer for pointing out this important issue. We agreed that the GFP⁺ MG shown in Figure 2F represents cells that have previously expressed cxcl18b and thus belong to the cxcl18b-expressing cell lineage, but this does not indicate that they continue to express cxcl18b at the time of sample collection. Performing clonal analysis using the Cre-loxp system, the GFP signal reflects historical cxcl18b promoter activity rather than ongoing transcription. We have revised the relevant sentence in our manuscript to clarify this point and now refer to these GFP⁺ cells as "cxcl18b lineage-traced MG" rather than "cxcl18b⁺ MG" to avoid any misinterpretation (revised line 207).

(6) Line 213: The statement that proliferative MG mostly originated from cxcl18b+ MG transitional states is a conclusion that does appear fully supported by the data. Whether those MG continue to express cxcl18b remains unanswered by the data in Figure 2 and would likely be inconsistent with the single-cell data in Figure 1.

We thank the reviewer for this valuable comment. We agree that the original statement on Line 213 regarding the lineage relationship between cxcl18b⁺ transitional MG and proliferative MG required clarification.

(1) The cxcl18b expression dynamics:

Our single-cell RNA-seq and ISH analyses consistently show that cxcl18b expression peaks as early as 24 hpi and declines rapidly, with significantly reduced expression by 48 and 72 hpi. These findings suggest that cxcl18b marks an early transitional MG state, rather than being maintained in proliferative MG. Indeed, in our scRNA-seq pseudotime trajectory analysis (Figure 1H), cxcl18b expression is highest in early transitional MG clusters (Clusters 1) and downregulated as cells progress toward proliferative states (Clusters 3/6), supporting a model in which cxcl18b is downregulated before cell-cycle re-entry.

(2) Prolonged stability of GFP protein:

The GFP signal observed in Tg(cxcl18b: GFP) retinas at 72 hpi may be because of the prolonged stability of GFP protein, rather than sustained cxcl18b transcription. The actual expression dynamics of cxcl18b are more directly reflected by our in situ hybridization and single-cell RNA-seq data, both showing a rapid decline after its early peak at 24 hpi. This explanation is also noted in the manuscript (revised lines 196–197).

(7) Line 246: The use of Dexamethasone to block inflammation is a widely used approach. However, dexamethasone is a broad-spectrum anti-inflammatory molecule that works through glucocorticoid signaling that may involve more than microglia. The observation that microglia recruitment and cxcl18a expression are both reduced is correlative but does not prove causation. Thus, the data are not sufficient to conclude that microglia-mediated inflammation is critical for activating cxcl18b expression. Indeed, data in Figure 1 indicate that cxcl18b expression occurs prior to microglia migration to the ONL.

We thank the reviewer for this thoughtful and important comment. We fully acknowledge that dexamethasone is a broad-spectrum anti-inflammatory agent that acts via glucocorticoid receptor signaling and may influence multiple immune and non-immune pathways beyond microglia.

In our study, dexamethasone treatment led to a reduction in both microglial recruitment and the number of cxcl18b⁺ MG at 72 hpi, suggesting a potential association between inflammation and cxcl18b activation. However, we agree that this observation remains correlative and is not sufficient to establish a direct link between microglia activity and cxcl18b induction. Our time-course analysis indicates that cxcl18b expression peaks at 24 hpi, preceding robust microglial accumulation in the ONL, further highlighting the need to clarify the temporal dynamics and cellular sources of inflammatory cues.

To address this question more conclusively, selective ablation of microglia during cone injury would be necessary. However, implementing such an approach would require a complex intersection of three transgenic lines—Tg(mpeg1: nfsB-mCherry) for microglia ablation, Tg(lws2: nfsB-mCherry) for cone ablation, and Tg(cxcl18b: GFP) for reporting—posing substantial genetic and experimental challenges.

We have revised the Results section accordingly to state: “These findings suggest that microglia-mediated inflammation may contribute to the activation of cxcl18b-defined transitional states that precede MG proliferation, although a causal relationship remains to be established.” (revised lines 251–253). We also added a new paragraph in the “Result: Clonal analysis reveals injury-induced MG proliferation via cxcl18b-defined transitional states associated with inflammation” as “While dexamethasone suppressed both microglial recruitment and cxcl18b⁺ MG generation, its broad anti-inflammatory action precludes definitive conclusions about microglial causality. Dissecting this relationship would require concurrent ablation of microglia and cone photoreceptors using a triple-transgenic strategy, which is beyond the scope of the current study. Targeted approaches will be necessary to resolve the specific role of microglia in initiating cxcl18b expression.” (revised lines 251–258) to explicitly acknowledge this limitation and the need for future studies using microglia-specific ablation models to resolve the mechanism.

(8) Could the authors clarify the basis of investigating NO signaling, given the relative expression of the genes by either cxcl18b+ MG or uninjured MG? Based on the expression illustrated in Supplemental Figure 4A, there is almost no expression of nos1 or nos2b in any MG. The authors are encouraged to revisit the earlier single-cell data sets to identify those cells that express components of NO signaling to determine the source(s) of NO that could be impacting the Muller glia.

We thank the reviewer for raising these important points.

Nitric oxide (NO) signaling has been implicated in the regeneration of multiple zebrafish tissues, including the heart (Rochon et al., 2020; Yu et al., 2024), spinal cord (Bradley et al., 2010), and fin (Matrone et al., 2021). Based on these findings, we hypothesized that NO signaling might also contribute to retinal regeneration.

As described in the manuscript, we compiled a redox-related gene list and systematically screened their roles in injury-induced MG proliferation using CRISPR-Cas9-mediated gene disruption. Among the candidates, disruption of nos genes significantly reduced the number of PCNA⁺ MG cells following G/R cone ablation (Figure 4), prompting us to further investigate the role of NO signaling.

(9) Line 319-320: this sentence appears to be missing text as "while no influenced across the nos mutants and gsnor mutants" does not make sense.

We appreciate the reviewer’s observation and agree that the original sentence was unclear. We have revised the sentence in the manuscript as follows:

“In contrast, no significant change in MG proliferation was observed in nos1, nos2a, or gsnor mutants compared to wild type (Figures 4F–4I)” (revised lines 326-328).

(10) Line 326-328: The text should be rewritten as the current meaning would suggest there was no significant loss of photoreceptors in the nos2b mutants. That is incorrect. Rather, there was no significant difference between WT and the nos2b mutants in the number of photoreceptors lost at 72 hpi following MTZ treatment. Both groups lost photoreceptors, but the number lost in nos2b hets and homozygotes was the same as WT.

We agree with the suggestion and have revised our manuscript. We have revised the sentence in the manuscript as follows:

“We observed no significant difference in the loss of cone photoreceptor at 72 hpi between nos2b mutants and WT, indicating that the reduced MG proliferation observed in nos2b mutants is independent of the injury (WT: 45 ± 8 remaining cones, n = 24; nos2b⁺/⁻: 49 ± 12, n = 20; nos2b⁻/⁻: 46 ± 9, n = 20; mean ± SEM) (Figure 4K).” (revised lines 331-335).

(11) There is concern over the inconsistencies with some of the data. In Figure 4, Supplement 1A, the single-cell data found virtually no expression of nos2b in either uninjured MG or cxcl18b+ MG. In contrast, the authors find nos2b expression by RT-PCR in the cxcl18b:GFP+ MG. The in situ expression of nos2b in Figure 5 - Supplement 1 is not persuasive. The red puncta are seen in a single cxcl18b:GFP+ cell but also in the plexiform layer and is other non cxcl18b:GFP+ cells.

We appreciate the concern regarding the apparent inconsistencies in nos2b expression across different datasets. We provide the following explanations:

(1) Low expression of nos2b in scRNA-seq data:

We propose a potential explanation: Nitric oxide (NO) signaling is known to exert its biological functions in a dose-dependent manner and is tightly regulated post-transcriptionally, especially in inducible nitric oxide synthase (iNOS) (Bogdan, 2001; Nathan and Xie, 1994; Thomas et al., 2008). Thus, even modest changes in nos2b expression may exert meaningful biological effects without producing strong transcriptional signals detectable by scRNA-seq, which could fall below the detection threshold of scRNA-seq methods. Supporting this idea, our functional assay (Figure 4J) reveals a clear concentration-dependent effect of NO on MG proliferation, consistent with the biological relevance of Nos2b activity despite its low transcript abundance.

(2) Regarding the in situ hybridization data:

We used both commercially available in situ hybridization probes from (HCR^TM) and RNAscope^TM (data not shown) to detect nos2b transcripts. While the nos2b signal was observed in other retinal cell types, including cells in the plexiform layer, our primary study was focused on examining its expression within the cxcl18b⁺ MG lineage.

(3) Regarding RT-PCR detection of nos2b in cxcl18b: GFP⁺ MG:

To enhance detection sensitivity, we enriched cxcl18b: GFP⁺ MG by FACS at 72 hpi and performed cDNA amplification before RT-PCR. This approach allowed the detection of low-abundance transcripts such as nos2b. It is also important to note that RT-PCR reflects fold changes in expression compared to MG to other retina cell type. The subtle but biologically upregulated of nos2b expression may not be readily captured by in situ hybridization or scRNA-seq.

(12) Line 356 - there is a disagreement over the interpretation of the current data. The statement that nos2b was specifically expressed in cxcl18b+ transitional MG states is not entirely accurate. This conclusion is based on expression of GFP from a cxcl18b promoter, which may reflect persistence of the GFP protein and not evidence of cxcl18b expression. Even assuming that the nos2b in situ hybridization and RT-PCR data are correct, the data would indicate that nos2b is expressed in proliferating MG that are derived from the cxcl18b+ transitional states. The single-cell trajectory analysis in Figure 2 indicates that cxcl18b is not co-expressed with PCNA. Furthermore, the single-cell data in Figure 4, Supplement 1, indicates no expression of nos2b in cxcl18b+ MG. The authors need to reconcile these seemingly contradictory pieces of data.

We thank the reviewer for this thoughtful and important comment. We agree that clarification is needed to accurately interpret the relationship between cxcl18b, nos2b, and MG proliferation, particularly considering the different temporal and technical contexts of our datasets.

(1) Lineage labeling and interpretation of GFP expression:

We acknowledge that in the Tg(cxcl18b: Cre-vhmc: mcherry::ef1a: loxP-dsRed-loxP-EGFP::lws2: nfsb-mCherry) line, GFP expression reflects historical activity of the cxcl18b promoter, rather than ongoing transcription. This GFP signal, due to its prolonged stay, may persist beyond the time window of endogenous cxcl18b expression. Accordingly, we have revised the manuscript to replace “cxcl18b⁺ MG” with “cxcl18b⁺ lineage-traced MG” throughout the relevant sections to prevent potential misinterpretation.

(2) Functional experiments support a lineage relationship between cxcl18b⁺ states and nos2b activity:

To further investigate the regulatory relationship between cxcl18b and nos2b, we conducted NO scavenging experiments using C-PTIO in the Tg(cxcl18b: GFP) background. We observed that the generation of cxcl18b: GFP⁺ MG after injury was not affected by NO depletion, indicating that cxcl18b activation precedes NO signaling (data not shown). However, PCNA⁺ MG was significantly reduced under the same treatment, suggesting that NO signaling is not required for cxcl18b⁺ transitional state formation, but is necessary for proliferation. Together with our MG-specific nos2b knockout data, these results support a model in which nos2b-derived NO acts downstream of the cxcl18b⁺ transitional state to promote MG cell-cycle re-entry.

(3) The scRNA-seq data with nos2b expression:

We agree with the reviewer that our scRNA-seq dataset shows minimal overlap between cxcl18b and pcna expression, which is consistent with our interpretation that cxcl18b expression marks a transitional phase before cell-cycle entry. Furthermore, nos2b transcripts were not robustly detected in cxcl18b⁺ MG clusters in our scRNA dataset. This discrepancy may be caused by technical limitations of scRNA-seq in capturing low-abundance or transient transcripts such as nos2b, as discussed in response to comment #11.

(13) The data in Figure 7 are interesting and suggest a link between NO signaling and notch activity. The use of the C-PTIO NO scavenger is not specific to MG, which limits the conclusions related to autocrine NO signaling in cxcl18b+ MG.

We acknowledge that the use of C-PTIO cannot distinguish between NO signaling within MG and paracrine effects from other retinal cells. Currently, technical limitations prevent MG-specific NO depletion. We have discussed this limitation accordingly in our revised “Limitations of this study” section (revised lines 540-545: “2. While our data suggest that injury-induced NO suppresses Notch signaling activation and promotes MG proliferation, the use of a general NO scavenger (C-PTIO) does not allow us to determine whether this regulation occurs in an autocrine or paracrine manner. The specific role of NO signaling within cxcl18b⁺ MG requires further validation using MG-specific NO depletion.”)

(14) Line 446-448. As mentioned before, the data do not support a causative link between microglia recruitment and cxcl18b induction. More specifically, dexamethasone is a broad-spectrum anti-inflammatory drug that blocks microglia activation and recruitment. Critically, the authors demonstrate that expression of cxcl18b occurs prior to microglia recruitment (see Figure 1, Supplement 1). Thus, the statement that cxcl18b induction depends on microglia recruitment is not accurate.

We thank the reviewer for reiterating this important point. We fully agree that the current data do not support a direct causal relationship between microglia recruitment and cxcl18b induction. As also addressed in our response to Comment 7, dexamethasone, as a broad-spectrum anti-inflammatory agent, cannot distinguish microglia-specific effects from those of other immune components. We have revised the text in revised lines 251–258 to clarify that microglia-mediated inflammation is associated with—but not required for—activation of cxcl18b-defined transitional MG states.

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eLife Assessment

This important study focuses on the molecular mechanisms underlying the generation of neuronal diversity. Taking advantage of a well-defined neuroblast lineage in Drosophila, the authors provide convincing evidence that two transcription factors of the conserved forkhead box (FOX) family provide a mechanistic link between transient spatial cues that initially specify neuroblast identity and terminal selector genes that define post-mitotic neuron identity. The findings will be of interest to developmental neurobiologists.

Reviewer #1 (Public review):

Summary:

Lai and Doe address the integration of spatial information with temporal patterning and genes that specify cell fate. They identify the Forkhead transcription factor Fd4 as a lineage-restricted cell fate regulator that bridges transient spatial transcription factors to terminal selector genes in the developing Drosophila ventral nerve cord. The experimental evidence convincingly demonstrates that Fd4 is both necessary for late-born NB7-1 neurons, but also sufficient to transform other neural stem cell lineages toward the NB7-1 identity. This work addresses an important question that will be of interest to developmental neurobiologists: How can cell identities defined by initial transient developmental cues be maintained in the progeny cells, even if the molecular mechanism remains to be investigated? In addition, the study proposes a broader concept of lineage identity genes that could be utilized in other lineages and regions in the Drosophila nervous system and in other species.

Strengths:

While the spatial factors patterning the neuroepithelium to define the neuroblast lineages in the Drosophila ventral nerve cord are known, these factors are sometimes absent or not required during neurogenesis. In the current work, Lai and Doe identified Fd4 in the NB7-1 lineage that bridges this gap and explains how NB7-1 neurons are specified after Engrailed (En) and Vnd cease their expression. They show that Fd4 is transiently co-expressed with En and Vnd and is present in all nascent NB7-1 progenies. They further demonstrate that Fd4 is required for later-born NB7-1 progenies and sufficient for the induction of NB7-1 markers (Eve and Dbx) while repressing markers of other lineages when force-expressed in neural progenitors, e.g., in the NB5-6 lineage and in the NB7-3 lineage. They also demonstrate that, when Fd4 is ectopically expressed in NB7-3 and NB5-6 lineages, this leads to the ectopic generation of dorsal muscle-innervating neurons. The inclusion of functional validation using axon projections demonstrates that the transformed neurons acquire appropriate NB7-1 characteristics beyond just molecular markers. Quantitative analyses are thorough and well-presented for all experiments.

Weaknesses:

(1) While Fd4 is required and sufficient for several later-born NB7-1 progeny features, a comparison between early-born (Hb/Eve) and later-born (Run/Eve) appears missing for pan-progenitor gain of Fd4 (with sca-Gal4; Figure 4) and for the NB7-3 lineage (Figure 6). Having a quantification for both could make it clearer whether Fd4 preferentially induces later-born neurons or is sufficient for NB7-1 features without temporal restriction.

(2) Fd4 and Fd5 are shown to be partially redundant, as Fd4 loss of function alone does not alter the number of Eve+ and Dbx+ neurons. This information is critical and should be included in Figure 3.

(3) Several observations suggest that lineage identity maintenance involves both Fd4-dependent and Fd4-independent mechanisms. In particular, the fact that fd4-Gal4 reporter remains active in fd4/fd5 mutants even after Vnd and En disappear indicates that Fd4's own expression, a key feature of NB7-1 identity, is maintained independently of Fd4 protein. This raises questions about what proportion of lineage identity features require Fd4 versus other maintenance mechanisms, which deserves discussion.

(4) Similarly, while gain of Fd4 induces NB7-1 lineage markers and dorsal muscle innervation in NB5-6 and NB7-3 lineages, drivers for the two lineages remain active despite the loss of molecular markers, indicating some regulatory elements retain activity consistent with their original lineage identity. It is therefore important to understand the degree of functional conversion in the gain-of-function experiments. Sparse labeling of Fd4 overexpressing NB5-6 and NB7-3 progenies, as was done in Seroka and Doe (2019), would be an option.

(5) The less-penetrant induction of Dbx+ neurons in NB5-6 with Fd4-overexpression is interesting. It might be worth the authors discussing whether it is an Fd4 feature or an NB5-6 feature by examining Dbx+ neuron number in NB7-3 with Fd4-overexpression.

(6) It is logical to hypothesize that spatial factors specify early-born neurons directly, so only late-born neurons require Fd4, but it was not tested. The model would be strengthened by examining whether Fd4-Gal4-driven Vnd rescues the generation of later-born neurons in fd4/fd5 mutants.

(7) It is mentioned that Fd5 is not sufficient for the NB7-1 lineage identity. The observation is intriguing in how similar regulators serve distinct roles, but the data are not shown. The analysis in Figure 4 should be performed for Fd5 as supplemental information.

Reviewer #2 (Public review):

Summary:

Via a detailed expression analysis, they find that Fd4 is selectively expressed in embryonic NB7-1 and newly born neurons within this lineage. They also undertake a comprehensive genetic analysis to provide evidence that fd4 is necessary and sufficient for the identity of NB7-1 progeny.

Strengths:

The analysis is both careful and rigorous, and the findings are of interest to developmental neurobiologists interested in molecular mechanisms underlying the generation of neuronal diversity. Great care was taken to make the figures clear and accessible. This work takes great advantage of years of painstaking descriptive work that has mapped embryonic neuroblast lineages in Drosophila.

Weaknesses:

The argument that Fd4 is necessary for NB7-1 lineage identity is based on a Fd4/Fd5 double mutant. Loss of fd4 alone did not alter the number of NB7-1-derived Eve+ or Dbx+ neurons. The authors clearly demonstrate redundancy between fd4 and fd5, and the fact that the LOF analysis is based on a double mutant should be better woven through the text. The authors generated an Fd5 mutant. I assume that Fd5 single mutants do not display NB7-1 lineage defects, but this is not stated. The focus on Fd4 over Fd5 is based on its highly specific expression profile and the dramatic misexpression phenotypes. But the LOF analysis demonstrates redundancy, and the conclusions in the abstract and through the results should reflect the existence of Fd5 in the conclusions of this manuscript.

It is notable that Fd4 overexpression can rewire motor circuits. This analysis adds another dimension to the changes in transcription factor expression and, importantly, demonstrates functional consequences. Could the authors test whether U4 and U5 motor axon targeting changes in the fd4/fd5 double mutant? To strengthen claims regarding the importance of fd4/fd5 for lineage identity, it would help to address terminal features of U motorneuron identity in the LOF condition.

Reviewer #3 (Public review):

The goal of the work is to establish the linkage between the spatial transcription factors (STFs) that function transiently to establish the identities of the individual NBs and the terminal selector genes (typically homeodomain genes) that appear in the newborn post-mitotic neurons. How is the identity of the NB maintained and carried forward after the spatial genes have faded away? Focusing on a single neuroblast (NB 7-1), the authors present evidence that the fork-head transcription factor, fd4, provides a bridge linking the transient spatial cues that initially specified neuroblast identity with the terminal selector genes that establish and maintain the identity of the stem cell's progeny.

The study is systematic, concise, and takes full advantage of 40+ years of work on the molecular players that establish neuronal identities in the Drosophila CNS. In the embryonic VNC, fd4 is expressed only in the NB 7-1 and its lineage. They show that Fd4 appears in the NB while the latter is still expressing the Spatial Transcription Factors and continues after the expression of the latter fades out. Fd4 is maintained through the early life of the neuronal progeny but then declines as the neurons turn on their terminal selector genes. Hence, fd4 expression is compatible with it being a bridging factor between the two sets of genes.

Experimental support for the "bridging" role of Fd4 comes from a set of loss-of-function and gain-of-function manipulations. The loss of function of Fd4, and the partially redundant gene Fd5, from lineage 7-1 does not affect the size of the lineage, but terminal markers of late-born neuronal phenotypes, like Eve and Dbx, are reduced or missing. By contrast, ectopic expression of fd4, but not fd5, results in ectopic expression of the terminal markers eve and Dbx throughout diverse VNC lineages.

A detailed test of fd4's expression was then carried out using lineages 7-3 and 5-6, two well-characterized lineages in Drosophila. Lineage 7-3 is much smaller than 7-1 and continues to be so when subjected to fd4 misexpression. However, under the influence of ectopic Fd4 expression, the lineage 7-3 neurons lost their expected serotonin and corazonin expression and showed Eve expression as well as motoneuron phenotypes that partially mimic the U motoneurons of lineage 7-1.

Ectopic expression of Fd4 also produced changes in the 5-6 lineage. Expression of apterous, a feature of lineage 5-6, was suppressed, and expression of the 7-1 marker, Eve, was evident. Dbx expression was also evident in the transformed 5-6 lineages, but extremely restricted as compared to a normal 7-1 lineage. Considering the partial redundancy of fd4 and fd5, it would have been interesting to express both genes in the 5-6 lineage. The anatomical changes that are exhibited by motoneurons in response to Fd4 expression confirm that these cells do, indeed, show a shift in their cellular identity.

Author response:

Reviewer #1 (Public Review):

Lai and Doe address the integration of spatial information with temporal patterning and genes that specify cell fate. They identify the Forkhead transcription factor Fd4 as a lineage-restricted cell fate regulator that bridges transient spatial transcription factors to terminal selector genes in the developing Drosophila ventral nerve cord. The experimental evidence convincingly demonstrates that Fd4 is both necessary for lateborn NB7-1 neurons, but also sufficient to transform other neural stem cell lineages toward the NB7-1 identity. This work addresses an important question that will be of interest to developmental neurobiologists: How can cell identities defined by initial transient developmental cues be maintained in the progeny cells, even if the molecular mechanism remains to be investigated? In addition, the study proposes a broader concept of lineage identity genes that could be utilized in other lineages and regions in the Drosophila nervous system and in other species. 

Thanks for the accurate summary and positive comments!

While the spatial factors patterning the neuroepithelium to define the neuroblast lineages in the Drosophila ventral nerve cord are known, these factors are sometimes absent or not required during neurogenesis. In the current work, Lai and Doe identified Fd4 in the NB7-1 lineage that bridges this gap and explains how NB7-1 neurons are specified after Engrailed (En) and Vnd cease their expression. They show that Fd4 is transiently co-expressed with En and Vnd and is present in all nascent NB7-1 progenies. They further demonstrate that Fd4 is required for later-born NB7-1 progenies and sufficient for the induction of NB7-1 markers (Eve and Dbx) while repressing markers of other lineages when force-expressed in neural progenitors, e.g., in the NB56 lineage and in the NB7-3 lineage. They also demonstrate that, when Fd4 is ectopically expressed in NB7-3 and NB5-6 lineages, this leads to the ectopic generation of dorsal muscle-innervating neurons. The inclusion of functional validation using axon projections demonstrates that the transformed neurons acquire appropriate NB7-1 characteristics beyond just molecular markers. Quantitative analyses are thorough and well-presented for all experiments.

Thanks for the positive comments!

(1) While Fd4 is required and sufficient for several later-born NB7-1 progeny features, a comparison between early-born (Hb/Eve) and later-born (Run/Eve) appears missing for pan-progenitor gain of Fd4 (with sca-Gal4; Figure 4) and for the NB7-3 lineage (Figure 6). Having a quantification for both could make it clearer whether Fd4 preferentially induces later-born neurons or is sufficient for NB7-1 features without temporal restriction.

We quantified the percentage of Hb+ and Runt+ cells among Eve+ cells with sca-gal4, and the results are shown in Figure 4-figure supplement 1. We found that the proportion of early-born cells is slightly reduced but the proportion of later-born cells remain similar. Interestingly, we also found a subset of Eve+ cells with a mixed fate (Hb+Runt+) but the reason remains unclear.

(2) Fd4 and Fd5 are shown to be partially redundant, as Fd4 loss of function alone does not alter the number of Eve+ and Dbx+ neurons. This information is critical and should be included in Figure 3.

Because every hemisegment in an fd4 single mutant is normal, we just added it as the following text: “In fd4 mutants, we observe no change in the number of Eve+ neurons or Dbx+ neurons (n=40 hemisegments).”

(3) Several observations suggest that lineage identity maintenance involves both Fd4dependent and Fd4-independent mechanisms. In particular, the fact that fd4-Gal4 reporter remains active in fd4/fd5 mutants even after Vnd and En disappear indicates that Fd4's own expression, a key feature of NB7-1 identity, is maintained independently of Fd4 protein. This raises questions about what proportion of lineage identity features require Fd4 versus other maintenance mechanisms, which deserves discussion.

We agree, thanks for raising this point. We add the following text to the Discussion. “Interestingly, the fd4 fd5 mutant maintains expression of fd4:gal4, suggesting that the fd4/fd5 locus may have established a chromatin state that allows “permanent” expression in the absence of Vnd, En, and Fd4/Fd5 proteins.”

(4) Similarly, while gain of Fd4 induces NB7-1 lineage markers and dorsal muscle innervation in NB5-6 and NB7-3 lineages, drivers for the two lineages remain active despite the loss of molecular markers, indicating some regulatory elements retain activity consistent with their original lineage identity. It is therefore important to understand the degree of functional conversion in the gain-of-function experiments. Sparse labeling of Fd4 overexpressing NB5-6 and NB7-3 progenies, as was done in Seroka and Doe (2019), would be an option.

We agree it is interesting that the NB7-3 and NB5-6 drivers remain on following Fd4 misexpression. To explore this, we used sca-gal4 to overexpress Fd4 and observed that Lbe expression persisted while Eg was largely repressed (see Author response image 1 below). The results show that Lbe and Eg respond differently to Fd4. A non-mutually exclusive possibility is that the continued expression of lbe-Gal4 UAS-GFP or eg-Gal4 UAS-GFP may be due to the lengthy perdurance of both Gal4 and GFP.

Author response image 1.

(5) The less-penetrant induction of Dbx+ neurons in NB5-6 with Fd4-overexpression is interesting. It might be worth the authors discussing whether it is an Fd4 feature or an NB56 feature by examining Dbx+ neuron number in NB7-3 with Fd4-overexpression.

In the NB7-3 lineages misexpressing Fd4, only 5 lineages generated Dbx+ cells (0.1±0.4, n=64 hemisegments), suggesting that the low penetrance of Dbx+ induction is an intrinsic feature of Fd4 rather than lineage context. We have added this information in the results section. 

(6) It is logical to hypothesize that spatial factors specify early-born neurons directly, so only late-born neurons require Fd4, but it was not tested. The model would be strengthened by examining whether Fd4-Gal4-driven Vnd rescues the generation of laterborn neurons in fd4/fd5 mutants.

When we used en-gal4 driver to express UAS-vnd in the fd4/fd5 mutant background, we found an average 7.4±2.2 Eve+ cells per hemisegment (n=36), significantly higher than fd4/fd5 mutant alone (3.9±0.8 cells, n=52, p=2.6x10<sup.-11</sup>) (Figure 3J). In addition, 0.2±0.5 Eve+ cells were ectopic Hb+ (excluding U1/U2), indicating that Vnd-En integration is sufficient to generate both early-born and late-born Eve+ cells in the fd4/fd5 mutants. We have added the results to the text.

(7) It is mentioned that Fd5 is not sufficient for the NB7-1 lineage identity. The observation is intriguing in how similar regulators serve distinct roles, but the data are not shown. The analysis in Figure 4 should be performed for Fd5 as supplemental information.

Thanks for the suggestion. Because the results are exactly the same as the wild type, we don’t think it is necessary to provide an additional images or analysis as supplemental information.

Reviewer #2 (Public review):

Via a detailed expression analysis, they find that Fd4 is selectively expressed in embryonic NB7-1 and newly born neurons within this lineage. They also undertake a comprehensive genetic analysis to provide evidence that fd4 is necessary and sufficient for the identity of NB7-1 progeny. 

Thanks for the accurate summary!

The analysis is both careful and rigorous, and the findings are of interest to developmental neurobiologists interested in molecular mechanisms underlying the generation of neuronal diversity. Great care was taken to make the figures clear and accessible. This work takes great advantage of years of painstaking descriptive work that has mapped embryonic neuroblast lineages in Drosophila. 

Thanks for the positive comments!

The argument that Fd4 is necessary for NB7-1 lineage identity is based on a Fd4/Fd5 double mutant. Loss of fd4 alone did not alter the number of NB7-1-derived Eve+ or Dbx+ neurons. The authors clearly demonstrate redundancy between fd4 and fd5, and the fact that the LOF analysis is based on a double mutant should be better woven through the text.

The authors generated an Fd5 mutant. I assume that Fd5 single mutants do not display NB7-1 lineage defects, but this is not stated. The focus on Fd4 over Fd5 is based on its highly specific expression profile and the dramatic misexpression phenotypes. But the LOF analysis demonstrates redundancy, and the conclusions in the abstract and through the results should reflect the existence of Fd5 in the conclusions of this manuscript.

We agree, and have added new text to clarify the single mutant phenotypes (there are none) and the double mutant phenotype (loss of NB7-1 molecular and morphological features. The following text is added to the manuscript: “Not surprisingly, we found that fd4 single mutants or fd5 single mutants had no phenotype (Eve+ neurons were all normal). Thus, to assess their roles, we generated a fd4 and fd5 double mutant. Because many Eve+ and Dbx+ cells are generated outside of NB7-1 lineage, it was also essential to identify the Eve+ or Dbx+ cells within NB7-1 lineage in wild type and fd4 mutant embryos. To achieve this, we replaced the open reading frame of fd4 with gal4 (called fd4-gal4) (see Methods); this stock simultaneously knocked out both fd4 and fd5 (called fd4/fd5 mutant hereafter) while specifically labeling the NB7-1 lineage. For the remainder of this paper we use the fd4/fd5 double mutant to assay for loss of function phenotypes.”

It is notable that Fd4 overexpression can rewire motor circuits. This analysis adds another dimension to the changes in transcription factor expression and, importantly, demonstrates functional consequences. Could the authors test whether U4 and U5 motor axon targeting changes in the fd4/fd5 double mutant? To strengthen claims regarding the importance of fd4/fd5 for lineage identity, it would help to address terminal features of U motorneuron identity in the LOF condition.

Thanks for raising this important point. We examined the axon targeting on body wall muscles in both wild type and in fd4/fd5 mutant background and added the results in Figure 3-figure supplement 2. We found that the axon targeting in the late-born neuron region (LL1) is significantly reduced, suggesting that the loss of late-born neurons in fd4/fd5 mutant leads to the absence of innervation of corresponding muscle targets.

Reviewer #3 (Public review):

The goal of the work is to establish the linkage between the spatial transcription factors (STFs) that function transiently to establish the identities of the individual NBs and the terminal selector genes (typically homeodomain genes) that appear in the newborn postmitotic neurons. How is the identity of the NB maintained and carried forward after the spatial genes have faded away? Focusing on a single neuroblast (NB 7-1), the authors present evidence that the fork-head transcription factor, fd4, provides a bridge linking the transient spatial cues that initially specified neuroblast identity with the terminal selector genes that establish and maintain the identity of the stem cell's progeny. 

Thanks for the positive comments!

The study is systematic, concise, and takes full advantage of 40+ years of work on the molecular players that establish neuronal identities in the Drosophila CNS. In the embryonic VNC, fd4 is expressed only in the NB 7-1 and its lineage. They show that Fd4 appears in the NB while the latter is still expressing the Spatial Transcription Factors and continues after the expression of the latter fades out. Fd4 is maintained through the early life of the neuronal progeny but then declines as the neurons turn on their terminal selector genes. Hence, fd4 expression is compatible with it being a bridging factor between the two sets of genes. 

Thanks for the accurate summary!

Experimental support for the "bridging" role of Fd4 comes from a set of loss-of-function and gain-of-function manipulations. The loss of function of Fd4, and the partially redundant gene Fd5, from lineage 7-1 does not aoect the size of the lineage, but terminal markers of late-born neuronal phenotypes, like Eve and Dbx, are reduced or missing. By contrast, ectopic expression of fd4, but not fd5, results in ectopic expression of the terminal markers eve and Dbx throughout diverse VNC lineages. 

Thanks for the accurate summary!

A detailed test of fd4's expression was then carried out using lineages 7-3 and 5-6, two well-characterized lineages in Drosophila. Lineage 7-3 is much smaller than 7-1 and continues to be so when subjected to fd4 misexpression. However, under the influence of ectopic Fd4 expression, the lineage 7-3 neurons lost their expected serotonin and corazonin expression and showed Eve expression as well as motoneuron phenotypes that partially mimic the U motoneurons of lineage 7-1.

Thanks for the positive comments!

Ectopic expression of Fd4 also produced changes in the 5-6 lineage. Expression of apterous, a feature of lineage 5-6, was suppressed, and expression of the 7-1 marker, Eve, was evident. Dbx expression was also evident in the transformed 5-6 lineages, but extremely restricted as compared to a normal 7-1 lineage. Considering the partial redundancy of fd4 and fd5, it would have been interesting to express both genes in the 5-6 lineage. The anatomical changes that are exhibited by motoneurons in response to Fd4 expression confirm that these cells do, indeed, show a shift in their cellular identity.

We appreciate the positive comments. We agree double misexpression of Fd4 and Fd5 might give a stronger phenotype (as the reviewer says) but the lack of this experiment does not change the conclusions that Fd4 can promote NB7-1 molecular and morphological aspects at the expense of NB5-6 molecular markers.

eLife Assessment

This study presents a valuable open-source and cost-effective method for automating the quantification of male aggression and courtship in Drosophila melanogaster. The work as presented provides solid evidence that the use of the behavioral setup that the authors designed - using readily available laboratory equipment and standardised high-performing classifiers they developed using existing software packages - accurately and reliably characterises social behavior in Drosophila. The work will be of interest to Drosophila neurobiologists and particularly to those working on male social behaviors.

Reviewer #1 (Public review):

The study introduces an open-source, cost-effective method for automating the quantification of male social behaviors in Drosophila melanogaster. It combines machine-learning based behavioral classifiers developed using JAABA (Janelia Automatic Animal Behavior Annotator) with inexpensive hardware constructed from off-the-shelf components. This approach addresses the limitations of existing methods, which often require expensive hardware and specialized setups. The authors demonstrate that their new "DANCE" classifiers accurately identify aggression (lunges) and courtship behaviors (wing extension, following, circling, attempted copulation, and copulation), closely matching manually annotated ground-truth data. Furthermore, DANCE classifiers outperform existing rule-based methods in accuracy. Finally, the study shows that DANCE classifiers perform as well when used with low-cost experimental hardware as with standard experimental setups across multiple paradigms, including RNAi knockdown of the neuropeptide Dsk and optogenetic silencing of dopaminergic neurons.

The authors make creative use of existing resources and technology to develop an inexpensive, flexible, and robust experimental tool for the quantitative analysis of Drosophila behavior. A key strength of this work is the thorough benchmarking of both the behavioral classifiers and the experimental hardware against existing methods. In particular, the direct comparison of their low-cost experimental system with established systems across different experimental paradigms is compelling. A weakness of the study is that the use of JAABA-based classifiers to analyze aggression and courtship is not novel (Tao et al., J. Neurosci., 2024; Sten et al., Cell, 2023; Chiu et al., Cell, 2021; Isshi et al., eLife, 2020; Duistermars et al., Neuron, 2018). However, the demonstration the JAABA classifiers they developed work as well without expensive experimental hardware opens the door to more low-cost systems for quantitative behavior analysis.

In summary, this work provides a practical and accessible approach to quantifying Drosophila behavior, reducing the economic barriers to the study of the neural and molecular mechanisms underlying social behavior.

Reviewer #2 (Public review):

Summary:

This manuscript addresses the development of a low-cost behavioural setup and standardised open-source high performing classifiers for aggression and courtship behaviour. It does so by using readily available laboratory equipment and previously developed software packages. By comparing the performance of the setup and the classifiers to previously developed ones, this study shows the classifier's overperformance and the reliability of the low-cost setup in recapitulating previously described effects of different manipulations on aggression and courtship.

Strengths:

The newly developed classifiers for lunges, wing extension, attempted copulation, copulation, following, circling, perform better than previously available developed ones. The behavioural setup developed is low cost and reliably allows analysis of both aggression and courtship behaviour, validated through social experience manipulation (social isolation), gene knock (Dsk in Dilp2 neurons) and neuronal inactivation (dopaminergic neurons) know to affect courtship and aggression.

Weaknesses:

This framework only encompasses analysis of lunges, while aggression encompasses multiple behaviours. Even though DANCE can serve as a template allowing future development of additional classifiers, the current study compares performance to CADABRA which analyses further aggression behaviours, making the comparisons incomplete.

Reviewer #3 (Public review):

The study by Yadav et al. describes a new setup to quantify a number of aggression and mating behaviors in Drosophila melanogaster. The investigation of these behaviors requires the analysis of large number of videos to identify each kind of behavior displayed by a fly. Several approaches to automatize this process have been published before, but each of them has their limitations. The authors set out to develop a new setup that includes a very low-cost, easy to acquire hardware and open-source machine-learning classifiers to identify and quantify the behavior.

Strengths:

(1) The study demonstrates that their cheap, simple, and easy to obtain hardware works just as well as custom-made, specialized hardware for analyzing aggression and mating behavior. This enables the setup to be used in a wide range of settings, from research with limited resources to classroom teaching.

(2) The authors used previously published software to train new classifiers for detecting a range of behaviors related to aggression and mating and make them freely available. The classifiers are very positively benchmarked against a manually acquired ground-truth as well as existing algorithms.

(3) The study demonstrates the applicability of the setup (hardware and classifiers) to common methods in the field by confirming a number of expected phenotypes with their setup.

Taken together, this work can greatly facilitate research of aggression and mating in Drosophila. The combination of low-cost, off-the-shelf hardware and open-source, robust software enables researchers with very little funding or technical expertise to contribute to the scientific process, and also allows large-scale experiments, for example, in classroom teaching with many students, or for systematic screenings.

Author response:

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

Reviewer #1 (Public review):

The study introduces an open-source, cost-effective method for automating the quantification of male social behaviors in Drosophila melanogaster. It combines machine-learning-based behavioral classifiers developed using JAABA (Janelia Automatic Animal Behavior Annotator) with inexpensive hardware constructed from off-the-shelf components. This approach addresses the limitations of existing methods, which often require expensive hardware and specialized setups. The authors demonstrate that their new "DANCE" classifiers accurately identify aggression (lunges) and courtship behaviors (wing extension, following, circling, attempted copulation, and copulation), closely matching manually annotated groundtruth data. Furthermore, DANCE classifiers outperform existing rule-based methods in accuracy. Finally, the study shows that DANCE classifiers perform as well when used with low-cost experimental hardware as with standard experimental setups across multiple paradigms, including RNAi knockdown of the neuropeptide Dsk and optogenetic silencing of dopaminergic neurons.

The authors make creative use of existing resources and technology to develop an inexpensive, flexible, and robust experimental tool for the quantitative analysis of Drosophila behavior. A key strength of this work is the thorough benchmarking of both the behavioral classifiers and the experimental hardware against existing methods. In particular, the direct comparison of their low-cost experimental system with established systems across different experimental paradigms is compelling.

While JAABA-based classifiers have been previously used to analyze aggression and courtship (Tao et al., J. Neurosci., 2024; Sten et al., Cell, 2023; Chiu et al., Cell, 2021; Isshi et al., eLife, 2020; Duistermars et al., Neuron, 2018), the demonstration that they work as well without expensive experimental hardware opens the door to more low-cost systems for quantitative behavior analysis.

We thank the reviewer for their positive assessment and constructive suggestions. We have cited these additional JAABA studies in the Introduction. We clarified that several prior JAABA-based classifiers were developed using specialized machinevision cameras or custom setups, and that in some cases the original code and classifiers were not made publicly available, which limits reproducibility and wider adoption. To address this, we explicitly note in the revised manuscript that DANCE was developed with accessibility in mind.

Although the study provides a detailed evaluation of DANCE classifier performance, its conclusions would be strengthened by a more comprehensive analysis. The authors assess classifier accuracy using a bout-level comparison rather than a frame-level analysis, as employed in previous studies (Kabra et al., Nat Methods, 2013). They define a true positive as any instance where a DANCE-detected bout overlaps with a manually annotated ground-truth bout by at least one frame. This criterion may inflate true positive rates and underestimate false positives, particularly for longer-duration courtship behaviors. For example, a 15-second DANCE-classified wing extension bout that overlaps with ground truth for only one frame would still be considered a true positive. A frame-level analysis performance would help address this possibility.

We thank the reviewer for raising this important point. Our original use of bout-level analysis followed existing literature (Duistermars et al., 2018; Ishii et al., 2020; Chiu et al., 2021; Tao et al., 2024; Hindmarsh Sten et al., 2025). While our lunge classifier already operates at the frame level, we have now performed additional frame-level evaluations for the duration based courtship classifiers. These analyses revealed only minor differences in precision, recall, and F1 scores compared with the original bout-level approach (see new Figure 5—Figure Supplement 3). Details of this analysis are now included in the Materials and Methods.

In summary, this work provides a practical and accessible approach to quantifying Drosophila behavior, reducing the economic barriers to the study of the neural and molecular mechanisms underlying social behavior.

We thank the reviewer for their encouraging comments and for recognizing the accessibility and practical value of our approach. We appreciate the constructive suggestions, which have helped strengthen the manuscript.

Reviewer #2 (Public review):

Summary:

This manuscript addresses the development of a low-cost behavioural setup and standardised open-source high-performing classifiers for aggression and courtship behaviour. It does so by using readily available laboratory equipment and previously developed software packages. By comparing the performance of the setup and the classifiers to previously developed ones, this study shows the classifier's overperformance and the reliability of the low-cost setup in recapitulating previously described effects of different manipulations on aggression and courtship.

Strengths:

The newly developed classifiers for lunges, wing extension, attempted copulation, copulation, following, and circling, perform better than available previously developed ones. The behavioural setup developed is low cost and reliably allows analysis of both aggression and courtship behaviour, validated through social experience manipulation (social isolation), gene knock (Dsk in Dilp2 neurons) and neuronal inactivation (dopaminergic neurons) known to affect courtship and aggression.

We thank the reviewer for the clear summary of our work and for highlighting its strengths. We appreciate these positive comments and suggestions, which have helped improve the clarity of the manuscript.

Weaknesses:

Aggression encompasses multiple defined behaviours, yet only lunges were analysed. Moreover, the CADABRA software to which DANCE was compared analyses further aggression behaviours, making their comparisons incomplete. In addition, though DANCE performs better than CADABRA and Divider in classifying lunges in the behavioural setup tested, it did not yield very high recall and F1 scores.

We thank the reviewer for raising this important point. We focused on lunges because they are widely used as a standard proxy for male aggression across multiple laboratories (Agrawal et al., 2020; Asahina et al., 2014; Chiu et al., 2021; Chowdhury et al., 2021; Dierick et al., 2007; Hoyer et al., 2008; Jung et al., 2020; Nilsen et al., 2004; Watanabe et al., 2017). As noted in the Discussion, our study also provides a template for the future development of additional aggression classifiers (fencing, wing flick, tussle, chase, female headbutt) and courtship classifiers (tapping, licking, rejection), which can be trained and shared through the same DANCE framework. Developing and validating these was beyond the scope of the present work.

To address the concern regarding precision, recall, and F1 scores, we performed additional analyses across all training videos and compiled these results in the new Figure 2—Figure Supplement 2. Our earlier lunge classifier had performance metrics obtained after training on a total of 11 videos. Our analysis shows performance metrics for classifiers trained on four independent datasets (Videos 8– 11). We found that the classifier trained on nine videos provided the best balance of precision, recall, and F1 (78.73%, 73.07%, and 75.79%, respectively), which was slightly better than the earlier classifier. We therefore updated the main figure, text, and Materials and Methods to use this version and uploaded the corresponding classifier and training details to the GitHub repository. 

DANCE is of limited use for neuronal circuit-level enquiries, since mechanisms for intensity and temporally controlled optogenetic manipulations, which are nowadays possible with open-source software and low-cost hardware, were not embedded in its development.

We thank the reviewer for this valuable point. The primary aim of DANCE is to provide an accessible, modular, and low-cost behavioural recording and analysis platform. It was designed so that users can readily integrate additional components such as optogenetic control when needed. As a proof of concept, we implemented optogenetic silencing of dopaminergic neurons using the DANCE hardware and confirmed that this manipulation increased aggression (Figure 7R). 

To facilitate adoption, we now provide schematic diagrams, LED control code, and instructions on our GitHub page and setup photographs in the manuscript (see new Figure 7—Figure Supplement 1). The released code allows programmable timing and intensity control, enabling users to reproduce temporally precise optogenetic protocols or extend the system for other stimulation paradigms.

Reviewer #3 (Public review):

The preprint by Yadav et al. describes a new setup to quantify a number of aggression and mating behaviors in Drosophila melanogaster. The investigation of these behaviors requires the analysis of a large number of videos to identify each kind of behavior displayed by a fly. Several approaches to automatize this process have been published before, but each of them has its limitations. The authors set out to develop a new setup that includes very low-cost, easy-to-acquire hardware and open-source machine-learning classifiers to identify and quantify the behavior.

Strengths:

(1) The study demonstrates that their cheap, simple, and easy-to-obtain hardware works just as well as custom-made, specialized hardware for analyzing aggression and mating behavior. This enables the setup to be used in a wide range of settings, from research with limited resources to classroom teaching.

(2) The authors used previously published software to train new classifiers for detecting a range of behaviors related to aggression and mating and to make them freely available. The classifiers are very positively benchmarked against a manually acquired ground truth as well as existing algorithms.

(3) The study demonstrates the applicability of the setup (hardware and classifiers) to common methods in the field by confirming a number of expected phenotypes with their setup.

We thank the reviewer for the positive assessment of our work and for highlighting its strengths. We appreciate these encouraging comments and suggestions, which have helped improve the clarity and presentation of the manuscript.

Weaknesses:

(1) When measuring the performance of the duration-based classifiers, the authors count any bout of behavior as true positive if it overlaps with a ground-truth positive for only 1 frame - despite the minimal duration of a bout is 10 frames, and most bouts are much longer. That way, true positives could contain cases that are almost totally wrong as long there was an overlap of a single frame. For the mating behaviors that are classified in ongoing bouts, I think performance should be evaluated based on the % of correctly classified frames, not bouts.

We thank the reviewer for raising this concern. In response to this point, and to Reviewer #1’s similar comment, we performed a frame-level evaluation of all duration-based courtship classifiers. The analysis revealed only minor differences compared with the original bout-level metrics (see new Figure 5—Figure Supplement 3), confirming the robustness of our classifiers. We have also added a description of this analysis in the Materials and Methods section.

(2) In the methods part, only one of the pre-existing algorithms (MateBook), is described. Given that the comparison with those algorithms is a so central part of the manuscript, each of them should be briefly explained and the settings used in this study should be described.

We thank the reviewer for this helpful suggestion. In the revised manuscript, we expanded the Materials and Methods to include concise descriptions and parameter settings for all pre-existing algorithms used for comparison. This includes dedicated subsections for CADABRA and the Divider assay, with explicit reference to their rulebased or geometric features. For MateBook, we specified the persistence filters used and the adjustments made for fair benchmarking. These changes ensure transparency and reproducibility.

Taken together, this work can greatly facilitate research on aggression and mating in Drosophila. The combination of low-cost, off-the-shelf hardware and open-source, robust software enables researchers with very little funding or technical expertise to contribute to the scientific process and also allows large-scale experiments, for example in classroom teaching with many students, or for systematic screenings.

We thank the reviewer for the encouraging comments and for recognizing the accessibility and broad applicability of DANCE. We believe these revisions have further strengthened the manuscript.

Reviewer #1 (Recommendations for the authors):

The following comments highlight areas where additional context, clarification, or further analysis could strengthen the manuscript. I hope these suggestions will be useful in refining your work.

(1) Lines 71-73: The authors state that Ctrax "leads to frequent identity switches among tracked flies, which is not the case while using FlyTracker." However, Ctrax was specifically designed to minimize identity errors, and Kabra et al. (2013) reported a low frequency of such errors-approximately one per five fly-hours in 10-fly videos. In contrast, Caltech FlyTracker does not correct identity errors automatically, requiring manual corrections, as noted in the Methods section of this study. If this is not an oversight, please provide further context to clarify this distinction.

We thank the reviewer for raising this clarification. As reported by Bentzur et al. (2021), when groups of flies were tracked simultaneously, Ctrax often generated multiple identities for the same individual, sometimes producing more trajectories than the actual number of flies. To prevent ambiguity, we revised the text to read: “While both Ctrax and FlyTracker (Eyjolfsdottir et al., 2014) may produce identity switches, when groups of flies were tracked simultaneously, Ctrax led to inaccuracies that required manual correction using specialized algorithms such as FixTrax (Bentzur et al., 2021).”  We also quantified FlyTracker identity-switch rates in our datasets and report them in new Supplementary File 5, confirming that such events were rare (< 2% of tracked intervals). We believe, this updated version provides the necessary context and ensures accuracy in describing each tracker’s limitations.

(2) Line 85: Providing additional context on how this study builds on previous work using JAABA-based classifiers for fly social behavior and comparing these classifiers to rule-based methods would more accurately situate it within the field. The authors state that "recently, a few JAABA-based classifiers have been developed for measuring aggression and courtship" and cite four related studies. However, this statement seems to underrepresent the use of JAABA-based classifiers for quantifying fly social behavior, which has become common in the field. Several additional studies (as noted in the public review) have developed JAABA-based classifiers for scoring aggression or courtship. Furthermore, other studies have compared the performance of JAABA-based classifiers with rule-based classifiers like CADABRA (e.g., Chowdhury et al., Comm Biology 2021; Leng et al., PlosOne 2020; Kabra et al., Nat Methods 2013). Mentioning the similar findings in those studies and your own helps strengthen the conclusion that machine-learning-based classifiers outperform rule-based classifiers in several experimental contexts.

We thank the reviewer for this helpful suggestion. We have revised the Introduction to include additional references to studies that applied JAABA-based classifiers for aggression and courtship and made textual edits to reflect this. We further noted that, unlike several previous studies, all DANCE classifiers and analysis code are publicly available.

Reviewer #2 (Recommendations for the authors):

(1) Suggestions for improved or additional experiments, data or analyses: As mentioned in the description of the effect of optogenetic inactivation of dopaminergic neurons, in the conclusion and also reported in the literature, there are other important identified aggression behaviours, such as fencing, wing flick, tussle, and chase. Similarly, for courtship, tapping and licking have also been defined. This study, as opposed to proposed future studies, would benefit from creating opensource classifiers for these established behaviours, which are important for the analysis of aggression and courtship.

We thank the reviewer for this valuable suggestion. As clarified in the Discussion, this manuscript intentionally focuses on six core, well-validated aggression and courtship behaviors to demonstrate DANCE’s modularity and reproducibility. Developing additional classifiers such as fencing, wing flick, tussle, chase, tapping, and licking would require extensive annotation and validation beyond the present scope. To address this point, we explicitly note in the revised text that the DANCE pipeline is readily extendable, allowing the community to build new classifiers within the same framework.

In terms of observer bias assessment for ground-truthing in courtship, this was only presented for circling and it would be beneficial to have encompassed all behaviours analysed.

We thank the reviewer for this suggestion. Observer-bias comparisons for all six classifiers are presented in Figure 2—Figure Supplement 1 (panels A–F). We clarified in the Results that annotations from two independent evaluators were compared for all classifiers, with no significant differences observed, confirming their robustness.

Finally, intensity and temporal optogenetic control are important for neuronal circuit analysis of underlying behaviour. The authors could embed this aspect in DANCE by integrating control of the green light LED strip used in this study using, for example, the open-source visual reactive programming software Bonsai (Lopes et al., 2015) and open-source electronics platform Arduino. This is an important and valuable addition in line with maintaining low cost.

We thank the reviewer for this valuable suggestion. DANCE was designed to be modular, allowing integration of temporal optogenetic control. To support immediate adoption, we now provide Arduino LED control code, setup schematics, and photographs (new Figure 7—Figure Supplement 1) along with step-by-step instructions on our GitHub page. We also note that Bonsai and Arduino frameworks are compatible with DANCE, enabling future extensions for closed-loop or behaviortriggered stimulation.

(2) Minor corrections to the text and figures:

Figure Supplement 1 refers only to Figure 2, yet panels D-F refer to the behaviour circling in courtship and therefore should be assigned to the respective figure.

Thanks, we have corrected this.

In lines 315-316, the cumbersome task of fluon coating for aggression assays seems to be ubiquitous across assays which is not the case, and therefore the sentence should include the word 'some'.

Thanks, we have edited this.

The cost of the phone and/or tablet should be included in the DANCE setup costs, as presumably these devices will be dedicated to the behavioural studies, for consistency purposes.

We thank the reviewer for this comment. We intentionally did not include smartphones or tablets in the setup cost because, in our experiments, these devices were not dedicated exclusively to DANCE but were repurposed from routine personal use. Our aim was to leverage readily available consumer electronics so that their cost does not become a barrier to adoption. We confirmed that commonly available Android phones capable of 30 fps at 1080p in H.264 format, as well as tablets or phones running a simple white-screen light app, are sufficient for reliable behavior classification and illumination. Since these devices can be returned to regular use after recordings, including their cost in the setup would not accurately reflect the intended accessibility of DANCE. For consistency, we now clarify in the Materials and Methods that such devices should be placed in airplane mode during recordings.

Reviewer #3 (Recommendations for the authors):

(1) For my taste, the authors put too much emphasis on the point that their method outperforms existing methods. I understand the value in comparing to published methods and it is of course fully justified to state the advantages of the new method. But the whole preprint is set up as a competition with the old algorithms, and the conclusion that the new classifier is better is repeated in each figure caption and after each paragraph of the results. This competitive mindset also extends to the selection of which results are presented as main figures and which as supplements - all cases in which the previous methods actually perform well are only presented in the supplement. I think this is simply unnecessary as the authors' results speak for themselves, and do not need the continuous competitive comparison.

We thank the reviewer for this thoughtful suggestion. Our intention was to benchmark DANCE rigorously against existing methods, not to frame the study competitively. We agree that repeated emphasis on relative performance was unnecessary. In the revised version, we streamlined figure captions and text throughout the manuscript to balance comparisons and removed redundant phrasing. Instances where other methods performed well are now presented with equal clarity to maintain a neutral and informative tone.

(2) When describing the DANCE hardware, as a reader I would find it interesting to also read about potential issues that the authors encountered. For example, how difficult is it to handle the materials without breaking or deforming them, which could affect the behavioral assays? How critical is it to use specific blister packs - the availability of which will likely vary strongly between countries? Did the authors try different sizes, and products? Such information, even as a supplement, could be very helpful for the widespread use of the hardware.

We thank the reviewer for this important point. To address this, we conducted additional tests comparing DANCE arenas of different diameters (new Figure 7— Figure Supplement 3A–C and new Figure 7—Figure Supplement 4A–L). We also consulted colleagues in multiple countries and verified that the blister packs used in our assays are readily available. The Materials and Methods now include practical handling notes: blister foils can be reused ~30–40 times for aggression assays and ~10–15 times for courtship assays before deformation. We also describe how to prevent agar surface damage during assembly and how to wash and dry the arenas for optimal reusability.

(3) I find the arrows pointing to several videos in a number of figures rather distracting and redundant, and suggest omitting them.

Thanks, we have omitted these arrows from all relevant figures and clarified the figure legends to enhance readability.

(4) P8, line 169 ff: this is a very long sentence that should be separated into several sentences.

We have rewritten this as follows: “DANCE scores remained comparable to groundtruth scores across all categories, whereas CADABRA and Divider underestimated the lunge counts (Figure 2B–E). Correlation analysis revealed a strong relationship between DANCE and ground-truth scores (Figure 2F, Supplementary File 2). In comparison, CADABRA and the Divider assay classifier showed a weaker correlation (Figure 2G-H, Supplementary File 2).”

(5) P10, line 216: please explain, here and in the methods, how these behavioral indices are calculated. I did not find this information anywhere in the paper.

We thank the reviewer for pointing this out. We now define the behavioral index explicitly in Materials and Methods: “For each assay, a behavioral index was calculated as the proportion of frames in which the male engaged in the specified behavior. This was obtained by dividing the total number of frames annotated for that behavior by the total number of frames in the recording.”

(6) P11, line 253: I don't understand the modifications to MateBook regarding attempted copulations, neither in the results nor the methods section. I would ask the authors to explain more explicitly what was done.

We thank the reviewer for this helpful suggestion. We have re-written several parts of the Materials and methods to clarify these details and streamline the text. To train the attempted copulation classifier, we combined datasets from assays with mated and decapitated virgin females, using manual annotations as ground truth. We also adapted MateBook’s persistence filters (Ribeiro et al., 2018) and defined thresholds explicitly: mounting lasting >45 s (>1350 frames at 30 fps) was defined as copulation, whereas abdominal curling without mounting, or mounting lasting 0.33– 45 s, was defined as attempted copulation.

(7) Figure 7F: this is the only case with a significant difference between the two setups. What explanations do the authors have for the discrepancy?

We thank the reviewer for raising this point. After repeating the experiments, we no longer found a significant difference between the setups. Figure 7 and its legend have been updated to reflect these results.

(8) Figure 2 - Supplement 1: I do not understand why the boxes for Observer 1 have different colors in different figures. Does this have a meaning?

Thanks for pointing this out. The color differences had no intended meaning, and we have corrected the figure for consistency across panels.

(9) P22, line 517ff: It would be interesting to know how frequently identity switches occurred. For large-scale, automatic behavioral screenings that step could be a crucial bottleneck.

We thank the reviewer for this valuable suggestion. We analyzed identity switches using the FlyTracker “Visualizer” package, which flags frames with possible overlaps or jumps. Flagged intervals were manually verified, and we report these data in new Supplementary File 5. Identity switch rates were very low: 0.66% for high-resolution recordings and 1.9% for smartphone DANCE videos in the most challenging decapitated-virgin dataset. These findings demonstrate robust tracking performance under both setups.

eLife Assessment

This important study presents a compelling theoretical framework for understanding condensation or phase separation of membrane-bound proteins, with a focus on the organization of tight junction components. By incorporating non-dilute binding effects into thermodynamic models and validating the model's predictions with in vitro experiments on the tight junction protein ZO-1, the authors provide a quantitative tool that combines theory and experiments and will help researchers in the field quantitatively interpret their findings. Given that phase separation of membrane bound molecules is becoming key in signaling, spanning from immune signaling to cell-cell adhesion, this work will be of broad interest for cell biologists and biophysicists.

Reviewer #1 (Public review):

Summary:

Biomolecular condensates are essential part of cellular homeostatic regulation. In this manuscript, authors develop a theoretical framework for phase separation of membrane bound proteins. They show the effect of non-dilute surface binding and phase separation on tight junction protein organization.

Strengths:

It is an important study considering the phase separation of membrane bound molecules are taking the center stage of signaling, spanning from immune signaling to cell-cell adhesion. A theoretical framework will help biologists to quantitatively interpret their findings.

Weaknesses:

Understandably, authors used one system to test their theory (ZO-1). However, to establish a theoretical framework, this is sufficient.

Comments on revisions:

I do not recommend new experiments. The manuscript is clear and establishes a new step in understanding the physical chemistry of biomolecular condensates.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

Biomolecular condensates are an essential part of cellular homeostatic regulation. In this manuscript, the authors develop a theoretical framework for the phase separation of membrane-bound proteins. They show the effect of non-dilute surface binding and phase separation on tight junction protein organization. 

Strengths: 

It is an important study, considering that the phase separation of membrane-bound molecules is taking the center stage of signaling, spanning from immune signaling to cell-cell adhesion. A theoretical framework will help biologists to quantitatively interpret their findings. 

Weaknesses: 

Understandably, the authors used one system to test their theory (ZO-1). However, to establish a theoretical framework, this is sufficient. 

We acknowledge this limitation. While we agree that additional systems would strengthen the generality of our theory, we note that the focus of this work is to introduce and validate a theoretical framework. As the reviewer notes, this is sufficient for establishing the framework. Nonetheless, we are open to further collaborations or future studies to test the model with other systems.

Reviewer #2 (Public review): 

Summary: 

The authors present a clear expansion of biophysical (thermodynamic) theory regarding the binding of proteins to membrane-bound receptors, accounting for higher local concentration effects of the protein. To partially test the expanded theory, the authors perform in vitro experiments on the binding of ZO1 proteins to Claudin2 C-terminal receptors anchored to a supported lipid bilayer, and capture the effects that surface phase separation of ZO1 has on its adsorption to the membrane. 

Strengths: 

(1) The derived theoretical framework is consistent and largely well-explained. 

(2) The experimental and numerical methodologies are transparent. 

(3) The comparison between the best parameterized non-dilute theory is in reasonable agreement with experiments. 

Weaknesses: 

(1) In the theoretical section, what has previously been known, compared to which equations are new, should be made more clear. 

We have revised the theory section to clearly distinguish previously established formulations from novel contributions following equation (4), which is .

(2) Some assumptions in the model are made purely for convenience and without sufficient accompanying physical justification. E.g., the authors should justify, on physical grounds, why binding rate effects are/could be larger than the other fluxes. 

For our problem, binding is relevant together with diffusive transport in each phase. Each process is accompanied by kinetic coefficients that we estimate for the experimental system. For the considered biological systems (and related ones), it is difficult to determine whether other fluxes (see, e.g., Eq. 8(e)) have relaxed or not. We note that their effects are, of course, included in the kinetic model applied to the coarsening of ZO1 surface condensates as boundary conditions. But we cannot exclude that the corresponding kinetic coefficient in the actual biological system is large enough such that, e.g., Eq. (9e) does not vanish to zero “quasi-statically”. We have now added a sentence to the outlook highlighting the relevance of testing those flux-force relationships in biological systems. 

(3) I feel that further mechanistic explanation as to why bulk phase separation widens the regime of surface phase separation is warranted.  

We have discussed the mechanistic explanation related to bulk protein interaction strength in the manuscript in the section: “Effects of binding affinity and interactions on surface phase separation”. We explained how the bulk interaction parameter affects the binding equilibrium. 

(4) The major advantage of the non-dilute theory as compared with a best parameterized dilute (or homogenous) theory requires further clarification/evidence with respect to capturing the experimental data. 

We thank reviewer for this helpful question. To address this point, we have added new paragraphs in the conclusion section, which explicitly discuss the necessity of employing the non-dilute theory for interpreting the experimental data.

(5) Discrete (particle-based) molecular modelling could help to delineate the quantitative improvements that the non-dilute theory has over the previous state-of-the-art. Also, this could help test theoretical statements regarding the roles of bulk-phase separation, which were not explored experimentally.  

We appreciate the suggestion and agree that such modeling would be valuable. However, this is beyond the scope of the current study. 

(6) Discussion of the caveats and limitations of the theory and modelling is missing from the text. 

We sincerely appreciate the reviewer’s helpful comment. We have added a discussion in the conclusion section outlining the caveats and limitations of our modeling approach.

Reviewing Editor Comments: 

Upon discussing with the reviewers, we feel that this manuscript could significantly be improved if testing the model with a different model system (beyond ZO1/tight junctions), in which case we foresee that we could enhance the strength of evidence from "compelling" to "exceptional". But of course, this is up to the authors to go for it or not, the paper is already very good. 

Reviewer #2 (Recommendations for the authors): 

(1) Lines 132-134: Re-word, the use of "complex" is confusing.

We have rephrased the sentence for clarity. The revised version reads: ṽ_{_𝑃𝑅}_ are the molecular volume and area of the protein-receptor complex ѵ_𝑃𝑅, respectively”, and the changes have been in the revised manuscript.

(2) Line 154 use of ""\nu"" for volume and area could be avoided for better clarity. 

We thank the reviewer for this helpful suggestion. We have removed the statement involving ""\nu"" as these quantities have already been defined in the preceding context.

(3) Line 158 the total "Helmholtz" free energy F... 

We have added the word "Helmholtz" to the sentence.

(4) Line 160 typo "In specific,..." 

We carefully checked this sentence but could not identify a typo.  

(5) For equation 5 explain the physical origins of each term, or provide a reference if this equation is explained elsewhere. 

Thank you very much for your valuable suggestions. We have carefully rephrased Equation (5) and added a paragraph immediately afterward to provide a detailed explanation of its physical meaning.

(6) Derivation on lines 163-174 is poorly written. Make the logical flow between the equations clearer. 

We greatly appreciate your insightful suggestions. Equation (6) has been carefully revised for clarity, and the explanation has been rewritten to ensure better readability. All modifications are Done.

(7) Define bold "t" in Equation 6. 

The variable “t” has been explicitly defined in the context for clarity.

(8) In equations. 7b-7c the nablas (gradients) should be the 2D versions.  

We have updated the gradient operators in Equations (7b) and (7c) [Eq. (9) in revised manuscript]  to their 2D forms for consistency. 

(9) Line 190, avoid referring to the future Equation 14, and state in words what is meant by "thermodynamic equilibrium". 

We have added the explanation of “thermodynamic equilibrium” and remove the reference to equation accordingly.

(10) In Equation 11 you don't explain what you are doing ( which is a perturbation around the minimum of the free energy). 

We have revised the paragraph before equation (11) [Eq. (13) in revised manuscript] to clarify that the expression represents a perturbation around the minimum of the free energy.

(11)  In Equation 12, doesn't this also depend on how you have written equation 6 (not just equation 5). 

Eq. (12) [Eq. (14) in revised manuscript] is derived directly from the variation of the total free energy F. In contrast, Eq. (6) contains the time derivative of free energies that were not written in their final form. In the revised version, we have now given the conjugate forces and fluxes in Eqs. (7) and (8) for clarity.

(12) Line 206 specify the threshold of local concentration (or provide a reference). 

We have specified the threshold of local concentration in the revised text, and the corresponding statement has been highlighted.

(13) Line 223 is the deviation from ideality captured in a pair-wise fashion? I presume it does not account for N many-body interactions?  

Yes, our model is formulated within a mean-field framework that incorporates pairwise (second order) interaction coefficients. For example, 𝜒_{𝑃𝑅 -𝑅} characterizes the interaction between the complex 𝑃𝑅 and the free receptor 𝑅, 𝜒_{𝑅 -L} the interaction between free receptor 𝑅 and free lipid 𝐿, 𝜒_{𝑃𝑅-𝐿} the interaction between complex 𝑃𝑅and free lipid 𝐿. We have stressed this choice of free energy in the revised manuscript.

(14) Line 274, how do the authors know the secondary effects (of which they should mention a few) do not significantly impact the observed behaviour?  

We sincerely thank the reviewer for the helpful comment. First, the parameters 𝜒_{𝑅 -L} and 𝜒_{𝑃𝑅 -𝑅} are not essential based on the experimental observations. For more information, please see our revised paragraph on the choice of the specific parameter values, which has been in the following Eq. (21).

(15) It's not clear how Figures 3 b and c are generated with reference to which parameters are changed to investigate with/without bulk phase separation. 

To improve clarity, we have revised Figure 3 to display the corresponding parameter values directly in each panel. Figures 3b and 3c were generated by computing the surface binding curves (as shown in Fig. 2) for each binding affinity 𝜔_𝑃𝑅 and membrane-complex interaction strength 𝜒_{𝑃𝑅-𝐿}, under different bulk interaction strengths chi, to compare the cases with and without bulk phase separation. 

(16) The jump between theory and the "Mechanism in ..." section is too much. The authors should include the biological context of tight junctions and ZO1 in the main introduction. 

We appreciate the reviewer’s suggestion. Following this comment, we have added an extended discussion in the main introduction to provide the necessary biological context of tight junctions and ZO1. In addition, we inserted new bridging paragraphs between the theoretical section and the section “Mechanism in tight junction formation” to create a smoother transition from theory to experiments. These revisions help to better connect the theoretical framework with the biological phenomena discussed in the later section.

eLife Assessment

This important study shows that orientation tuning of V1 neurons is suppressed during a continuous flash suppression paradigm, especially when the neurons have a binocular receptive field. However, the evidence presented is incomplete and, in particular, does not distinguish whether this suppression is due to reduced contrast or due to masking.

Reviewer #1 (Public review):

Disclaimer: While I am familiar with the CFS method and the CFS literature, I am not familiar with primate research or two-photon calcium imaging. Additionally, I may be biased regarding unconscious processing under CFS, as I have extensively investigated this area but have found no compelling evidence in favor of unconscious processing under CFS.

This manuscript reports the results of a nonhuman-primate study (N=2 behaving macaque monkeys) investigating V1 responses under continuous flash suppression (CFS). The results show that CFS substantially suppressed V1 orientation responses, albeit slightly differently in the two monkeys. The authors conclude that CFS-suppressed orientation information "may not suffice for high-level visual and cognitive processing" (abstract).

The manuscript is clearly written and well-organized. The conclusions are supported by the data and analyses presented (but see disclaimer). However, I believe that the manuscript would benefit from a more detailed discussion of the different results observed for monkeys A and B (i.e., inter-individual differences), and how exactly the observed results are related to findings of higher-order cognitive processing under CFS, on the one hand, and the "dorsal-ventral CFS hypothesis", on the other hand.

Major Comments:

(1) Some references are imprecise. For example, l.53: "Nevertheless, two fMRI studies reported that V1 activity is either unaffected or only weakly affected (Watanabe et al., 2011; Yuval-Greenberg & Heeger, 2013)". "To the best of my understanding, the second study reaches a conclusion that is entirely opposite to that of the first, specifically that for low-contrast, invisible stimuli, stimulus-evoked fMRI BOLD activity in the early visual cortex (V1-V3) is statistically indistinguishable from activity observed during stimulus-absent (mask-only) trials. Therefore, high-level unconscious processing under CFS should not be possible if Yuval-Greenberg & Heeger are correct. The two studies contradict each other; they do not imply the same thing.

(2) Line 354: "The flashing masker was a circular white noise pattern with a diameter of 1.89{degree sign}{degree sign}, a contrast of 0.5, and a flickering rate of 10 Hz. The white noise consisted of randomly generated black and white blocks (0.07 × 0.07 each)." Why did the authors choose a white noise stimulus as the CFS mask? It has previously been shown that the depth of suppression engendered by CFS depends jointly on the spatiotemporal composition of the CFS and the stimulus it is competing with (Yang & Blake, 2012). For example, Hesselmann et al. (2016) compared Mondrian versus random dot masks using the probe detection technique (see Supplementary Figure S4 in the reference below) and found only a poor masking performance of the random dot masks.

Yang, E., & Blake, R. (2012). Deconstructing continuous flash suppression. Journal of Vision, 12(3), 8. https://doi.org/10.1167/12.3.8

Hesselmann, G., Darcy, N., Ludwig, K., & Sterzer, P. (2016). Priming in a shape task but not in a category task under continuous flash suppression. Journal of Vision, 16, 1-17.

(3) Related to my previous point: I guess we do not know whether the monkeys saw the CF-suppressed grating stimuli or not? Therefore, could it be that the differences between monkey A and B are due to a different individual visibility of the suppressed stimuli? Interocular suppression has been shown to be extremely variable between participants (see reference below). This inter-individual variability may, in fact, be one of the reasons why the CFS literature is so heterogeneous in terms of unconscious cognitive processing: due to the variability in interocular suppression, a significant amount of data is often excluded prior to analysis, leading to statistical inconsistencies. Moreover, the authors' main conclusion (lines 305-307) builds on the assumption that the stimuli were rendered invisible, but isn't this speculation without a measure of awareness?

Yamashiro, H., Yamamoto, H., Mano, H., Umeda, M., Higuchi, T., & Saiki, J. (2014). Activity in early visual areas predicts interindividual differences in binocular rivalry dynamics. Journal of Neurophysiology, 111(6), 1190-1202. https://doi.org/10.1152/jn.00509.2013

(4) The authors refer to the "tool priming" CFS studies by Almeida et al. (l.33, l.280, and elsewhere) and Sakuraba et al. (l.284). A thorough critique of this line of research can be found here:

Hesselmann, G., Darcy, N., Rothkirch, M., & Sterzer, P. (2018). Investigating Masked Priming Along the "Vision-for-Perception" and "Vision-for-Action" Dimensions of Unconscious Processing. Journal of Experimental Psychology. General. https://doi.org/10.1037/xge0000420

This line of research ("dorsal-ventral CFS hypothesis") has inspired a significant body of behavioral and fMRI/EEG studies (see reference for a review below). The manuscript would benefit from a brief paragraph in the discussion section that addresses how the observed results contribute to this area of research.

Ludwig, K., & Hesselmann, G. (2015). Weighing the evidence for a dorsal processing bias under continuous flash suppression. Consciousness and Cognition, 35, 251-259. https://doi.org/10.1016/j.concog.2014.12.010

Reviewer #2 (Public review):

Summary:

The goal of this study was to investigate the degree to which low-level stimulus features (i.e., grating orientation) are processed in V1 when stimuli are not consciously perceived under conditions of continuous flash suppression (CFS). The authors measured the activity of a population of V1 neurons at single neuron resolution in awake fixating monkeys while they viewed dichoptic stimuli that consisted of an oriented grating presented to one eye and a noise stimulus to the other eye. Under such conditions, the mask stimulus can prevent conscious perception of the grating stimulus. By measuring the activity of neurons (with Ca2+ imaging) that preferred one or the other eye, the authors tested the degree of orientation processing that occurs during CFS.

Strengths:

The greatest strength of this study is the spatial resolution of the measurement and the ability to quantify stimulus representations during CSF in populations of neurons, preferring the eye stimulated by either the grating or the mask. There have been a number of prominent fMRI studies of CFS, but all of them have had the limitation of pooling responses across neurons preferring either eye, effectively measuring the summed response across ocular dominance columns. The ability to isolate separate populations offers an exciting opportunity to study the precise neural mechanisms that give rise to CFS, and potentially provide insights into nonconscious stimulus processing.

Weaknesses:

While this is an impressive experimental setup, the major weakness of this study is that the experiments don't advance any theoretical account of why CFS occurs or what CFS implies for conscious visual perception. There are two broad camps of thinking with regard to CFS. On the one hand, Watanabe et al. (2011) reported that V1 activity remained intact during CFS, implying that CFS interrupts stimulus processing downstream of V1. On the other hand, Yuval-Greenberg and Heeger (2013) showed that V1 activity is, in fact, reduced during CFS. By using a parametric experimental design, they measured the impact of the mask on the stimulus response as a function of contrast and concluded that the mask reduces the gain of neural responses to the grating stimulus. They presented a theoretical model in which the mask effectively reduced the SNR of the grating, making it invisible in the same way that reducing contrast makes a stimulus invisible.

An important discussion point of Yuval-Greenberg and Heeger is that null results (such as those presented by Watanabe et al.) are difficult to interpret, as the lack of an effect may be simply due to insufficient data. I am afraid that this critique also applies to the present study. Here, the authors report that CFS effectively 'abolishes' tuning for stimuli in neurons preferring the eye with the grating stimulus. The authors would have been in a much stronger position to make this claim if they had varied the contrast of the stimulus to show that the loss of tuning was not simply due to masking. So, while this is an incredibly impressive set of measurements that in many ways raises the bar for in vivo Ca2+ imaging in behaving macaques, there isn't anything in the results that constitutes a real theoretical advance.

Reviewer #3 (Public review):

Summary:

In this study, Tang, Yu & colleagues investigate the impact of continuous flash suppression (CFS) on the responses of V1 neurons using 2-photon calcium imaging. The report that CFS substantially suppressed V1 orientation responses. This suppression happens in a graded fashion depending on the binocular preference of the neuron: neurons preferring the eye that was presented with the marker stimuli were most suppressed, while the neurons preferring the eye to which the grating stimuli were presented were least suppressed. The binocular neuron exhibited an intermediate level of suppression.

Strengths:

The imaging techniques are cutting-edge, and the imaging results are convincing and consistent across animals.

Weaknesses:

I am not totally convinced by the conclusions that the authors draw based on their machine learning models.

Author response:

Reviewer #2

We respectfully disagree with Reviewer 2’s critiques, upon which the eLife assessment of “incomplete evidence” is primarily based. We believe these critiques do not accurately reflect our study and are rooted in a misinterpretation of the evidence. Consequently, we suggest that the conclusion of “incomplete evidence” is not warranted.

On the basis of Reviewer 2’s critiques, the eLife assessment states: “However, the evidence presented is incomplete and, in particular, does not distinguish whether this suppression is due to reduced contrast or due to masking.” We emphasize that the suppression we observed is a consequence of interocular masking, not contrast reduction. Reviewer 2 cites Yuval-Greenberg and Heeger (2013), which proposes that during CFS, the mask reduces the gain of neural responses in V1 in a manner analogous to reducing stimulus contrast. We agree that both CFS masking and contrast reduction can decrease signal-to-noise ratio and thereby reduce visibility. However, in our paradigm, the physical stimulus contrast was held constant, while suppression was induced by interocular competition under CFS. This is a fundamentally different mechanism from lowering stimulus contrast. Our results therefore reflect genuine masking-induced suppression, rather than the effect of physical contrast reduction.

Furthermore, Reviewer 2 cited Yuval-Greenberg and Heeger’s discussion that null results can arise from insufficient data, and suggested that this applies to our study. This main critique from Reviewer 2 is misplaced for two reasons: First, our main result is not a null effect. A null effect would mean that CFS masking had no impact on population orientation responses. Instead, we observed significant suppression, including abolished tuning in some conditions, which clearly indicates a strong effect of masking. Second, our findings are based on large neural populations recorded using two-photon calcium imaging, providing extensive sampling and high statistical power. Thus, concerns about “insufficient data” do not apply to our study.

Finally, we used machine learning approaches to examine the effects of CFS masking on orientation discrimination and recognition, providing new insight into the long-standing debate over whether the brain can perform high-level cognitive processing under CFS. Although it is, to some extent, a matter of personal judgment whether our work represents a theoretical advance, Reviewer 2 made no comment, positive or negative, on this major component of our study while forming his/her judgment. (In response to Reviewer 3’s main concern about the suitability of SVMs, we now performed a multi-way classification analysis, which yielded results largely consistent with those obtained using the SVM approach in the original manuscript, confirming the robustness of our mechine learning results.

eLife Assessment

In this important paper, Garcia et al seek to determine whether the superior frontal sulcus (SFS), an area previously implicated in evidence accumulation for perceptual decisions, plays a causal role in perceptual and/or value-based decisions. Through a combination of careful paradigm design, computational modelling, transcranial magnetic stimulation and fMRI analyses, the authors provide convincing evidence that the SFS supports perceptual but not value-based decisions and that its disruption leads to a lowering of decision boundaries.

Reviewer #1 (Public review):

Summary:

In this study, participants completed two different tasks. A perceptual choice task in which they compared the sizes of pairs of items and a value-different task in which they identified the higher value option among pairs of items with the two tasks involving the same stimuli. Based on previous fMRI research, the authors sought to determine whether the superior frontal sulcus (SFS) is involved in both perceptual and value-based decisions or just one or the other. Initial fMRI analyses were devised to isolate brain regions that were activated for both types of choices and also regions that were unique to each. Transcranial magnetic stimulation was applied to the SFS in between fMRI sessions and it was found to lead to a significant decrease in accuracy and RT on the perceptual choice task but only a decrease in RT on the value-different task. Hierarchical drift diffusion modelling of the data indicated that the TMS had led to a lowering of decision boundaries in the perceptual task and a lowering of non-decision times on the value-based task. Additional analyses show that SFS covaries with model derived estimates of cumulative evidence, that this relationship is weakened by TMS.

The paper has many strengths including the rigorous multi-pronged approach of causal manipulation, fMRI and computational modelling which offers a fresh perspective on the neural drivers of decision making. Some additional strengths include the careful paradigm design which ensured that the two types of tasks were matched for their perceptual content while orthogonalizing trial-to-trial variations in choice difficulty. The paper also lays out a number of specific hypotheses at the outset regarding the behavioural outcomes that are tied to decision model parameters and well justified.

Reviewer #2 (Public review):

Summary:

The authors set out to test whether a TMS-induced reduction in excitability of the left Superior Frontal Sulcus influenced evidence integration in perceptual and value-based decisions. They directly compared behaviour-including fits to a computational decision process model---and fMRI pre and post TMS in one of each type of decision-making task. Their goal was to test domain-specific theories of the prefrontal cortex by examining whether the proposed role of the SFS in evidence integration was selective for perceptual but not value-based evidence.

Strengths:

The paper presents multiple credible sources of evidence for the role of the left SFS in perceptual decision making, finding similar mechanisms to prior literature and a nuanced discussion of where they diverge from prior findings. The value-based and perceptual decision making tasks were carefully matched in terms of stimulus display and motor response, making their comparison credible.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, participants completed two different tasks. A perceptual choice task in which they compared the sizes of pairs of items and a value-different task in which they identified the higher value option among pairs of items with the two tasks involving the same stimuli. Based on previous fMRI research, the authors sought to determine whether the superior frontal sulcus (SFS) is involved in both perceptual and value-based decisions or just one or the other. Initial fMRI analyses were devised to isolate brain regions that were activated for both types of choices and also regions that were unique to each. Transcranial magnetic stimulation was applied to the SFS in between fMRI sessions and it was found to lead to a significant decrease in accuracy and RT on the perceptual choice task but only a decrease in RT on the value-different task. Hierarchical drift diffusion modelling of the data indicated that the TMS had led to a lowering of decision boundaries in the perceptual task and a lower of nondecision times on the value-based task. Additional analyses show that SFS covaries with model derived estimates of cumulative evidence, that this relationship is weakened by TMS.

Strengths:

The paper has many strengths, including the rigorous multi-pronged approach of causal manipulation, fMRI and computational modelling, which offers a fresh perspective on the neural drivers of decision making. Some additional strengths include the careful paradigm design, which ensured that the two types of tasks were matched for their perceptual content while orthogonalizing trial-to-trial variations in choice difficulty. The paper also lays out a number of specific hypotheses at the outset regarding the behavioural outcomes that are tied to decision model parameters and well justified.

We thank the reviewer for their thoughtful summary of the study and for highlighting these strengths. We are pleased that the multi-pronged approach combining causal manipulation, fMRI, and hierarchical drift–diffusion modelling, as well as the careful matching of perceptual content across the two tasks, came across clearly. We also appreciate the reviewer’s positive remarks on the specificity of our a priori hypotheses and their links to decision-model parameters. In revising the manuscript, we have aimed to further streamline the presentation of these hypotheses and to more explicitly connect the behavioural predictions, model parameters, and neural readouts throughout the Results and Discussion sections.

Weaknesses:

In my previous comments (1.3.1 and 1.3.2) I noted that key results could be potentially explained by cTBS leading to faster perceptual decision making in both the perceptual and value-based tasks. The authors responded that if this were the case then we would expect either a reduction in NDT in both tasks or a reduction in decision boundaries in both tasks (whereas they observed a lowering of boundaries in the perceptual task and a shortening of NDT in the value task). I disagree with this statement. First, it is important to note that the perceptual decision that must be completed before the value-based choice process can even be initiated (i.e. the identification of the two stimuli) is no less trivial than that involved in the perceptual choice task (comparison of stimulus size). Given that the perceptual choice must be completed before the value comparison can begin, it would be expected that the model would capture any variations in RT due to the perceptual choice in the NDT parameter and not as the authors suggest in the bound or drift rate parameters since they are designed to account for the strength and final quantity of value evidence specifically. If, in fact, cTBS causes a general lowering of decision boundaries for perceptual decisions (and hence speeding of RTs) then it would be predicted that this would manifest as a short NDT in the value task model, which is what the authors see.

We thank the reviewer for raising these points and for the helpful clarification. We agree that, in principle, the architecture of the value-based task can be conceived as involving an upstream perceptual process that must be completed, to some degree, before value comparison can proceed. Under such a multistage framework, it is indeed possible that cTBS-induced changes in a perceptual decision stage could manifest as a reduction in boundary separation in the pure perceptual task, while the same perturbation appears as a shortening of non-decision time (NDT) when fitting a single-stage DDM to the value task. In this sense, our earlier statement that a “general speeding effect” would necessarily produce identical parameter changes (either NDT or boundaries) in both tasks was too strong, and we are grateful to the reviewer for pointing this out.

At the same time, this alternative explanation remains fully compatible with our central claim that the left SFS plays a perceptual rather than value-based role. We agree with the reviewer that there must be a stimulus-related circuit (in visual and parietal regions) that encodes the physical attributes of the options, and that this upstream processing can influence both tasks. However, a large body of work suggests that left SFS is not part of this primary identification circuitry, but rather contributes specifically to the accumulation and comparison of sensory evidence (e.g., Heekeren et al., 2004, 2006), downstream from areas such as FFA, PPA, or MT/V5 that encode stimulus identity. In other words, stimulus identification (forming a representation of “what is where”) is anatomically and functionally distinct from the accumulation of evidence toward a perceptual decision. Within this framework, the reviewer’s proposal that cTBS speeds “perceptual decisions” across tasks can be understood as targeting precisely the evidence-accumulation stage we ascribe to SFS, with the value-comparison stage proper likely implemented in other regions (e.g., vmPFC and connected valuation circuitry).

We therefore do not rely solely on the dissociation between boundary changes in the perceptual task and NDT changes in the value task as decisive evidence against a “general speeding” account. Instead, our interpretation is based on the convergence of behavioural, model-based, and neural results. First, in the perceptual task, cTBS to left SFS leads to a selective reduction in decision boundary and a concomitant change in trialwise BOLD activity within the stimulated region that covaries with perceptual choice behaviour and with the latent decision variable inferred from the HDDM. Second, in the value task, cTBS does not affect value sensitivity or accuracy, nor does it alter value-related drift or boundary parameters; the only robust HDDM effect is a modest shortening of NDT. Third, critically, left SFS BOLD activity is modulated by perceptual evidence and by cTBS in the perceptual task, but we observe no evidence that SFS activity encodes value evidence or shows value-related cTBS neuronal effects in the value task.

Taken together, these findings indicate that the left SFS serves a causal role in the accumulation of perceptual evidence and in the setting of the choice criterion for perceptual decisions. The reviewer’s suggestion that cTBS may induce a general speeding of perceptual processes that also influences the value task is compatible with this conclusion, in the sense that any contribution of SFS to the value task is best understood as acting via a perceptual component that is upstream of value comparison, rather than via the value accumulation process itself. We have clarified this point in the Discussion of the revised manuscript and now explicitly acknowledge that our DDM dissociation alone does not exclude a general perceptual speeding account, but that the combination of task-specific neural effects in SFS, preserved value-based choice behaviour, and the absence of value-related BOLD changes in SFS strongly support a primarily perceptual role for this region.

Reviewer #2 (Public review):

Summary:

The authors set out to test whether a TMS-induced reduction in excitability of the left Superior Frontal Sulcus influenced evidence integration in perceptual and value-based decisions. They directly compared behaviour-including fits to a computational decision process model---and fMRI pre and post TMS in one of each type of decision-making task. Their goal was to test domain-specific theories of the prefrontal cortex by examining whether the proposed role of the SFS in evidence integration was selective for perceptual but not value-based evidence.

Strengths:

The paper presents multiple credible sources of evidence for the role of the left SFS in perceptual decision making, finding similar mechanisms to prior literature and a nuanced discussion of where they diverge from prior findings. The value-based and perceptual decision-making tasks were carefully matched in terms of stimulus display and motor response, making their comparison credible.

We thank the reviewer for their clear summary of our aims and approach, and for highlighting these strengths. We are pleased that the convergence between causal TMS, fMRI, and hierarchical modelling comes across as providing credible evidence for the role of left SFS in perceptual decision-making, and that our attempt to link these results to the existing literature is seen as appropriately nuanced. We also appreciate the reviewer’s positive assessment of the task design, in particular the close matching of perceptual content and motor output across perceptual and value-based decisions, which was central to our goal of testing domain-specific theories of prefrontal function. In revising the manuscript, we have further clarified these design choices and their rationale, and we have streamlined the exposition of how the hypotheses, model parameters, and neural readouts are connected across the two decision domains.

Weaknesses:

I was confused about the model specification in terms of the relationship between evidence level and drift rate. While the methods (and e.g. supplementary figure 3) specify a linear relationship between evidence level and drift rate, suggesting, unless I misunderstood, that only a single drift rate parameter (kappa) is fit. However, the drift rate parameter estimates in the supplementary tables (and response to reviewers) do not scale linearly with evidence level.

We thank the reviewer for raising this point and appreciate the opportunity to clarify the model specification. In our hierarchical DDM, we did not fit separate, free drift parameters for each evidence level. As shown in Supplementary Fig. 3, the drift on each trial is specified as

where 𝐸_{𝑐,𝑠,𝑖} the trial-wise evidence (difference in size or value) and κ_𝑐,𝑠 is a single drift-scaling parameter per condition and session. Thus, the linear dependence of drift on evidence is implemented at the trial level via 𝜅; we do not estimate independent 𝛿 parameters for each evidence level.

In Supplementary Tables 8 and 9 we report, for descriptive purposes, the posterior means of 𝛿 conditional on each evidence bin (levels 1–4), alongside the corresponding decision boundary and nondecision time summaries. These values are therefore derived quantities that reflect the combination of (i) the single κ_𝑐,𝑠 parameter, (ii) the empirical distribution of continuous evidence values 𝐸 within each bin, and (iii) hierarchical pooling across subjects and sessions. Consequently, they are expected to increase monotonically with evidence level—as they do in our data—but not to lie exactly on a straight line in the discrete level index, because the underlying evidence bins are not equally spaced in physical units and because of between-subject variability and posterior uncertainty.

We will revise the text and table captions to make clear that the evidence-level entries are descriptive summaries of 𝛿 implied by the 𝜅×𝐸 formulation, rather than independently estimated drift parameters, in order to avoid this confusion.

-The fit quality for the value-based decision task is not as good as that for the PDM, and this would be worth commenting on in the paper.

We agree that the HDDM fit for the value-based task is somewhat weaker than for the perceptual task. This is reflected in the somewhat higher DIC values for VDM compared with PDM and in slightly broader posterior-predictive distributions (Supplementary Tables 8–11 and Supplementary Figs. 11–16). We believe this difference primarily reflects the greater intrinsic variability of subjective value-based choices (e.g. trial-to-trial fluctuations in preferences, satiety, or attention), coupled with our decision to use the same relatively simple DDM architecture for both tasks to allow a principled cross-task comparison. Importantly, posterior-predictive checks show that, for VDM as well, the model adequately reproduces both accuracy and full RT distributions at the group and subject level (Supplementary Figs. 11–16), indicating that the fit quality is sufficient for our purposes. In the revised manuscript we now explicitly note that the model captures PDM behaviour more tightly than VDM and that this may reduce sensitivity to very small cTBS effects on value-based decision parameters, even though no systematic effects are evident in our data. Crucially, our central conclusion—that left SFS plays a domain-specific role in setting the decision boundary for perceptual evidence—relies on the robust behavioural, computational, and neural effects observed in PDM and does not depend on assuming a perfect model fit for VDM.

- Supplementary Figure 3 specifies the distribution for kappa hyper-parameter twice.

We thank the reviewer for spotting this typo. We have revised Supplementary Figure 3 legend.

eLife Assessment

Combining state-of-the-art in-situ cell-surface proteomics, functional genetic screening, and single-nucleus RNA sequencing, this fundamental work substantially advances our understanding of glial contributions to organismal lifespan. The evidence supporting the conclusions is compelling, although additional clarification, control experiments, and analysis would further strengthen the study. The work will be of broad interest to researchers studying aging biology, glia-neuron communication, and in vivo proteomic profiling.

Reviewer #1 (Public review):

Summary:

Age-related synaptic dysfunction can have detrimental effects on cognitive and locomotor function. Additionally, aging makes the nervous system vulnerable to late-onset neurodegenerative diseases. This manuscript by Marques et al. seeks to profile the cell surface proteomes of glia to uncover signaling pathways that are implicated in age-related neurodegeneration. They compared the glial cell-surface proteomes in the central brain of young (day 5) and old (day 50) flies, and identified the most up- and down-regulated proteins during the aging process. 48 genes were selected for analysis in a lifespan screen, and interestingly, most sex-specific phenotypes. Among these, adult-specific pan-glial DIP-β overexpression (OE) significantly increased the lifespan of both males and females and improved their motor control ability. To investigate the effect of DIP-β in the aging brain, Marques et al. performed snRNA-seq on 50-day-old Drosophila brains with or without DIP-β OE in glia. Cortex and ensheathing glia showed the most differentially expressed genes. Computational analysis revealed that glial DIP-β OE increased cell-cell communication, particularly with neurons and fat cells.

Strengths:

(1) State-of-the-art methodology to reveal the cell surface proteomes of glia in young and old flies.

(2) Rigorous analyses to identify differentially expressed proteins.

(3) Examination of up- and down-regulated candidates and identification of glial-expressed mediators that impact fly lifespan.

(4) Intriguing sex-specific glial genes that regulate life span.

(5) Follow-up RNA-seq analysis to examine cellular transcriptomes upon overexpression of an identified candidate (DIP-β).

(6) A compelling dataset for the community that should generate extensive interest and spawn many projects.

Weaknesses:

(1) DIP-β OE using flySAM:

a) These flies showed a larger increase in lifespan compared to using UAS-DIP-β (Figure 2 C, D). Do the authors think that flySAM is a more efficient way of OE than UAS? Also, the UAS construct would be specific to one DIP-β isoform, while flySAM would likely express all isoforms. Could this also contribute to the phenotypes observed?

b) The Glial-GS>DIP-β flySAM flies without RU-486 have significantly shorter lifespans (Figure 2C) than their UAS-DIP-β counterparts. flySAM is lethal when expressed under the control of tubulin-GAL4 (Jia et al. 2018), likely due tothe toxicity of such high levels of overexpression. Is it possible that a larger increase in lifespan is due to the already reduced viability of these flies?

c) Statistics: It is stated in the Methods that "statistical methods used are described in the figure legend of each relevant panel." However, there is no description of the statistics or sample sizes used in Figure 2.

(2) Figure 3: The authors use a glial GeneSwitch (GS) to knock down and overexpress candidate genes. In Figure 3A, they look at glial-GS>UAS-GFP with and without RU. Without RU, there is no GFP expression, as expected. With RU, there is GFP expression. It is expected that all cell body GFP signal should colocalize with a glial nuclear marker (Repo). However, there is some signal that does not appear to be glia. Also, many glia do not express GFP, suggesting the glial GS driver does not label all glia. This could impact which glia are being targeted in several experiments.

(3) It is interesting that sex-specific lifespan effects were observed in the candidate screen.

a) The authors should provide a discussion about these sex-specific differences and their thoughts about why these were observed.

b) The authors should also provide information regarding the sex of the flies used in the glial cell surface proteome study.

c) Also, beyond the scope of this study, examining sex-specific glial proteomes could reveal additional insights into age-related pathways affecting males and females differentially.

(4) The behavioral assay used in this study (climbing) tests locomotion driven by motor neurons. The proteomic analysis was performed with the central adult brain, which does not include the nerve cord, where motor neurons reside. While likely beyond the scope of this study, it would be informative to test other behaviors, including learning, circadian rhythms, etc.

(5) It is surprising that overexpressing a CAM in glia has such a broad impact on the transcriptomes of so many different cell types. Could this be due to DIP-β OE maintaining the brain in a "younger" state and indirectly influencing the transcriptomes? Instead of DIP-β OE in glia directly influencing cell-cell interactions? Can the authors comment on this?

Reviewer #2 (Public review):

This manuscript presents an ambitious and technically innovative study that combines in situ cell-surface proteomics, functional genetic screening, and single-nucleus RNA sequencing to uncover glial factors that influence aging in Drosophila. The authors identify DIP-β as a glial protein whose overexpression extends lifespan and report intriguing sex-specific differences in lifespan outcomes. Overall, the study is conceptually compelling and offers a valuable dataset that will be of considerable interest to researchers studying glia-neuron communication, aging biology, and proteomic profiling in vivo.

The in-situ proteomic labeling approach represents a notable methodological advance. If validated more extensively, it has the potential to become a widely used resource for probing glial aging mechanisms. The use of an inducible glial GeneSwitch driver is another strength, enabling the authors to carefully separate aging-relevant effects from developmental confounds. These technical choices meaningfully elevate the rigor of the study and support its central conclusions. The discovery of new candidate genes from the proteomics pipeline, including DIP-β, is intriguing and opens new avenues for understanding glial contributions to organismal lifespan. The observation of sex-specific lifespan effects is particularly interesting and warrants further exploration; the study sets the stage for future work in this direction.

At the same time, several areas would benefit from clarification or additional analysis to fully support the manuscript's claims:

(1) The manuscript frequently refers to "improved" or "increased" cell-cell communication following DIP-β overexpression, but the meaning of this term remains somewhat vague. Because the current analysis relies largely on transcriptomic predictions, it would be helpful to define precisely what metric is being used, e.g., increased numbers of predicted ligand-receptor interactions, enrichment of specific signaling pathways, or altered expression of communication-related components. Strengthening the mechanistic link between DIP-β, cell-cell communication, and lifespan extension, potentially through targeted validation of specific glial interactions, would substantially reinforce the interpretation.

(2) The lifespan screen is central to the paper, and clearer visualization and contextualization of these results would significantly improve the manuscript's impact. For example, Figure 3D is challenging to interpret in its current form. More explicit presentation of which manipulations extend lifespan in each sex, along with effect sizes and significance values, would provide clarity. Including positive controls for lifespan extension would also help contextualize the magnitude of the observed effects. The reported effects of DIP-β, while promising, are modest relative to baseline effects of RU feeding, and a discussion of this would help appropriately calibrate the conclusions.

(3) Several figures would benefit from improved labeling or more detailed legends. For instance, the meaning of "N" and "C" in Figure 1D is unclear; Figure 3A should clarify that Repo is a glial marker; and Figure 5C appears to have truncated labels. Reordering certain panels (e.g., moving control data in Figure 4A-B) may also improve narrative flow. These refinements would greatly aid reader comprehension.

(4) A few claims would be strengthened by more specific references or acknowledgment of alternative interpretations. Examples include the phenoxy-radical labeling radius, the impact of H₂O₂ exposure, and the specificity of neutravidin. Additionally, downregulation of synapse-related GO terms may reflect age-related transcriptional changes rather than impaired glia-neuron communication per se, and this possibility should be recognized. The term "unbiased" to describe the screen may also be reconsidered, given the preselection of candidate genes.

(5) Clarifying the rationale for focusing on central brain glia over optic-lobe glia would be useful.

Reviewer #3 (Public review):

Summary:

Razlan and colleagues provide a detailed anatomical characterization of lamina I projection neurons in the mouse spinal cord that are densely innervated by primary afferents activated by cooling of the skin. The authors, building on their previous anatomical work, validate a Trpm8-Flp mouse line, show synaptic contacts between Trpm8⁺ boutons and projection neurons at the ultrastructural level, and demonstrate at the physiological level that these neurons specifically respond to cooling stimuli. Next, by taking advantage of their previous transcriptomic analysis of ALS neurons, they identify calbindin as a marker for cold-activated lamina I projection neurons and map their ascending projections to the rostral lateral parabrachial area, caudal periaqueductal gray, and ventral posterolateral thalamus, well-known thermosensory and thermoregulatory centers. Altogether, these findings provide strong anatomical and functional evidence for a direct line of transmission from Trpm8⁺ sensory afferents through Calb1⁺ lamina I neurons to key supraspinal centers controlling perception of cold and thermoregulatory responses.

Strengths:

The combination of mouse genetics, electron microscopy, ex vivo physiology, and viral tracing provides convincing evidence for a direct cold pathway. The work validates the Trpm8-Flp line by extensive anatomical and molecular characterization. Integration with previous transcriptomic and anatomical data neatly links the cold-selective lamina I neurons to a molecularly defined cluster of ALS neurons, strengthening the bridge between molecular identity, anatomy, and physiological function.

Weaknesses:

While anatomical evidence for direct synaptic connectivity between Trpm8+ afferents and lamina I projection neurons is compelling, a physiological demonstration of strict monosynaptic transmission is not shown. The conclusion that these inputs are exclusively monosynaptic should be toned down. Similarly, the statement that "Lamina I ALS neurons that are surrounded by Trpm8 afferents are cold-selective" should also be toned down as only a few neurons have been tested and it cannot be excluded that other neurons with similar characteristics may be polymodal.

eLife Assessment

This important study presents novel data on temporal variation in sperm whale communication, contributing to a richer understanding of the social transmission of vocal styles across neighbouring clans. The evidence is solid, although some terminology limits comparisons to other taxa. This research will be of interest to bioacoustics and cetacean communication specialists, particularly those working on social learning and culture.

Reviewer #2 (Public review):

Summary:

The current article adapts standard rhythmic measures to describe the temporal organisation of whale song units.

Strengths:

The detailed description of the internal temporal structure of whale songs is something that has thus far been lacking.

Weaknesses:

Conceptual and terminological bases of the paper are problematical and hamper comparison with other taxa, including humans. According to signal theory, codas are indexical rather than symbolic. They signal an individual's group identity. Borrowing from humans and linguistics, coda inter-group variation represents a case of accents -- phonologically different varieties of the same call -- not dialects, confirming they are an index. Moreover, symbolism is not a feature detectable or confirmed through rhythmic analyses or temporal characterisation. This raises serious doubt whether alleged "dialects," "symbolism" and similarity between whales and humans is factual. The comparative scope and relevance of this paper for the broader field is limited and evolutionary claims are potentially misleading and perilous.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #2 (Public review):

Summary:

The current article presents a new type of analytical approach to the sequential organisation of whale song units.

Strengths:

The detailed description of the internal temporal structure of whale songs is something that has been thus far lacking.

Weaknesses:

The conceptual and terminological bases of the paper are problematical and hamper comparison with other taxa, including humans. According to signal theory, codas are indexical rather than symbolic. They signal an individual's group identity. Borrowing from humans and linguistics, coda inter-group variation represents a case of accents - phonologically different varieties of the same call - not dialects, confirming they are an index. This raises serious doubt about whether alleged "symbolism" and similarity between whale and human vocal behaviour is factual.

We respect that the reviewer does not agree with describing codas as symbolic markers of cultural identity in sperm whales, but ultimately we find the quantitative evidence presented in Hersh et al. (2022) compelling, and stand by the framing of our manuscript, which builds on this foundation.

The same applies to the difference between ICIs (inter-click interval) and IOIs (inter-onset interval). If the two are equivalent, variation in click duration needs to be shown so small that can be considered negligible. This raises serious doubt about whether the alleged variation in whale codas is indeed rhythmic in nature and prevents future efforts for comparison with the vocal capacities of other species. The scope and relevance of this paper for the broader field is limited.

We believe there has been a miscommunication. Coda inter-click intervals are calculated as the time between the onsets of sequential clicks within a coda. This is identical to definitions of inter-onset intervals in many publications, including:

• Burchardt and Knörnschild (2020): “the duration between the beginning of one element and the next”

• Friberg and Battel (2002): “the time interval between the onset of the tone and the onset of the immediately following tone”

• De Gregorio et al. (2021): “the time between the onset of a note and the next one”

In response to a comment from this reviewer in the first round of revisions, we made the point that we do not believe rhythm analyses need be restricted to inter-onset intervals alone. Regardless of that stance, we did analyze inter-onset intervals in this manuscript and accordingly are capturing aspects of rhythm in our analyses. We have removed a poorly worded sentence in our introduction and apologize for any confusion it caused. We have also made this explicit in lines 30–35: “This classification is based on the total number of clicks and their rhythm and tempo extrapolated from the time interval between the onsets of consecutive clicks: the inter-click interval (ICI) [15, 16] (Fig. 1A). This measure is equivalent to the inter-onset intervals (IOIs) often used in rhythm analyses [17, 18, 19] but for the sake of compatibility with studies on sperm whale acoustics, we use ICI terminology throughout this paper.”

In our analyses, inter-click intervals and inter-onset intervals are equivalent measures.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

My concerns regarding interdisciplinary terminology and methods remain unaddressed. The study's inaccurate terminology hinders reliable comparison with other taxa, including humans. Being "symbolic" bears no weight on the new method that the authors present, thus, the unwillingness for compatibility is limiting and perplexing. The authors state that codas have been previously described as being symbolic, but just because poor terminology has been used before doesn't justify perpetuating it, especially when it confounds and conflicts with broader comparative efforts.

We agree that being symbolic bears no weight on the new method we present, but we believe it does bear weight on our interpretation of what our method reveals about patterns in sperm whale communication. For that reason, we have opted to maintain the current framing of our manuscript.

The same applies to the difference between ICIs and IOIs. The authors resist amending terminology, even though they state the two represent the same measure. If so, want prevents the correct use of IOIs?

We have opted to use ICI throughout the paper because it is standard terminology in sperm whale acoustics, but we have now made the ICI/IOI equivalence explicitly clear in the introduction.

References:

Burchardt LS, Knörnschild M. 2020. Comparison of methods for rhythm analysis of complex animals’ acoustic signals. PLoS Computational Biology 16. doi:10.1371/journal.pcbi.1007755

De Gregorio C, Valente D, Raimondi T, Torti V, Miaretsoa L, Friard O, Giacoma C, Ravignani A, Gamba M. 2021. Categorical rhythms in a singing primate. Current Biology 31:R1379–R1380. doi:10.1016/j.cub.2021.09.032

Friberg A, Battel GU. 2002. Structural communication In: Parncutt R, McPherson G, editors. The Science & Psychology of Music Performance: Creative Strategies for Teaching and Learning. Oxford University Press. doi:10.1093/acprof:oso/9780195138108.001.0001

Hersh TA, Gero S, Rendell L, Cantor M, Weilgart L, Amano M, Dawson SM, Slooten E, Johnson CM, Kerr I, Payne R, Rogan A, Andrews O, Ferguson EL, Hom-Weaver CA, Norris TF, Barkley YM, Merkens KP, Oleson EM, Doniol-Valcroze T, Pilkington J, Gordon J, Fernandes M, Guerra M, Hickmott L, Whitehead H. 2022. Evidence from sperm whale clans of symbolic marking in non-human cultures. Proceedings of the National Academy of Sciences 119:e2201692119. doi:10.1073/pnas.2201692119

eLife Assessment

This study makes a valuable contribution by elucidating the genetic determinants of growth and fitness across multiple clinical strains of Mycobacterium intracellulare, an understudied non-tuberculous mycobacterium. Using transposon sequencing (Tn-seq), the authors identify a core set of 131 genes essential for bacterial adaptation to hypoxia, providing a convincing foundation for anti-mycobacterial drug discovery.

Reviewer #1 (Public review):

Summary:

In this descriptive study, Tateishi et al. report a Tn-seq based analysis of genetic requirements for growth and fitness in 8 clinical strains of Mycobacterium intracellulare Mi), and compare the findings with a type strain ATCC13950. The study finds a core set of 131 genes that are essential in all nine strains, and therefore are reasonably argued as potential drug targets. Multiple other genes required for fitness in clinical isolates have been found to be important for hypoxic growth in the type strain.

Strengths:

The study has generated a large volume of Tn-seq datasets of multiple clinical strains of Mi from multiple growth conditions, including from mouse lungs. The dataset can serve as an important resource for future studies on Mi, which despite being clinically significant, remains a relatively understudied species of mycobacteria.

Weaknesses:

The primary claim of the study that the clinical strains are better adapted for hypoxic growth is yet to be comprehensively investigated. However, this reviewer thinks such an investigation would require a complex experimental design and perhaps form an independent study.

Comments on revisions:

The revised paper has satisfactorily addressed my previous concerns, and I have no further issues with this paper.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review) :

Comments on revisions:

The revised manuscript has responded to the previous concerns of the reviewers, albeit modestly. The overemphasis on hypoxic adaptation of the clinical isolates persist as a key concern in the paper. The authors have compared the growth-curve of each of the clinical and ATCC strains under normal and hypoxic conditions (Fig. 8), but don't show how mutations in some of the genes identified in Tn-seq would impact the growth phenotype under hypoxia. They largely base their arguments on previously published results.

As I mentioned previously, the paper will be better without over-interpreting the TnSeq data in the context of hypoxia.

Thank you for the comment on the issue of not determining the impact of individual gene mutations identified in TnSeq on the growth phenotypes under hypoxia.

We agree that the lack of validation of TnSeq results is a limitation of this study. Without evidence of growth pattern of each gene-deletion mutant under hypoxia there might be a risk of over-interpretating the data, even though the data are carefully interpreted based on previous reports. We consider that it is necessary to confirm the phenomenon by using knockout mutants.

We have just recently succeeded in constructing the vector plasmids for making knockout mutants of M intracellulare (Tateishi. Microbiol Immunol. 2024). We will proceed to the validation experiment of TnSeq-hit genes by constructing knockout mutants. We already mentioned this point as a limitation of this study in the Discussion (pages 35-36 lines 630-640 in the revised manuscript).

Reference.

Tateishi, Y., Nishiyama, A., Ozeki, Y. & Matsumoto, S. Construction of knockout mutants in Mycobacterium intracellulare ATCC13950 strain using a thermosensitive plasmid containing negative selection marker rpsL+. Microbiol Immunol 68, 339-347 (2024).

Other points:

The y-axis legends of plots in Fig.8c are illegible.

Following the comment, we have corrected Figure 8c and checked the uploaded PDF

The statements in lines 376-389 are convoluted and need some explanation. If the clinical strains enter the log phase sooner than ATCC strain under hypoxia, then how come their growth rates (fig. 8c) are lower? Aren't they expected to grow faster?

Thank you for the comment on the interpretation of the difference in bacterial growth under hypoxia between MAC-PD strains and the ATCC type strain. The growth curve consists of the onset of logarithmic growth and its growth speed. In this study, we evaluated the former as timing of midpoint and the latter as growth rate at midpoint. Timing of midpoint and growth rate at midpoint are individual parameters. The early entry to log-phase does not mean the fast growth rate at midpoint.

Our results demonstrated that 5 (M.i.198, M.i.27, M003, M019 and M021) out of 8 clinical MAC-PD strains entered log-phase early and continued to grow logarithmically long time (slow growth). This data suggests the capacity for MAC-PD to continue replication long time under hypoxic conditions. By contrast, the ATCC type strain showed delayed onset of logarithmic growth caused by long-term lag phase. The duration of logarithmic growth was short even once after it started. The log phase soon transited to the stationary phase. This data suggests the lower capacity for the ATCC strain to continue replication under hypoxic conditions.

Following the comment, we have added the interpretation of the growth curve pattern as follows (page 22 lines 379-392 in the revised manuscript): “The growth rate at midpoint under hypoxic conditions was significantly lower in these 5 clinical MAC-PD strains than in ATCC13950. The early entry to log phase followed by long-term logarithmic growth (slow growth rate at midpoint) suggests the capacity for these 5 clinical MAC-PD strains to continue replication long time under hypoxic conditions. On the other hand, the rest 3 clinical MAC-PD strains (M018, M001 and MOTT64) did not show significant change in the growth rate between aerobic and hypoxic conditions, suggesting that there are different levels of capacity in maintaining long-term replication under hypoxia among clinical MAC-PD strains. In ATCC13950, the entry to log phase was significantly delayed under 5% oxygen compared to aerobic conditions, and the growth rate at midpoint was significantly increased under hypoxic conditions compared to aerobic conditions in ATCC13950. Such long-term lag phase followed by short-term log phase suggests lower capacity for ATCC13950 to continue replication under hypoxic conditions compared to clinical MAC-PD strains.”

Reviewer #4 (Public review):

Comments on revisions:

The revised version has satisfactorily addressed my initial comments in the discussion section.

The authors thank the Reviewer for understanding our reply.

Reviewer #5 (Public review):

Comments on revisions:

There is quite a lot of data and this could have been a really impactful study if the authors had channelized the Tn mutagenesis by focusing on one pathway or network. It looks scattered. However, from the previous version, the authors have made significant improvements to the manuscript and have provided comments that fairly address my questions.

The authors thank the Reviewer for understanding our reply. And the authors thank the Reviewer for the comments suggesting the future studies of TnSeq that focus on one pathway or network.

eLife Assessment

This is an important study that utilized in vivo optical measurements of the cortical metabolic rate of O2 and blood flow, as well as measurements in isolated mitochondria to assess the uncoupling of the oxidative phosphorylation due to hypoxia-ischemia injury of the neonatal brain, and effects of the hypothermia treatment. The combination of state-of-the-art optical measurements, mitochondrial assays, and the use of various control experiments provides convincing evidence for the derived conclusions. This work will be of interest to those in the mitochrondrial metabolomics, brain injury and hypoxia fields.

Reviewer #1 (Public review):

Summary:

This manuscript addresses the important problem of the uncoupling of oxidative phosphorylation due to hypoxia-ischemia injury in the neonatal brain and provides insight into the neuroprotective mechanisms of hypothermia treatment.

Strengths:

The authors used a combination of in vivo imaging of awake P10 mice and experiments on isolated mitochondria to assess various key parameters of brain metabolism during hypoxia-ischemia with and without hypothermia treatment. This unique approach resulted in a comprehensive data set that provides solid evidence to support the derived conclusions.

Weaknesses:

Several potential weaknesses were identified in the original submission, which the authors subsequently addressed in the revised manuscript. Here is the brief list of the questions:

(1) Is it possible that the observed relatively low baseline OEF and trends of increased OEF and CBF over several hours after the imaging start were partially due to slow recovery from anesthesia?

(2) What was the pain management, and is there a possibility that some of the observations were influenced by the pain-reducing drugs or their absence?

(3) Were P10 mice significantly stressed during imaging in the awake state because they didn't have head-restraint habituation training?

(4) Considering high metabolism and blood flow in the cortex, it could be potentially challenging to predict cortical temperature based on the skull temperature, particularly in the deeper part of the cortex.

(5) The map of estimated CMRO2 looks quite heterogeneous across the brain surface. Could this be partially resulting from the measurement artefact?

(6) It would be beneficial to provide more detailed justification for using P10 mice in the experiments.

Reviewer #3 (Public review):

Sun et al. present a comprehensive study using a novel photoacoustic microscopy setup and mitochondrial analysis to investigate the impact of hypoxia-ischemia (HI) on brain metabolism and the protective role of therapeutic hypothermia. The authors elegantly demonstrate three connected findings: (1) HI initially suppresses brain metabolism, (2) subsequently triggers a metabolic surge linked to oxidative phosphorylation uncoupling and brain damage, and (3) therapeutic hypothermia mitigates HI-induced damage by blocking this surge and reducing mitochondrial stress.

The study's design and execution are great, with a clear presentation of results and methods. Data is nicely presented, and methodological details are thorough.

However, a minor concern is the extensive use of abbreviations, which can hinder readability. As all the abbreviations are introduced in the text, their overuse may render the text hard to read to non-specialist audiences. Additionally, sharing the custom Matlab and other software scripts online, particularly those used for blood vessel segmentation, would be a valuable resource for the scientific community. In addition, while the study focuses on the short-term effects of HI, exploring the long-term consequences and definitively elucidating HI's impact on mitochondria would further strengthen the manuscript's impact.

Despite these minor points, this manuscript is very interesting.

Comments on revisions:

All addressed.

Author response:

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

Reviewer #1 (Public review)

(1) This manuscript addresses an important problem of the uncoupling of oxidative phosphorylation due to hypoxia-ischemia injury of the neonatal brain and provides insight into the neuroprotective mechanisms of hypothermia treatment.

The authors used a combination of in vivo imaging of awake P10 mice and experiments on isolated mitochondria to assess various key parameters of the brain metabolism during hypoxia-ischemia with and without hypothermia treatment. This unique approach resulted in a comprehensive data set that provides solid evidence for the derived conclusions

We thank the reviewer for the positive feedback.

(2) The experiments were performed acutely on the same day when the surgery was performed. There is a possibility that the physiology of mice at the time of imaging was still affected by the previously applied anesthesia. This is particularly of concern since the duration of anesthesia was relatively long. Is it possible that the observed relatively low baseline OEF (~20%) and trends of increased OEF and CBF over several hours after the imaging start were partially due to slow recovery from prolonged anesthesia? The potential effects of long exposure to anesthesia before imaging experiments were not discussed.

We thank the reviewer for this important comment and for pointing out the potential influence of anesthesia on the physiological state of the animals. We apologize for any confusion. To clarify, all PAM imaging experiments were conducted in awake animals. Isoflurane anesthesia was used only during two brief surgical procedures: (1) the installation of the head-restraint plastic head plate and (2) the right common carotid artery (CCA) ligation. Each anesthesia session lasted less than 20 minutes.

We have revised the Methods section to provide additional details:

For the subsection Procedures for PAM Imaging on page 17, we clarified the sequence of procedures during the head plate installation, as well as the corresponding anesthesia duration:

“After the applied glue was solidified (~20 min), the animal was first returned to its cage for full recovery from anesthesia, and then carefully moved to the treadmill and secured to the metal arm-piece with two #4–40 screws for awake PAM imaging. The total duration of anesthesia, including preparation and glue solidification, was approximately 20 minutes.”

For the subsection Neonatal Cerebral HI and Hypothermia Treatment on page 19, we also clarified the CCA ligation procedure:

“Briefly, P10 mice of both sexes anesthetized with 2% isoflurane were subjected to the right CCA-ligation. To manage pain, 0.25% Bupivacaine was administered locally prior to the surgical procedures, which took less than 10 minutes. After a recovery period for one hour, awake mice were exposed to 10% O₂ for 40 minutes in a hypoxic chamber at 37 °C.”

Regarding the reviewer’s concern about the observed trends in OEF and CBF, we agree that residual effects of anesthesia could, in principle, influence physiological parameters. However, we believe this is unlikely in this study for the following reasons. First, all imaging was conducted in awake animals after a clearly defined recovery period. Second, the trend of increasing OEF and CBF over time was consistent across animals and aligned with expected physiological responses following hypoxic-ischemic injury. In particular, the relatively low baseline OEF (0.21 at 37°C) is consistent with our previous study (0.25; (Cao et al., 2018)). The gradual increase in CBF and OEF reflects metabolic compensation and reperfusion following hypoxia-ischemia, as previously described (Lin and Powers, 2018). Therefore, we believe the observed changes are of physiological origin rather than anesthesia-related artifacts.

(3) The Methods Section does not provide information about drugs administered to reduce the pain. If pain was not managed, mice could be experiencing significant pain during experiments in the awake state after the surgery. Since the imaging sessions were long (my impression based on information from the manuscript is that imaging sessions were ~4 hours long or even longer), the level of pain was also likely to change during the experiments. It was not discussed how significant and potentially evolving pain during imaging sessions could have affected the measurements (e.g., blood flow and CMRO₂). If mice received pain management during experiments, then it was not discussed if there are known effects of used drugs on CBF, CMRO₂, and lesion size after 24 hr.

We thank the reviewer for this valuable comment regarding pain management. We confirm that local analgesia was administered to all animals prior to surgical procedures. Specifically, 0.25% Bupivacaine was applied locally before both the head-restraint plate installation and the CCA ligation. These details have now been clarified in the Methods section:

For the subsection Procedures for PAM Imaging on page 16, we added:

“To manage pain, 0.25% Bupivacaine was administered locally prior to the surgical procedures.”

For the subsection Neonatal Cerebral HI and Hypothermia Treatment on page 18, we added:

“To manage pain, 0.25% Bupivacaine was administered locally prior to the surgical procedures, which took less than 10 minutes.”

To our knowledge, Bupivacaine has minimal systemic effects at the dose used and is unlikely to significantly alter CBF, CMRO₂, or lesion development (Greenberg et al., 1998). No other analgesics (e.g., NSAIDs or opioids) were administered unless distress symptoms were observed—which did not occur in this study.

Additionally, although imaging sessions were extended (up to 2 hours), animals remained calm and showed no signs of pain or distress during or after the procedures. Throughout the experimental period (up to 24 hours post-surgery), animals were monitored for signs of discomfort (e.g., abnormal activity, breathing, or weight gain), but no additional analgesia was required. The neonatal HI procedures are considered minimally invasive, and based on our protocol and prior experience, local Bupivacaine provides effective analgesia during and after the brief surgeries. We have added a corresponding note in the Discussion section (newly added subsection: Limitations in this study, the last paragraph) on page 15:

“We observed no signs of distress or pain and did not use stress- or pain-reducing drugs during imaging. However, potential effects of stress or residual pain on CBF and CMRO₂ cannot be fully ruled out. Future studies could incorporate more detailed pain assessment and stress-mitigation strategies to further enhance physiological reliability.”

(4) Animals were imaged in the awake state, but they were not previously trained for the imaging procedure with head restraint. Did animals receive any drugs to reduce stress? Our experience with well-trained young-adult as well as old mice is that they can typically endure 2 and sometimes up to 3 hours of head-restrained awake imaging with intermittent breaks for receiving the rewards before showing signs of anxiety. We do not have experience with imaging P10 mice in the awake state. Is it possible that P10 mice were significantly stressed during imaging and that their stress level changed during the imaging session? This concern about the potential effects of stress on the various measured parameters was not discussed.

We thank the reviewer for this important comment regarding the potential effects of stress during awake imaging. The neonatal mice used in our study were P10, a stage at which animals are still physiologically immature and relatively inactive. Due to their small size and limited mobility, these animals did not struggle or show signs of distress during the imaging sessions. All animals remained calm and stable throughout the procedure, and no stress-reducing drugs were administered.

We agree that, unlike older animals, P10 mice are not amenable to prior behavioral training. However, their underdeveloped motor activity and natural docility at this stage allowed for stable head-restrained imaging without inducing overt stress responses. Although no behavioral signs of stress were observed, we acknowledge that subtle physiological effects cannot be entirely excluded. We have added a brief discussion in the Discussion section (newly added subsection: Limitations in this study, the last paragraph) on page 15:

“Lastly, for awake imaging, the small size of neonatal mice at P10 aids stability during awake PAM imaging, though it limits the feasibility of prior training, which is typically possible in older animals.”

(5) The temperature of the skull was measured during the hypothermia experiment by lowering the water temperature in the water bath above the animal's head. Considering high metabolism and blood flow in the cortex, it could be challenging to predict cortical temperature based on the skull temperature, particularly in the deeper part of the cortex.

We thank the reviewer for this helpful comment and for highlighting an important technical consideration. We acknowledge that we did not directly measure intracortical tissue temperature during the hypothermia experiments. While we recognize that relying on skull temperature may have limitations—particularly in reflecting temperature changes in deeper cortical regions—this approach is consistent with clinical practice, where intracortical temperature is typically not measured. Moreover, prior studies have shown that skull or brain surface temperature generally reflects cortical thermal dynamics to a reasonable extent under controlled conditions (Kiyatkin, 2007). We have added the following note in the Discussion section (newly added subsection: Limitations in this study, the 2^nd paragraph) on page 14:

“A technical limitation is the absence of direct intracortical temperature measurements during hypothermia; we relied on skull temperature, which may not fully capture temperature dynamics in deeper cortical layers. However, this approach aligns with clinical practice, where intracortical temperature is not typically measured. Future studies could benefit from more precise intracortical assessments.”

(6) The map of estimated CMRO₂ (Fig. 4B) looks very heterogeneous across the brain surface. Is it a coincidence that the highest CMRO₂ is observed within the central part of the field of view? Is there previous evidence that CMRO₂ in these parts of the mouse cortex could vary a few folds over a 1-2 mm distance?

We appreciate the reviewer’s insightful observation regarding the spatial heterogeneity observed in the estimated CMRO₂ map (Fig. 4B). This heterogeneity is not a result of scanning bias, as uniform contour scanning was performed across the entire field of view. The higher CMRO₂ values observed in the central region are unlikely to be artifacts and more likely reflect underlying physiological variability.

Our CMRO₂ estimation is based on an algorithm we previously developed and validated in other tissues. Specifically, we have successfully applied this algorithm to assess oxygen metabolism in the mouse kidney (Sun et al., 2021) and to monitor vascular adaptation and tissue oxygen metabolism during cutaneous wound healing (Sun et al., 2022). These studies demonstrated the algorithm's capability to capture spatial variations in oxygen metabolism. Although the current application to the brain is novel, the algorithm has been validated in controlled experimental settings and shown to produce consistent results. We acknowledge that the observed range of CMRO₂ appears relatively broad across a 1–2 mm distance; however, such heterogeneity may arise from local differences in vascular density, metabolic demand, or tissue oxygenation — all of which can vary across cortical regions, even within small spatial scales. We have added a brief note in the Discussion (Subsection: Optical CMRO₂ detection in neonatal care) on page 13 to acknowledge this point:

“Additionally, the spatial heterogeneity in estimated CMRO₂ observed in our data may reflect underlying physiological variability, including differences in vascular structure or metabolic demand across cortical regions. Future studies will aim to further validate and interpret these spatial patterns.”

(7) The justification for using P10 mice in the experiments has not been well presented in the manuscript.

We thank the reviewer for pointing out the need to clarify our choice of developmental stage. We chose P10 mice for our hypoxia-ischemia injury model because this stage is widely recognized as developmentally comparable to human term infants in terms of brain maturation. This approach has been validated by several previous studies (Clancy et al., 2007; Mallard and Vexler, 2015; Sheldon et al., 2018). We have added the following clarification to the Methods section (Subsection: Neonatal Cerebral HI and Hypothermia Treatment) on page 18:

“P10 mice were chosen for our experiments as they are widely used to model near-term infants in humans. At this developmental stage, the brain maturation in mice closely parallels that of near-term infants, making them an appropriate model for studying neonatal brain injury and therapeutic interventions (Clancy et al., 2007; Mallard and Vexler, 2015; Sheldon et al., 2018).”

(8) It was not discussed how the observations made in this manuscript could be affected by the potential discrepancy between the developmental stages of P10 mice and human babies regarding cellular metabolism and neurovascular coupling.

We thank the reviewer for raising this important point regarding developmental differences between P10 mice and human infants. We have discussed this issue by adding the following statement to the Discussion section (newly added subsection: Limitations in this study, the 1^st paragraph) on page 15, where we summarize the overall study design and model selection:

“While P10 mice are widely used to model near-term human infants, developmental differences in cellular metabolism and neurovascular coupling may affect the observed outcomes and limit direct clinical translation (Clancy et al., 2007; Mallard and Vexler, 2015; Sheldon et al., 2018). Nevertheless, the P10 model remains a valuable and widely accepted tool for studying neonatal hypoxia-ischemia mechanisms and evaluating therapeutic interventions.”

(9) Regarding the brain temperature measurements, the authors should use a new cohort of mice, implant the miniature thermocouples 1 mm, 0.5 mm, and immediately below the skull in different mice, and verify the temperature in the brain cortex under conditions applied in the experiments. The same approach could be applied to a few mice undergoing 4-hr-long hypothermia treatment in a chamber, which will provide information about the brain temperature that resulted in observed protection from the injury.

We thank the reviewer for this helpful recommendation. We fully agree that direct intracortical temperature measurement would provide more accurate insight into thermal dynamics during hypothermia treatment. However, the primary aim of this study was not to characterize the precise intracortical temperature response under hypothermic conditions, but rather to examine the effects of hypothermia on CMRO₂ and mitochondrial function. Due to the substantial time and resources required to perform direct intracortical temperature monitoring—and considering the technical focus of the current work—we respectfully suggest reserving such investigations for a future study specifically focused on thermal dynamics in hypoxia-ischemia models.

We have acknowledged this limitation in the subsection Limitations in this study of the Discussion on page 15, noting that skull temperature was used as an approximation of brain temperature and that this approach is consistent with clinical practice, where intracortical temperature is typically not measured. We also note that future studies may benefit from more precise assessments using intracortical probes.

(10) The mean values presented in Fig. 4G are much lower than the peak values in the 2D panels and potentially were calculated as the average values over the entire field of view. Please provide more details on how CMRO₂ was estimated and if the validity of the measurements is expected across the entire field of view. If there are parts of the field of view where the estimation of CMRO₂ is more reliable for technical reasons, maybe one way to compute the mean values is to restrict the usable data to the more centralized part of the field of view.

We thank the reviewer for this thoughtful comment. We confirm that CMRO₂ values shown in Figure 4G were calculated as spatial averages over the entire field of view (FOV; ~5 × 3 mm²) encompassing both hemicortices, as shown in Figure 1C. Regarding the observed CMRO₂ values, The apparent difference likely reflects a comparison between two different post-HI time points. Specifically, the ~0.5 value shown for the 37°C ipsilateral group in Figure 4G reflects the average CMRO₂ measured 24 hours after HI, while the ~1.5 value in Figure 2D (red line) corresponds to CMRO₂ during the early 0–2 hour post-HI period. The temporal difference accounts for the apparent discrepancy in magnitude. We understand the importance of consistency across the field of view and have clarified this point in the subsection Procedures for PAM Imaging in the Methods on page 17 “For the imaging field covering both hemicortices between the Bregma and Lambda of the neonatal mouse (5 × 3 mm² as shown in Figure 1C, with each hemicortex measuring 2.5 × 3 mm²)”, as well as in the Figure 4 legend on page 34 “Correlation of CMRO₂ and post-HI brain infarction in mouse neonates at 24 hours”.

In our model and setup, CMRO₂ estimation is spatially robust across the FOV under standard imaging conditions. We recognize, however, that certain peripheral regions may be more prone to signal attenuation. Future refinement of region selection could further improve spatial averaging strategies. For the current study, full-FOV averaging was used consistently across all groups to maintain comparability.

(11) Minor: Results presented in Supplementary Tables have too many significant digits.

Thank you for the helpful suggestion. We have revised Supplementary Tables S1 and S2 to reduce the number of significant digits and improve clarity.

Reviewer #2 (Public review)

(1) In this study, authors have hypothesized that mitochondrial injury in HIE is caused by OXPHOS-uncoupling, which is the cause of secondary energy failure in HI. In addition, therapeutic hypothermia rescues secondary energy failure. The methodologies used are state-of-the art and include PAM technique in live animal, bioenergetic studies in the isolated mitochondria, and others.

The study is comprehensive and impressive. The article is well written and statistical analyses are appropriate.

We thank the reviewer for the positive feedback.

(2) The manuscript does not discuss the limitation of this animal model study in view of the clinical scenario of neonatal hypoxia-ischemia.

We thank the reviewer for this valuable feedback. In response, we have added a dedicated “Limitations in this study” subsection in the Discussion, where we address the potential limitations of this animal model in the context of the clinical scenario of neonatal hypoxia-ischemia in the first paragraph on page 14, including the developmental differences between P10 mice and human infants.

(3) I see many studies on Pubmed on bioenergetics and HI. Hence, it is unclear what is novel and what is known.

We thank the reviewer for this important comment regarding the novelty of our study in the context of existing research on bioenergetics and hypoxia-ischemia (HI). To better clarify the novel aspects of our work, we have highlighted the relevant content in the Abstract (page 4) and Introduction (page 5). Specifically, while many studies have explored HI-related bioenergetic dysfunction, the mechanisms by which therapeutic hypothermia modulates CMRO₂ and mitochondrial function post-HI remain poorly understood.

Abstract on page 4: “However, it is unclear how post-HI hypothermia helps to restore the balance, as cooling reduces CMRO₂. Also, how transient HI leads to secondary energy failure (SEF) in neonatal brains remains elusive. Using photoacoustic microscopy, we examined the effects of HI on CMRO₂ in awake 10-day-old mice, supplemented by bioenergetic analysis of purified cortical mitochondria.”

Introduction on page 5: “The use of awake mouse neonates avoided the confounding effects of anesthesia on CBF and CMRO₂ (Cao et al., 2017; Gao et al., 2017; Sciortino et al., 2021; Slupe and Kirsch, 2018). In addition, we measured the oxygen consumption rate (OCR), reactive oxygen species (ROS), and the membrane potential of mitochondria that were immediately purified from the same cortical area imaged by PAM. This dual-modal analysis enabled a direct comparison of cerebral oxygen metabolism and cortical mitochondrial respiration in the same animal. Moreover, we compared the effects of therapeutic hypothermia on oxygen metabolism and mitochondrial respiration, and correlated the extent of CMRO₂-reduction with the severity of infarction at 24 hours after HI. Our results suggest that blocking HI-induced OXPHOS-uncoupling is an acute effect of hypothermia and that optical detection of CMRO₂ may have clinical applications in HIE.”

In this study, we propose that uncoupled oxidative phosphorylation (OXPHOS) underlies the secondary energy failure observed after HI, and we demonstrate that hypothermia suppresses this pathological CMRO₂ surge, thereby protecting mitochondrial integrity and preventing injury. Additionally, our use of photoacoustic microscopy (PAM) in awake neonatal mice represents a novel, non-invasive approach to track cerebral oxygen metabolism, with potential clinical relevance for guiding hypothermia therapy.

(4) What are the limitations of ex-vivo mitochondrial studies?

We thank the reviewer for this insightful comment. We acknowledge that ex-vivo mitochondrial assays do not fully replicate in vivo physiological conditions, as they lack systemic factors such as blood flow, cellular interactions, and intact tissue architecture. However, these assays are well-established and widely accepted in the field for evaluating mitochondrial function under controlled conditions (Caspersen et al., 2008; Niatsetskaya et al., 2012). Despite their limitations, they enable direct comparisons of mitochondrial activity across experimental groups and provide valuable mechanistic insights that complement in vivo observations.

(5) PAM technique limits the resolution of the image beyond 500-750 micron depth. Assessing basal ganglia may not be possible with this approach?

We thank the reviewer for this important comment. We agree that the imaging depth of PAM is limited and may not allow assessment of deeper brain structures such as the basal ganglia. However, in our neonatal HI model—as in many clinical cases of HIE—cortical injury is typically more severe and represents a major focus for mechanistic and therapeutic investigations. The cortical regions assessed with PAM are thus highly relevant to the pathophysiology of neonatal HI. We have now acknowledged this depth limitation in the third paragraph of the newly added Limitations in this study subsection of the Discussion on page 15:

“Another limitation of this study is the restricted imaging depth of the PAM technique, which is typically less than 1 mm and therefore does not allow assessment of deeper brain structures such as the basal ganglia. However, in both our neonatal HI model and most clinical cases of neonatal hypoxia-ischemia, cortical injury tends to be more prominent and functionally significant. As such, our cortical measurements remain highly relevant for investigating the mechanisms of injury and evaluating therapeutic interventions.”

(6) Hypothermia in present study reduces the brain temperature from 37 to 29-32 degree centigrade. In clinical set up, head temp is reduced to 33-34.5 in neonatal hypoxia ischemia. Hence a drop in temperature to 29 degrees is much lower relative to the clinical practice. How the present study with greater drop in head temperature can be interpreted for understanding the pathophysiology of therapeutic hypothermia in neonatal HIE. Moreover, in HIE model using higher temperature of 37 and dropping to 29 seems to be much different than the clinical scenario. Please discuss.

We thank the reviewer for raising this important point regarding temperature ranges in our study. In Figure 1, we used a broader temperature range (down to 29°C) to explore the general relationship between temperature and CMRO₂ in uninjured neonatal mice. This experiment was not intended to model therapeutic hypothermia directly, but rather to characterize the baseline physiological responses.

For all experiments involving hypothermia as a therapeutic intervention following HI, we consistently maintained a brain temperature of 32°C, which falls within the clinically accepted mild hypothermia range for neonatal HIE (typically 33–34.5°C). We believe this temperature closely mimics clinical practice and supports the translational relevance of our findings.

(7) NMR was assessed ex-vivo. How does it relate to in vivo assessment. Infants admitted in Neonatal intensive Care Unit, frequently get MRI with spectroscopy. How do the MRS findings in human newborns with HIE correlate with the ex-vivo evaluation of metabolites.

We thank the reviewer for this insightful question. While our study assessed brain metabolites ex vivo, similar metabolic changes have been observed in vivo using proton magnetic resonance spectroscopy (¹H-MRS) in infants with HIE. Specifically, reductions in N-acetylaspartate (NAA) — a marker of neuronal integrity — have been reported in neonates with severe brain injury, aligning with our ex vivo findings. This correlation between in vivo and ex vivo assessments supports the translational relevance of our model for studying metabolic disruption in neonatal HIE. We have added this point to the subsection Using Optically Measured CMRO₂ to Detect Neonatal HI Brain Injury of the Results on page 8, along with a supporting clinical reference (Lally et al., 2019):

“In addition, in vivo proton MRS in infants with HIE has also shown a reduction in NAA, particularly in cases of severe injury (Lally et al., 2019). This reduction in NAA, observed in neonatal intensive care settings, reflects neuronal and axonal loss or dysfunction and serves as a biomarker for injury severity. The alignment between our ex vivo observations and in vivo MRS findings in clinical studies reinforces the translational relevance of our model for investigating metabolic disturbances in neonatal HIE.”

Reviewer #3 (Public review)

(1) In Sun et al. present a comprehensive study using a novel photoacoustic microscopy setup and mitochondrial analysis to investigate the impact of hypoxia-ischemia (HI) on brain metabolism and the protective role of therapeutic hypothermia. The authors elegantly demonstrate three connected findings: (1) HI initially suppresses brain metabolism, (2) subsequently triggers a metabolic surge linked to oxidative phosphorylation uncoupling and brain damage, and (3) therapeutic hypothermia mitigates HI-induced damage by blocking this surge and reducing mitochondrial stress.

The study's design and execution are great, with a clear presentation of results and methods. Data is nicely presented, and methodological details are thorough.

We thank the reviewer for the positive feedback.

(2) However, a minor concern is the extensive use of abbreviations, which can hinder readability. As all the abbreviations are introduced in the text, their overuse may render the text hard to read to non-specialist audiences. Additionally, sharing the custom Matlab and other software scripts online, particularly those used for blood vessel segmentation, would be a valuable resource for the scientific community. In addition, while the study focuses on the short-term effects of HI, exploring the long-term consequences and definitively elucidating HI's impact on mitochondria would further strengthen the manuscript's impact.

We thank the reviewer for these valuable suggestions. Please find our point-by-point responses below:

Abbreviations: To improve readability, we have added a List of Abbreviations on page 3 to help readers, especially non-specialists, navigate the terminology more easily.

MATLAB Code Availability: The methodology for blood vessel segmentation was described in detail in our previous publication (Sun et al., 2020). We have now updated the subsection Quantification of Cerebral Hemodynamics and Oxygen Metabolism by PAM of the Methods on page 18 to provide additional details and have indicated that the MATLAB scripts are available upon request.

“Briefly, this process involves generating a vascular map using signal amplitude from the Hilbert transformation, selecting a region slightly larger than the vessel of interest, and applying Otsu’s thresholding method to remove background pixels. Isolated or spurious boundary fragments are then removed to improve boundary smoothness. The customized MATLAB code used for vessel segmentation is available upon request.”

Long-Term Effects of Hypothermia: We agree that exploring long-term outcomes would enhance the broader impact of this research. While our study focuses on the acute phase following HI, prior studies have shown long-term neuroprotective benefits of therapeutic hypothermia, such as enhanced white matter development (Koo et al., 2017). We have added this point to the fourth paragraph in the subsection Limitations in this study of the Discussion on page 15:

“While our study focuses on the acute effects of hypothermia, previous research has shown long-term neuroprotective benefits, including improved white matter development post-injury (Koo et al., 2017). These findings highlight hypothermia's potential for both immediate and extended recovery, warranting further study of long-term outcomes.”

(3) Extensive use of abbreviations.

Thank you for the helpful suggestion. To improve readability for a broader audience, we have added a List of Abbreviations on page 3 of the manuscript to assist readers in navigating terminology used throughout the text. This has been included as Response #2 to Reviewer #3.

(4) Share code used to conduct the study.

Thank you for the suggestion. The methodology for vessel segmentation was previously published (Sun et al., 2020), and we have noted in the subsection Quantification of Cerebral Hemodynamics and Oxygen Metabolism by PAM of the Methods on page 18 that the MATLAB code is available upon request. This has also been included as Response #2 to Reviewer #3.

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eLife Assessment

This valuable study presents a well-designed set of experiments demonstrating how a planthopper salivary carbonic anhydrase can promote rice stripe virus infection by modulating callose deposition in the host plant. The authors provide solid data for the proposed protein-protein interactions, including strengthened evidence for the LssaCA-NP-OsTLP complex and clarified dynamics of LssaCA presence in planta. Overall, the work reveals a mechanistic link whereby a vector salivary protein enhances a plant β-1,3-glucanase to suppress callose-based defense, thereby facilitating early viral establishment.

Reviewer #2 (Public Review):

There is increasing evidence that viruses manipulate vectors and hosts to facilitate transmission. For arthropods, saliva plays an essential role for successful feeding on a host and consequently for arthropod-borne viruses that are transmitted during arthropod feeding on new hosts. This is so because saliva constitutes the interaction interface between arthropod and host and contains many enzymes and effectors that allow feeding on a compatible host by neutralizing host defenses. Therefore, it is not surprising that viruses change saliva composition or use saliva proteins to provoke altered vector-host interactions that are favorable for virus transmission. However, detailed mechanistic analyses are scarce. Here, Zhao and coworkers study transmission of rice stripe virus (RSV) by the planthopper Laodelphax striatellus. RSV infects plants as well as the vector, accumulates in salivary glands and is injected together with saliva into a new host during vector feeding.

The authors present evidence that a saliva-contained enzyme - carbonic anhydrase (CA) - might facilitate virus infection of rice by interfering with callose deposition, a plant defense response. In vitro pull-down experiments, yeast two hybrid assay and binding affinity assays show convincingly interaction between CA and a plant thaumatin-like protein (TLP) that degrades callose. Similar experiments show that CA and TLP interact with the RSV nuclear capsid protein NT to form a complex. Formation of the CA-TLP complex increases TLP activity by roughly 30% and integration of NT increases TLP activity further. This correlates with lower callose content in RSV-infected plants and higher virus titer. Further, silencing CA in vectors decreases virus titers in infected plants. Interestingly, aphid CA was found to play a role in plant infection with two non-persistent non-circulative viruses, turnip mosaic virus and cucumber mosaic virus (Guo et al. 2023 doi.org/10.1073/pnas.2222040120), but the proposed mode of action is entirely different.

Editors' note: this version was assessed by the editors, without further input from the reviewers.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

In this study, the authors identified an insect salivary protein LssaCA participating viral initial infection in plant host. LssaCA directly bond to RSV nucleocapsid protein and then interacted with a rice OsTLP that possessed endo-β-1,3-glucanase activity to enhance OsTLP enzymatic activity and degrade callose caused by insects feeding. The manuscript suffers from fundamental logical issues, making its central narrative highly unconvincing.

(1) These results suggested that LssaCA promoted RSV infection through a mechanism occurring not in insects or during early stages of viral entry in plants, but in planta after viral inoculation. As we all know that callose deposition affects the feeding of piercing-sucking insects and viral entry, this is contradictory to the results in Fig. S4 and Fig. 2. It is difficult to understand callose functioned in virus reproduction in 3 days post virus inoculation. And authors also avoided to explain this mechanism.

We appreciate your insightful comment and acknowledge that our initial description may not have been sufficiently clear.

(1) Based on the EPG results, we found that LssaCA deficiency did not significantly affect total feeding time, time to first non-phloem phase, or time to first phloem feeding (Fig. S8A-D in the revised manuscript). However, the continuity of sap ingestion was disturbed—the N4 waveform of dsLssaCA SBPHs was occasionally interrupted for brief periods (newly added Fig. S8E in the revised manuscript), likely due to phloem blockage. In the revised manuscript, we have added this analysis to the Result section (Lines 285-291 and 578-587) and provided the EPG procedure in Material and Methods section (Lines 670-680).

(2) We assessed RSV titers immediately post-feeding to confirm the inoculation viral loads (Fig. 2G) and at 3 dpf (Fig. 2H-I) to assess the in-planta effects following viral inoculation. This did not mean that callose functions in virus reproduction at 3 days post viral inoculation. Rather, callose deposition typically occurs immediately in response to insect feeding and virus inoculation. When measuring callose deposition, we allowed insects to feed for 24 h and quantified the callose levels immediately post feeding. The EPG results showed that sap ingestion continuity was disrupted—the N4 waveform of dsLssaCA-treated SBPHs was occasionally interrupted for brief periods (newly added Fig. S8E in the revised manuscript), likely due to phloem blockage. We have reorganized the description to avoid confusion. Please see Lines 139-144 and Fig. S8E for detail.

(1) Missing significant data. For example, the phenotypes of the transgenic plants, the RSV titers in the transgenic plants (OsTLP OE, ostlp). The staining of callose deposition were also hard to convince. The evidence about RSV NP-LssaCA-OsTLP tripartite interaction to enhance OsTLP enzymatic activity is not enough.

We thank the reviewer for this insightful comment.

(1) We constructed OsTLP overexpression and mutant transgenic plants (OsTLP OE and ostlp) and assessed their phenotypes regarding RSV infection levels. Compared with wild-type plants, OsTLP OE plants exhibited accelerated growth, while ostlp plants showed growth inhibition. Following feeding by viruliferous L. striatellus, OsTLP OE plants had significantly higher RSV titers compared with wild-type plants, whereas ostlp mutant plants exhibited significantly lower RSV titers (Lines 221-228 and new Fig. 3I). These results indicate that OsTLP facilitates RSV infection in planta.

(2) The images showing callose deposition staining are representative of 15 images from 3 independent insect treatments. In addition to the staining images, we quantified fluorescence intensity and measured callose concentration by ELISA.

(2)  Figure 4a, there was the LssaCA signal in the fourth lane of pull-down data. Did MBP also bind LsssCA? The characterization of pull-down methods was rough a little bit. The method of GST pull-down and MBP pull-down should be characterized more in more detail.

We thank the reviewer for this helpful comment. MBP did not bind LssaCA. We have repeated the pull-down experiment and provide clearer figure with improved results. We have also revised and provided more detailed descriptions of the GST pull-down and MBP pull-down methods. Please refer to Lines 744-774 and Figure 4A for details.

eLife Assessment

The medicinal leech preparation is an amenable system in which to understand the neural basis of locomotion. Here a previously identified non-spiking neuron was studied in leech and found to alter the mean firing frequency of a crawl-related motoneuron, which fires during the contraction phase of crawling. The findings are valuable and the experiments were diligently done and considered solid. The results lay a foundation for additional studies in this system.

Reviewer #1 (Public review):

The medicinal leech preparation is an amenable system in which to understand how the underlying cellular networks for locomotion function. A previously identified non-spiking neuron (NS) was studied and found to alter the mean firing frequency of a crawl-related motoneuron (DE-3), which fires during the contraction phase of crawling. The data are solid. Identifying upstream neurons responsible for crawl motor patterning is essential for understanding how rhythmic behavior is controlled.

Reviewer #2 (Public review):

This study by Radice et al., takes advantage of the very well-established leach preparation to investigate questions related to motor control, more precisely the question of how the activity of motoneurons taking part in leach crawling behavior are finely tuned.

The paper is overall well written. The findings are clearly presented, and the data seems solid overall.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review): 

The medicinal leech preparation is an amenable system in which to understand how the underlying cellular networks for locomotion function. A previously identified non-spiking neuron (NS) was studied and found to alter the mean firing frequency of a crawl-related motoneuron (DE-3), which fires during the contraction phase of crawling. The data are mostly solid. Identifying upstream neurons responsible for crawl motor patterning is essential for understanding how rhythmic behavior is controlled.

Review of Revision: 

On a positive note, the rationale for the study is clearer to me now after reading the authors' responses to both reviewers, but that information, as described in the authors' responses, is minimally incorporated into the current revised paper. Incorporating a discussion of previous work on the NS cell has, indeed, improved the paper. 

I suggested earlier that the paper be edited for clarity but not much text has been changed since the first draft. I will provide an example of the types of sentences that are confusing. The title of the paper is: "Phase-specific premotor inhibition modulates leech rhythmic motor output". Are the authors referring to the inhibition created by premotor neurons (e.g., on to the motoneurons) or the inhibition that the premotor neurons receive? 

In this case, this is an interesting ambiguity: NS is inhibited and that inhibition is directly transmitted to the motoneurons because both cells are electrically coupled.  We believe that the title does not disguise the findings conveyed by the manuscript.

I also find the paper still confusing with regard to the suggested "functional homology" with the vertebrate Renshaw cells. When the authors set up this expectation of homology (should be analogy) in the introduction and other sections of the paper, one would assume that the NS cell would be directly receiving excitation from a motoneuron (like DE-3) and, in turn, the motoneuron would then receive some sort of inhibitory input to regulate its firing frequency. Essentially, I have always viewed the Renshaw cells as nature's clever way to monitor the ongoing activity of a motoneuron while also providing recurrent feedback or "recurrent inhibition" to modify that cell's excitatory state. The authors present their initial idea below on line 62. Authors write: "These neurons are present as bilateral pairs in each segmental ganglion and are functional homologs of the mammalian Renshaw cells (Szczupak, 2014). These spinal cord cells receive excitatory inputs from motoneurons and, in turn, transmit inhibitory signals to the motoneurons (Alvarez and Fyffe, 2007)." 

We agree with Reviewer #2: the correct term is "analogous," not "homologous." Thanks for pointing this out. We changed the term throughout the text.

The Reviewer is also right in the appreciation of the role of Renshaw cells. NS plays exactly the role that the Reviewer expresses. The ONLY difference is that NS is inhibited by the motoneurons, and in turn transmits this inhibition to the motoneurons via the rectifying electrical junctions. Attending the confusion that our description caused in the Reviewer, we have modified the cited sentence accordingly now in lines 65-67.

Minor note:

I suggest re-writing this last sentence as "these" is confusing. Change to: 'In the spinal cord, Renshaw interneurons receive excitatory inputs from motoneurons and, in turn, transmit inhibitory signals to them (Alvarez and Fyffe, 2007).'] 

Please, see the changes mentioned above.

Furthermore, the authors note that (line 69 on): "In the context of this circuit the activity of excitatory motoneurons evokes chemically mediated inhibitory synaptic potentials in NS. Additionally, the NS neurons are electrically coupled......In physiological conditions this coupling favors the transmission of inhibitory signals from NS to motoneurons." Based on what is being conveyed here, I see a disconnect with the "functional homology" being presented earlier. I may be missing something, but the Renshaw analogy seems to be quite different compared to what looks like reciprocal inhibition in the leech. If the authors want to make the analogy to Renshaw cells clearer, then they should make a simple ball and stick diagram of the leech system and visually compare it to the Renshaw/motoneuron circuit with regard to functionality. This simple addition would help many readers. 

We have simplified the description regarding the Renshaw cell (lines 65-67) to avoid the “details” of the connectivity between the two circuits.

This report focuses on NS neurons and their role in crawling; we mention the analogy with Renshaw cells to widen the interest of the results. We do not think that making a special diagram to compare how the two neurons play a similar role via different connections among the players is useful in the context of this manuscript.

The Abstract, Authors write (line 19), "Specifically, we analyzed how electrophysiological manipulation of a premotor nonspiking (NS) neuron, that forms a recurrent inhibitory circuit (homologous to vertebrate Renshaw cells)...."

First, a circuit would not be homologous to a cell, and the term homology implies a strict developmental/evolutionary commonality. At best, I would use the term functionally analogous but even then I am still not sure that they are functionally that similar (see comments above). 

Reviewer #2 is right. We changed the sentence in line 20.

Line 22: "The study included a quantitative analysis of motor units active throughout the fictive crawling cycle that shows that the rhythmic motor output in isolated ganglia mirrors the phase relationships observed in vivo." This sentence must be revised to indicate that not all of the extracellular units were demonstrated to be motor units. Revise to: "The study included a quantitative analysis of identified and putative motor units active throughout the fictive crawling cycle that shows.....' 

Line 187 regarding identifying units as motoneurons: Authors write, "While multiple extracellular recordings have been performed previously (Eisenhart et al., 2000), these results (Figure 4) present the first quantitative analysis of motor units activated throughout the crawling cycle in this type of recordings." The authors cannot assume that the units in the recorded nerves belong only to motoneurons. Based on their first rebuttal, the authors seem to be reluctant to accept the idea that the extracellularly recorded units might represent a different class of neurons. They admit that some sensory neurons (with somata located centrally) do, indeed, travel out the same nerves recorded, but go on to explain why they would not be active. 

The leech has a variety of sensory organs that are located in the periphery, and some of these sensory neurons do show rhythmic activity correlated with locomotor activity (see Blackshaw's early work). The numerous stretch receptors, in fact, have very large axons that pass through all the nerves recorded in the current paper. 

In Fig. 4, it is interesting that the waveforms of all the units recorded in the PP nerve exhibit a reversal in waveform as compared to those in the DP nerve, which might indicate (based on bipolar differential recording) that the units in the PP nerve are being propagated in the opposite direction (i.e., are perhaps afferent). Rhythmic presynaptic inhibition and excitation is commonly seen for stretch receptors within the CNS (see the work of Burrows) and many such cells are under modulatory control. 

Most likely, the majority of the units are from motoneurons, but we do not really know at this point. The authors should reframe their statements throughout the paper as: 'While multiple extracellular recordings have been performed previously (Eisenhart et al., 2000), these results (Figure 4) present the first quantitative analysis of multiple extracellular units, using spike sorting methods, which are activated throughout the crawling cycle.' In cases where the identity of the unit is known, then it is fine to state that, but when the identity of the unit is not known, then there should be some qualification and stated as 'putative motor units' 

We understand the concern of Reviewer #2 regarding the type of neurons active during dopamine-induced crawling in isolated ganglia. However, we believe there is sufficient evidence to support that the recorded spikes originate from motoneurons. As readers may share the same concern, we have added a paragraph explaining why spikes from somatic sensory neurons such as P or T cells, or from stretch receptors, are unlikely to contribute (lines 206-214). We included the term putative in the abstract.

The Methods section:

Needs to include the full parameters that were used to assess whether bursting activity was qualified in ways to be considered crawling activity or not. Typically, crawl-like burst periods of no more than 25 seconds have been the limit for their qualification as crawling activity. In Fig 2F, for example, the inter-burst period is over 35 seconds; that coupled with an average 5 second burst duration would bring the burst period to 40 seconds, which is substantially out of range for there to be bursting relevant to crawl activity. Simply put, long DE-3 burst periods are often observed but may not be indicative of a crawl state as the CV motoneurons are no longer out of phase with DE-3. A number of papers have adopted this criterion. 

We now indicate in the methods the range of period values measured in our experiments.  For the reviewer informatio we show here histograms depicting the variability of period and duty cycle values recorded in our experiments (control conditions). The Reviewer can see that the bursting activity of DE-3 fall within what has been published.

Author response image 1.

Crawling in isolated ganglia. A. Histogram of periods end-to-end during crawling in isolated ganglia. The dotted line indicates the mean obtained from the averages of all experiments. The solid black line represents the mean of all cycles across all experiments. B. As in A, for the duty cycle calculated using end-to-end periods.  (n = 210 cycles from 45 ganglia obtained from 32 leeches in all cases).

Reviewer #1 (Recommendations for the authors): 

Minor comments-

Line 100: "In the frame of the recurrent inhibitory circuit, NS is the target of inhibitory signals". Suggestion: 'Within the framework of the recurrent inhibitory circuit, NS is the target of inhibitory signals.' 

Changed as suggested (line 107).

Line 163: "This series of experiments proves that, as predicted based on the known circuit (Figure 164 1C), inhibitory signals onto NS premotor neurons were transmitted to DE-3 motoneurons and counteracted their excitatory drive during crawling, limiting their firing frequency". I think this sentence is too strong plus needs some editing. Suggestion: 'As predicted based on the known circuit (Figure 164 1C), this series of experiments indicates that inhibitory signals onto NS premotor neurons are transmitted to DE-3 motoneurons, thus limiting their firing frequency and counteracting their excitatory drive during crawling."

Changed as suggested.

Lines 86, 292 and 304 and Fig 4 legend: "Different from DE-3, In-Phase units showed a marked decrease in the maximum bFF along time." Suggestion: Replace the word "along" with 'across' time. Also replace those words in the Fig 4 legend and Line 80...."along" (replace with 'across') the different stages of crawling. 

Changed as suggested.

Line 311: "bursts and a concurrent inhibitory input via NS (Figure 7). Coherent with this interpretation, the activity level of the Anti- Phase units was not influenced by these inhibitory signals". Suggestion: Replace the word "coherent" with 'consistent'. 

Changed as suggested.

Line 332: "...offer the particular advantage of allowing electrical manipulation of individual neurons in wildtype adults," I am unsure what the authors are attempting to convey. Not sure what they mean by "wildtype" in this context and why that would matter. 

“wildtype” was eliminated

We thank Reviewer #2 for the suggested edits to the text.

eLife Assessment

This important Research Advance builds on the authors' previous work delineating the roles of the rodent perirhinal cortex and the basolateral amygdala in first- and second-order learning. The convincing results show that serial exposure of non-motivationally relevant stimuli influences how those stimuli are encoded within the perirhinal cortex and basolateral amygdala when paired with a shock. This manuscript will be interesting for researchers in cognitive and behavioral neuroscience.

Reviewer #1 (Public review):

Summary:

This study advances the lab's growing body of evidence exploring higher-order learning and its neural mechanisms. They recently found that NMDA receptor activity in the perirhinal cortex was necessary for integrating stimulus-stimulus associations with stimulus-shock associations (mediated learning) to produce preconditioned fear, but it was not necessary for forming stimulus-shock associations. On the other hand, basolateral amygdala NMDA receptor activity is required for forming stimulus-shock memories. Based on these facts, the authors assessed: 1. why the perirhinal cortex is necessary for mediated learning but not direct fear learning and 2. the determinants of perirhinal cortex versus basolateral amygdala necessity for forming direct versus indirect fear memories. The authors used standard sensory preconditioning and variants designed to manipulate the novelty and temporal relationship between stimuli and shock and, therefore, the attentional state under which associative information might be processed. Under experimental conditions where information would presumably be processed primarily in the periphery of attention (temporal distance between stimulus/shock or stimulus pre-exposure), perirhinal cortex NMDA receptor activation was required for learning indirect associations. On the other hand, when information would likely be processed in focal attention (novel stimulus contiguous with shock), basolateral amygdala NMDA activity was required for learning direct associations. Together, the findings indicate that the perirhinal cortex and basolateral amygdala subserve peripheral and focal attention, respectively. The authors provide support for their conclusions using careful, hypothesis-driven experimental design, rigorous methods, and integrating their findings with the relevant literature on learning theory, information processing, and neurobiology. Therefore, this work will be highly interesting to several fields.

Strengths:

(1) The experiments were carefully constructed and designed to test hypotheses that were rooted in the lab's previous work, in addition to established learning theory and information processing background literature.

(2) There are clear predictions and alternative outcomes. The provided table does an excellent job of condensing and enhancing the readability of a large amount of data.

(3) In a broad sense, attention states are a component of nearly every behavioral experiment. Therefore, identifying their engagement by dissociable brain areas and under different learning conditions is an important area of research.

(4) The authors clearly note where they replicated their own findings, report full statistical measures, effect sizes, and confidence intervals, indicating the level of scientific rigor.

(5) The findings raise questions for future experiments that will further test the authors' hypotheses; this is well discussed.

Reviewer #2 (Public review):

This paper continues the authors' research on the roles of the basolateral amygdala (BLA) and the perirhinal cortex (PRh) in sensory preconditioning (SPC) and second order conditioning (SOC). In this manuscript, the authors explore how prior exposure to stimuli may influence which regions are necessary for conditioning to the second-order cue (S2). The authors perform a series of experiments which first confirm prior results shown by the author - that NMDA receptors in the PRh are necessary in SPC during conditioning of the first-order cue (S1) with shock to allow for freezing to S2 at test; and that NMDA receptors in the BLA are necessary for S1 conditioning during the S1-shock pairings. The authors then set out to test the hypothesis that the PRh encodes associations in a peripheral state of attention whereas the BLA encodes associations in a focal state of attention, similar to the A1 and A2 states in Wagner's theory of SOP. To do this, they show that BLA is necessary for conditioning to S2 when the S2 is first exposed during a serial compound procedure - S2-S1-shock. To determine whether pre-exposure of S2 will shift S2 to a peripheral focal state, the authors run a design in which S2-S1 presentations are given prior to the serial compound phase. The authors show that this restores NMDA receptor activity within the PRh as necessary for fear response to S2 at test. They then test whether the presence of S1 during the serial compound conditioning allows the PRh to support the fear responses to S2 by introducing a delay conditioning paradigm in which S1 is no longer present. The authors find that PRh is no longer required and suggest that this is due to S2 remaining in the primary focal state.

Strengths:

As with their earlier work, the authors have performed a rigorous series of experiments to better understand the roles of the BLA and PRh in the learning of first- and second-order stimuli. The experiments are well-designed and clearly presented, and the results show definitive differences in functionality between the PRh and BLA. The first experiment confirms earlier findings from the lab (and others), and the authors then build on their previous work to more deeply reveal how these regions differ in how they encode associations between stimuli. The authors have done a commendable job on pursuing these questions.

Table 1 is an excellent way to highlight the results and provide the reader with a quick look-up table of the findings.

Reviewer #3 (Public review):

Summary:

This manuscript presents a series of experiments that further investigate the roles of the BLA and PRH in sensory preconditioning, with a particular focus on understanding their differential involvement in the association of S1 and S2 with shock.

Strengths:

The motivation for the study is clearly articulated, and the experimental designs are thoughtfully constructed. I especially appreciate the inclusion of Table 1, which makes the designs easy to follow. The results are clearly presented, and the statistical analyses are rigorous.

During the revision, the authors have adequately addressed my minor suggestions from the original version.

Author response:

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

Reviewer #1 (Public review):

Summary:

This study advances the lab's growing body of evidence exploring higher-order learning and its neural mechanisms. They recently found that NMDA receptor activity in the perirhinal cortex was necessary for integrating stimulus-stimulus associations with stimulus-shock associations (mediated learning) to produce preconditioned fear, but it was not necessary for forming stimulus-shock associations. On the other hand, basolateral amygdala NMDA receptor activity is required for forming stimulus-shock memories. Based on these facts, the authors assessed: (1) why the perirhinal cortex is necessary for mediated learning but not direct fear learning, and (2) the determinants of perirhinal cortex versus basolateral amygdala necessity for forming direct versus indirect fear memories. The authors used standard sensory preconditioning and variants designed to manipulate the novelty and temporal relationship between stimuli and shock and, therefore, the attentional state under which associative information might be processed. Under experimental conditions where information would presumably be processed primarily in the periphery of attention (temporal distance between stimulus/shock or stimulus pre-exposure), perirhinal cortex NMDA receptor activation was required for learning indirect associations. On the other hand, when information would likely be processed in focal attention (novel stimulus contiguous with shock), basolateral amygdala NMDA activity was required for learning direct associations. Together, the findings indicate that the perirhinal cortex and basolateral amygdala subserve peripheral and focal attention, respectively. The authors provide support for their conclusions using careful, hypothesis-driven experimental design, rigorous methods, and integrating their findings with the relevant literature on learning theory, information processing, and neurobiology. Therefore, this work will be highly interesting to several fields.

Strengths:

(1) The experiments were carefully constructed and designed to test hypotheses that were rooted in the lab's previous work, in addition to established learning theory and information processing background literature.

(2) There are clear predictions and alternative outcomes. The provided table does an excellent job of condensing and enhancing the readability of a large amount of data.

(3) In a broad sense, attention states are a component of nearly every behavioral experiment. Therefore, identifying their engagement by dissociable brain areas and under different learning conditions is an important area of research.

(4) The authors clearly note where they replicated their own findings, report full statistical measures, effect sizes, and confidence intervals, indicating the level of scientific rigor.

(5) The findings raise questions for future experiments that will further test the authors' hypotheses; this is well discussed.

Weaknesses:

As a reader, it is difficult to interpret how first-order fear could be impaired while preconditioned fear is intact; it requires a bit of "reading between the lines".

We appreciate the Reviewer’s point and have attempted to address on lines 55-63 of the revised paper: “In a recent pair of studies, we extended these findings in two ways. First, we showed that S1 does not just form an association with shock in stage 2; it also mediates an association between S2 and the shock. Thus, S2 enters testing in stage 3 already conditioned, able to elicit fear responses (Wong et al., 2019). Second, we showed that this mediated S2-shock association requires NMDAR-activation in the PRh, as well as communication between the PRh and BLA (Wong et al., 2025). These findings raise two critical questions: 1) why is the PRh engaged for mediated conditioning of S2 but not for direct conditioning of S1; and 2) more generally, what determines whether the BLA and/or PRh is engaged for conditioning of the S1 and/or S2?”

Reviewer #2 (Public review):

Summary:

This paper continues the authors' research on the roles of the basolateral amygdala (BLA) and the perirhinal cortex (PRh) in sensory preconditioning (SPC) and second-order conditioning (SOC). In this manuscript, the authors explore how prior exposure to stimuli may influence which regions are necessary for conditioning to the second-order cue (S2). The authors perform a series of experiments which first confirm prior results shown by the author - that NMDA receptors in the PRh are necessary in SPC during conditioning of the first-order cue (S1) with shock to allow for freezing to S2 at test; and that NMDA receptors in the BLA are necessary for S1 conditioning during the S1-shock pairings. The authors then set out to test the hypothesis that the PRh encodes associations in a peripheral state of attention, whereas the BLA encodes associations in a focal state of attention, similar to the A1 and A2 states in Wagner's theory of SOP. To do this, they show that BLA is necessary for conditioning to S2 when the S2 is first exposed during a serial compound procedure - S2-S1-shock. To determine whether pre-exposure of S2 will shift S2 to a peripheral focal state, the authors run a design in which S2-S1 presentations are given prior to the serial compound phase. The authors show that this restores NMDA receptor activity within the PRh as necessary for the fear response to S2 at test. They then test whether the presence of S1 during the serial compound conditioning allows the PRh to support the fear responses to S2 by introducing a delay conditioning paradigm in which S1 is no longer present. The authors find that PRh is no longer required and suggest that this is due to S2 remaining in the primary focal state.

Strengths:

As with their earlier work, the authors have performed a rigorous series of experiments to better understand the roles of the BLA and PRh in the learning of first- and second-order stimuli. The experiments are well-designed and clearly presented, and the results show definitive differences in functionality between the PRh and BLA. The first experiment confirms earlier findings from the lab (and others), and the authors then build on their previous work to more deeply reveal how these regions differ in how they encode associations between stimuli. The authors have done a commendable job of pursuing these questions.

Table 1 is an excellent way to highlight the results and provide the reader with a quick look-up table of the findings.

Weaknesses:

The authors have attempted to resolve the question of the roles of the PRh and BLA in SPC and SOC, which the authors have explored in previous papers. Laudably, the authors have produced substantial results indicating how these two regions function in the learning of first- and second-order cues, providing an opportunity to narrow in on possible theories for their functionality. Yet the authors have framed this experiment in terms of an attentional framework and have argued that the results support this particular framework and hypothesis - that the PRh encodes peripheral and the BLA encodes focal states of learning. This certainly seems like a viable and exciting hypothesis, yet I don't see why the results have been completely framed and interpreted this way. It seems to me that there are still some alternative interpretations that are plausible and should be included in the paper.

We appreciate the Reviewer’s point and have attempted to address it on lines 566-594 of the Discussion: “An additional point to consider in relation to Experiments 3A, 3B, 4A and 4B is the level of surprise that rats experienced following presentations of the familiar S2 in stage 2. Specifically, in Experiments 3A and 3B, S2 was followed by the expected S1 (low surprise) and its conditioning required activation of NMDA receptors in the PRh and not the BLA. By contrast, in Experiments 4A and 4B, S2 was followed by omission of the expected S1 (high surprise) and its conditioning required activation of NMDA receptors in the BLA and not the PRh. This raises the possibility that surprise, or prediction error, also influences the way that S2 is processed in focal and peripheral states of attention. When prediction error is low, S2 is processed in the peripheral state of attention: hence, learning under these circumstances requires NMDA receptor activation in the PRh and not the BLA. By contrast, when prediction error is high, S2 is preserved in the focal state of attention: hence, learning under these circumstances requires NMDA receptor activation in the BLA and not the PRh. The impact of prediction error on the processing of S2 could be assessed using two types of designs. In the first design, rats are pre-exposed to S2-S1 pairings in stage 1 and this is followed by S2-S3-shock pairings in stage 2. The important feature of this design is that, in stage 2, the S2 is followed by surprise in omission of S1 and presentation of S3. Thus, if a large prediction error maintains processing of the familiar S2 in the BLA, we might expect that its conditioning in this design would require NMDA receptor activation in the BLA (in contrast to the results of Experiment 3B) and no longer require NMDA receptor activation in the PRh (in contrast to the results of Experiment 3A). In the second design, rats are pre-exposed to S2 alone in stage 1 and this is followed by S2-[trace]-shock pairings in stage 2. The important feature of this design is that, in stage 2, the S2 is not followed by the surprising omission of any stimulus. Thus, if a small prediction error shifts processing of the familiar S2 to the PRh, we might expect that its conditioning in this design would no longer require NMDA receptor activation in the BLA (in contrast to the results of Experiment 4B) but, instead, require NMDA receptor activation in the PRh (in contrast to the results of Experiment 4A). Future studies will use both designs to determine whether prediction error influences the processing of S2 in the focus versus periphery of attention and, thereby, whether learning about this stimulus requires NMDA receptor activation in the BLA or PRh.”

Reviewer #3 (Public review):

Summary:

This manuscript presents a series of experiments that further investigate the roles of the BLA and PRH in sensory preconditioning, with a particular focus on understanding their differential involvement in the association of S1 and S2 with shock.

Strengths:

The motivation for the study is clearly articulated, and the experimental designs are thoughtfully constructed. I especially appreciate the inclusion of Table 1, which makes the designs easy to follow. The results are clearly presented, and the statistical analyses are rigorous. My comments below mainly concern areas where the writing could be improved to help readers more easily grasp the logic behind the experiments.

Weaknesses:

(1) Lines 56-58: The two previous findings should be more clearly summarized. Specifically, it's unclear whether the "mediated S2-shock" association occurred during Stage 2 or Stage 3. I assume the authors mean Stage 2, but Stage 2 alone would not yet involve "fear of S2," making this expression a bit confusing.

We apologise for the confusion and have revised the summary of our previous findings on lines 55-63. The revised text now states: “In a recent pair of studies, we extended these findings in two ways. First, we showed that S1 does not just form an association with shock in stage 2; it also mediates an association between S2 and the shock. Thus, S2 enters testing in stage 3 already conditioned, able to elicit fear responses (Wong et al., 2019). Second, we showed that this mediated S2-shock association requires NMDAR-activation in the PRh, as well as communication between the PRh and BLA (Wong et al., 2025). These findings raise two critical questions: 1) why is the PRh engaged for mediated conditioning of S2 but not for direct conditioning of S1; and 2) more generally, what determines whether the BLA and/or PRh is engaged for conditioning of the S1 and/or S2?”

(2) Line 61: The phrase "Pavlovian fear conditioning" is ambiguous in this context. I assume it refers to S1-shock or S2-shock conditioning. If so, it would be clearer to state this explicitly.

Apologies for the ambiguity - we have omitted the term “Pavlovian” which may have been the source of confusion: The revised text on lines 60-63 now states: “These findings raise two critical questions: 1) why is the PRh engaged for mediated conditioning of S2 but not for direct conditioning of S1; and 2) more generally, what determines whether the BLA and/or PRh is engaged for conditioning of the S1 and/or S2?”

(3) Regarding the distinction between having or not having Stage 1 S2-S1 pairings, is "novel vs. familiar" the most accurate way to frame this? This terminology could be misleading, especially since one might wonder why S2 couldn't just be presented alone on Stage 1 if novelty is the critical factor. Would "outcome relevance" or "predictability" be more appropriate descriptors? If the authors choose to retain the "novel vs. familiar" framing, I suggest providing a clear explanation of this rationale before introducing the predictions around Line 118.

We have incorporated the suggestion regarding “predictability” while also retaining “novelty” as follows. 

L76-85: “For example, different types of arrangements may influence the substrates of conditioning to S2 by influencing its novelty and/or its predictive value at the time of the shock, on the supposition that familiar stimuli are processed in the periphery of attention and, thereby, the PRh (Bogacz & Brown, 2003; Brown & Banks, 2015; Brown & Bashir, 2002; Martin et al., 2013; McClelland et al., 2014; Morillas et al., 2017; Murray & Wise, 2012; Robinson et al., 2010; Suzuki & Naya, 2014; Voss et al., 2009; Yang et al., 2023) whereas novel stimuli are processed in the focus of attention and, thereby, the amygdala (Holmes et al., 2018; Qureshi et al., 2023; Roozendaal et al., 2006; Rutishauser et al., 2006; Schomaker & Meeter, 2015; Wright et al., 2003).”

L116-120: “Subsequent experiments then used variations of this protocol to examine whether the engagement of NMDAR in the PRh or BLA for Pavlovian fear conditioning is influenced by the novelty/predictive value of the stimuli at the time of the shock (second implication of theory) as well as their distance or separation from the shock (third implication of theory; Table 1).”

(4) Line 121: This statement should refer to S1, not S2.

(5) Line 124: This one should refer to S2, not S1.

We have checked the text on these lines for errors and confirmed that the statements are correct. The lines encompassing this text (L121-130) are reproduced here for convenience:

(1) When rats are exposed to novel S2-S1-shock sequences, conditioning of S2 and S1 will be disrupted by a DAP5 infusion into the BLA but not into the PRh (Experiments 2A and 2B);

(2) When rats are exposed to S2-S1 pairings and then to S2-S1-shock sequences, conditioning of S2 will be disrupted by a DAP5 infusion into the PRh but not the BLA whereas conditioning of S1 will be disrupted by a DAP5 infusion into the BLA not the PRh (Experiments 3A and 3B);

(3) When rats are exposed to S2-S1 pairings and then to S2 (trace)-shock pairings, conditioning of S2 will be disrupted by a DAP5 into the BLA not the PRh (Experiments 4A and 4B).

(6) Additionally, the rationale for Experiment 4 is not introduced before the Results section. While it is understandable that Experiment 4 functions as a follow-up to Experiment 3, it would be helpful to briefly explain the reasoning behind its inclusion.

Experiment 4 follows from the results obtained in Experiment 3; and, as noted, the reasoning for its inclusion is provided locally in its introduction. We attempted to flag this experiment earlier in the general introduction to the paper; but this came at the cost of clarity to the overall story. As such, our revised paper retains the local introduction to this experiment. It is reproduced here for convenience:

“In Experiments 3A and 3B, conditioning of the pre-exposed S1 required NMDAR-activation in the BLA and not the PRh; whereas conditioning of the pre-exposed S2 required NMDAR-activation in the PRh and not the BLA. We attributed these findings to the fact that the pre-exposed S2 was separated from the shock by S1 during conditioning of the S2-S1-shock sequences in stage 2: hence, at the time of the shock, S2 was no longer processed in the focal state of attention supported by the BLA; instead, it was processed in the peripheral state of attention supported by the PRh.

“Experiments 4A and 4B employed a modification of the protocol used in Experiments 3A and 3B to examine whether a pre-exposed S1 influences the processing of a pre-exposed S2 across conditioning with S2-S1-shock sequences. The design of these experiments is shown in Figure 4A. Briefly, in each experiment, two groups of rats were exposed to a session of S2-S1 pairings in stage 1 and, 24 hours later, a session of S2-[trace]-shock pairings in stage 2, where the duration of the trace interval was equivalent to that of S1 in the preceding experiments. Immediately prior to the trace conditioning session in stage 2, one group in each experiment received an infusion of DAP5 or vehicle only into either the PRh (Experiment 4A) or BLA (Experiment 4B). Finally, all rats were tested with presentations of the S2 alone in stage 3. If the substrates of conditioning to S2 are determined only by the amount of time between presentations of this stimulus and foot shock in stage 2, the results obtained in Experiments 4A and 4B should be the same as those obtained in Experiments 3A and 3B: acquisition of freezing to S2 will require activation of NMDARs in the PRh and not the BLA. If, however, the presence of S1 in the preceding experiments (Experiments 3A and 3B) accelerated the rate at which processing of S2 transitioned from the focus of attention to its periphery, the results obtained in Experiments 4A and 4B will differ from those obtained in Experiments 3A and 3B. That is, in contrast to the preceding experiments where acquisition of freezing to S2 required NMDAR-activation in the PRh and not the BLA, here acquisition of freezing to S2 should require NMDAR-activation in the BLA but not the PRh.”

Reviewer #1 (Recommendations for the authors):

I greatly enjoyed reading and reviewing this manuscript, and so I only have boilerplate recommendations.

(1) I might add a couple of sentences discussing how/why preconditioned fear could be intact while first-order fear is impaired. Of course, if I am interpreting the provided interpretation correctly, the reason is that peripheral processing is still intact even when BLA NMDA receptors are blocked, and so mediated conditioning still occurs. Does this mean that mediated conditioning does not require learning the first-order relationship, and that they occur in parallel? Perhaps I just missed this, but I cannot help but wonder whether/how the psychological processes at play might change when first-order learning is impaired, so this would be greatly appreciated.

As noted above, we have revised the general introduction (around lines 55-59) to clarify that the direct S1-shock and mediated S2-shock associations form in parallel. Hence, manipulations that disrupt first-order fear to the S1 (such as a BLA infusion of the NMDA receptor antagonist, DAP5) do not automatically disrupt the expression of sensory preconditioned fear to the S2.

(2) Adding to the above - does the SOP or another theory predict serial vs parallel information flow from focal state to peripheral, or perhaps it is both to some extent?

SOP predicts both serial and parallel processing of information in its focal and peripheral states. That is, some proportion of the elements that comprise a stimulus may decay from the focal state of attention to the periphery (serial processing); hence, at any given moment, the elements that comprise a stimulus can be represented in both focal and peripheral states (parallel processing).

Given the nature of the designs and tools used in the present study (between-subject assessment of a DAP5 effect in the BLA or PRh), we selected parameters that would maximize the processing of the S2 and S1 stimuli in one or the other state of activation; hence the results of the present study. We are currently examining the joint processing of stimulus elements across focal and peripheral states using simultaneous recordings of activity in the BLA and PRh. These recordings are collected from rats trained in the different stages of a within-subject sensory preconditioning protocol. The present study created the basis for this work, which will be published separately in due course.

(3) The organization of PRh vs BLA is nice and consistent across each figure, but I would suggest adding any kind of additional demarcation beyond the colors and text, maybe just more space between AB / CD. The figure text indicating PRh/BLA is a bit small.

Thank you for the suggestion – we have added more space between the top and bottom panels of the figure.

(4) Line 496 typo ..."in the BLA but not the BLA".

Apologies for the type - this has been corrected.

Reviewer #2 (Recommendations for the authors):

I found the experiments to be extremely well-designed and the results convincing and exciting. The hypothesis of the focal and peripheral states of attention being encoded by BLA and PRh respectively, is enticing, yet as indicated in the public review, this does not seem to be the only possible interpretation. This is my only serious comment for the authors.

(1) I think it would be worth reframing the article slightly to give credence to alternative hypotheses. Not to say that the authors' intriguing hypothesis shouldn't be an integral part of the introduction, but no alternatives are mentioned. In experiment 2, could the fact that S2 is already being a predictor of S1, not block new learning to S2? In the framework of stimulus-stimulus associations, there would be no surprise in the serial-compound stage of conditioning at the onset of S1. This may prevent direct learning of the S2-shock association within the BLA. This type of association may as well (S2 predicts S1, but it's omitted), which could support learning by S2. fall under the peripheral/focal theory, but I don't think it's necessary to frame this possibility in terms of a peripheral/focal theory. To build on this alternative interpretation, the absence of S1 in experiment 4 may induce a prediction error. The peripheral and focal states appear to correspond to A2 and A1 in SOP extremely well, and I think it would potentially add interest and support. If the authors do intend to make the paper a strong argument for their hypothesis, perhaps a few additional experiments may be introduced. If the novelty of S2 is critical for S2 not to be processed in a focal state during the serial compound stage, could pre-exposure of S2 alone allow for dependence of S2-shock on the PRh? Assuming this is what the authors would predict, this might disentangle the S-S theory mentioned above from the peripheral/focal theory. Or perhaps run an experiment S2-X in stage 1 and S2-S1-shock in stage 2? This said, I think the experiments are more than sufficient for an exciting paper as is, and I don't think running additional experiments is necessary. I would only argue for this if the authors make a hard claim about the peripheral/focal theory, as is the case for the way the paper is currently written.

We appreciate the reviewer’s excellent point and suggestions. We have included an additional paragraph in the Discussion on page 24 (lines 566-594).  “An additional point to consider in relation to Experiments 3A, 3B, 4A and 4B is the level of surprise that rats experienced following presentations of the familiar S2 in stage 2. Specifically, in Experiments 3A and 3B, S2 was followed by the expected S1 (low surprise) and its conditioning required activation of NMDA receptors in the PRh and not the BLA. By contrast, in Experiments 4A and 4B, S2 was followed by omission of the expected S1 (high surprise) and its conditioning required activation of NMDA receptors in the BLA and not the PRh. This raises the possibility that surprise, or prediction error, also influences the way that S2 is processed in focal and peripheral states of attention. When prediction error is low, S2 is processed in the peripheral state of attention: hence, learning under these circumstances requires NMDA receptor activation in the PRh and not the BLA. By contrast, when prediction error is high, S2 is preserved in the focal state of attention: hence, learning under these circumstances requires NMDA receptor activation in the BLA and not the PRh. The impact of prediction error on the processing of S2 could be assessed using two types of designs. In the first design, rats are pre-exposed to S2-S1 pairings in stage 1 and this is followed by S2-S3-shock pairings in stage 2. The important feature of this design is that, in stage 2, the S2 is followed by surprise in omission of S1 and presentation of S3. Thus, if a large prediction error maintains processing of the familiar S2 in the BLA, we might expect that its conditioning in this design would require NMDA receptor activation in the BLA (in contrast to the results of Experiment 3B) and no longer require NMDA receptor activation in the PRh (in contrast to the results of Experiment 3A). In the second design, rats are pre-exposed to S2 alone in stage 1 and this is followed by S2-[trace]-shock pairings in stage 2. The important feature of this design is that, in stage 2, the S2 is not followed by the surprising omission of any stimulus. Thus, if a small prediction error shifts processing of the familiar S2 to the PRh, we might expect that its conditioning in this design would no longer require NMDA receptor activation in the BLA (in contrast to the results of Experiment 4B) but, instead, require NMDA receptor activation in the PRh (in contrast to the results of Experiment 4A). Future studies will use both designs to determine whether prediction error influences the processing of S2 in the focus versus periphery of attention and, thereby, whether learning about this stimulus requires NMDA receptor activation in the BLA or PRh.”

(3) I was surprised the authors didn't frame their hypothesis more in terms of Wagner's SOP model. It was minimally mentioned in the introduction or the authors' theory if it were included more in the introduction. I was wondering whether the authors may have avoided this framing to avoid an expectation for modeling SOP in their design. If this were the case, I think the paper stands on its own without modeling, and at least for myself, a comparison to SOP would not require modeling of SOP. If this was the authors' concern for avoiding it, I would suggest to the authors that they need not be concerned about it.

We appreciate the endorsement of Wagner’s SOP theory as a nice way of framing our results. We are currently working on a paper in which we use simulations to show how Wagner’s theory can accommodate the present findings as well as others in the literature on sensory preconditioning. For this reason, we have not changed the current paper in relation to this point.

eLife Assessment

This study presents an important new approach to quantifying parsimony preferences in human inference. The work provides convincing evidence that humans are sensitive to specific formalizations of parsimony, such as the dimensionality of perceptual shapes. The work is considered timely, well-written, and technically sophisticated, effectively bridging concepts from statistical inference and human decision-making.

Reviewer #1 (Public review):

I have to preface my evaluation with a disclosure that I lack the mathematical expertise to fully assess what seems to be the authors' main theoretical contribution. I am providing this assessment to the best of my ability, but I cannot substitute for a reviewer with more advanced mathematical/physical training.

Summary:

This paper describes a new theoretical framework for measuring parsimony preferences in human judgments. The authors derive four metrics that they associate with parsimony (dimensionality, boundary, volume, and robustness) and measure whether human adults are sensitive to these metrics. In two tasks, adults had to choose one of two flower beds which a statistical sample was generated from, with or without explicit instruction to choose the flower bed perceptually closest to the sample. The authors conduct extensive statistical analyses showing that humans are sensitive to most of the derived quantities, even when the instructions encouraged participants to choose only based on perceptual distance. The authors complement their study with a computational neural network model that learns to make judgments about the same stimuli with feedback. They show that the computational model is sensitive to the tasks communicated by feedback and only uses the parsimony-associated metrics when feedback trains it to do so.

Strengths:

(1) The paper derives and applies new mathematical quantities associated with parsimony. The mathematical rigor is very impressive and is much more extensive than in most other work in the field, where studies often adopt only one metric (such as the number of causes or parameters). These formal metrics can be very useful for the field.

(2) The studies are preregistered, and the statistical analyses are strong.

(3) The computational model complements the behavioral findings, showing that the derived quantities are not simply equivalent to maximum-likelihood inference in the task.

(4) The speculations in the discussion section (e.g., the idea that human sensitivity is driven by the computational demands each metric requires) are intriguing and could usefully guide future work.

Weaknesses:

(1) The paper is very hard to understand. Many of the key details of the derived metrics are in the appendix, with very little accessible explanation in the main text. The figures helped me understand the metrics somewhat, although I am still not sure how some of them (such as boundary or robustness as measured here) are linked to parsimony. I understand that this is addressed by the derivations in the appendix, but as a computational cognitive scientist, I would have benefited from more accessible explanations. Important aspects of the human studies are also missing from the main text, such as the sample size for Experiment 2.

(2) It is not fully clear whether the sensitivity of human participants to some of the quantities convincingly reported here actually means that participants preferred shapes according to the corresponding aspect of parsimony. The title and framing suggest that parsimony "guides" human decision-making, which may lead readers to conclude that humans prefer more parsimonious shapes. I am not sure the sensitivity findings alone support this framing, but it might just be my misunderstanding of the analyses.

(3) The stimulus set included only four combinations of shapes, each designed to diagnostically target one of the theoretical quantities. It is unclear whether the results are robust or specific to these particular 4 stimuli.

(4) The study is framed as measuring "decision-making," but the task resembles statistical inference (e.g., which shape generated the data) or perceptual judgment. This is a minor point since "decision-making" is not well defined in the literature, yet the current framing in the title gave me the initial impression that humans would be making preference choices and learning about them over time with feedback.

Reviewer #2 (Public review):

This manuscript presents a sophisticated investigation into the computational mechanisms underlying human decision-making, and it presents evidence for a preference for simpler explanations (Occam's razor). The authors dissect the simplicity bias into four different components, and they design experiments to target each of them by presenting choices whose underlying models differ only in one of these components. In the learning tasks, participants must infer a "law" (a logical rule) from observed data in a way that operationalizes the process of scientific reasoning in a controlled laboratory setting. The tasks are complex enough to be engaging but simple enough to allow for precise computational modeling.

As a further novel feature, authors derive a further term in the expansion of the log-evidence, which arises from boundary terms. This is combined with a choice model, which is the one that is tested in experiments. Experiments are run, but with humans and with artificial intelligence agents, showing that humans have an enhanced preference for simplicity as compared to artificial neural networks.

Overall, the work is well written, interesting, and timely, bridging concepts in statistical inference and human decision making. Although technical details are rather elaborate, my understanding is that they represent the state of the art.

I have only one main comment that I think deserves more comments. Computing the complexity penalty of models may be hard. It is unlikely that humans can perform such a calculation on the fly. As authors discuss in the final section, while the dimensionality term may be easier to compute, others (e.g., the volume term, which requires an integral) may be considerably harder to compute (it is true that they should be computed once and for all for each task, but still...). I wonder whether the sensitivity of human decision making with reference to the different terms is so different, and in particular whether it aligns with computational simplicity, or with the possibility of approximating each term by simple heuristics. Indeed, the sensitivity to the volume term is significantly and systematically lower than that of other terms. I wonder whether this relation could be made more quantitative using neural networks, using as a proxy of computational hardness the number of samples needed to reach a given error level in learning each of these terms.

Reviewer #3 (Public review):

Summary:

This is a very interesting paper that documents how humans use a variety of factors that penalize model complexity and integrate over a possible set of parameters within each model. By comparison, trained neural networks also use these biases, but only on tasks where model selection was part of the reward structure. In the situation where training emphasizes maximum-likelihood decisions, only neural networks, but not humans, were able to adapt their decision-making. Humans continue to use model integration simplicity biases.

Strengths:

This study used a pre-registered plan for analyzing human data, which exceeds the standards compared to other current studies.

The results are technically correct.

Weaknesses:

The presentation of the results could be improved.

Author response:

Reviewer #1 (Public review)

I have to preface my evaluation with a disclosure that I lack the mathematical expertise to fully assess what seems to be the authors' main theoretical contribution. I am providing this assessment to the best of my ability, but I cannot substitute for a reviewer with more advanced mathematical/physical training.

Summary:

This paper describes a new theoretical framework for measuring parsimony preferences in human judgments. The authors derive four metrics that they associate with parsimony (dimensionality, boundary, volume, and robustness) and measure whether human adults are sensitive to these metrics. In two tasks, adults had to choose one of two flower beds which a statistical sample was generated from, with or without explicit instruction to choose the flower bed perceptually closest to the sample. The authors conduct extensive statistical analyses showing that humans are sensitive to most of the derived quantities, even when the instructions encouraged participants to choose only based on perceptual distance. The authors complement their study with a computational neural network model that learns to make judgments about the same stimuli with feedback. They show that the computational model is sensitive to the tasks communicated by feedback and only uses the parsimony-associated metrics when feedback trains it to do so.

Strengths:

(1)  The paper derives and applies new mathematical quantities associated with parsimony. The mathematical rigor is very impressive and is much more extensive than in most other work in the field, where studies often adopt only one metric (such as the number of causes or parameters). These formal metrics can be very useful for the field.

(2)  The studies are preregistered, and the statistical analyses are strong.

(3)  The computational model complements the behavioral findings, showing that the derived quantities are not simply equivalent to maximum-likelihood inference in the task.

(4)  The speculations in the discussion section (e.g., the idea that human sensitivity is driven by the computational demands each metric requires) are intriguing and could usefully guide future work.

Weaknesses:

(1) The paper is very hard to understand. Many of the key details of the derived metrics are in the appendix, with very little accessible explanation in the main text. The figures helped me understand the metrics somewhat, although I am still not sure how some of them (such as boundary or robustness as measured here) are linked to parsimony. I understand that this is addressed by the derivations in the appendix, but as a computational cognitive scientist, I would have benefited from more accessible explanations. Important aspects of the human studies are also missing from the main text, such as the sample size for Experiment 2.

(2) It is not fully clear whether the sensitivity of human participants to some of the quantities convincingly reported here actually means that participants preferred shapes according to the corresponding aspect of parsimony. The title and framing suggest that parsimony "guides" human decision-making, which may lead readers to conclude that humans prefer more parsimonious shapes. I am not sure the sensitivity findings alone support this framing, but it might just be my misunderstanding of the analyses.

(3) The stimulus set included only four combinations of shapes, each designed to diagnostically target one of the theoretical quantities. It is unclear whether the results are robust or specific to these particular 4 stimuli.

(4) The study is framed as measuring "decision-making," but the task resembles statistical inference (e.g., which shape generated the data) or perceptual judgment. This is a minor point since "decision-making" is not well defined in the literature, yet the current framing in the title gave me the initial impression that humans would be making preference choices and learning about them over time with feedback.

We are grateful for the supportive comments highlighting the rigor of our experimental design and data analysis. The Reviewer lists four points under “weaknesses”, to which we reply below. 

(1)  The paper is very hard to understand

In the revised version of the paper, we will expand the main text to include a more detailed and intuitive description of the terms of the Fisher Information Approximation, in particular clarifying the interpretation of robustness and boundary as parsimony. We also will include more details that are now given only in Methods, such as the sample size for the second experiment. 

(2) Sensitivity of human participants 

We do argue, and believe, that our data show that people tend to prefer simpler shapes. However, giving a well-posed definition of "preference" in this context turns out to be nontrivial.

At the very least, any statement such as "people prefer shape A over B" should be qualified with something like “when the distance of the data from both shapes is the same.” In other words, one should control for goodness-of-fit. Even before making any reference to our behavioral model, this phenomenon (a preference for the simpler model when goodness of fit is matched between models) is visible in Figure 3a, where the effective decision boundary used by human participants is closer to the more complex model than the cyan line representing the locus of points with equal goodness of fit under the two models (or equivalently, with the same Euclidean distance from the two shapes). The goal of our theory and our behavioral model is precisely to systematize this sort of control, extending it beyond just goodness-of-fit and allowing us to control simultaneously for multiple features of model complexity that may affect human behavior in different ways. In other words, it allows us not only to ask whether people prefer shape A over B after controlling for the distance of the data to the shapes, but also to understand to what extent this preference is driven by important geometrical features such as dimensionality, volume, curvature, and boundaries of the shapes. More specifically, and importantly, our theory makes it possible to measure the strength of the preference, rather than merely asserting its existence. In our modeling framework, the existence of a preference for simpler shapes is captured by the fact that the estimated sensitivities to the complexity penalties are positive (and although they differ in magnitude, all are statistically reliable).

(3) Generalization to different shapes  

Thank you for bringing up this important topic. First, note that while dimensionality and volume are global properties of models and only take two possible values in our human tasks, the boundary and robustness penalties depend on the model and on the data and therefore assume a continuum of values through the tasks (note also that the boundary penalty is relevant for all task types, not just the one designed specifically to study it, because all models except the zero-dimensional dot have boundaries). Therefore, our experimental setting is less restrictive of what it may seem, because it explores a range of possible values for two of the four model features. However, we agree that it would be interesting to repeat our experiment with a broader range of models, perhaps allowing their dimensionality and volume to vary more. In the same spirit, it would be interesting to study the dependence of human behavior on the amount of available data. We believe that these are all excellent ideas for further study that exceed the scope of the present paper. We will include these important points in a revised Discussion. 

(4) Usage of “decision making” vs “perceptual judgment”

Thank you. We will clarify better in the text that our usage of “decision making” overlaps with the idea of a perceptual judgment and that our experiments do not tackle sequential aspects of repeated decisions. 

Reviewer #2 (Public review):

This manuscript presents a sophisticated investigation into the computational mechanisms underlying human decision-making, and it presents evidence for a preference for simpler explanations (Occam's razor). The authors dissect the simplicity bias into four different components, and they design experiments to target each of them by presenting choices whose underlying models differ only in one of these components. In the learning tasks, participants must infer a "law" (a logical rule) from observed data in a way that operationalizes the process of scientific reasoning in a controlled laboratory setting. The tasks are complex enough to be engaging but simple enough to allow for precise computational modeling.

As a further novel feature, authors derive a further term in the expansion of the logevidence, which arises from boundary terms. This is combined with a choice model, which is the one that is tested in experiments. Experiments are run, but with humans and with artificial intelligence agents, showing that humans have an enhanced preference for simplicity as compared to artificial neural networks.

Overall, the work is well written, interesting, and timely, bridging concepts in statistical inference and human decision making. Although technical details are rather elaborate, my understanding is that they represent the state of the art.

I have only one main comment that I think deserves more comments. Computing the complexity penalty of models may be hard. It is unlikely that humans can perform such a calculation on the fly. As authors discuss in the final section, while the dimensionality term may be easier to compute, others (e.g., the volume term, which requires an integral) may be considerably harder to compute (it is true that they should be computed once and for all for each task, but still...). I wonder whether the sensitivity of human decision making with reference to the different terms is so different, and in particular whether it aligns with computational simplicity, or with the possibility of approximating each term by simple heuristics. Indeed, the sensitivity to the volume term is significantly and systematically lower than that of other terms. I wonder whether this relation could be made more quantitative using neural networks, using as a proxy of computational hardness the number of samples needed to reach a given error level in learning each of these terms.

Thank you. The computational complexity associated with calculating the different terms and its potential connection to human sensitivity to the terms is an intriguing topic. As we hinted at in the discussion, we agree with the reviewer that this is a natural candidate for further research, which likely deserves its own study and exceeds the scope of the present paper. 

As a minor aside, at least for the present task the volume term may not be that hard to compute, because it can be expressed with the number of distinguishable probability distributions in the model (Balasubramanian 1996). Given the nature of our task, where noise is Gaussian, isotropic and with known variance, the geometry of the model is actually the Euclidean geometry of the plane, and the volume is simply the (log of the) length of the line that represents the one-dimensional models, measured in units of the standard deviation of the noise.

Reviewer #3 (Public review):

Summary:

This is a very interesting paper that documents how humans use a variety of factors that penalize model complexity and integrate over a possible set of parameters within each model. By comparison, trained neural networks also use these biases, but only on tasks where model selection was part of the reward structure. In the situation where training emphasizes maximum-likelihood decisions, only neural networks, but not humans, were able to adapt their decision-making. Humans continue to use model integration simplicity biases.

Strengths:

This study used a pre-registered plan for analyzing human data, which exceeds the standards compared to other current studies.

The results are technically correct.

Weaknesses:

The presentation of the results could be improved.

We thank the reviewer for their appreciation of our experimental design and methodology, and for pointing out (in the separate "recommendations to authors") a few passages of the paper where the presentation could be improved. We will clarify these passages in the revision.

eLife Assessment

This valuable study successfully decoded visual representations of facial expressions and stereoscopic depth information from electroencephalogram (EEG) signals recorded in an immersive virtual reality (VR) environment. The evidence is solid in demonstrating the technical feasibility of integrating state-of-the-art EEG decoding and VR with eye tracking. This work will interest neuroscience researchers, as well as engineers developing brain-machine interfaces and/or virtual reality displays.

Reviewer #1 (Public review):

Summary:

The study by Klotzsche et al. examines whether emotional facial expressions can be decoded from EEG while participants view 3D faces in immersive VR and whether stereoscopic depth cues affect these neural representations. Participants viewed computer-generated faces (three identities, four emotions) rendered either stereoscopically or monoscopically, while performing an emotion recognition task. Time-resolved multivariate decoding revealed above-chance decodability of facial expressions from EEG. Importantly, decoding accuracy did not differ between monoscopic and stereoscopic viewing. This indicates that the neural representation of expressions is robust against stereoscopic disparity for the relevant features. However, a separate classifier could distinguish the depth condition (mono vs. stereo) from EEG, i.e., the pattern of neuronal activity differs between conditions, but not in ways relevant for the decoding of emotions. It had an early peak and a temporal profile similar to identity decoding, suggesting that early, task-irrelevant visual differences are captured neurally. Cross-decoding further demonstrated that expression decoders trained in one depth condition could generalize to the other, supporting the idea of representational invariance. Eye-tracking analyses showed that expressions and identities could be decoded from gaze patterns, but not the depth condition, and EEG- and gaze-based decoding performances were not correlated across participants. Overall, this work shows that EEG decoding in VR is feasible and sensitive, and suggests that stereoscopic cues are represented in the brain but do not influence the neural processing of facial expressions. This study addresses a relevant question with state-of-the-art experimental and data analysis techniques.

Strengths:

(1) It combines EEG, virtual reality stereoscoptic and monoscopic presentation of visual stimuli, and advanced data analysis methods to address a timely question.

(2) The figures are of very high quality.

(3) The reference list is appropriate and up to date.

Weaknesses:

(1) The introduction-results-discussion-methods order makes it hard to follow the Results without repeatedly consulting the Methods. Please introduce minimal, critical methodological context at the start of each Results subsection; reserve technical details for Methods/Supplement.

(2) Many Results subsections begin with a crisp question and present rich analyses, but end without a short synthesis. Please add 1-2 sentences that explicitly answer the opening question and state what the analyses demonstrate.

(3) The Results compellingly show that (a) expressions are decodable from EEG and (b) mono vs stereo trials are decodable from EEG; yet expression decoding is comparable across mono and stereo. It would help if you articulate why depth is neurally distinguishable while leaving expression representations unchanged. Maybe improve the discussion of the results of source localization and give a more detailed connection to what we already know about the processing of disparity.

Reviewer #2 (Public review):

Summary:

The authors' main aim was to determine the extent to which the emotional expression of face images could be inferred from electrophysiological data under the viewing conditions imposed by immersive virtual reality displays. Further, given that stereoscopic depth cues can be easily manipulated in such displays, the authors wished to investigate whether successful emotion decoding was affected by the presence or absence of these depth cues, and also if the presence/absence of depth cues was itself a property of the viewing experience that could be decoded from neural data.

Overall, the authors use fairly standard approaches to decoding neural data to demonstrate that above-chance results (slightly above the 0.5 chance threshold for their measure of choice) are in general achievable for emotion decoding, decoding the identity of faces from neural data, and decoding the presence/absence of depth cues in an immersive virtual reality display. They further examine the contribution of specific components of the response to visual stimuli with similar outcomes.

Strengths:

The main contribution of the manuscript is methodological. Rather than shedding particular light on the neural mechanisms supporting depth processing or face perception, what is on offer is primarily a straightforward examination of an applied question. With regard to the goal of answering that applied question, I think the paper succeeds. The overall experimental design is not novel, but in this case, that is a good thing. The authors have used relatively unadorned tasks and previous approaches to applying decoding tools to EEG data to see what they can get out of the neural data collected under these viewing conditions. While I would say that there is not a great deal that is especially surprising about these results, the authors do meet the goal they set for themselves.

Weaknesses:

Some of the key weaknesses I see are points that the authors raise themselves in their discussion, particularly with regard to the generalizability of their results. In particular, the 3D faces they have employed here perhaps exhibit a somewhat limited repertoire of emotional expression and do not necessarily cover a representative gamut of emotional face appearances, such as one would encounter in naturalistic settings. Then again, part of the goal of the paper was to examine the decodability of emotional expression in a specific, non-natural viewing environment - a viewing environment in which one could reasonably expect to encounter artificial faces like these. Still, the limitations of the stimuli potentially limit the scope of the conclusions one should draw from the data. I also think that there is a great deal of room for low-level image properties to drive the decoding results for faces, which could have been addressed in a number of ways (matching power spectra, for example, or using an inverted-image control condition). The absence of such control comparisons means that it is difficult to know if this is really a result that reflects face processing or much lower-level image differences that are diagnostic of emotion or identity in this subset of images. Again, to some extent, this is potentially acceptable - if one is mostly interested in whether this result is achievable at all (by hook or by crook), then it is not so important how the goal is met. Then again, one would perhaps like to know if what has been measured here is more a reflection of spatial vision vs. face processing mechanisms.

Reviewer #3 (Public review):

Summary:

This study investigates two main questions:

(1) whether brain activity recorded during immersive virtual reality can differentiate facial expressions and stereoscopic depth, and

(2) whether depth cues modulate facial information processing.

The results show that both expression and depth information can be decoded from multivariate EEG recorded in a head-mounted VR setup. However, the results show that the decoding performance of facial expressions does not benefit from depth information.

Strengths:

The study is technically strong and well executed. EEG data are of high quality despite the challenges of recording inside a head-mounted VR system. The work effectively combines stereoscopic stimulus presentation, eye-tracking to monitor gaze behavior, and time-resolved multivariate decoding techniques. Together, these elements provide an exemplary demonstration of how to collect and analyze high-quality EEG data in immersive VR environments.

Weaknesses:

The major limitation concerns the theoretical question about how stereoscopic depth modulates facial expression processing. While previous work has suggested that stereoscopic depth cues can shape natural face perception and emphasize the importance of binocular information in recognizing facial expressions (lines 95-97), the present study reports a null effect of depth. However, the stimulus configuration they used likely constrained the ability to detect any depth-related effects. All facial stimuli were static, frontal, and presented at a fixed distance. This design leads to near-ceiling behavioral performance and no behavioral effect of depth on expression recognition. It makes the null modulation of depth on expression processing unsurprising and limits the theoretical reach of the study. Adding more subtle or naturalistic features (such as various viewing angles and dynamic expressions) to the stimulus set if the authors aim to advance a strong theoretical claim about the role of binocular disparity. Or reframing the work as a technical validation of EEG decoding in this context.

Another issue relates to the claim that eye movements cannot explain the EEG decoding results. It is a real challenge to remove eye-movement-related artifacts and confounds, as the VR setup tends to encourage viewers to explore the environment freely. However, nearly half of the eye-tracking datasets were lost (usable in only 17 of 33 participants), which substantially weakens the evidence for EEG-gaze dissociation. Moreover, it would be almost impossible to decode facial information from only two-dimensional gaze direction, given that with 60 EEG channels, the decoding accuracy was modest (AUC ≈ 0.60). These two factors together limited the strength of the reported null correlation between neural and eye-data decoding.

The decoding analysis appears to use all 60 EEG channels as input features. I wonder why the authors did not examine using more spatially specific channel subsets. Facial expression and depth cues are known to preferentially engage occipito-temporal regions (e.g., N170-related sites), yet the current approach treats all sensors equally. Including all the channels may add noise and irrelevant signals to facial information decoding. Besides, using a subset of spatial-specific channels would align more directly with the subsequent source reconstruction.

Author response:

We thank the reviewers for their thoughtful and constructive comments. We are pleased that they found the study technically strong and the integration of EEG decoding, immersive VR, and eye tracking valuable.

Across all three reviews, several points of clarification emerged. In our revision, we will focus on:

(1) Improving clarity and structure of the manuscript (Reviewer #1).

We will strengthen the flow between the Methods and Results subsections and include explicit concluding statements for the single results.

(2) Emphasize methodological scope and limitations in terms of stimulus set and generalizability (Reviewers #2 and #3).

We will further emphasize that a key objective was to establish, for the first time, the methodological feasibility of decoding facial features (especially emotional expressions) under VR conditions, and that our stimulus set (consisting of facial expressions that were easy to distinguish) limits (a) the task-relevance (and thus possibly the neural integration) of depth information and (b) the generalizability to less easily distinguishable settings. We appreciate the suggestion of an inverted-face control to further investigate the extent to which the decoding results were based on low-level features; however, we do not plan a follow-up experiment at this stage; instead, we will discuss this limitation more explicitly.

We believe these revisions will substantially strengthen the manuscript and further highlight its methodological focus.

eLife Assessment

This important study reveals that mitotic release of an ER-microtubule tether is critical for normal mitotic progression. Manipulating CLIMP63 phosphorylation, the authors provide convincing evidence that persistent microtubule-ER contacts activate the spindle assembly checkpoint and, if mitosis is forced to proceed, drive severe micronucleation. While the study provides new mechanistic insights, some evidence is indirect, and additional experiments would further refine the model.