Author response:

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

We wanted to clarify Reviewer #1’s latest comment in the last round of review, “Furthermore, the referee appreciates that the authors have echoed the concern regarding the limited statistical robustness of the observed scrambling events.” We appreciate the follow up information provided from Reviewer #1 that their comment is specifically about the low count alternative pathway events that we view at the dimer interface, and not the statistics of the manuscript overall as they believe that “the study presents a statistically rigorous analysis of lipid scrambling events across multiple structures and conformations (Reviewer #1)”. We agree with the Reviewer and acknowledge that overall our coarse-grained study represents the most comprehensive single manuscript of the entire TMEM16 family to date.

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

Public Review:

Reviewer #1 (Public review):

Summary:

The manuscript investigates lipid scrambling mechanisms across TMEM16 family members using coarse-grained molecular dynamics (MD) simulations. While the study presents a statistically rigorous analysis of lipid scrambling events across multiple structures and conformations, several critical issues undermine its novelty, impact, and alignment with experimental observations.

Critical issues:

(1) Lack of Novelty:

The phenomenon of lipid scrambling via an open hydrophilic groove is already well-established in the literature, including through atomistic MD simulations. The authors themselves acknowledge this fact in their introduction and discussion. By employing coarse-grained simulations, the study essentially reiterates previously known findings with limited additional mechanistic insight. The repeated observation of scrambling occurring predominantly via the groove does not offer significant advancement beyond prior work.

We agree with the reviewer’s statement regarding the lack of novelty when it comes to our observations of scrambling in the groove of open Ca2+-bound TMEM16 structures. However, we feel that the inclusion of closed structures in this study, which attempts to address the yet unanswered question of how scrambling by TMEM16s occurs in the absence of Ca2+, offers new observations for the field. In our study we specifically address to what extent the induced membrane deformation, which has been theorized to aid lipids cross the bilayer especially in the absence of Ca2+, contributes to the rate of scrambling (see references 36, 59, and 66). There are also several TMEM16F structures solved under activating conditions (bound to Ca2+ and in the presence of PIP2) which feature structural rearrangements to TM6 that may be indicative of an open state (PDB 6P48) and had not been tested in simulations. We show that these structures do not scramble and thereby present evidence against an out-of-the-groove scrambling mechanism for these states. Although we find a handful of examples of lipids being scrambled by Ca2+-free structures of TMEM16 scramblases, none of our simulations suggest that these events are related to the degree of deformation.

(2) Redundancy Across Systems:

The manuscript explores multiple TMEM16 family members in activating and non-activating conformations, but the conclusions remain largely confirmatory. The extensive dataset generated through coarse-grained MD simulations primarily reinforces established mechanistic models rather than uncovering fundamentally new insights. The effort, while statistically robust, feels excessive given the incremental nature of the findings.

Again, we agree with the reviewer’s statement that our results largely confirm those published by other groups and our own. We think there is however value in comparing the scrambling competence of these TMEM16 structures in a consistent manner in a single study to reduce inconsistencies that may be introduced by different simulation methods, parameters, environmental variables such as lipid composition as used in other published works of single family members. The consistency across our simulations and high number of observed scrambling events have allowed us to confirm that the mechanism of scrambling is shared by multiple family members and relies most obviously on groove dilation.

(3) Discrepancy with Experimental Observations:

The use of coarse-grained simulations introduces inherent limitations in accurately representing lipid scrambling dynamics at the atomistic level. Experimental studies have highlighted nuances in lipid permeation that are not fully captured by coarse-grained models. This discrepancy raises questions about the biological relevance of the reported scrambling events, especially those occurring outside the canonical groove.

We thank the reviewer for bringing up the possible inaccuracies introduced by coarse graining our simulations. This is also a concern for us, and we address this issue extensively in our discussion. As the reviewer pointed out above, our CG simulations have largely confirmed existing evidence in the field which we think speaks well to the transferability of observations from atomistic simulations to the coarse-grained level of detail. We have made both qualitative and quantitative comparisons between atomistic and coarse-grained simulations of nhTMEM16 and TMEM16F (Figure 1, Figure 4-figure supplement 1, Figure 4-figure supplement 5) showing the two methods give similar answers for where lipids interact with the protein, including outside of the canonical groove. We do not dispute the possible discrepancy between our simulations and experiment, but our goal is to share new nuanced ideas for the predicted TMEM16 scrambling mechanism that we hope will be tested by future experimental studies.

(4) Alternative Scrambling Sites:

The manuscript reports scrambling events at the dimer-dimer interface as a novel mechanism. While this observation is intriguing, it is not explored in sufficient detail to establish its functional significance. Furthermore, the low frequency of these events (relative to groove-mediated scrambling) suggests they may be artifacts of the simulation model rather than biologically meaningful pathways.

We agree with the reviewer that our observed number of scrambling events in the dimer interface is too low to present it as strong evidence for it being the alternative mechanism for Ca2+-independent scrambling. This will require additional experiments and computational studies which we plan to do in future research. However, we are less certain that these are artifacts of the coarse-grained simulation system as we observed a similar event in an atomistic simulation of TMEM16F.

Conclusion:

Overall, while the study is technically sound and presents a large dataset of lipid scrambling events across multiple TMEM16 structures, it falls short in terms of novelty and mechanistic advancement. The findings are largely confirmatory and do not bridge the gap between coarse-grained simulations and experimental observations. Future efforts should focus on resolving these limitations, possibly through atomistic simulations or experimental validation of the alternative scrambling pathways.

Reviewer #2 (Public review):

Summary:

Stephens et al. present a comprehensive study of TMEM16-members via coarse-grained MD simulations (CGMD). They particularly focus on the scramblase ability of these proteins and aim to characterize the "energetics of scrambling". Through their simulations, the authors interestingly relate protein conformational states to the membrane's thickness and link those to the scrambling ability of TMEM members, measured as the trespassing tendency of lipids across leaflets. They validate their simulation with a direct qualitative comparison with Cryo-EM maps.

Strengths:

The study demonstrates an efficient use of CGMD simulations to explore lipid scrambling across various TMEM16 family members. By leveraging this approach, the authors are able to bypass some of the sampling limitations inherent in all-atom simulations, providing a more comprehensive and high-throughput analysis of lipid scrambling. Their comparison of different protein conformations, including open and closed groove states, presents a detailed exploration of how structural features influence scrambling activity, adding significant value to the field. A key contribution of this study is the finding that groove dilation plays a central role in lipid scrambling. The authors observe that for scrambling-competent TMEM16 structures, there is substantial membrane thinning and groove widening. The open Ca2+-bound nhTMEM16 structure (PDB ID 4WIS) was identified as the fastest scrambler in their simulations, with scrambling rates as high as 24.4 {plus minus} 5.2 events per μs. This structure also shows significant membrane thinning (up to 18 Å), which supports the hypothesis that groove dilation lowers the energetic barrier for lipid translocation, facilitating scrambling.

The study also establishes a correlation between structural features and scrambling competence, though analyses often lack statistical robustness and quantitative comparisons. The simulations differentiate between open and closed conformations of TMEM16 structures, with open-groove structures exhibiting increased scrambling activity, while closed-groove structures do not. This finding aligns with previous research suggesting that the structural dynamics of the groove are critical for scrambling. Furthermore, the authors explore how the physical dimensions of the groove qualitatively correlate with observed scrambling rates. For example, TMEM16K induces increased membrane thinning in its open form, suggesting that membrane properties, along with structural features, play a role in modulating scrambling activity.

Another significant finding is the concept of "out-of-the-groove" scrambling, where lipid translocation occurs outside the protein's groove. This observation introduces the possibility of alternate scrambling mechanisms that do not follow the traditional "credit-card model" of groove-mediated lipid scrambling. In their simulations, the authors note that these out-of-the-groove events predominantly occur at the dimer interface between TM3 and TM10, especially in mammalian TMEM16 structures. While these events were not observed in fungal TMEM16s, they may provide insight into Ca2+-independent scrambling mechanisms, as they do not require groove opening.

Weaknesses:

A significant challenge of the study is the discrepancy between the scrambling rates observed in CGMD simulations and those reported experimentally. Despite the authors' claim that the rates are in line experimentally, the observed differences can mean large energetic discrepancies in describing scrambling (larger than 1kT barrier in reality). For instance, the authors report scrambling rates of 10.7 events per μs for TMEM16F and 24.4 events per μs for nhTMEM16, which are several orders of magnitude faster than experimental rates. While the authors suggest that this discrepancy could be due to the Martini 3 force field's faster diffusion dynamics, this explanation does not fully account for the large difference in rates. A more thorough discussion on how the choice of force field and simulation parameters influence the results, and how these discrepancies can be reconciled with experimental data, would strengthen the conclusions. Likewise, rate calculations in the study are based on 10 μs simulations, while experimental scrambling rates occur over seconds. This timescale discrepancy limits the study's accuracy, as the simulations may not capture rare or slow scrambling events that are observed experimentally and therefore might underestimate the kinetics of scrambling. It's however important to recognize that it's hard (borderline unachievable) to pinpoint reasonable kinetics for systems like this using the currently available computational power and force field accuracy. The faster diffusion in simulations may lead to overestimated scrambling rates, making the simulation results less comparable to real-world observations. Thus, I would therefore read the findings qualitatively rather than quantitatively. An interesting observation is the asymmetry observed in the scrambling rates of the two monomers. Since MARTINI is known to be limited in correctly sampling protein dynamics, the authors - in order to preserve the fold - have applied a strong (500 kJ mol-1 nm-2) elastic network. However, I am wondering how the ENM applies across the dimer and if any asymmetry can be noticed in the application of restraints for each monomer and at the dimer interface. How can this have potentially biased the asymmetry in the scrambling rates observed between the monomers? Is this artificially obtained from restraining the initial structure, or is the asymmetry somehow gatekeeping the scrambling mechanism to occur majorly across a single monomer? Answering this question would have far-reaching implications to better describe the mechanism of scrambling.

The main aim of our computational survey was to directly compare all relevant published TMEM16 structures in both open and closed states using the Martini 3 CGMD force field. Our standardized simulation and analysis protocol allowed us to quantitatively compare scrambling rates across the TMEM16 family, something that has never been done before. We do acknowledge that direct comparison between simulated versus experimental scrambling rates is complicated and is best to be interpreted qualitatively. In line with other reports (e.g., Li et al, PNAS 2024), lipid scrambling in CGMD is 2-3 orders of magnitude faster than typical experimental findings. In the CG simulation field, these increased dynamics due to the smoother energy landscape are a well known phenomenon. In our view, this is a valuable trade-off for being able to capture statistically robust scrambling dynamics and gain mechanistic understanding in the first place, since these are currently challenging to obtain otherwise. For example, with all-atom MD it would have been near-impossible to conclude that groove openness and high scrambling rates are closely related, simply because one would only measure a handful of scrambling events in (at most) a handful of structures.

Considering the elastic network: the reviewer is correct in that the elastic network restrains the overall structure to the experimental conformation. This is necessary because the Martini 3 force field does not accurately model changes in secondary (and tertiary) structure. In fact, by retaining the structural information from the experimental structures, we argue that the elastic network helped us arrive at the conclusion that groove openness is the major contributing factor in determining a protein’s scrambling rate. This is best exemplified by the asymmetric X-ray structure of TMEM16K (5OC9), in which the groove of one subunit is more dilated than the other. In our simulation, this information was stored in the elastic network, yielding a 4x higher rate in the open groove than in the closed groove, within the same trajectory.

Notably, the manuscript does not explore the impact of membrane composition on scrambling rates. While the authors use a specific lipid composition (DOPC) in their simulations, they acknowledge that membrane composition can influence scrambling activity. However, the study does not explore how different lipids or membrane environments or varying membrane curvature and tension, could alter scrambling behaviour. I appreciate that this might have been beyond the scope of this particular paper and the authors plan to further chase these questions, as this work sets a strong protocol for this study. Contextualizing scrambling in the context of membrane composition is particularly relevant since the authors note that TMEM16K's scrambling rate increases tenfold in thinner membranes, suggesting that lipid-specific or membrane-thickness-dependent effects could play a role.

Considering different membrane compositions: for this study, we chose to keep the membranes as simple as possible. We opted for pure DOPC membranes, because it has (1) negligible intrinsic curvature, (2) forms fluid membranes, and (3) was used previously by others (Li et al, PNAS 2024). As mentioned by the reviewer, we believe our current study defines a good, standardized protocol and solid baseline for future efforts looking into the additional effects of membrane composition, tension, and curvature that could all affect TMEM16-mediated lipid scrambling.

Reviewer #3 (Public review):

Strengths:

The strength of this study emerges from a comparative analysis of multiple structural starting points and understanding global/local motions of the protein with respect to lipid movement. Although the protein is well-studied, both experimentally and computationally, the understanding of conformational events in different family members, especially membrane thickness less compared to fungal scramblases offers good insights.

We appreciate the reviewer recognizing the value of the comparative study. In addition to valuable insights from previous experimental and computational work, we hope to put forward a unifying framework that highlights various TMEM16 structural features and membrane properties that underlie scrambling function.

Weaknesses:

The weakness of the work is to fully reconcile with experimental evidence of Ca²⁺-independent scrambling rates observed in prior studies, but this part is also challenging using coarse-grain molecular simulations. Previous reports have identified lipid crossing, packing defects, and other associated events, so it is difficult to place this paper in that context. However, the absence of validation leaves certain claims, like alternative scrambling pathways, speculative.

Answer: It is generally difficult to quantitatively compare bulk measurements of scrambling phenomena with simulation results. The advantage of simulations is to directly observe the transient scrambling events at a spatial and temporal resolution that is currently unattainable for experiments. The current experimental evidence for the precise mechanism of Ca2+-independent scrambling is still under debate. We therefore hope to leverage the strength of MD and statistical rigor of coarse-grained simulations to generate testable hypotheses for further structural, biochemical, and computational studies.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The findings are largely confirmatory and do not bridge the gap between coarse-grained simulations and experimental observations. Future efforts should focus on resolving these limitations, possibly through atomistic simulations or experimental validation of the alternative scrambling pathways.

While we agree with what the reviewer may be hinting at regarding limitations of coarse-grained MD simulations, we believe that our study holds much more merit than this comment suggests. We have provided something that has yet to be done in the field: a comprehensive study that directly compares the scrambling rates of multiple TMEM16 family members in different conformations using identical simulation conditions. Our work clearly shows that a sufficiently dilated grooves is the major structural feature that enables robust scrambling for all TMEM16 scramblases members with solved structures. While all TMEM16s cause significant distortion and thinning of the membrane, we assert that the extreme thinning observed around open grooves is significantly enhanced by the lipid scrambling itself as the two leaflets merge through lipid exchange.  We saw no evidence that membrane thinning/distortion alone, in the absence of an open groove, could support scrambling at the rates observed under activating conditions or even the low rates observed in Ca2+-independent scrambling. Moreover, our handful of observations of scrambling events outside of the groove, which has not yet been reported in any study, opens an exciting new direction for studying alternative scrambling mechanisms. That said, we are currently following up on many of the observations reported here such as: scrambling events outside the groove, the kinetics of scrambling, the possibility that lipids line the groove of non-scramblers like TMEM16A, etc. This is being done experimentally with our collaborators through site directed mutagenesis and with all-atom MD in our lab. Unfortunately, it is well beyond the scope of the current study to include all of this in the current paper.

Reviewer #2 (Recommendations for the authors):

Major comments and questions:

(1) Line 214 and Figure 1- Figure Supplement 1: why have you only compared the final frame of the trajectory to the cryo-EM structure? Even if these comparisons are qualitative, they should be representative of the entire trajectory, not a single frame.

We thank the reviewer for this suggestion and replaced the single-frame snapshots in Figure 1-figure supplement 1 for ensemble-averaged head groups densities. The overall agreement between membrane shapes in CGMD and cryo-EM was not affected by this change.

(2) Lines 228-231: You comment 'Residues in this site on nhTMEM16 and TMEMF also seem to play a role in scrambling but the mechanism by which they do so is unclear.' This is something you could attempt to quantify in the simulations by calculating the correlation between scrambling and protein-membrane interactions/contacts in this site. Can you speculate on a mechanism that might be a contributing factor?

We probed the correlation between these residues and scrambling lipids, as suggested by the reviewer, and interestingly not all scrambling lipids interact with these residues. Yet there is strong lipid density in this vicinity (see insets in Figure 1 and Figure 4-figure supplement 2). These observations lead us to suspect these residues impact scrambling indirectly through influencing the conformation of the protein or flexibility and shape of the membrane. This interpretation fits with mutagenesis studies highlighting a role for these residues in scrambling (see refs 59, 62, and 67). Specifically, Falzone et al. 2022 (ref 59) suggested that they may thin the membrane near the groove, but this has not been tested via structure determination and a detailed model of how they impact scrambling is missing. We could address this question with in silico mutations; however, CG simulation is not an appropriate method to study large scale protein dynamics, and AA simulations are likely best, but beyond the scope of this paper.

(3) Lines 240-245 and Figure 1B: This section discusses the coupling between membrane distortions and the sinusoidal curve around the protein, however, Figure 1B only shows snapshots of the membrane distortions. Is it possible to understand how these two collective variables are correlated quantitatively (as opposed to the current qualitative analysis)?

We believe that it may be possible to quantitatively capture these two key features of the membrane, as we did previously with nhTMEM16 using our continuum elasticity-based model of the membrane (Bethel and Grabe 2016). Our model agreed with all atom MD surfaces to within ~1 Å, hence showing good quantitative agreement throughout the entire membrane. However, we doubt that we could distill the essence of our model down to a simple functional relationship between the sinusoidal wave and pinching, which we think the reviewer is asking. Rather, we believe that the large-scale sinusoidal distortion (collective variable 1) and pinching/distortion (collective variable 2) near the groove arise from the interplay of the specific protein surface chemistry for each protein (patterning of polar and non-polar residues) and the membrane. This is why we chose to simply report the distinct patterns that the family members impose on the surrounding membrane, which we think is fascinating. Specifically, Fig. 1B shows that different TMEM16 family members distort the membrane in different ways. Most notably, fungal TMEM16s feature a more pronounced sinusoidal deformation, whereas the mammalian members primarily produce local pinching. Then, in Fig. 3A we show that the thinning at the groove happens in all structures and is more pronounced in open, scrambling-competent conformations. In other words, proteins can show very strong thinning (e.g. TMEM16K, 5OC9) even though the membrane generally remains flat.

(4) Lines 257-258: Authors comment that TMEM16A lacks scramblase activity yet can achieve a fully lipid-lined groove (note the typo - should be lipid-lined, not lipid-line). Is a fully lipid-lined groove a prerequisite for scramblase activity? Are lipid-lined grooves the only requirement for scramblase activity? Could the authors clarify exactly what the prerequisite for scramblase activity is to avoid any confusion; this will be useful for later descriptions (i.e. line 295) where scrambling competence is again referred to. Additionally, the associated figure panel (Figure 1D) shows a snapshot of this finding but lacks any statistical quantifications - is a fully lipid-lined groove a single event? Perhaps the additional analyses, such as the groove-lipid contacts, may be useful here.

The definition of lipid scrambling is that a lipid fully transitions from one membrane leaflet to the other. While a single lipid could transition through the groove on its own, it is well documented in both atomistic and CG MD simulations, that lipid scrambling typically happens through a lipid-lined groove, as shown in Fig. 1A-B. The lipids tend to form strong choline-to-phosphate interactions with nearest neighbors that make this energetically favorable. That said, lipid-lined grooves are not sufficient for robust scrambling, which is what we show in Fig. 1D where the non-scrambler TMEM16A did in fact feature a lipid-lined groove. As suggested, we performed contact analysis and found that residue K645 on TM6 in the middle of the groove contacts lipids in 9.2% of the simulation frames.

To get a better understanding of how populated the TM4-TM6 pathway is with lipids across all simulated structures, we determined for every simulation frame how many headgroup beads resided in the groove. This indicates that the ion-conductive state of TMEM16A (5OYB*, Fig. 1D) only had 1 lipid in the pathway, on average, meaning that the configuration shown Fig. 1D is indeed exceptional. As a reference, our strongest scrambler nhTMEM16 4WIS, had an average of 2.8 lipids in the groove. We added a table containing the means and standard deviations that resulted from this analysis as Figure 1-Table supplement 1.

(5) Lines 295-298 : The scrambling rates of the Ca²⁺-bound and Ca²⁺-free structures fall within overlapping error margins, it becomes difficult to definitively state that Ca²⁺ binding significantly enhances scrambling activity. This undermines the claim that the Ca²⁺-bound structure is the strongest scrambler. The authors should conduct statistical analyses to determine if the difference between the two conditions is statistically significant.

In contrast to the reviewer’s comment, we do not claim that Ca2+-binding itself enhances lipid scrambling. Instead, what we show is that WT structures that are solved in an open confirmation (all of which are Ca2+-bound, except 6QM6) are robust scramblers. For nhTMEM16, we did not observe any scrambling events for the closed-groove proteins, making further statistical analysis redundant.

(6) The authors claim that the scrambling rates derived from their MD simulations are in "excellent agreement" with experimental findings (lines 294-295), despite significant discrepancy between simulated and experimentally measured rates. For example, the simulated rate of 24.4 {plus minus} 5.2 events/µs for the open, Ca²⁺-bound fungal nhTMEM16 (PDB ID 4WIS) corresponds to approximately 24 million events per second, which is vastly higher than experimental rates. Experimental studies have reported scrambling rate constants of ~0.003 s⁻¹ for TMEM16 family members in the absence of Ca²⁺, measured under physiological conditions (https://doi.org/10.1038/s41467-019-11753-1 ). Even with Ca²⁺ activation, scrambling rates remain several orders of magnitude lower than the rates observed in simulations. Moreover, this highlights a larger problem: lipid scrambling rates occur over timescales that are not captured by these simulations. While the authors elude to these discrepancies (lines 605-606), they should be emphasised in the text, as opposed to the table caption. These should also be reconducted to differences between the membrane compositions of different studies.

We agree with the spirit of the reviewer’s comment, and because of that, we were very careful not to claim that we reproduce experimental scrambling rates, just that the trends (scrambling-competent, or not) are correct. On lines 294-295, we actually said that the scrambling rates in our simulations excellently agree with “the presumed scrambling competence of each experimental structure”, which is true. 

As explained extensively in the discussion section of our paper (and by many others), direct comparison between MD (e.g., Martini 3, but also atomistic force fields) dynamics and experimental measurements is challenging. The primary goal of our paper is to quantify and compare the scrambling capacity of different TMEM16 family members and different states, within a CGMD context.

That said, we agree with the reviewer that we may have missed rare or long-timescale events (as is the case in any MD experiment) and added this point to the discussion.

(7) To address these discrepancies, the authors should: i) emphasize that simulated rates serve as qualitative indicators of scrambling competence rather than absolute values comparable to experimental findings and ii) discuss potential reasons for the divergence, such as simulation timescale limitations or lipid bilayer compositions that may favor scrambling and force field inaccuracies.

Please see our answer to question 6. Within the context of our CGMD survey, we confidently call our results quantitative. However, we agree with the reviewer that comparison with experimental scrambling rates is qualitative and should be interpreted with caution. To reflect this, we rewrote the first sentence of the relevant paragraph in the discussion section.

(8) Line 310: Can the authors provide a rationale as to why one monomer has a wider groove than the other? Perhaps a contact analysis could be useful. See the comment above about ENM.

The simulation of Ca2+-bound TMEM16K was initiated from an asymmetric X-ray structure in which chain B features a more dilated groove than chain A (PDB 5OC9). The backbones of TM4 and TM6 in the closed groove (A) are close enough together to be directly interconnected by the elastic network. In contrast, TM4 and TM6 in the more dilated subunit (B) are not restricted by the elastic network and, as a consequence, display some “breathing” behavior (Fig. 3B and Fig. 3-Suppl. 6A), giving rise to a ~4x higher scrambling rate. We explicitly added the word “cryo-EM” and the PDB ID to the sentence to emphasize that the asymmetry stems from the original experimental structure.

When answering this question, we also corrected a mislabeled chain identifier which was in the original manuscript ‘chain A’ when it is actually ‘chain B’ in Fig.2-Suppl. 3A.

(9) Line 312: Authors speculate that increased groove width likely accounts for increased scrambling rates. For statistical significance, authors should attempt to correlate scrambling rates and groove width over the simulation period.

The Reviewer is referring to our description of scrambling rates we measured for TMEM16K where we noted that on average the groove with the highest scrambling rate is also on average wider than the opposite subunit which is below 6 Å. We do not suggest that the correlation between scrambling and groove width is continuous, as the Reviewer may have interpreted from our original submission, but we think it is a binary outcome – lipids cannot easily enter narrow grooves (< 6 Å) and hence scrambling can only occur once this threshold is reached at which point it occurs at a near constant rate. We showed this for 4 different family members in the original Fig. 3B, where scrambling events (black dots) were much more likely during, or right after, groove dilation to distances > 6 Å. 

(10) Line 359: Authors have plotted the minimum distance between residues TM4 and TM6 in Fig. 3A/B, claiming that a wide groove is required for scrambling. Upon closer examination, it is clear that several of these distributions overlap, reducing the statistical significance of these claims. Statistical tests (i.e. KS-tests) should be performed to determine whether the differences in distributions are significant.

The Reviewer appears to be asking for a statistical test between the six distance distributions represented by the data in Fig. 3A for the scrambling competent structures (6QP6*, 8B8J, 6QM6, 7RXG, 4WIS, 5OC9), and we think this is being asked because it is believed that we are making a claim that the greater the distance, the greater the scrambling rate. If we have interpreted this comment correctly, we are not making this claim. Rather, we are simply stating that we only observe robust scrambling when the groove width regularly separates beyond 6 Å. The full distance distributions can now be found in Figure 3-figure supplement 6B, and we agree there is significant overlap between some of these distributions. However, the distinguishing characteristic of the 6 distributions from scrambling competent proteins is that they all access large distances, while the others do not. Notably, TMEM16F proteins (6QP6*, 8B8J) are below the 6 Å threshold on average, but they have wide standard deviations and spend well over ¼ of their time in the permissive regime (the upper error bar in the whisker plots in Fig. 3A is the 75% boundary).

(11) Line 363-364: The authors state that all TMEM16 structures thin the membrane. Could the authors include a description of how membrane thinning is calculated, for instance, is the entire membrane considered, or is thinning calculated on a membrane patch close to the protein? Do membrane patches closer to the transmembrane protein increase or decrease thickness due to hydrophobic packing interactions? The latter question is of particular concern since Martini3 has been shown to induce local thinning of the membrane close to transmembrane helices, yielding thicknesses 2-3 Å thinner than those reported experimentally (https://doi.org/10.1016/j.cplett.2023.140436). This could be an important consideration in the authors' comparison to the bulk membrane thickness (line 364). Finally, how is the 'bulk membrane thickness' measured (i.e., from the CG simulations, from AA simulations, or from experiments)?

Regarding the calculation of thinning and bulk membrane thickness, as described in Method “Quantification of membrane deformations”, the minimal membrane thickness, or thinning, is defined as the shortest distance between any two points from the interpolated upper and lower leaflet surfaces constructed using the glycerol beads (GL1 and GL2). Bulk membrane thickness is calculated by taking the vertical distance between the averaged glycerol surfaces at the membrane edge.

The concern of localized membrane deformation due to force field artifacts is well-founded. However, the sinusoidal deformations shown here are much greater than 2-3 Å Martini3 imperfections, and they extend for up to 10 Å radially away from the protein into the bulk membrane (see Figure 3-figure supplement 1-5 for more of a description). Most importantly, the sinusoidal wave patterns set up by the proteins is very similar to those described in the previous continuum calculation and all-atom MD for nhTMEM16 (https://www.pnas.org/doi/full/10.1073/pnas.1607574113).

(12) Line 374: The authors state a 'positive correlation' between membrane thinning/groove opening and scrambling rates. To support this claim, the authors should report. the correlation coefficients.

We have removed any discussion concerning correlations between the magnitude of the scrambling rate and the degree of membrane thinning/groove opening. Rather we simply state that opening beyond a threshold distance is required for robust scrambling, as shown in our analysis in Fig. 3A.

Concerning the relation between thinning and scrambling: Instantaneous membrane thinning is poorly defined (because it is governed by fluctuations of single lipids), and therefore difficult to correlate with the timing of individual scrambling events in a meaningful way.  Moreover, as we state later in that same section, “we argue that the extremely thin membranes are likely correlated with groove opening, rather than being an independent contributing factor to lipid scrambling”.

(13) Line 396: It is stated that TMEM16A is not a scramblase but the simulating scrambling activity is not zero. How can you be sure that you are monitoring the correct collective variable if you are getting a false positive with respect to experiments?

We only observe 2 scrambling events in 10 ms, which is a very small rate compared to the scrambling competent states. In a previous large survey Martini CG simulation study that inspired our protocol (Li et al, PNAS 2024), they employed a 1 event/ms cut-off to distinguish scramblers from non-scramblers. Hence, they would have called TMEM16A a non-scrambler as well. We expect that false negatives in this context might be an artifact of the CG forcefield, or it could be that TMEM16A can scramble but too slowly to be experimentally detected. Regarding the collective variable for lipid flipping, it is correct, and we know that this lipid actually flipped.

(14) Line 402: Distance distributions for the electrostatic interactions between E633 and K645 should be included in the manuscript. This is also the case for the interactions between E843-K850 (lines 491-492).

Our description of interactions between lipid headgroups and E633 and K645 in TMEM16A (5OYB*) are based on qualitative observations of the MD trajectory, and we highlight an example of this interaction in Figure 3-video 4. The video clearly shows that the lipid headgroups in the center of the groove orient themselves such that the phosphate bead (red) rests just above K645 (blue) and at other times the choline bead (blue) rests just below E633 (red). We do not think an additional plot with the distance distributions between lipids and these residues will add to our understanding of how lipids interact residues in the TMEM16A pore.

We made a similar qualitative observation for the interaction between the POPC choline to E843 and POPC phosphate to K850 while watching the AAMD simulation trajectory of TMEM16F (PDB ID 6QP6). Given that this was a single observation, and the same interactions does not appear in CG simulation of the same structure (see simulation snapshots in Figure 4-figure supplement 5) we do not think additional analysis would add significantly to our understanding of which residues may stabilize lipids in the dimer interface.

(15) Lines 450-451: 'As the groove opens, water is exposed to the membrane core and lipid headgroups insert themselves into the water-filled groove to bridge the leaflets.' Is this a qualitative observation? Could the authors report the correlation between groove dilation and the number of water permeation events?

Yes, this is qualitative, and it sketches the order of events during scrambling, and we revised the main text starting at line 450 to indicate this. As illustrated by the density isosurfaces in Appendix 1-Figure 2A, the amount of water found in the closed versus open grooves is striking – there is a significant flood of water that connects the upper and lower solutions upon groove opening. Moreover, Appendix 1-Figure 2B shows much greater water permeation for open structures (4WIS, 7RXG, 5OC9, 8B8J, …) compared to closed structures (6QMB, 6QMA, 8B8Q, and many of the non-labeled data in the figure that all have closed grooves and near 0 water permeation). A notable exception is TMEM16A (7ZK3*8), which has water permeation but a closed groove and little-to-no lipid scrambling.

Minor Comments:

(1) Inconsistent use of '10' and 'ten' throughout.

We like to kindly point out that we do not find examples of inconsistent use.

(2) Line 32: 'TM6 along with 3, 4 and 5...' should be 'TM6 along with TM3, TM4 and TM5...'. Same in line 142. Naming should stay consistent.

Changes are reflected in the updated manuscript.

(3) Line 141: do you mean traverse (i.e. to travel across)? Or transverse (i.e. to extend across the membrane)?

This is a typo. We meant “traverse”. Thanks for pointing it out.

(4) Line 142: 'greasy' should be 'strongly hydrophobic'.

Changes are reflected in the updated manuscript.

(5) Line 143-144: "credit card mechanism" requires quotation marks.

Changes are reflected in the updated manuscript.

(6) Line 144: state if Nectria haematococca is mammalian or fungal, this is not obvious for all readers.

Changes are reflected in the updated manuscript.

(7) Line 147-148: Is TMEM16A/TMEM16K fungal or mammalian? What was the residue before the mutation and which residue is mutated? Perhaps the nomenclature should read as TMEM16X10Y where X=the residue prior to the mutation, 10 is a placeholder for the residue number that is mutated and Y=the new residue following mutation.

“TMEM16” is the protein family. “A” denotes the specific homolog rather than residue.  

(8) Lines 157-158: same as 10, it is unclear if these are fungal or mammalian.

Clarifications added.

(9) Line 184: "...CGMD simulation" should be "...CGMD simulations".

Changes made.

(10) Line 191-192: It would help to create a table of all of the mutants (including if they are mammalian or fungal) summarizing the salt concentrations, lipid and detergent environments, the presence of modulators/activators, etc.

We added this information to Appendix 1-Table 1 in the supplemental information. We did not specify NaCl concentrations, because they all experimental procedures used standard physiological values for this (100-150 mM).

(11) Line 210: inconsistencies with 'CG' and 'coarse-grain'.

Changes made.

(12) Figure 1 caption: '...totaling ~2μs (B)...' is missing the fullstop after 2μs.

Changes made.

(13) Figure 1B: it may be useful to label where the Ca2+ ion binds or include a schematic.

We updated Fig. 1A to illustrate where Ca2+ binds.

(14) Line 311: Are these mean distances? The authors should add standard deviations.

Yes, they are. We added the standard deviations to the text.

(15) Line 321-322: Perhaps a schematic in Figure 2 would be useful to visualize the structural features described here.

We would kindly refer interested readers to reference [60].

(16) Line 377: '...are likely a correlate of groove opening...' should read as: '...are likely correlated to groove opening...'.

Thank you for pointing it out. Changes made.

(17) Line 398: the '...empirically determined 6Å threshold for scrambling.' Was this determined from the simulations or from experiments? What does "empirically" mean here? Please state this.

This value was determined from the simulations. Based on our analysis of the correlation between scrambling rate and groove dilation, we found that the minimal TM4/6 distance of 6 Å can distinguish between the high and low activity scramblers. The exact numerical value is somewhat arbitrary as there is a range of values around 6 Å that serve to distinguish scramblers from non-scramblers.

(18) Figure 4: This figure should be labelled as A, B, C and D, with the figure caption updated accordingly.

We updated Figure 4 and its caption.

Reviewer #3 (Recommendations for Authors):

The authors must do additional simulations to further validate their claim with different lipids and further substantiate dimer interface independent of Ca2+ ions.

Thank you for the suggestion. We completely agree that studying scrambling in the context of a diverse lipid environment is an exciting area to explore. We are indeed actively working on a project that shares the similar idea. We decided not to include that study because we think the additional discussion involved would be excessive for the current manuscript. We, however, look forward to publishing our findings in a separate manuscript in the near future. In terms of Ca2+-independent scrambling, we are planning with our experimental collaborator for mutagenesis studies that target the residues we identified along the dimer interface.

Since calcium ions are critical for the stability of these structures, authors should show that they were placed throughout the simulations consistently.

As stated in the method section “Coarse-grained system preparation and simulation detail”, all Ca2+ ions are manually placed into the coarse-grained structure from the beginning of the simulation at their identical corresponding position in the experimental structure and harmonically bonded to adjacent acidic residues throughout the duration of simulation. We have also added a label to Fig 1A to indicate where the two Ca2+ ions are located.

The comparison with experimental structures should be consistent with complete simulation, and not the last structure of the trajectory. Depending on the conformational variability, this might be misleading.

We agree and updated Fig. 1-supplement figure 1 accordingly. The overall agreement between membrane shapes in CGMD and cryo-EM was not affected by this change.

I appreciate the two extra opportunities to revise essays 1 and 2 for a different grade. I am not a big fan of essays therefore, I find it really helpful just in case I don't get a satisfying grade. This shows how you value learning and growth for every student.

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

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Reply to the reviewers

Reviewer #1

Evidence, reproducibility and clarity

In their manuscript de las Mercedes Carro et al investigated the role of Ago proteins during spermatogenesis by producing a triple knockout of Ago 1, 3 and 4. They first describe the pattern of expression of each protein and of Ago2 during the differentiation of male germ cells, then they describe the spermatogenesis phenotype of triple knockout males, study gene deregulation by scRNA seq and identify novel interacting proteins by co-IP mass spectrometry, in particular BRG1/SMARCA4, a chromatin remodeling factor and ATF2 a transcription factor. The main message is that Ago3 and 4 are involved in the regulation of XY gene silencing during meiosis, and also in the control of autosomal gene expression during meiosis. Overall the manuscript is well written, the topic, very interesting and the experiments, well-executed. However, there are some parts of the methodology and data interpretation that are unclear (see below).

Major comments

1= Please clarify how the triple KO was obtained, and if it is constitutive or specific to the male germline. In the result section a Cre (which cre?) is mentioned but it is not mentioned in the M&M. On Figure S1, a MICER VECTOR is shown instead of a deletion, but nothing is explained in the text nor legend. Could the authors provide more details in the results section as well as in the M&M ? This is essential to fully interpret the results obtained for this KO line, and to compare its phenotype to other lines (such as lines 184-9 Comparison of triple KO phenotype with that of Ago4 KO). Also, if it is a constitutive KO, the authors should mention if they observed other phenotypes in triple KO mice since AGO proteins are not only expressed in the male germline.

Response: We apologize for omitting this vital information. We have now incorporated a more detailed description of how the Ago413 mutant was created in the results and M&M sections (line 120 and 686 respectively).

As mentioned in the manuscript, Ago4, Ago1 and Ago3 are widely expressed in mammalian somatic tissues. Mutations or deletions of these genes does not disrupt development; however, there is limited research on the impact of these mutations in mammalian models in vivo. In humans, mutations in Ago1 and Ago3 genes are associated with neurological disorders, autism and intellectual disability (Tokita, M.J.,et al. 2015- doi: 10.1038/ejhg.2014.202., Sakaguchi et al. 2019- doi: 10.1016/j.ejmg.2018.09.004, Schalk et al 2021- doi: 10.1136/jmedgenet-2021-107751). In mouse, global deletion of Ago1 and Ago3 simultaneously was shown to increase mice susceptibility to influenza virus through impaired inflammation responses (Van Stry et al 2012- doi.org/10.1128/jvi.05303-11). Studies performed in female Ago413 mutants (the same mutant line used herein) have shown that knockout mice present postnatal growth retardation with elevated circulating leukocytes (Guidi et al 2023- doi: 10.1016/j.celrep.2023.113515). Other studies of double conditional knockout of Ago1 and Ago3 in the skin associated the loss of these Argonautes with decreased weight of the offspring and severe skin morphogenesis defects (Wang et al 2012- doi: 10.1101/gad.182758.111). In our study, we did not observe major somatic or overt behavioral phenotypes, and we did not observe statistical differences in body weights of null males compared to WT as shown in figure below.

2= The paragraph corresponding to G2/M analysis is unclear to me. Why was this analysis performed? What does the heatmap show in Figure S4? What is G2/M score? (Fig 2D). Lines 219-220, do the authors mean that Pachytene cells are in a cell phase equivalent to G2/M? All this paragraph and associated figures require more explanation to clarify the method and interpretation.

__Response: __We have modified the methods to include more information about how the cell cycle scoring used in Figures 2D and S4 were calculated and will add more information regarding the interpretation of these figures.

3= I have concerns regarding Fig2G: to be convincing the analysis needs to be performed on several replicates, and, it is essential to compare tubules of the same stage - which does not seem to be the case. This does not appear to be the case. Besides, co (immunofluorescent) staining with markers of different cell types should be shown to demonstrate the earlier expression of some markers and their colocalization with markers of the earlier stages.

__Response: __We agree with the Reviewer. New images with staged tubules will be added to the analysis of Figure 2G.

4= one important question that I think the authors should discuss regarding their scRNAseq: clusters are defined using well characterized markers. But Ago triple KO appears to alter the timing of expression of genes... could this deregulation affects the interperetation of scRNAseq clusters and results?

__Response: __We thank the reviewer for this suggestion and agree that including this information is important. We expect that, at most, this dysregulation impacts the edges of these clusters slightly. Given that marker genes that have been used to define cell types in these data are consistently expressed between the knockout and wildtype mice (see Figure S4A), we do not think that the cells in these clusters have different identities, just dysregulated expression programs. We have added the relevant sentence to the discussion, and will include additional supplemental figure panels to document this point more comprehensively.

5= XY gene deregulation is mentioned throughout the result section but only X chromosome genes seem to have been investigated.... Even the gene content of the Y is highly repetitive, it would be very interesting to show the level of expression of Y single copy and Y multicopy genes in a figure 3 panel.

__Response: __We agree with the reviewer that including analysis of Y-linked genes is important. We will add a supplemental figure which includes the Y:Autosome ratio and differential expression analysis.

6= Can the authors elaborate on the observation that X gene upregulation is visible in the KO before MSCI; that is in lept/zygotene clusters (and in spermatogonia, if the difference visible in 3A is significant?)

Response: We do see that X gene expression is upregulated before pachynema. Previous scRNA-seq studies that have looked at MCSI have seen that silencing of genes on the X and Y chromosomes starts before the cell clusters that are defined as pachynema, though silencing is not fully completed until pachynema. We have clarified this point in the manuscript.

7 = miRNA analysis: could the authors indicate if X encoded miRNA were identified and found deregulated? Because Ago4 has been shown to lead to a downregulation of miRNA, among which many X encoded. It is therefore puzzling to see that the triple KO does not recapitulate this observation. Were the analyses performed differently in the present study and in Ago4 KO study?

__Response: __The analysis identifying downregulation of miRNA in the original Ago4 mutant analysis was conducted relative to total small RNA expression. Amongst those altered miRNA families in the Ago4 mutants, we demonstrated both upregulation and downregulation of miRNA. We agree that confirming a similar global downregulation of miRNA counts compared to other small RNAs is important. Therefore, in a revised manuscript, we will add this information to the miRNA analysis section, especially highlighting the X chromosome-associated miRNAs, as well as whether the ratios between other small RNA classes change.

8 = The last results paragraph would also benefit from some additional information. It is not clear why the authors focused on enhancers and did not investigate promoters (or maybe they were but it's unclear). Which regions (size and location from TSS) were investigated for motif enrichment analyses? To what correspond the "transcriptional regulatory regions previously identified using dREG" mentioned in the M&M? I understand it's based on a previous article, but more info in the present manuscript would be useful.

Response: We thank the reviewer for this suggestion. The regions that were used for motif enrichment will be included as a supplementary information in the fully revised manuscript. We have also clarified in the methods that these transcriptional regulatory regions were downloaded from GEO and obtained from previous ChRO-seq data (from GEO) analysis. These data are run through the dREG pipeline that identifies regions predicted to contain transcription start sites, which include promoters and enhancers.

Minor comments

1) In the introduction: The sentence "Ago1 is not expressed in the germline from the spermatogonia stage onwards allowing us to use this model to study the roles of Ago4 and Ago3 in spermatogenesis." is misleading because Ago1 is expressed at least in spermatogonia; It would be more precise to write "after spermatogonia stage" and rephrase the sentence. Otherwise it is surprising to see AGO1 protein in testis lysate and it is not in line with the scRNA seq shown in figure 2.

__Response: __We agree with the Reviewers suggestion and have edited the sentence on line 100. This sentence now reads "Ago1 is not expressed in the germline after the spermatogonia stage allowing us to use this model to study the roles of Ago4 and Ago3 in spermatogenesis".

2) Could the authors precise if AGO proteins are expressed in other tissues? In somatic testicular cells?

__Response: __Expression patterns of mammalian AGOs have been described in somatic and testicular tissues for the mouse by Gonzales-Gonzales et al (2008) by qPCR. They found that Ago2 is expressed in all the somatic tissues analyzed (brain, spleen, heart, muscle and lung) as well as the testis, with the highest expression in brain and lowest in heart. Ago1 is highly expressed in spleen compared to all the tissues analyzed, while Ago3 and Ago4 showed highest expression in testis and brain. Within somatic tissues of the testis, the four argonautes are expressed in Sertoli cells, however, Ago1,3 and 4 expression is very low compared to Ago2, with the latter showing a 10-fold higher transcript level. We have included a sentence with this information in the introduction in line 89.

3) Pattern of expression: How do the authors explain that AGO3 disappears at the diplotene stage and reappears in spermatids?

__Response: __ Single cell RNAseq data in the germline shows reduced transcript for Ago3 from the Pachytene stage onwards, suggesting minimal if any new transcription in round spermatids. We hypothesize that the AGO3 protein present in the round spermatid stage is cytoplasmic, presumably coming from the pool of AGO3 in the chromatoid body, a cytoplasmic structure with functional association with the nucleus in round spermatids (Kotaja et al, 2003 doi: 10.1073/pnas.05093331).

4) It would be useful to show the timing of expression of AGO 1 to 4 throughout spermatogenesis in the first paragraph of the article. Maybe the authors could present data from fig2B earlier?

Response: We understand the Reviewers concern, however, given that Ago expression throughout spermatogenesis was obtained from scRNA seq, we consider that this data should be presented after introducing the Ago413 knockout and the scRNA seq experiment. As Ago1-4 expression was also described in an earlier manuscript by Gonzales-Gonzales et al in the mouse male germline, and our data aligns with this report, we included a sentence about these previous findings in the earlier results section.

5) Line 190: please modify the sentence "reveal no differences in cellular architecture of the seminiferous tubules when compared to wild-type males" to " reveal no gross differences..." since even without quantification of the different cell types it is visible that KO seminiferous tubules are different from WT tubules.

__Response: __We agree with the reviewer, and we modified line 190 (now 173) as suggested. Grossly, seminiferous tubules from Ago413 null males contain the same cell types as in wild type tubules, including spermatozoa. However, our studies show that the number and quality of germ cells is compromised in knockouts, as shown by sperm counts and TUNEL staining.

6) TUNEL analysis: please stage the tubules to determine the stage(s) at which apoptosis is the most predominant.

__Response: __We have complied with the reviewer suggestion. Figure 1G now shows staged seminiferous tubules, and we have replaced the wild type image for one where the staged tubules match the knockout image.

7) Figure S4B does not show an increase of cells at Pachytene stage but at Lepto/zygotene stage (as well as an increase of spermatogonia). Please comment this discrepancy with results shown in Fig2.

__Response: __Figures 2 and S4 show distribution of cells in different substages of spermatogenesis and prophase I measured with very different methods: a cytological approach using chromosome spreads cells vs a transcriptomic approach that involves clustering of cells. We attribute the differences in cell type distribution to differences in the sensitivity of the methods to identify each cell type and therefore identify differences between the number of cells for each group. Moreover, our scRNA-seq data groups the leptotene and zygotene stages together, while the cytological approach allows for separation of these two sub-stages. Importantly, both results show that Ago413 spermatocytes are progressing slower from pachynema into diplonema and/or are dying after pachynema, as stated in line 194 in our manuscript.

8) Fig5H and 5I are not mentioned in the result section. Also, it would be useful to label them with "all chromosomes" and "XY" to differentiate them easily

__Response: __We apologize for the omission and have now cited Figures 5H and 5I in the manuscript (line 453). We have added the suggested labels.

9) Line 530 "data provide further evidence for a functional association between AGO-dependent small RNAs and heterochromatin formation, maintenance and/or silencing." Please rephrase, the present article does not really show that AGO nuclear role depends on small RNAs.

__Response____: __We agree with the reviewer that these data do not directly show a dependence on small RNAs. As our identified localization of AGO proteins to the pericentric heterochromatin coincides with localization of DICER shown previously by Yadav and collaborators (2020, doi: 10.1093/nar/gkaa460), we do believe that our data further implicates small RNAs in the silencing of heterochromatin. Yadav et al shows that DICER localizes to pericentromeric heterochromatin and processes major satellite transcripts into small RNAs in mouse spermatocytes, and cKO germ cells have reduced localization of SUV39H2 and H3K9me3 to the pericentromeric heterochromatin. Given the colocalization of both small RNA producing machinery and AGOs at pericentromeric heterochromatin, the AGOs may bind these small RNAs, and the statement in line 530 refers to how our results provide evidence for the involvement of other RNAi machinery in the silencing of pericentromeric heterochromatin investigated by Yadav et al which likely includes small RNAs.

To clarify this point, we have modified the text accordingly.

10) Line 1256: replace "cite here " by appropriate reference

__Response: __The reference was added to line 1256.

11) Please use SMARCA4 instead of BRG1 name as it is its official name.

__Response: __We have replaced BRG1 with SMARCA4 in the text and figures.

Figures:

Figure 1: Are the pictures shown for Ago3-tagged and floxed from the same stages ? The leptotene stage in 1A looks like a zygotene, while some pachytene/diplotene stage pictures do not look alike.

__Response: __New representative images have been added to figure 1 to match the same substages across the figure.

Figure 1D, please label the Y scale properly (testis weight related to body weight)

__Response: __We have fixed this.

FigS1: Please comment the presence of non-specific bands in the figure legend

__Response: __We have added a sentence in Figure S1 Legend.

Fig 2E and F, please indicate on the figure (in addition to its legend), what are the X and Y axes respectively to facilitate its reading.

__Response: __X and Y axes are now labelled in Figure 2E and F.

2F: please use an easier abbreviation for Spermatocyte than Sp (which could spermatogonia, sperm etc..) such as Scyte I ? (same comment for Fig 3C)

Response: The abbreviation for spermatocyte was changed from Sp to Scyte I in Figures 2 and 3.

Overall, for all figures showing GSEA analyses, could the authors explain what a High positive NES and a High negative NES mean in the results section?

Response: Thank you for this suggestion. We have added this information where the GSEA score of the cell markers is initially introduced.

Significance

Ago proteins are known for their roles in post transcriptional gene regulation via small RNA mediated cleavage of mRNA, which takes places in the cytoplasm. Some Ago proteins have been shown to be also located in the nucleus suggesting other non-canonical roles. It is the case of Ago4 which has been shown to localize to the transcriptionally silenced sex chromosomes (called sex body) of the spermatocyte nucleus, where it contributes to regulate their silencing (Modzelewski et al 2012). Interestingly, Ago4 knockout leads to Ago3 upregulation, including on the sex body indicating that Ago3 and Ago4 are involved in the same nuclear process. In their manuscript, de las Mercedes Carro et al., investigate the consequences of loss of both Ago3 and Ago4 in the male germline by the production of a triple knockout of Ago1, 3 and 4 in the mouse. With this model, the authors describe the role of Ago3 and Ago4 during spermatogenesis and show that they are involved in sex chromosome gene repression in spermatocytes and in round spermatids, as well as in the control of autosomal meiotic gene expression. Triple KO males have impaired meiosis and spermiogenesis, with fewer and abnormal spermatozoa resulting in reduced fertility. Since Ago1 male germline expression is restricted to pre-meiotic germ cells, it is not expected to contribute to the meiotic and postmeiotic phenotypes observed in the triple KO. The strengths of the study are i) the thorough analyses of mRNA expression at the single cell level, and in purified spermatocytes and spermatids (bulk RNAseq), ii) the identification of novel nuclear partners of AGO3/4 relevant for their described nuclear role: ATF2, which they show to also co-localize with the sex body, and BRG1/SMARCA4, a SWI/SNF chromatin remodeler. The main limitation of the study is the lack of information in the method regarding the production of the triple KO, as well as some aspects of the transcriptome and motif analyses. It is also surprising to see that the triple KO does not recapitulate the miRNA deregulation observed in Ago4 KO. The characterization of a non-canonical role of AGO3/4 in male germ cells will certainly influence researchers of the field, and also interest a broader audience studying Argonaute proteins and gene regulation at transcriptional and posttranscriptional levels.

Reviewer #2

Evidence, reproducibility and clarity

In the manuscript titled "Argonaute proteins regulate the timing of the spermatogenic transcriptional program" by Carro et al., the authors present their findings on how Argonaute proteins regulate spermatogenic development. They utilize a mouse model featuring a deletion of the gene cluster on chromosome 4 that contains Ago1, Ago3, and Ago4 to investigate the cumulative roles of AGO3 and AGO4 in spermatogenic cells. The authors characterize the distribution of AGO proteins and their effects on key meiotic milestones such as synapsis, recombination, meiotic transcriptional regulation, and meiotic sex chromosome inactivation (MSCI). They analyze stage-specific transcriptomes in spermatogenic cells using single-cell and bulk RNA sequencing and determine the interactome of AGO3 and AGO4 through mass spectrometry to examine how AGO proteins may regulate gene expression in these cells during meiotic and post-meiotic development. The authors conclude that both AGO3 and AGO4 are essential for regulating the overall gene expression program in spermatogenic cells and specifically modulate MSCI to repress sex-linked genes in pachytene spermatocytes, which may be partially mediated by the proper distribution of DNA damage repair factors. Additionally, AGO3 is suggested to interact with the chromatin remodeler SWI/SNF factor BRG1, facilitating its removal from the sex-chromatin to enable the repression of sex-linked genes during MSCI.

Major Comments: 1. The study utilized a triple knockout mouse model to determine the effect of AGO3 on spermatogenesis, following up on their previous report about the role of AGO4 in spermatogenesis, which resulted from an upregulation of AGO3 in Ago4-/- spermatocytes. However, the results are more difficult to interpret and ascertain the role of AGO3 in these cells, given the absence of any observable phenotype from Ago3 interruption. AGO4 regulates sex body formation, meiotic sex chromosome inactivation (MSCI), and miRNA production in spermatocytes, all of which were noted in the absence of both AGO3 and AGO4, with only an increased incidence of cells containing abnormal RNAPII at the sex chromosomes. It will be necessary to characterize how AGO3 regulates spermatogenic development, including meiotic progression and the regulation of the meiotic transcriptome, and compare these findings with the current observations to determine if the proposed mechanism involving AGO3, BRG1, and possibly AP2 is relevant in this context.

__Response: __While we agree with Reviewer that a single Ago3 knockout will help understand distinct roles of AGO3 and AGO4 in spermatogenesis, the time and resources required to generate a new mouse model are substantial. The analysis included in this current manuscript has already taken over seven years, and with the lengthy production of a new single mutant mouse, validation of the new mouse, and then final analysis, we would be looking at another 3-5 years of analysis. In the current funding climate, and with strong concerns over ensuring reduction in utilization of laboratory mice, we consider this request to be far in excess of what is required to move this important story forward.

The Ago413-/- mouse model has allowed us to associate a nuclear role of Argonaute proteins with a strong reproductive phenotype in the mouse germline. Given the redundancy between Ago3 and Ago4, it is likely that a single Ago3 knockout would have a mild phenotype just like the Ago4 KO. All this said, we agree with the reviewer that analysis of an Ago3 knockout mouse is a valuable next step, just not within this chapter of the story.

1. Does Ago413-/- mice recapitulate the early meiotic entry phenotype observed in Ago4-/- mice? If not, could it be possible that AGO3 promotes meiotic entry, given its strong mRNA expression in spermatogonia according to the scRNAseq data (Fig. 2B)

Response: Our scRNA-seq data shows strong expression of Ago3 in spermatogonia, as mentioned by the Reviewer. Analysis of cell cycle marker expression also shows that the transcriptomic profile of spermatogonia is altered, with higher levels of transcripts corresponding to the later G2/M stages (Figure 2D). Moreover, Ago413 knockouts present an increase in the number of spermatogonial stem cells (Supplementary Figure S4B). However, this cluster represents a pool of quiescent and mitotically active cells entering meiosis, therefore interpretation of these data might be challenging. While specific experiments could be conducted to answer this question, this is outside of the scope of our manuscript. The manuscript as it stands is already rather large, and a full analysis of meiotic entry dynamics would dilute the core message relating to chromatin regulation in the sex body.

1. The authors suggested that the removal of BRG1 by AGO3 is necessary during sex body formation and the eventual establishment of MSCI. However, the BAF complex subunit ARID1A has been shown to facilitate MSCI by regulating promoter accessibility. It will be interesting to determine how BRG1 distribution changes across the genome in the absence of AGO proteins and how that correlates with alterations in sex-linked gene expression.

__Response: __We agree that changes in BRG1 distribution across the genome would be very interesting to identify. However, in this work we show that BRG1/SMARCA4 protein changes its localization in the sex body very rapidly between early to late pachynema. These two substages are only discernable by immunofluorescence using synaptonemal complex markers, as there are currently no available techniques to enrich for these subfractions. Therefore, study of genome occupancy of BRG1 in these specific substages by techniques such as CUT&Tag are not currently possible. However, we are currently working on new methods to distinguish these cell populations and hope eventually to use these purification strategies to perform the studies suggested by this reviewer. Alternatively, the hope is that single cell CUT&Tag methods will become more reliable, and will enable us to address these questions. Both of these options are not currently available to us. The studies by Menon et al (2024-doi:10.7554/eLife.88024.5) provide strong evidence to support that ARID1A is needed to reduce promoter accessibility of XY silenced genes in prophase I through modulation of H3.3 distribution. However, this mechanism and our identification of the removal of BRG1 between early and late pachytema are not inconsistent with one another, as either SMARCA4 or SMARCA2 can associate with ARID1A as part of the cBAF complex, and ARID1A is also not in all forms of the BAF complex which BRG1 are in. The difference between our results and those seen in Menon et al likely indicate that there are multiple forms of the BAF complex which are differentially regulated during MSCI and play different roles in silencing transcription. Further studies of specific BAF subunits are needed to elucidate how different flavors of the BAF complex act at specific genomic locations and meiotic time points.

1. The observations presented in this manuscript (Fig. 1D, 2C, 3D, and 4) suggest a haploinsufficiency of the deleted locus in spermatogenic development. How does this compare with the ablation of either Ago3 or Ago4? Please explain.

Response: Our previous studies in single Ago4 knockouts did not present a heterozygous phenotype (Modzelewski et al 2012, doi: 10.1016/j.devcel.2012.07.003, data not shown). Triple Ago413 knockouts show a much stronger fertility phenotype than single Ago4 knockout. Testis weight of Ago413 homozygous null present a 30% reduction while heterozygous mice show a 15% reduction (Figure 1D), comparable to the 13% reduction previously observed in Ago4-/- males. Sperm counts of Ago413 null and heterozygous males are reduced by 60% and 39% compared to wild type (Figure 1E), respectively, whereas Ago4 null mice have a milder phenotype, with only a 22% reduction in sperm counts. At the MSCI level, both homozygous and heterozygous Ago413 mutant spermatocytes show a similar increase in pachytene spermatocytes with increased RNA pol II ingression into the sex body with respect to wild-type of 35% and 30%, respectively. Ago4 single knockouts show an almost 18% increase in Pol II ingression when compared to wild type. These comparisons are now included in our manuscript in lines 170, 172 and 288. A milder phenotype of the Ago4 knockout and haploinsufficiency in triple Ago413 knockouts but not in Ago4 single knockouts is likely a consequence of the overlapping functions of Ago3 and Ago4 in mammals (and/or overexpression of Ago3 in Ago4 knockouts). In the context of their role in RISC, Wang et al (doi: 10.1101/gad.182758.111) studied the effects of single and double conditional knockouts for Ago1 and Ago2 in miRNA-mediated silencing. They discovered that the interaction between miRNAs and AGOs is highly correlated with the abundance of each AGO protein, and only double knockouts presented an observable phenotype.

Minor Comments: Based on the interactome analysis, it was argued that AGO3 and AGO4 may function separately. Please discuss how AGO3 might compensate for AGO4 (Line 109).

Response: We hypothesize that the combined function of AGO3 and AGO4 is needed for proper sex chromosome inactivation during meiosis. We base this hypothesis on the facts that (i) both proteins localize to the sex body in pachytene spermatocytes, (ii) loss of Ago4 leads to upregulation of Ago3, and (iii) the MSCI phenotype of Ago413 knockout mice is much stronger than the single Ago4 knockout (see above). However, AGO3 and AGO4 might not induce silencing through the same mechanism or pathway. In this work, we observed that their temporal expression in prophase I is different; while AGO3 protein seems to disappear by the diplotene stage, AGO4 is present in the sex body of these cells. Moreover, the proteomic analysis revealed a very low number of common interactors, an observation which could support the idea of AGO3 and AGO4 acting by different (albeit perhaps related) mechanisms to achieve MSCI. It is also possible that common interactors were not identified in our proteomic analysis due to the low abundance of AGO3 and AGO4 in the germ cells, limiting the resolution of the proteomics analysis (note that in order to visualize AGO proteins in WB experiments, at least 60 μg of enriched germ cell lysate must be loaded per lane). Moreover, given the difficulty in obtaining enough isolated pachytene and diplotene spermatocytes to perform immunoprecipitation experiments, we performed IP experiments in whole germ cell lysates, which limits the interpretation of our analysis. If AGO3 and AGO4 protein interactors overlap, then AGO3 would directly substitute for AGO4 leading to silencing in single Ago4 knockouts. However, if AGO3 and AGO4 work together through different, complementary mechanisms, then Ago4 mutant mice likely compensates loss of Ago4 by upregulation of Ago3along with specific interactors of the given pathway. We have added a sentence addressing this matter in line 411 of the results section and lines 506 and 513 of the discussion in the revised manuscript.

In Line 221, it is unclear what is meant by 'cell cycle transcripts'. Does this refer to meiotic transcripts? It is also important to discuss the relevance of the G2/M cell cycle marker genes at later stages of meiotic prophase.

Response: Thank you for this suggestion. We have changed the relevant text to remove redundancies and include more information. We agree that considering the importance of these genes across meiotic prophase is needed, as cells which are in the dividing stage will already have produced the proteins necessary for division. These cells likely correspond to the diplotene/M cluster cells that have a lower G2/M score, potentially causing the bimodal distribution seen in Figure 2D. We have added a sentence addressing this to the manuscript.

While identified as a common interactor of both AGO3 and AGO4 in lines 440-445, HNRNPD is not listed among AGO4 interactors in Table S6. Please correct or explain this discrepancy.

Response: HNRPD was originally identified as an AGO4 interactor using a less strict criteria than the one used in our manuscript: we required consistent enrichment in at least two rounds of IP MS experiments. This reference to HNRNPD was a mistake, given that HNRPD was only enriched in one of our three replicates. Thus, we apologize and have removed the sentence in lines 440-445.

It is unclear whether wild-type cell lysate or lysate containing FLAG-tagged AGO3 was used for BRG1 immunoprecipitation, and which antibody was used to detect AGO3 in the BRG1 IP sample. A co-IP experiment demonstrating interaction between BRG1 and wild-type AGO3 would be ideal in this context. Furthermore, co-localization by IF would be beneficial to determine the subcellular localization and the cell stages the interaction may be occurring. Additionally, co-IP and Western blot methodologies should be included in the methods section.

__Response: __MYC-FLAG tagged AGO3 protein lysates were used for BRG1 Co-Immunoprecipitation, along with an anti MYC antibody to detect AGO3. This is now detailed in the Methods section of our revised manuscript (line 1133).

Regarding BRG1 and AGO3 colocalization by IF, we can confidently show that both AGO3 and BRG1 localize to the sex chromosomes in early pachynema by comparing BRG1/SYCP3 and FLAG-AGO3/SYCP3 stained spreads. We were not able to show colocalization simultaneously on the same cells, given the lack of appropriate antibodies. Our anti FLAG antibody is raised in mouse, while anti BRG1 is raised in rabbit, therefore a non-rabbit, non-mouse anti SYCP3 would be needed to identify prophase I substages, and our lab does not possess such a validated antibody. However, we now have access to a multiplexing kit that allows to use same-species antibodies for immunofluorescence and we can perform these experiments for a revised manuscript.

__Response: __The methods section now includes description of co-IP methodologies (line 1132). Western Blot methodologies are explained in lane 718, under the "Immunoblotting" title.

In line 599, it is unclear what is meant by 'persistence of sex chromosome de-repression'. Please correct or clarify this.

Response: This sentence has been changed and reads: "The persistence of sex chromosome gene expression".

If possible, please add an illustration to summarize the findings together.

Response: We thank the reviewer for this suggestion, and have now added this in Figure 6

Significance

Overall, this study enhances the understanding of gene expression regulation by AGO proteins during spermatogenesis. Several approaches, including functional, histological, and molecular characterization of the triple knockout phenotype, were instrumental in elucidating the role of AGO proteins in MSCI and meiotic as well as postmeiotic gene regulation. The main limitation of the study is that it is challenging to appreciate the role of AGO3 in addition to the previously published role of AGO4 without the inclusion of necessary control groups. Furthermore, the mechanism of action for AGO proteins in meiotic gene regulation was left relatively unexplored. This study presents new findings that will be significant for the research community interested in gene regulation, chromatin biology, and reproductive biology with the above suggestions considered.

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

The authors characterize a CRISPR-Cas9 mouse mutant that targets 3 genes that encode AGO family proteins, 2 of which are expressed during spermatogenesis (AGO3 and AGO4) and one that is said is not expressed, AGO1. This mouse mutant showed that AGO3 and AGO4 both contribute to spermatogenesis success as the "Ago413" mutation gave rise to an additive reduction in testis weight, due to spermatocyte apoptosis, and reduction in sperm count. Furthermore, they use insertion mouse mutants for Ago3 and Ago2 that express tagged versions of their corresponding proteins, which they use in combination with pan-AGO antibodies and Ago mutants to show differential expression and localization properties of AGO2, AGO3, and AGO4 (and the absence of AGO1) during spermatogenesis with a particular focus on meiotic prophase. They perform single-cell RNAseq and intricate analyses to demonstrate a change in distribution of meiotic stages in Ago413 mutants, and the overall cell cycle in spermatogonia and spermatocytes is altered. This analysis shows that the mutation leads to an inability to downregulate prior spermatogonia/spermatocyte stage transcripts in a timely manner. On the other hand, later-stage spermatocytes are abnormally expressing spermiogenesis genes. Similar to the Ago4 mutant previously characterized MSCI is disrupted. The authors also show that AGO3 has different interaction partners compared to AGO4 and focus their final assessment on a novel interaction partner of AGO3, BRG1. They show that this factor, which is involved in chromatin remodeling, is aberrantly localized to the sex body during meiotic prophase and diplonema. As BRG1 is involved in open chromatin, it is proposed that AGO3 restricts BRG1 (and related proteins) from the XY chromosome to ensure MSCI. Overall, this paper is very well constructed with mechanistic insights that make this a very impactful contribution to the research community. Major Comments:

1. The abstract contains "Ago413-/- mouse" without any explanation of what that is. The abstract needs to be a stand-alone document that does not require any referencing for context.

Response: We have included a sentence describing Ago413 in line 27

Figure 2C. - The significance bars are confusing as they appear to overlap strangely.

Response: We have modified this figure and now present the significance bars are on top of the data points.

On line 235, the authors state that "we first identified the top non-overlapping upregulated genes for Ago413+/+ germ cells in each cluster. Why did the authors not also select down-regulated genes in each cluster to perform a similar analysis?

__Response: __Thank you for this question. As our goal was to identify genes that are markers of the transcriptional program in each cell type, we used only uniquely upregulated genes for each cluster. Genes that are downregulated for a cluster may be indicative of the transcription in several other cell types, which is not easily interpretable. For a revised manuscript, we will perform this analysis to determine if there is any specific alterations in these downregulated genes.

Their Ago413 mutant characterization does a good job of assessing meiotic prophase and spermatozoa. However, their assessment of the stages in between these is lacking (meiotic divisions and spermiogenesis).

Response: We understand the reviewer's concern, however, it is not usual to study stages between the first meiotic division and spermiogenesis because meiosis II is so rapid and thus we lack tools to dissect it. In general, any defect that impacts meiosis I (and particularly prophase I) leads to cell death during prophase I or at metaphase I due to strictly adhered checkpoints that eradicate defective cells. Thus, the increased TUNEL staining in prophase I indicates to us that defective cells are cleared before exit from meiosis I, and those cells progressing to the spermatid stage are "normal" for meiosis II progression. For these cells that did complete meiosis I and progressed normally through meiosis II, we analyzed their spermiogenic outcome extensively (see section entitled "Post-meiotic spermatids from Ago413-/- males exhibit defective spermiogenesis and poor spermatozoa function"). This section included extensive sperm morphology, sperm motility and sperm fertility through in vitro fertilization assays. That said, we have added a sentence on line 268 to explain the transit through meiosis II.

The discovery of the interaction between BRG1 and AGO3 is exciting. They should assess BRG1 localization in later sub-stages, including late diplonema and diakinesis.

__Response: __BRG1(SMARCA4) was analyzed throughout prophase I, as shown in image 5G, including quantification of fluorescence intensity included the analysis of diplonema (5H-I). However, diakinesis was not included here since there was no observable signal of BRG1 in these cells. We have explained this in lines 459.

ATF2 should have been assessed in more detail, as was done for BRG1 in Figure 5.

__Response: __We agree with the Reviewer, however, staining of chromosome spreads with the anti ATF2 antibody was not possible in our hands after several attempts and changes in staining conditions. However, as staining of sections was successful, we showed localization of ATF2 on spermatocytes by co staining sections with SYCP3 and ATF2.

Reviewer #3 (Significance (Required)): Overall, this paper is very well constructed with mechanistic insights, as described in my reviewer comments, that make this a very impactful contribution to the research community.

Reviewer #2 (Public review):

Summary:

The authors repeatedly measured the behavior of individual flies across several environmental situations in custom-made behavioral phenotyping rigs.

Strengths:

The study uses several different behavioral phenotyping devices to quantify individual behavior in a number of different situations and over time. It seems to be a very impressive amount of data. The authors also make all their behavioral phenotyping rig design and tracking software available, which I think is great, and I'm sure other folks will be interested in using and adapting to their own needs.

Weaknesses/Limitations:

I think an important limitation is that while the authors measured the flies under different environmental scenarios (i.e. with different lighting, temperature) they didn't really alter the "context" of the environment. At least within behavioral ecology, context would refer to the potential functionality of the expressed behaviors so for example, an anti-predator context, or a mating context, or foraging. Here, the authors seem to really just be measuring aspects of locomotion under benign (relatively low risk perception) contexts. This is not a flaw of the study, but rather a limitation to how strongly the authors can really say that this demonstrates that individuality is generalized across many different contexts. It's quite possible that rank-order of locomotor (or other) behaviors may shift when the flies are in a mating or risky context.

I think the authors are missing an opportunity to use much more robust statistical methods It appears as though the authors used pearson correlations across time/situations to estimate individual variation; however far more sophisticated and elegant methods exist. The problem is that pearson correlation coefficients can be anti-conservative and additionally, the authors have thus had to perform many many tests to correlate behaviors across the different trials/scenarios. I don't see any evidence that the authors are controlling for multiple testing which I think would also help. Alternatively, though, the paper would be a lot stronger, and my guess is, much more streamlined if the authors employ hierarchical mixed models to analyse these data, which are the standard analytical tools in the study of individual behavioral variation. In this way, the authors could partition the behavioral variance into its among- and within-individual components and quantify repeatability of different behaviors across trials/scenarios simultaneously. This would remove the need to estimate 3 different correlations for day 1 & day 2, day 1 & 3, day 2 & 3 (or stripe 0 & stripe 1, etc) and instead just report a single repeatability for e.g. the time spent walking among the different strip patterns (eg. figure 3). Additionally, the authors could then use multivariate models where the response variables are all the behaviors combined and the authors could estimate the among-individual covariance in these behaviors. I see that the authors state they include generalized linear mixed models in their updated MS, but I struggled a bit to understand exactly how these models were fit? What exactly was the response? what exactly were the predictors (I just don't understand what Line404 means "a GLM was trained using the environmental parameters as predictors (0 when the parameter was not changed, 1 if it was) and the resulting individual rank differences as the response"). So were different models run for each scenario? for different behaviors? Across scenarios? What exactly? I just harp on this because I'm actually really interested in these data and think that updating these methods can really help clarify the results and make the main messages much clearer!

I appreciate that the authors now included their sample sizes in the main body of text (as opposed to the supplement) but I think that it would still help if the authors included a brief overview of their design at the start of the methods. It is still unclear to me how many rigs each individual fly was run through? Were the same individuals measured in multiple different rigs/scenarios? Or just one?

I really think a variance partitioning modeling framework could certainly improve their statistical inference and likely highlight some other cool patterns as these methods could better estimate stability and covariance in individual intercepts (and potentially slopes) across time and situation. I also genuinely think that this will improve the impact and reach of this paper as they'll be using methods that are standard in the study of individual behavioral variation

Author response:

The following is the authors’ response to the original reviews

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors state the study's goal clearly: "The goal of our study was to understand to what extent animal individuality is influenced by situational changes in the environment, i.e., how much of an animal's individuality remains after one or more environmental features change." They use visually guided behavioral features to examine the extent of correlation over time and in a variety of contexts. They develop new behavioral instrumentation and software to measure behavior in Buridan's paradigm (and variations thereof), the Y-maze, and a flight simulator. Using these assays, they examine the correlations between conditions for a panel of locomotion parameters. They propose that inter-assay correlations will determine the persistence of locomotion individuality.

Strengths: 

The OED defines individuality as "the sum of the attributes which distinguish a person or thing from others of the same kind," a definition mirrored by other dictionaries and the scientific literature on the topic. The concept of behavioral individuality can be characterized as: 

(1) a large set of behavioral attributes, 

(2) with inter-individual variability, that are 

(3) stable over time. 

A previous study examined walking parameters in Buridan's paradigm, finding that several parameters were variable between individuals, and that these showed stability over separate days and up to 4 weeks (DOI: 10.1126/science.aaw718). The present study replicates some of those findings and extends the experiments from temporal stability to examining the correlation of locomotion features between different contexts.

The major strength of the study is using a range of different behavioral assays to examine the correlations of several different behavior parameters. It shows clearly that the inter-individual variability of some parameters is at least partially preserved between some contexts, and not preserved between others. The development of highthroughput behavior assays and sharing the information on how to make the assays is a commendable contribution.

We thank the reviewer for his exceptionally kind assessment of our work!

Weaknesses: 

The definition of individuality considers a comprehensive or large set of attributes, but the authors consider only a handful. In Supplemental Fig. S8, the authors show a large correlation matrix of many behavioral parameters, but these are illegible and are only mentioned briefly in Results. 

We have now uploaded a high-resolution PDF to the Github Address: https://github.com/LinneweberLab/Mathejczyk_2024_eLife_Individuality/blob/main/S8.pdf, and this is also mentioned in the figure legend for Fig. S8

Why were five or so parameters selected from the full set? How were these selected? 

The five parameters (% of time walked, walking speed, vector strength, angular velocity, and centrophobicity) were selected because they describe key aspects of the investigated behaviors that can be compared directly across assays. Importantly, several parameters we typically use (e.g., Linneweber et al., 2020) cannot be applied under certain conditions, such as darkness or the absence of visual cues. Furthermore, these five parameters encompass three critical aspects of navigation across standard visual behavioral arenas: (1) The “exploration” category is characterized by parameters describing the fly’s activity. (2) Parameters related to “attention” reflect heightened responses to visual cues, but unlike commonly used metrics such as angle or stripe deviations (e.g., Coulomb, 2012; Linneweber et al., 2020), they can also be measured in absence of visual cues and are therefore suitable for cross-assay comparisons. (3) The parameter “centrophobicity,” used as a potential indicator of anxiety, is conceptually linked to the open-field test in mice, where the ratio of wall-to-open-field activity is frequently calculated as a measurement of anxiety (see for example Carter, Sheh, 2015, chapter 2. https://www.sciencedirect.com/book/9780128005118/guide-to-researchtechniques-in-neuroscience). Admittedly, this view is frequently challenged in mice, but it has a long history which is why we use it.

Do the correlation trends hold true across all parameters? For assays in which only a subset of parameters can be directly compared, were all of these included in the analysis, or only a subset? 

As noted above, we only included a subset of parameters in our final analysis, as many were unsuitable for comparison across assays while still providing valuable assayspecific information which are important to relate these results to previous publications.

The correlation analysis is used to establish stability between assays. For temporal retesting, "stability" is certainly the appropriate word, but between contexts, it implies that there could be 'instability'. Rather, instead of the 'instability' of a single brain process, a different behavior in a different context could arise from engaging largely (or entirely?) distinct context-dependent internal processes, and have nothing to do with process stability per se. For inter-context similarities, perhaps a better word would be "consistency". 

Thank you for this suggestion. During the preparation of the manuscript, we indeed frequently alternated between the terms “stability” and “consistency.” And decided to go with “stability” as the only descriptor, to keep it simple. We now fully agree with the reviewer’s argument and have replaced “stability” by “consistency” throughout the current version of the manuscript in order to increase clarity and coherence.

The parameters are considered one by one, not in aggregate. This focuses on the stability/consistency of the variability of a single parameter at a time, rather than holistic individuality. It would appear that an appropriate measure of individuality stability (or individuality consistency) that accounts for the high-dimensional nature of individuality would somehow summarize correlations across all parameters. Why was a multivariate approach (e.g. multiple regression/correlation) not used? Treating the data with a multivariate or averaged approach would allow the authors to directly address 'individuality stability' and analyses of single-parameter variability stability.

We agree with the reviewer that a multivariate analysis adds clear advantages in terms of statistical power, in addition to our chosen approach. On one hand, we believe that the simplicity of our initial analysis, both for correlational and mean data, makes easy for readers to understand and reproduce our data. While preparing the previous version of the manuscript we were skeptical since more complex analyses often involve numerous choices, which can complicate reproducibility. For instance, a recent study in personality psychology (Paul et al., 2024) highlighted the risks of “forking paths” in statistical analysis, showing that certain choices of statistical methods could even reverse findings—a concern mitigated by our simplistic straightforward approach. Still, in preparation of this revised version of the manuscript, we accepted the reviewer’s advice and reanalyzed the data using a generalized linear model. This analysis nicely recapitulates our initial findings and is now summarized in a single figure (Fig. 9).

The correlation coefficients are sometimes quite low, though highly significant, and are deemed to indicate stability. For example, in Figure 4C top left, the % of time walked at 23{degree sign}C and 32{degree sign}C are correlated by 0.263, which corresponds to an R2 of 0.069 i.e. just 7% of the 32{degree sign}C variance is predictable by the 23{degree sign}C variance. Is it fair to say that a 7% determination indicates parameter stability? Another example: "Vector strength was the most correlated attention parameter... correlations ranged... to -0.197," which implies that 96% (1 - R2) of Y-maze variance is not predicted by Buridan variance. At what level does an r value not represent stability?

We agree that this is an important question. Our paper clearly demonstrates that individuality always plays a role in decision-making (and, in this context, any behavioral output can be considered a decision). However, the non-linear relationship between certain situations and the individual’s behavior often reduces the predictive value (or correlation) across contexts, sometimes quite drastically.

For instance, temperature has a relatively linear effect on certain behavioral parameters, leading to predictable changes across individuals. As a result, correlations across temperature conditions are often similar to those observed across time within the same situation. In contrast, this predictability diminishes when comparing conditions like the presence or absence of visual stimuli, the use of different arenas, or different modalities.

For this reason, we believe that significance remains the best indicator for describing how measurable individuality persists, even across vastly different situations.

The authors describe a dissociation between inter-group differences and interindividual variation stability, i.e. sometimes large mean differences between contexts, but significant correlation between individual test and retest data. Given that correlation is sensitive to slope, this might be expected to underestimate the variability stability (or consistency). Is there a way to adjust for the group differences before examining the correlation? For example, would it be possible to transform the values to in-group ranks prior to correlation analysis?  

We thank the reviewer for this suggestion, and we have now addressed this point. To account for slope effects, we have now introduced in-group ranks for our linear model computation (see Fig. 9). 

What is gained by classifying the five parameters into exploration, attention, and anxiety? To what extent have these classifications been validated, both in general and with regard to these specific parameters? Is the increased walking speed at higher temperatures necessarily due to an increased 'explorative' nature, or could it be attributed to increased metabolism, dehydration stress, or a heat-pain response? To what extent are these categories subjective?

We agree that grouping our parameters into traits like exploration, attention, and anxiety always includes subjective decisions. The classification into these three categories is even considered partially controversial in the mouse specific literature, which uses the term “anxiety” in similar experiments (see for exampler Carter, Sheh, 2015, chapter 2 . https://www.sciencedirect.com/book/9780128005118/guide-to-research-techniquesin-neuroscience). Nevertheless, we believe that readers greatly benefit from these categories, since they make it easier to understand (beyond mathematical correlations) which aspects of the flies’ individuality can be considered consistent across situations. Furthermore, these categories serve as a bridge to compare insight from very distinct models.

The legends are quite brief and do not link to descriptions of specific experiments. For example, Figure 4a depicts a graphical overview of the procedure, but I could not find a detailed description of this experiment's protocol.

We assume the reviewer is referring to Figure 3a. The detailed experimental protocol can be found in the Materials and Methods section under Setup 2: IndyTrax Multi-Arena Platform. We have now clarified this in the mentioned figure legend.

Using the current single-correlation analysis approach, the aims would benefit from rewording to appropriately address single-parameter variability stability/consistency (as distinct from holistic individuality). Alternatively, the analysis could be adjusted to address the multivariate nature of individuality, so that the claims and the analysis are in concordance with each other.

The reviewer raises an important point about hierarchies within the concept of animal individuality or personality. We agree that this is best addressed by first focusing on single behavioral traits/parameters and then integrating several trait properties into a cohesive concept of animal personality (holistic individuality). To ensure consistency throughout the text, we have now thoroughly reviewed the entire manuscript clearly distinguish between single-parameter variability stability/consistency and holistic individuality/personality.

The study presents a bounty of new technology to study visually guided behaviors. The GitHub link to the software was not available. To verify the successful transfer of open hardware and open-software, a report would demonstrate transfer by collaboration with one or more other laboratories, which the present manuscript does not appear to do. Nevertheless, making the technology available to readers is commendable.

We have now uploaded all codes and materials to GitHub and made them available as soon as we received the reviewers’ comments. All files and materials can be accessed at https://github.com/LinneweberLab/Mathejczyk_2024_eLife_Individuality, which is now frequently mentioned throughout the revised manuscript.

The study discusses a number of interesting, stimulating ideas about inter-individual variability, and presents intriguing data that speaks to those ideas, albeit with the issues outlined above.

While the current work does not present any mechanistic analysis of inter-individual variability, the implementation of high-throughput assays sets up the field to more systematically investigate fly visual behaviors, their variability, and their underlying mechanisms. 

We thank the reviewer again for the extensive and constructive feedback.

Reviewer #2 (Public Review): 

Summary: 

The authors repeatedly measured the behavior of individual flies across several environmental situations in custom-made behavioral phenotyping rigs.

Strengths: 

The study uses several different behavioral phenotyping devices to quantify individual behavior in a number of different situations and over time. It seems to be a very impressive amount of data. The authors also make all their behavioral phenotyping rig design and tracking software available, which I think is great and I'm sure other folks will be interested in using and adapting it to their own needs.

We thank the reviewer for highlighting the strengths of our study.

Weaknesses/Limitations: 

I think an important limitation is that while the authors measured the flies under different environmental scenarios (i.e. with different lighting and temperature) they didn't really alter the "context" of the environment. At least within behavioral ecology, context would refer to the potential functionality of the expressed behaviors so for example, an anti-predator context, a mating context, or foraging. Here, the authors seem to really just be measuring aspects of locomotion under benign (relatively low-risk perception) contexts. This is not a flaw of the study, but rather a limitation to how strongly the authors can really say that this demonstrates that individuality is generalized across many different contexts. It's quite possible that rank order of locomotor (or other) behaviors may shift when the flies are in a mating or risky context. 

We agree with the reviewer that the definition of environmental context can differ between fields and that behavioral context is differently defined, particularly in ecology. Nevertheless, we highlight that our alternations of environmental context are highly stereotypic, well-defined, and unbiased from any interpretation (we only modified what we stated in the experimental description without designing a specific situation that might be again perceived individually differently. E.g., comparing a context with a predator and one without might result in a binary response because one fraction of the tested individuals might perceive the predator in the predator situation, and the other half does not. 

The analytical framework in terms of statistical methods is lacking. It appears as though the authors used correlations across time/situations to estimate individual variation; however, far more sophisticated and elegant methods exist. The paper would be a lot stronger, and my guess is, much more streamlined if the authors employ hierarchical mixed models to analyse these data these models could capture and estimate differences in individual behavior across time and situations simultaneously. Along with this, it's currently unclear whether and how any statistical inference was performed. Right now, it appears as though any results describing how individuality changes across situations are largely descriptive (i.e. a visual comparison of the strengths of the correlation coefficients?). 

The reviewer raises an important point, also raised by reviewer #1. On one hand, we agree with both reviewers that a more aggregated analysis has clear advantages like more statistical power and has the potential to streamline our manuscript, which is why we added such an analysis (see below). On the other hand, we would also like to defend the initial approach we took, since we think that the simplicity of the analysis for both correlational and mean data is easy to understand and reproduce. More complex analyses necessarily include the selection of a specific statistical toolbox by the experimenters and based on these decisions, different analyses become less comparable and more and more complicated to reproduce, unless the entire decision tree is flawlessly documented. For instance, a recent personality psychology paper investigated the relationship between statistical paths within the decision tree (forking analysis) and their results, leading to very surprising results (Paul et al., 2024), since some paths even reversed their findings. Such a variance in conclusions is hardly possible with the rather simplistic and easily reproducible analysis we performed. One of the major strengths of our study is the simple experimental design, allowing for rather simple and easy to understand analyses.

We nevertheless took the reviewer’s advice very seriously and reanalyzed the data using a generalized linear model, which largely recapitulated the findings of our previously performed “low-tech” analysis in a single figure (Fig. 9).

Another pretty major weakness is that right now, I can't find any explicit mention of how many flies were used and whether they were re-used across situations. Some sort of overall schematic showing exactly how many measurements were made in which rigs and with which flies would be very beneficial. 

We apologize for this inconvenience. A detailed overview of male and female sample sizes has been listed in the supplemental boxplots next to the plots (e.g, Fig S6). Apparently, this was not visible enough. Therefore, we have now also uniformly added the sample sizes to the main figure legends.

I don't necessarily doubt the robustness of the results and my guess is that the author's interpretations would remain the same, but a more appropriate modeling framework could certainly improve their statistical inference and likely highlight some other cool patterns as these methods could better estimate stability and covariance in individual intercepts (and potentially slopes) across time and situation.

As described above, we have now added the suggested analyses. We hope that the reviewer will appreciate the new Fig. 9, which, in our opinion, largely confirms our previous findings using a more appropriate generalized linear modelling framework.

Reviewer #3 (Public Review): 

This manuscript is a continuation of past work by the last author where they looked at stochasticity in developmental processes leading to inter-individual behavioural differences. In that work, the focus was on a specific behaviour under specific conditions while probing the neural basis of the variability. In this work, the authors set out to describe in detail how stable the individuality of animal behaviours is in the context of various external and internal influences. They identify a few behaviours to monitor (read outs of attention, exploration, and 'anxiety'); some external stimuli (temperature, contrast, nature of visual cues, and spatial environment); and two internal states (walking and flying).

They then use high-throughput behavioural arenas - most of which they have built and made plans available for others to replicate - to quantify and compare combinations of these behaviours, stimuli, and internal states. This detailed analysis reveals that:

(1) Many individualistic behaviours remain stable over the course of many days. 

(2) That some of these (walking speed) remain stable over changing visual cues. Others (walking speed and centrophobicity) remain stable at different temperatures.

(3) All the behaviours they tested failed to remain stable over the spatially varying environment (arena shape).

(4) Only angular velocity (a readout of attention) remains stable across varying internal states (walking and flying).

Thus, the authors conclude that there is a hierarchy in the influence of external stimuli and internal states on the stability of individual behaviours.

The manuscript is a technical feat with the authors having built many new highthroughput assays. The number of animals is large and many variables have been tested - different types of behavioural paradigms, flying vs walking, varying visual stimuli, and different temperatures among others. 

We thank the reviewer for this extraordinary kind assessment of our work!

Recommendations for the authors:  

Reviewing Editor (Recommendations For The Authors): 

While appreciating the effort and quality of the work that went into this manuscript, the reviewers identified a few key points that would greatly benefit this work.

(1) Statistical methods adopted. The dataset produced through this work is large, with multiple conditions and comparisons that can be made to infer parameters that both define and affect the individualistic behaviour of an animal. Hierarchical mixed models would be a more appropriate approach to handle such datasets and infer statistically the influence of different parameters on behaviours. We recommend that the authors take this approach in the analyses of their data.

(2) Brevity in the text. We urge the authors to take advantage of eLife's flexible template and take care to elaborate on the text in the results section, the methods adopted, the legends, and the guides to the legends embedded in the main text. The findings are likely to be of interest to a broad audience, and the writing currently targets the specialist.

Reviewer #2 (Recommendations For The Authors): 

I want to start by saying this seems like a really cool study! It's an impressive amount of work and addressing a pretty basic question that is interesting (at least I think so!)

We thank the reviewer again for this assessment!

That said, I would really strongly recommend the authors embrace using mixed/hierarchical models to analyze their data. They're producing some really impressive data and just doing Pearson correlation coefficients across time points and situations is very clunky and actually losing out on a lot of information. The most up-todate, state-of-the-art are mixed models - these models can handle very complex (or not so complex) random structures which can estimate variance and importantly, covariance, in individual intercepts both over time and across situations. I actually think this could add some really cool insights into the data and allow you to characterize the patterns you're seeing in far more detail. It's datasets exactly like this that are tailormade for these complex variance partitioning models! 

As mentioned before, we have now adopted a more appropriate GLM-based data analysis (see above).

Regardless of which statistical methods you decide to use, please explicitly state in your methods exactly what analyses you did. That is completely lacking now and was a bit frustrating. As such, it's completely unclear whether or how statistical inference was performed. How did you do the behavioral clustering? 

We apologize that these points were not clearly represented in the previous version of the manuscript. We have now significantly extended the methods section to include a separate paragraph on the statistical methods used, in order to address this critique and hope that the revised version is clear now.

Also, I could not for the life of me figure out how many flies had been measured. Were they reused across the situation? Or not?

We reused the same flies across situations whenever possible. However, having one fly experience all assays consecutively was not feasible due to their fragility. Instead, individual flies were exposed to at least 2 of the 3 groups of assays used here: in the Indytrax setup ,  the Buridan arenas and variants thereof, and the virtual arenas Hence, we have compared flies across entirely different setups, but the number of times flies can be retested is limited (as otherwise, sample sizes will drop over time, and the flies will have gone through too many experimental alternations). To make this more clear, we have elaborated on this point in the main text, and we added group sample sizes to figure legends r.

What are these "groups" and "populations" that are referred to in the results (e.g. lines 384, 391, 409)?

We apologize for using these two terms somewhat interchangeably without proper introduction/distinction. We have now made this more clear in at the beginning of the results in the main text, by focusing on the term ‘group’. By ‘group’ we refer to the average of all individuals tested in the same situation. Sample sizes in the figure legends now indicate group/population sizes to make this clearer.

Some of the rationale for the development of the behavioral rigs would have actually been nice to include in the intro, rather than in the results.

This rationale is introduced at the beginning of the last paragraph of the introduction. We hope that this now becomes clear in the revised version of the manuscript.

Reviewer #3 (Recommendations For The Authors): 

This manuscript would do well to take advantage of eLife's flexible word limit. I sense that it has been written in brevity for a different journal but I would urge the authors to revisit this and unpack the language here - in the text, in the figure legends, in references to the figures within the text. The way it's currently written, though not misleading, will only speak to the super-specialist or the super-invested :). But the findings are nice, and it would be nice to tailor it to a broader audience.

We appreciate this suggestion. Initially, we were hoping that we had described our results as clearly and brief as possible. We apologize if that was not always the case. The comments and requests of all three reviewers now led to a series of additions to both main text and methods, leading to a significantly expanded manuscript. We hope that these additons are helpful for the general, non-expert audience.

Include in paper

Author response:

The following is the authors’ response to the original reviews

Overview of changes in the revision

We thank the reviewers for the very helpful comments and have extensively revised the paper. We provide point-by-point responses below and here briefly highlight the major changes:

(1) We expanded the discussion of the relevant literature in children and adults.

(2) We improved the contextualization of our experimental design within previous reinforcement studies in both cognitive and motor domains highlighting the interplay between the two.

(3) We reorganized the primary and supplementary results to better communicate the findings of the studies.

(4) The modeling has been significantly revised and extended. We now formally compare 31 noise-based models and one value-based model and this led to a different model from the original being the preferred model. This has to a large extent cleaned up the modeling results. The preferred model is a special case (with no exploration after success) of the model proposed in Therrien et al. (2018). We also provide examples of individual fits of the model, fit all four tasks and show group fits for all, examine fits vs. data for the clamp phases by age, provide measures of relative and absolute goodness of fit, and examine how the optimal level of exploration varies with motor noise.

Reviewer #1 (Public review):

Summary:

Here the authors address how reinforcement-based sensorimotor adaptation changes throughout development. To address this question, they collected many participants in ages that ranged from small children (3 years old) to adulthood (1 8+ years old). The authors used four experiments to manipulate whether binary and positive reinforcement was provided probabilistically (e.g., 30 or 50%) versus deterministically (e.g., 100%), and continuous (infinite possible locations) versus discrete (binned possible locations) when the probability of reinforcement varied along the span of a large redundant target. The authors found that both movement variability and the extent of adaptation changed with age.

Thank you for reviewing our work. One note of clarification. This work focuses on reinforcementbased learning throughout development but does not evaluate sensorimotor adaptation. The four tasks presented in this work are completed with veridical trajectory feedback (no perturbation).

The goal is to understand how children at different ages adjust their movements in response to reward feedback but does not evaluate sensorimotor adaptation. We now explain this distinction on line 35.

Strengths:

The major strength of the paper is the number of participants collected (n = 385). The authors also answer their primary question, that reinforcement-based sensorimotor adaptation changes throughout development, which was shown by utilizing established experimental designs and computational modelling.

Thank you.

Weaknesses:

Potential concerns involve inconsistent findings with secondary analyses, current assumptions that impact both interpr tation and computational modelling, and a lack of clearly stated hypotheses.

(1) Multiple regression and Mediation Analyses.

The challenge with these secondary analyses is that:

(a) The results are inconsistent between Experiments 1 and 2, and the analysis was not performed for Experiments 3 and 4,

(b) The authors used a two-stage procedure of using multiple regression to determine what variables to use for the mediation analysis, and

(c)The authors already have a trial-by-trial model that is arguably more insightful.

Given this, some suggested changes are to:

(a) Perform the mediation analysis with all the possible variables (i.e., not informed by multiple regression) to see if the results are consistent.

(b) Move the regression/mediation analysis to Supplementary, since it is slightly distracting given current inconsistencies and that the trial-by-trial model is arguably more insightful.

Based on these comments, we have chosen to remove the multiple regression and mediation analyses. We agree that they were distracting and that the trial-by-trial model allows for differentiation of motor noise from exploration variability in the learning block.

(2) Variability for different phases and model assumptions:

A nice feature of the experimental design is the use of success and failure clamps. These clamped phases, along with baseline, are useful because they can provide insights into the partitioning of motor and exploratory noise. Based on the assumptions of the model, the success clamp would only reflect variability due to motor noise (excludes variability due to exploratory noise and any variability due to updates in reach aim). Thus, it is reasonable to expect that the success clamps would have lower variability than the failure clamps (which it obviously does in Figure 6), and presumably baseline (which provides success and failure feedback, thus would contain motor noise and likely some exploratory noise).

However, in Figure 6, one visually observes greater variability during the success clamp (where it is assumed variability only comes from motor noise) compared to baseline (where variability would come from: (a) Motor noise.

(b) Likely some exploratory noise since there were some failures.

(c) Updates in reach aim.

Thanks for this comment. It made us realize that some of our terminology was unintentionally misleading. Reaching to discrete targets in the Baseline block was done to a) determine if participants could move successfully to targets that are the same width as the 100% reward zone in the continuous targets and b) determine if there are age dependent changes in movement precision. We now realize that the term Baseline Variability was misleading and should really be called Baseline Precision.

This is an important distinction that bears on this reviewer's comment. In clamp trials, participants move to continuous targets. In baseline, participants move to discrete targets presented at different locations. Clamp Variability cannot be directly compared to Baseline Precision because they are qualitatively different. Since the target changes on each baseline trial, we would not expect updating of desired reach (the target is the desired reach) and there is therefore no updating of reach based on success or failure. The SD we calculate over baseline trials is the endpoint variability of the reach locations relative to the target centers. In success clamp, there are no targets so the task is qualitatively different.

We have updated the text to clarify terminology, expand upon our operational definitions, and motivate the distinct role of the baseline block in our task paradigm (line 674).

Given the comment above, can the authors please:

(a) Statistically compare movement variability between the baseline, success clamp, and failure clamp phases.

Given our explanation in the previous point we don't think that comparing baseline to the clamp makes sense as the trials are qualitatively different.

(b) The authors have examined how their model predicts variability during success clamps and failure clamps, but can they also please show predictions for baseline (similar to that of Cashaback et al., 2019; Supplementary B, which alternatively used a no feedback baseline)?

Again, we do not think it makes sense to predict the baseline which as we mention above has discrete targets compared to the continuous targets in the learning phase.

(c) Can the authors show whether participants updated their aim towards their last successful reach during the success clamp? This would be a particularly insightful analysis of model assumptions.

We have now compared 31 models (see full details in next response) which include the 7 models in Roth et al. (2023). Several of these model variants have updating even after success with so called planning noise). We also now fit the model to the data that includes the clamp phases (we can't easily fit to success clamp alone as there are only 10 trials). We find that the preferred model is one that does not include updating after success.

(d) Different sources of movement variability have been proposed in the literature, as have different related models. One possibility is that the nervous system has knowledge of 'planned (noise)' movement variability that is always present, irrespective of success (van Beers, R.J. (2009). Motor learning is optimally tuned to the properties of motor noise. Neuron, 63(3), 406-417). The authors have used slightly different variations of their model in the past. Roth et al (2023) directly Rill compared several different plausible models with various combinations of motor, planned, and exploratory noise (Roth A, 2023, "Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds." Proceedings of the Royal Society B 290: 20231475: see Supplemental). Their best-fit model seems similar to the one the authors propose here, but the current paper has the added benefit of the success and failure clamps to tease the different potential models apart. In light of the results of a), b), and c), the authors are encouraged to provide a paragraph on how their model relates to the various sources of movement variability and ther models proposed in the literature.

Thank you for this. We realized that the models presented in Roth et al. (2023) as well as in other papers, are all special cases of a more general model. Moreover, in total there are 30 possible variants of the full model so we have now fit all 31 models to our larger datasets and performed model selection (Results and Methods). All the models can be efficiently fit by Kalman smoother to the actual data (rather than to summary statistics which has sometimes been done). For model selection, we fit only the 100 learning trials and chose the preferred model based on BIC on the children's data (Figure 5—figure Supplement 1). After selecting the preferred model we then refit this model to all trials including the clamps so as to obtain the best parameter estimates.

The preferred model was the same whether we combined the continuous and discrete probabilistic data or just examin d each task separately either for only the children or for the children and adults combined. The preferred model is a pecial case (no exploration after success) of the one proposed in Therrien et al. (2018) and has exploration variability (after failure) and motor noise with full updating with exploration variability (if any) after success. This model differs from the model in the original submission which included a partial update of the desired reach after exploration this was considered the learning rate. The current model suggests a unity learning rate.

In addition, as suggested by another reviewer, we also fit a value-based model which we adapted from the model described in Giron et al. (2023). This model was not preferred.

We have added a paragraph to the Discussion highlighting different sources of variability and links to our model comparison.

(e) line 155. Why would the success clamp be composed of both motor and exploratory noise? Please clarify in the text

This sentence was written to refer to clamps in general and not just success clamps. However, in the revision this sentence seemed unnecessary so we have removed it.

(3) Hypotheses:

The introduction did not have any hypotheses of development and reinforcement, despite the discussion above setting up potential hypotheses. Did the authors have any hypotheses related to why they might expect age to change motor noise, exploratory noise, and learning rates? If so, what would the experimental behaviour look like to confirm these hypotheses? Currently, the manuscript reads more as an exploratory study, which is certainly fine if true, it should just be explicitly stated in the introduction. Note: on line 144, this is a prediction, not a hypothesis. Line 225: this idea could be sharpened. I believe the authors are speaking to the idea of having more explicit knowledge of action-target pairings changing behaviour.

We have included our hypotheses and predictions at two points in the paper In the introduction we modified the text to:

"We hypothesized that children's reinforcement learning abilities would improve with age, and depend on the developmental trajectory of exploration variability, learning rate (how much people adjust their reach after success), and motor noise (here defined as all sources of noise associated with movement, including sensory noise, memory noise, and motor noise). We think that these factors depend on the developmental progression of neural circuits that contribute to reinforcement learning abilities (Raznahan et al., 2014; Nelson et al., 2000; Schultz, 1998)."

In results we modified the sentence to:

"We predicted that discrete targets could increase exploration by encouraging children to move to a different target after failure.”

Reviewer #2 (Public review):

Summary:

In this study, Hill and colleagues use a novel reinforcement-based motor learning task ("RML"), asking how aspects of RML change over the course of development from toddler years through adolescence. Multiple versions of the RML task were used in different samples, which varied on two dimensions: whether the reward probability of a given hand movement direction was deterministic or probabilistic, and whether the solution space had continuous reach targets or discrete reach targets. Using analyses of both raw behavioral data and model fits, the authors report four main results: First, developmental improvements reflected 3 clear changes, including increases in exploration, an increase in the RL learning rate, and a reduction of intrinsic motor noise. Second, changes to the task that made it discrete and/or deterministic both rescued performance in the youngest age groups, suggesting that observed deficits could be linked to continuous/probabilistic learning settings. Overall, the results shed light on how RML changes throughout human development, and the modeling characterizes the specific learning deficits seen in the youngest ages.

Strengths:

(1) This impressive work addresses an understudied subfield of motor control/psychology - the developmental trajectory of motor learning. It is thus timely and will interest many researchers.

(2) The task, analysis, and modeling methods are very strong. The empirical findings are rather clear and compelling, and the analysis approaches are convincing. Thus, at the empirical level, this study has very few weaknesses.

(3) The large sample sizes and in-lab replications further reflect the laudable rigor of the study.

(4) The main and supplemental figures are clear and concise.

Thank you.

Weaknesses:

(1) Framing.

One weakness of the current paper is the framing, namely w/r/t what can be considered "cognitive" versus "non-cognitive" ("procedural?") here. In the Intro, for example, it is stated that there are specific features of RML tasks that deviate from cognitive tasks. This is of course true in terms of having a continuous choice space and motor noise, but spatially correlated reward functions are not a unique feature of motor learning (see e.g. Giron et al., 2023, NHB). Given the result here that simplifying the spatial memory demands of the task greatly improved learning for the youngest cohort, it is hard to say whether the task is truly getting at a motor learning process or more generic cognitive capacities for spatial learning, working memory, and hypothesis testing. This is not a logical problem with the design, as spatial reasoning and working memory are intrinsically tied to motor learning. However, I think the framing of the study could be revised to focus in on what the authors truly think is motor about the task versus more general psychological mechanisms. Indeed, it may be the case that deficits in motor learning in young children are mostly about cognitive factors, which is still an interesting result!

Thank you for these comments on the framing of our study. We now clearly acknowledge that all motor tasks have cognitive components (new paragraph at line 65). We also explain why we think our tasks has features not present in typical cognitive tasks.

(2) Links to other scholarship.

If I'm not mistaken a common observation in tudies of the development of reinforcement learning is a decrease in exploration over-development (e.g., Nussenbaum and Hartley, 2019; Giron et al., 2023; Schulz et al., 2019); this contrasts with the current results which instead show an increase. It would be nice to see a more direct discussion of previous findings showing decreases in exploration over development, and why the current study deviates from that. It could also be useful for the authors to bring in concepts of different types of exploration (e.g. "directed" vs "random"), in their interpretations and potentially in their modeling.

We recognize that our results differ from prior work. The optimal exploration pattern differs from task to task. We now discuss that exploration is not one size fits all, it's benefits vary depending upon the task. We have added the following paragraphs to the Discussion section:

"One major finding from this study is that exploration variability increases with age. Some other studies of development have shown that exploration can decrease with age indicating that adults explore less compared to children (Schulz et al., 2019; Meder et al., 2021; Giron et al., 2023). We believe the divergence between our work and these previous findings is largely due to the experimental design of our study and the role of motor noise. In the paradigm used initially by Schulz et al. (2019) and replicated in different age groups by Meder et al. (2021) and Giron et al. (2023), participants push buttons on a two-dimensional grid to reveal continuous-valued rewards that are spatially correlated. Participants are unaware that there is a maximum reward available and therefore children may continue to explore to reduce uncertainty if they have difficulty evaluating whether they have reached a maxima. In our task by contrast, participants are given binary reward and told that there is a region in which reaches will always be rewarded. Motor noise is an additional factor which plays a key role in our reaching task but minimal if any role in the discretized grid task. As we show in simulations of our task, as motor noise goes down (as it is known to do through development) the optimal amount of exploration goes up (see Figure 7—figure Supplement 2 and Appendix 1). Therefore, the behavior of our participants is rational in terms of R230 increasing exploration as motor noise decreases.

A key result in our study is that exploration in our task reflects sensitivity to failure. Older children make larger adjustments after failure compared to younger children to find the highly rewarded zone more quickly. Dhawale et al. (2017) discuss the different contexts in which a participant may explore versus exploit (i.e., stick at the same position). Exploration is beneficial when reward is low as this indicates that the current solution is no longer ideal, and the participant should search for a better solution. Konrad et al. (2025) have recently shown this behavior in a real-world throwing task where 6 to 12 year old children increased throwing variability after missed trials and minimized variability after successful trials. This has also been shown in a postural motor control task where participants were more variable after non-rewarded trials compared to rewarded trials (Van Mastrigt et al., 2020). In general, these studies suggest that the optimal amount of exploration is dependent on the specifics of the task."

(3) Modeling.

First, I may have missed something, but it is unclear to me if the model is actually accounting for the gradient of rewards (e.g., if I get a probabilistic reward moving at 45°, but then don't get one at 40°, I should be more likely to try 50° next then 35°). I couldn't tell from the current equations if this was the case, or if exploration was essentially "unsigned," nor if the multiple-trials-back regression analysis would truly capture signed behavior. If the model is sensitive to the gradient, it would be nice if this was more clear in the Methods. If not, it would be interesting to have a model that does "function approximation" of the task space, and see if that improves the fit or explains developmental changes.

The model we use (similar to Roth et al. (2023) and Therrien et al. (2016, 2018)) does not model the gradient. Exploration is always zero-mean Gaussian. As suggested by the reviewer, we now also fit a value-based model (described starting at line 810) which we adapted from the model presented in Giron et al. (2023). We show that the exploration and noise-based model is preferred over the value-based model.

The multiple-trials-back regression was unsigned as the intent was to look at the magnitude and not the direction of the change in movement. We have decided to remove this analysis from the manuscript as it was a source of confusion and secondary analysis that did not add substantially to the findings of these studies.

Second, I am curious if the current modeling approach could incorporate a kind of "action hysteresis" (aka perseveration), such that regardless of previous outcomes, the same action is biased to be repeated (or, based on parameter settings, avoided).

In some sense, the learning rate in the model in the original submission is highly related to perseveration. For example if the learning rate is 0, then there is complete perseveration as you simply repeat the same desired movement. If the rate is 1, there is no perseveration and values in between reflect different amounts of perseveration. Therefore, it is not easy to separate learning rate from perseveration. Adding perseveration as another parameter would likely make it and the learning unidentifiable. However, we now compare 31 models and those that have a non-unity learning rate are not preferred suggesting there is little perseveration.

(4) Psychological mechanisms. There is a line of work that shows that when children and adults perform RL tasks they use a combination of working memory and trial-by-trial incremental learning processes (e.g., Master et al., 2020; Collins and Frank 2012). Thus, the observed increase in the learning rate over development could in theory reflect improvements in instrumental learning, working memory, or both. Could it be that older participants are better at remembering their recent movements in short-term memory (Hadjiosif et al., 2023; Hillman et al., 2024)?

We agree that cognitive processes, such as working memory or visuospatial processing, play a role in our task and describe cognitive elements of our task in the introduction (new paragraph at line 65). However, the sensorimotor model we fit to the data does a good job of explaining the variation across age, which suggests that that age-dependent cognitive processes probably play a smaller role.

Reviewer #3 (Public review):

Summary:

The study investigates reinforcement learning across the lifespan with a large sample of participants recruited for an online game. It finds that children gradually develop their abilities to learn reward probability, possibly hindered by their immature spatial processing and probabilistic reasoning abilities. Motor noise, reinforcement learning rate, and exploration after a failure all contribute to children's subpar performance.

Strengths:

(1) The paradigm is novel because it requires continuous movement to indicate people's choices, as opposed to discrete actions in previous studies.

(2) A large sample of participants were recruited.

(3) The model-based analysis provides further insights into the development of reinforcement learning ability.

Thank you.

Weaknesses:

(1 ) The adequacy of model-based analysis is questionable, given the current presentation and some inconsistency in the results.

Thank you for raising this concern. We have substantially revised the model from our first submission. We now compare 31 noise-based models and 1 value-based model and fit all of the tasks with the preferred model. We perform model selection using the two tasks with the largest datasets to identify the preferred model. From the preferred model, we found the parameter fits for each individual dataset and simulated the trial by trial behavior allowing comparison between all four tasks. We now show examples of individual fits as well as provide a measure of goodness of fit. The expansion of our modeling approach has resolved inconsistencies and sharpened the conclusions drawn from our model.

(2) The task should not be labeled as reinforcement motor learning, as it is not about learning a motor skill or adapting to sensorimotor perturbations. It is a classical reinforcement learning paradigm.

We now make it clear that our reinforcement learning task has both motor and cognitive demands, but does not fall entirely within one of these domains. We use the term motor learning because it captures the fact that participants maximize reward by making different movements, corrupted by motor noise, to unmarked locations on a continuous target zone. When we look at previous ublications, it is clear that our task is similar to those that also refer to this as reinforcement motor learning Cashaback et al. (2019) (reaching task using a robotic arm in adults), Van Mastrigt et al. (2020) (weight shifting task in adults), and Konrad et al. (2025) (real-world throwing task in children). All of these tasks involve trial-by-trial learning through reinforcement to make the movement that is most effective for a given situation. We feel it is important to link our work to these previous studies and prefer to preserve the terminology of reinforcement motor learning.

Recommendations for the authors:

Reviewing Editor Comments:

Thank you for this summary. Rather than repeat the extended text from the responses to the reviewers here, we point the Editor to the appropriate reviewer responses for each issue raised.

The reviewers and editors have rated the significance of the findings in your manuscript as "Valuable" and the strength of evidence as "Solid" (see eLife evalutation). A consultancy discussion session to integrate the public reviews and recommendations per reviewer (listed below), has resulted in key recommendations for increasing the significance and strength of evidence:

To increase the Significance of the findings, please consider the following:

(1) Address and reframe the paper around whether the task is truly getting at a motor learning process or more generic cognitive decision-making capacities such as spatial memory, reward processing, and hypothesis testing.

We have revised the paper to address the comments on the framing of our work. Please see responses to the public review comments of Reviewers #2 and #3.

(2) It would be beneficial to specify the differences between traditional reinforcement algorithms (i.e., using softmax functions to explore, and build representations of state-action-reward) and the reinforcement learning models used here (i.e., explore with movement variability, update reach aim towards the last successful action), and compare present findings to previous cognitive reinforcement learning studies in children.

Please see response to the public review comments of Reviewer #1 in which we explain the expansion of our modeling approach to fit a value-based model as well as 31 other noise-based models. In our response to the public review comments of Reviewer #2, we comment on our expanded discussion of how our findings compare with previous cognitive reinforcement learning studies.

To move the "Strength of Evidence" to "Convincing", please consider doing the following:

(1 ) Address some apparently inconsistent and unrealistic values of motor noise, exploration noise, and learning rate shown for individual participants (e.g., Figure 5b; see comments reviewers 1 and take the following additional steps: plotting r squares for individual participants, discussing whether individual values of the fitted parameters are plausible and whether model parameters in each age group can extrapolate to the two clamp conditions and baselines.

We have substantially updated our modeling approach. Now that we compare 31 noise-based models, the preferred model does not show any inconsistent or unrealistic values (see response to Reviewer #3). Additionally, we now show example individual fits and provide both relative and absolute goodness of fit (see response to Reviewer #3).

(2) Relatedly, to further justify if model assumptions are met, it would be valuable to show that the current learning model fits the data better than alternative models presented in the literature by the authors themselves and by others (reviewer 1). This could include alternative development models that formalise the proposed explanations for age-related change: poor spatial memory, reward/outcome processing, and exploration strategies (reviewer 2).

Please see response to public review comments of Reviewer #1 in which we explain that we have now fit a value-based model as well as 31 other noise-based models providing a comparison of previous models as well as novel models. This led to a slightly different model being preferred over the model in the original submission (updated model has a learning rate of unity). These models span many of the processes previously proposed for such tasks. We feel that 32 models span a reasonable amount of space and do not believe we have the power to include memory issues or heuristic exploration strategies in the model.

(3) Perform the mediation analysis with all the possible variables (i.e., not informed by multiple regression) to see if the results are more consistent across studies and with the current approach (see comments reviewer 1).

Please see response to public review comments of Reviewer #1. We chose to focus only on the model based analysis because it allowed us to distinguish between exploration variability and motor noise.

Please see below for further specific recommendations from each reviewer.

Reviewer #1 (Recommendations for the author):

(1) In general, there should be more discussion and contextualization of other binary reinforcement tasks used in the motor literature. For example, work from Jeroen Smeets, Katinka van der Kooij, and Joseph Galea.

Thank you for this comment. We have edited the Introduction to better contextualize our work within the reinforcement motor learning literature (see line 67 and line 83).

(2) Line 32. Very minor. This sentence is fine, but perhaps could be slightly improved. “select a location along a continuous and infinite set of possible options (anywhere along the span of the bridge)"

Thank you for this comment. We have edited the sentence to reflect this suggestion.

(3) Line 57. To avoid some confusion in successive sentences: Perhaps, "Both children over 12 and adolescents...".

Thank you for this comment. We have edited the sentence to reflect this suggestion.

(4) Line 80. This is arguably not a mechanistic model, since it is likely not capturing the reward/reinforcement machinery used by the nervous system, such as updating the expected value using reward predic tion errors/dopamine. That said, this phenomenological model, and other similar models in the field, do very well to capture behaviour with a very simple set of explore and update rules.

We use mechanistic in the standard use in modeling, as in Levenstein et al. (2023), for example. The contrast is not with neural modeling, but with normative modeling, in which one develops a model to optimize a function (or descriptive models as to what a system is trying to achieve). In mechanistic modeling one proposes a mechanism and this can be at a state-space level (as in our case) or a neural level (as suggested my the reviewer) but both are considered mechanistic, just at different levels. Quoting Levenstein "... mechanistic models, in which complex processes are summarized in schematic or conceptual structures that represent general properties of components and their interactions, are also commonly used." We now reference the Levenstein paper to clarify what we mean by mechanistic.

(5) Figure 1. It would be useful to state that the x-axis in Figure 1 is in normalized units, depending on the device.

Thank you for this comment. We have added a description of the x-axis units to the Fig. 1 caption.

(6) Were there differences in behaviour for these different devices? e.g., how different was motor noise for the mouse, trackpad, and touchscreen?

Thank you for this question. We did not find a significant effect of device on learning or precision in the baseline block. We have added these one way ANOVA results for each task in Supplementary Table 1.

(7) Line 98. Please state that participants received reinforcement feedback during baseline.

Thank you for this comment. We have updated the text to specify that participants receive reward feedback during the baseline block.

(8) Line 99. Did the distance from the last baseline trial influence whether the participant learned or did not learn? For example, would it place them too far from the peak success location such that it impacted learning?

Thank you for this question. We looked at whether the position of movement on the last baseline block trial was correlated with the first movement position in the learning block. We did not find any correlations between these positions for any of the tasks. Interestingly, we found that the majority of participants move to the center of the workspace on the first trial of the learning block for all tasks (either in the presence of the novel continuous target scene or the presentation of 7 targets all at once). We do not think that the last movement in the baseline block "primed" the participant for the location of the success zone in the learning block. We have added the following sentence to the Results section:

"Note that the reach location for the first learning trial was not affected by (correlated with) the target position on the last baseline trial (p > 0.3 for both children and adults, separately)."

(9) The term learning distance could be improved. Perhaps use distance from target.

Thank you for this comment. We appreciate that learning distance defined with 0 as the best value is counter intuitive. We have changed the language to be "distance from target" as the learning metric.

(10) Line 188. This equation is correct, but to estimate what the standard deviation by the distribution of changes in reach position is more involved. Not sure if the authors carried out this full procedure, which is described in Cashaback et al., 2019; Supplemental 2.

There appear to be no Supplemental 2 in the referenced paper so we assume the reviewer is referring to Supplemental B which deals with a shuffling procedure to examine lag-1 correlations.

In our tasks, we are limited to only 9 trials to analyze in each clamp phase so do not feel a shuffling analysis is warranted. In these blocks, we are not trying to 'estimate what the standard deviation by the distribution of changes in reach position' but instead are calculating the standard deviation of the reach locations and comparing the model fit (for which the reviewer says the formula is correct) with the data. We are unclear what additional steps the reviewer is suggesting. In our updated model analysis, we fit the data including the clamp phases for better parameter estimation. We use simulations to estimate s.d. in the clamp phase (as we ensure in simulations the data does not fall outside the workspace) making the previous analytic formulas an approximation that are no longer used.

(11) Line 197-199. Having done the demo task, it is somewhat surprising that a 3-year-old could understand these instructions (whose comprehension can be very different from even a 5-year old).

Thank you for raising this concern. We recognize that the younger participants likely have different comprehension levels compared to older participants. However, we believe that the majority of even the youngest participants were able to sufficiently understand the goal of the task to move in a way to get the video clip to play. We intentionally designed the tasks to be simple such that the only instructions the child needed to understand were that the goal was to get the video clip to play as much as possible and the video clip played based on their movement. Though the majority of younger children struggled to learn well on the probabilistic tasks, they were able to learn well on the deterministic tasks where the task instructions were virtually identical with the exception of how many places in the workspace could gain reward. On the continuous probabilistic task, we did have a small number (n = 3) of 3 to 5 year olds who exhibited more mature learning ability which gives us confidence that the younger children were able to understand the task goal.

(12) Line 497: Can the authors please report the F-score and p-value separately for each of these one-way ANOVA (the device is of particular interest here).

Thank you for this request. We have added ina upplementarytable (Supplementary Table 1) with the results of these ANOVAs.

(13) Past work has discussed how motivation influences learning, which is a function of success rate (van der Kooij, K., in 't Veld, L., & Hennink, T. (2021). Motivation as a function of success frequency. Motivation and Emotion, 45, 759-768.). Can the authors please discuss how that may change throughout development?

Thank you for this comment. While motivation most probably plays a role in learning, in particular in a game environment, this was out of the scope of the direct focus of this work and not something that our studies were designed to test. We have added the following sentence to the discussion section to address this comment:

"We also recognize that other processes, such as memory and motivation, could affect performance on these tasks however our study was not designed to test these processes directly and future work would benefit from exploring these other components more explicitly."

(14) Supplement 6. This analysis is somewhat incomplete because it does not consider success.

Pekny and collegues (2015) looked at 3 trials back but considered both success and reward. However, their analysis has issues since successive time points are not i.i.d., and spurious relationships can arise. This issue is brought up by Dwahale (Dhawale, A. K., Miyamoto, Y. R., Smith, M. A., & R475 Ölveczky, B. P. (2019). Adaptive regulation of motor variability. Current Biology, 29(21), 3551-3562.). Perhaps it is best to remove this analysis from the paper.

Thank you for this comment. We have decided to remove this secondary analysis from the paper as it was a source of confusion and did not add to the understanding and interpretation of our behavioral results.

Reviewer #2 (Recommendations for the author):

(1 ) the path length ratio analyses in the supplemental are interesting but are not mentioned in the main paper. I think it would be helpful to mention these as they are somewhat dramatic effects

Thank you for this comment. Path length ratios are defined in the Methods and results are briefly summarized in the Results section with a point to the supplementary figures. We have updated the text to more explicitly report the age related differences in path length ratios.

(2) The second to last paragraph of the intro could use a sentence motivating the use ofthe different task features (deterministic/probabilistic and discrete/continuous).

Thank you for this comment. We have added an additional motivating sentence to the introduction.

Reviewer #3 (Recommendations for the author):

The paper labeled the task as one for reinforcement motor learning, which is not quite appropriate in my opinion. Motor learning typically refers to either skill learning or motor adaptation, the former for improving speed-accuracy tradeoffs in a certain (often new) motor skill task and the latter for accommodating some sensorimotor perturbations for an existing motor skill task. The gaming task here is for neither. It is more like a

decision-making task with a slight contribution to motor execution, i.e., motor noise. I would recommend the authors label the learning as reinforcement learning instead of reinforcement motor learning.

Thank you for this comment. As noted in the response to the public review comments, we agree that this task has components of classical reinforcement learning (i.e. responding to a binary reward) but we specifically designed it to require the learning of a movement within a novel game environment. We have added a new paragraph to the introduction where we acknowledge the interplay between cognitive and motor mechanisms while also underscoring the features in our task that we think are not present in typical cognitive tasks.

My major concern is whether the model adequately captures subjects' behavior and whether we can conclude with confidence from model fitting. Motor noise, exploration noise, and learning rate, which fit individual learning patterns (Figure 5b), show some quite unrealistic values. For example, some subjects have nearly zero motor noise and a 100% learning rate.

We have now compared 31 models and the preferred model is different from the one in the first submission. The parameter fits of the new model do not saturate in any way and appear reasonable to us. The updates to the model analysis have addressed the concern of previously seen unrealistic values in the prior draft.

Currently, the paper does not report the fitting quality for individual subjects. It is good to have an exemplary subject's fit shown, too. My guess is that the r-squared would be quite low for this type of data. Still, given that the children's data is noisier, it might be good to use the adult data to show how good the fitting can be (individual fits, r squares, whether the fitted parameters make sense, whether it can extrapolate to the two clamp phases). Indeed, the reliability of model fitting affects how we should view the age effect of these model parameters.

We now show fits to individual subjects. But since this is a Kalman smoother it fits the data perfectly by generating its best estimate of motor noise and exploration variability on each trial to fully account for the data — so in that sense R² is always 1 so that is not helpful.

While the BIC analysis with the other model variants provides a relative goodness of fit, it is not straightforward to provide an absolute goodness of fit such as standard R² for a feedforward simulation of the model given the parameters (rather than the output of the Kalman smoother). There are two problems. First, there is no single model output. Each time the model is simulated with the fit parameters it produces a different output (due to motor noise, exploration variability and reward stochasticity). Second, the model is not meant to reproduce the actual motor noise, exploration variability and reward stochasticity of a trial. For example, the model could fit pure Gaussian motor noise across trials (for a poor learner) by accurately fitting the standard deviation of motor noise but would not be expected to actually match each data point so would have a traditional R² of O.

To provide an overall goodness of fit we have to reduce the noise component and to do so we exam ined the traditional R² between the average of all the children's data and the average simulation of the model (from the median of 1000 simulations per participant) so as to reduce the stochastic variation. The results for the continuous probabilistic and discrete probabilistic task are R² of 0.41 and 0.72, respectively.

Not that variability in the "success clamp" doe not change across ages (Figure 4C) and does not contribute to the learning effect (Figure 4F). However, it is regarded as reflecting motor noise (Figure SC), which then decreases over age from the model fitting (Figure 5B). How do we reconcile these contradictions? Again, this calls the model fitting into question.

For the success clamp, we only have 9 trials to calculate variability which limits our power to detect significance with age. In contrast, the model uses all 120 trials to estimate motor noise. There is a downward trend with age in the behavioral data which we now show overlaid on the fits of the model for both probabilistic conditions (Figure 5—figure Supplement 4) and Figure 6—figure Supplement 4). These show a reasonable match and although the variance explained is 1 6 and 56% (we limit to 9 trials so as to match the fail clamp), the correlations are 0.52 and 0.78 suggesting we have reasonable relation although there may be other small sources of variability not captured in the model.

Figure 5C: it appears one bivariate outlier contributes a lot to the overall significant correlation here for the "success clamp".

Recalculating after removing that point in original Fig 5C was still significant and we feel the plots mentioned in the previous point add useful information to this issue. With the new model this figure has changed.

It is still a concern that the young children did not understand the instructions. Nine 3-to-8 children (out of 48) were better explained by the noisy only model than the full model. In contrast, ten of the rest of the participants (out of 98) were better explained by the noisy-only model. It appears that there is a higher percentage of the "young" children who didn't get the instruction than the older ones.

Thank you for this comment. We did take participant comprehension of the task into consideration during the task design. We specifically designed it so that the instructions were simple and straight forward. The child simply needs to understand the underlying goal to make the video clip play as often as possible and that they must move the penguin to certain positions to get it to play. By having a very simple task goal, we are able to test a naturalistic response to reinforcement in the absence of an explicit strategy in a task suited even for young children.

We used the updated reinforcement learning model to assess whether an individual's performance is consistent with understanding the task. In the case of a child who does not understand the task, we expect that they simply have motor noise on their reach, and crucially, that they would not explore more after failure, nor update their reach after success. Therefore, we used a likelihood ratio test to examine whether the preferred model was significantly better at explaining each participant's data compared to the model variant which had only motor noise (Model 1). Focusing on only the youngest children (age 3-5), this analysis showed that that 43, 59, 65 and 86% of children (out of N = 21, 22, 20 and 21 ) for the continuous probabilistic, discrete probabilistic, continuous deterministic, and discrete deterministic conditions, respectively, were better fit with the preferred model, indicating non-zero exploration after failure. In the 3-5 year old group for the discrete deterministic condition, 18 out of 21 had performance better fit by the preferred model, suggesting this age group understands the basic task of moving in different directions to find a rewarding location.

The reduced numbers fit by the preferred model for the other conditions likely reflects differences in the task conditions (continuous and/or probabilistic) rather than a lack of understanding of the goal of the task. We include this analysis as a new subsection at the end of the Results.

Supplementary Figure 2: the first panel should belong to a 3-year-old not a 5-year-old? How are these panels organized? This is kind of confusing.

Thank you for this comment. Figure 2—figure Supplement 1 and Figure 2—figure Supplement 2 are arranged with devices in the columns and a sample from each age bin in the rows. For example in Figure 2—figure Supplement 1, column 1, row 1 is a mouse using participant age 3 to 5 years old while column 3, row 2 is a touch screen using participant age 6 to 8 years old. We have edited the labeling on both figures to make the arrangement of the data more clear.

Line 222: make this a complete sentence.

This sentence has been edited to a complete sentence.

Line 331: grammar.

This sentence has been edited for grammar.

Author response:

The following is the authors’ response to the original reviews

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

This article investigates the phenotype of macrophages with a pathogenic role in arthritis, particularly focusing on arthritis induced by immune checkpoint inhibitor (ICI) therapy. 

Building on prior data from monocyte-macrophage coculture with fibroblasts, the authors hypothesized a unique role for the combined actions of prostaglandin PGE2 and TNF. The authors studied this combined state using an in vitro model with macrophages derived from monocytes of healthy donors. They complemented this with single-cell transcriptomic and epigenetic data from patients with ICI-RA, specifically, macrophages sorted out of synovial fluid and tissue samples. The study addressed critical questions regarding the regulation of PGE2 and TNF: Are their actions co-regulated or antagonistic? How do they interact with IFN-γ in shaping macrophage responses? 

This study is the first to specifically investigate a macrophage subset responsive to the PGE2 and TNF combination in the context of ICI-RA, describes a new and easily reproducible in vitro model, and studies the role of IFNgamma regulation of this particular Mф subset. 

Strengths: 

Methodological quality: The authors employed a robust combination of approaches, including validation of bulk RNA-seq findings through complementary methods. The methods description is excellent and allows for reproducible research. Importantly, the authors compared their in vitro model with ex vivo single-cell data, demonstrating that their model accurately reflects the molecular mechanisms driving the pathogenicity of this macrophage subset. 

Weaknesses: 

Introduction: The introduction lacks a paragraph providing an overview of ICI-induced arthritis pathogenesis and a comparison with other types of arthritis. Including this would help contextualize the study for a broader audience.

Thank you for this suggestion, we have added a paragraph on ICI-arthritis to intro (pg. 4, middle paragraph).  

Results Section: At the beginning of the results section, the experimental setup should be described in greater detail to make an easier transition into the results for the reader, rather than relying just on references to Figure 1 captions.

We have clarified the experimental setup (pg. 5).  

There is insufficient comparison between single-cell RNA-seq data from ICI-induced arthritis and previously published single-cell RA datasets. Such a comparison may include DEGs and GSEA, pathway analysis comparison for similar subsets of cells. Ideally, an integration with previous datasets with RA-tissue-derived primary monocytes would allow for a direct comparison of subsets and their transcriptomic features.

We thank the Reviewer for this suggestion, which has increased the impact of our data and analysis. A computationally rigorous representation mapping approach showed that ICI-arthritis myeloid subsets predominantly mapped onto 4 previously defined RA subsets including IL-1β+ cells. This result was corroborated using a complementary data integration approach. Analysis of (TNF + PGE)-induced gene sets (TP signatures) in ICI-arthritis myeloid cells projected onto the RA subsets using the AUCell package showed elevated TP gene expression in similar ICI-arthritis and RA monocytic cell subsets. We also found mutually exclusive expression of TP and IFN signatures in distinct RA and ICI-arthritis myeloid cell subsets, which supports that the opposing cross-regulation between IFN-γ and PGE2 pathways that we identified in vitro also functions similarly in vivo. This analysis is shown in the new Fig. 3, described on pg. 7, and discussed on pp. 13-14.

While it's understandable that arthritis samples are limited in numbers and myeloid cell numbers, it would still be interesting to see the results of PGE2+TNF in vitro stimulation on the primary RA or ICI-RA macrophages. It would be valuable to see RNA-Seq signatures of patient cell reactivation in comparison to primary stimulation of healthy donor-derived monocytes.

We agree that this would be interesting but given limited samples and distribution of samples amongst many studies and investigators this is beyond the scope of the current study.  

Discussion: Prior single-cell studies of RA and RA macrophage subpopulations from 2019, 2020, 2023 publications deserve more discussion. A thorough comparison with these datasets would place the study in a broader scientific context. 

Creating an integrated RA myeloid cell atlas that combines ICI-RA data into the RA landscape would be ideal to add value to the field. 

As one of the next research goals, TNF blockade data in RA and ICI-RA patients would be interesting to add to such an integrated atlas. Combining responders and non-responders to TNF blockade would help to understand patient stratification with the myeloid pathogenic phenotypes. It would be great to read the authors' opinion on this in the Discussion section. 

Please see our response to point 3 above. This point is addressed in Fig. 3, pg. 7, and pp. 13-14, which includes a discussion of responders and nonresponders and patient stratification.  

Conclusion: The authors demonstrated that while PGE2 maintains the inflammatory profile of macrophages, it also induces a distinct phenotype in simultaneous PGE2 and TNF treatment. The study of this specific subset in single-cell data from ICI-RA patients sheds light on the pathogenic mechanisms underlying this condition, however, how it compares with conventional RA is not clear from the manuscript. 

Given the substantial incidence of ICI-induced autoimmune arthritis, understanding the unique macrophage subsets involved for future targeting them therapeutically is an important challenge. The findings are significant for immunologists, cancer researchers, and specialists in autoimmune diseases, making the study relevant to a broad scientific audience. 

Reviewer #2 (Public review): 

Summary/Significance of the findings: 

The authors have done a great job by extensively carrying out transcriptomic and epigenomic analyses in the primary human/mouse monocytes/macrophages to investigate TNF-PGE2 (TP) crosstalk and their regulation by IFN-γ in the Rheumatoid arthritis (RA) synovial macrophages. They proposed that TP induces inflammatory genes via a novel regulatory axis whereby IFN-γ and PGE2 oppose each other to determine the balance between two distinct TNF-induced inflammatory gene expression programs relevant to RA and ICI-arthritis. 

Strengths: 

The authors have done a great job on RT-qPCR analysis of gene expression in primary human monocytes stimulated with TNF and showing the selective agonists of PGE2 receptors EP2 and EP4 22 that signal predominantly via cAMP. They have beautifully shown IFN-γ opposes the effects of PGE2 on TNF-induced gene expression. They found that TP signature genes are activated by cooperation of PGE2-induced AP-1, CEBP, and NR4A with TNF-induced NF-κB activity. On the other hand, they found that IFN-γ suppressed induction of AP-1, CEBP, and NR4A activity to ablate induction of IL-1, Notch, and neutrophil chemokine genes but promoted expression of distinct inflammatory genes such as TNF and T cell chemokines like CXCL10 indicating that TP induces inflammatory genes via IFN-γ in the RA and ICI-arthritis. 

Weaknesses: 

(1) The authors carried out most of the assays in the monocytes/macrophages. How do APCcells like Dendritic cells behave with respect to this TP treatment similar dosing? 

We agree that this is an interesting topic especially as TNF + PGE2 is one of the standard methods of maturing in vitro generated human DCs and promoting antigen-presenting function. As DC maturation is quite different from monocyte activation this would represent a new study and is beyond the scope of the current manuscript. We have instead added a paragraph to the discussion (pg. 12) and cited the literature on DC maturation by TNF + PGE2 including one of our older papers (PMID: 18678606; 2008)  

(2) The authors studied 3h and 24h post-treatment transcriptomic and epigenomic. What happens to TP induce inflammatory genes post-treatment 12h, 36h, 48h, 72h. It is critical to see the upregulated/downregulated genes get normalised or stay the same throughout the innate immune response.

We now clarify that subsets of inducible genes showed distinct kinetics of induction with transient expression at 3 hr versus sustained expression over the 24 hr stimulation period as shown in Supplementary Fig. 1 (pg. 5).

(3) The authors showed IL1-axis in response to the TP-treatment. Do other cytokine axes get modulated? If yes, then how do they cooperate to reduce/induce inflammatory responses along this proposed axis?

This is an interesting question, which we approached using a combination of pathway analysis and targeted inspection of pathways important pathogenesis of RA, which is the inflammatory condition most relevant for this study. In addition to genes in the IL-1-NF-κB core inflammatory pathway, pathway analysis of genes induced by TP co-stimulation showed enrichment of genes related to leukocyte chemotaxis, in particular neutrophil migration. Accordingly, TP costimulation increased expression of CSF3, which plays a key role in mobilizing neutrophils from the bone marrow, and major neutrophil chemokines CXCL1, CXCL2, CXCL3 and CXCL5 that recruit neutrophils to sites of inflammation including in inflammatory arthritis. Analysis of the late response to TNF similarly showed enrichment of genes important in chemotaxis, and suppression of genes in the cholesterol biosynthetic pathway, which we and others have previously linked to IFN responses. Targeted inspection of genes in additional pathways implicated in RA pathogenesis showed increased expression of genes in the Notch pathway. We believe that these pathways work together with the IL-1 pathway to increase immune cell recruitment and activation in inflammatory responses; these results are described on pp. 5-6 and are incorporated into Figures 1, 2 and Supplementary Fig. 2. 

Overall, the data looks good and acceptable but I need to confirm the above-mentioned criticisms. 

Recommendations for the authors: 

Reviewer #1 (Recommendations for the authors):   

The discussion section of the manuscript claims: "In this study, we utilized transcriptomics to demonstrate a 'TNF + PGE2' (TP) signature in RA and ICI-arthritis IL-1β+ synovial macrophages." This statement is misleading, as no new transcriptomic data from RA synovial samples were generated in this study. To support such a claim, the authors would need to compare primary monocytes or macrophages from RA patients using bulk RNA-seq or singlecell RNA-seq. Based on the current data, the comparison is limited to bulk RNA-seq findings from the authors' in vitro model and prior monocyte-fibroblast coculture studies. 

We have modified the abstract and discussion (pg. 10) to reflect that we have compared an in vitro generated TP signature with gene expression in previously identified RA macrophage subsets.

Author response:

The following is the authors’ response to the original reviews

Main revision made to the manuscript

The main revision made to the manuscript is to reconcile our findings with the line attractor model. The revision is based on Reviewer 1’s comment on reinterpreting our results as a superposition of an attractor model with fast timescale dynamics. We expanded our analysis regime to the start of a trial and characterized the overall within-trial dynamics to reinterpret our findings.

We first acknolwedge that our results are not in contradiction with evidence integration on a line attractor. As pointed out by the reviewers, our finding that the integration of reward outcome explains the reversal probability activity x_rev (Figure 3) is compatible with the line attractor model. However, the reward integration equation is an algebraic relation and does not characterize the dynamics of reversal probability activity. So a closer analysis on the neural dynamics is needed to assess the feasibility of line attractor.

In the revised manuscript, we show that x_rev exhibits two different activity modes (Figure 4). First, x_rev has substantial non-stationary dynamics during a trial, and this non-stationary activity is incompatible with the line attractor model, as claimed in the original manuscript. Second, we present new results showing that x_rev is stationary (i.e., constant in time) and stable (i.e., contracting) at the start of a trial. These two properties of x_rev support that it is a point attractor at the start of a trial and is compatible with the line attractor model. 

We further analyze how the two activity modes are linked (Figure 4, Support vector regression). We show that the non-stationary activity is predictable from the stationary activity if the underlying dynamics can be inferred. In other words, the non-stationary activity during a trial is generated by an underlying dynamics with the initial condition provided by the stationary state at the start of trial.

These results suggest an extension of the line attractor model where an attractor state at the start of a trial provides an initial condition from which non-stationary activity is generated during a trial by an underlying dynamics associated with task-related behavior (Figure 4, Augmented model). 

The separability of non-stationary trajectories (Figure 5 and 6) is a property of the non-stationary dynamics that allows separable points in the initial stationary state to remain separable during a trial, thus making it possible to represent distinct probabilistic values in non-stationary activity.

This revised interpretation of our results (1) retains our original claim that the non-stationary dynamics during a trial is incompatible with the line attractor model and (2) introduces attractor state at the start of a trial which is compatible with the line attractor model. Our anlaysis shows that the two activity modes are linked by an underlying dynamics, and the attractor state serves as initial state to launch the non-stationary activity.

Responses to the Public Reviews:

Reviewer # 1:

(1) To provide better explanation of the reversal learning task and network training method, we added detailed description of RNN and monkey task structure (Result Section 1), included a schematic of target outputs (Figure1B), explained the rationale behind using inhibitory network model (Method Section 1) and explained the supervised RNN training scheme (Result Section 1). This information can also be found in the Methods.

(2) Our understanding is that the augmented model discussed in the previous page is aligned with the model suggested by Reviewer 1: “a curved line attractor, with faster timescale dynamics superimposed on this structure”. It is likely that the “fast” non-stationary activity observed during the trial is driven by task-related behavior, thus is transient. For instance, we do not observe such non-stationary activity in the inter-trial-interval when the task-related behavior is absent. For this reason, the non-stationary trajectories were not considered to be part of the attractor. Instead, they are transient activity generated by the underlying neural dynamics associated with task-related behavior. We believe such characterization of faster timescale dynamics is consistent with Reviewer 1’s view and wanted to clarify that there are two different activity modes.

(3) We appreciate the reviewers (Reviewer 1 and Reviewer 2) comment that TDR may be limited in isolating the neural subspace of interest. Our study presents what could be learned from TDR but is by no means the only way to interpret the neural data. It would be of future work to apply other methods for isolating task-related neural activities.

We would appreciate it if the reviewers could share thoughts on what other alternative methods could better isolate the reversal probability activity.

Reviewer # 2:

(1) (i) We respectfully disagree with Reviewer 2’s comment that “no action is required to be performed by neurons in the RNN”. In our network setup, the output of RNN learns to choose a sign (+ or -), as Reviewer 2 pointed out, to make a choice. This is how the RNN takes an action. It is unclear to us what Reviewer 2 has intended by “action” and how reaching a target value (not just taking a sign) would make a significant difference in how the network performs the task. 

(ii)  From Reviewer 2’s comment that “no intervening behavior is thus performed by neurons”, we noticed that the term “intervening behavior” has caused confusion. It refers to task-related behavior, such as making choices or receiving reward, that the subject must perform across trials before reversing its preferred choice. These are the behaviors that intervene the reversal of preferred choice. To clarify its meaning, in the revised manuscript, we changed the term to “task-related behavior” and put them in context. For example, in the Introduction we state that “However, during a trial, task-related behavior, such as making decisions or receiving feedback, produced …”

(iii) As pointed out by Reviewer 2, the lack of fixation period in the RNN could make differences in the neural dynamics of RNN and PFC, especially at the start of a trial. We demonstrate this issue in Result Section 4 where we analyze the stationary activity at the start of a trial. We find that fixating the choice output to zero before making a choice promotes stationary activity and makes the RNN activity more similar to the PFC activity.

Reviewer #3:

(1) (i) In the previous study (Figure 1 in [Bartolo and Averbeck ‘20]), it was shown that neural activity can predict the behavioral reversal trial. This is the reason we examined the neural activity in the trials centered at the behavioral reversal trial. We explained in Result Section 2 that we followed this line of analysis in our study.

(ii) We would like to emphasize that the main point of Figures 4 and 5 is to show the separability of neural trajectories: the entire trajectory shifts without overlapping. It is not obvious that high-dimensional neural population activity from two trials should remain separated when their activities are compressed into a one-dimensional subspace. The onedimensional activities can easily collide since their activities are compressed into a lowdimensional space. We revised the manuscript to bring out these points. We added an opening paragraph that discusses separability of trajectories and revised the main text to bring out the findings on separability. 

(iii) We agree with Reviewer 3 that it would be interesting to look at what happens in other subspace of neural activity that are not related to reversal probability and characterize how different neural subspace interact with each. However, the focus of this paper was the reversal probability activity, and we’d consider these questions out of the scope of current paper. We point out that, using the same dataset, neural activity related to other experimental variables were analyzed in other papers [Bartolo and Averbeck ’20; Tang, Bartolo and Averbeck ‘21] 

(2) (i) In the revised manuscript, we added explanation on the rational behind choosing inhibitory network as a simplified model for the balanced state. In brief, strong inhibitory recurrent connections with strong excitatory external input operates in the balanced state, as in the standard excitatory-inhibitory network. We included references that studied this inhibitory network. We also explained the technical reason (GPU memory) for choosing the inhibitory model.

(ii) We thank the reviewer for pointing out that the original manuscript did not mention how the feedback and cue were initialized. They were random vectors sample from Gaussian distribution. We added this information in the revised manuscript. In our opinion, it is common to use random external inputs for training RNNs, as it is a priori unclear how to choose them. In fact, it is possible to analyze the effects of random feedback on one-dimensional x_rev dynamics by projecting the random feedback vector to the reversal probability vector. This is shown in Figure 4F.

(iii) We agree that it would be more natural to train the RNN to solve the task without using the Bayesian model. We point out this issue in the Discussion in the revised manuscript.

Recommendations for the authors:

Reviewer #1:

(1) My understanding of network training was that a Bayesian ideal observer signaled target output based on previous reward outcomes. However, the authors never mention that networks are trained by supervised learning in the main text until the last paragraph of the discussion. There is no mention that there was an offset in the target based on the behavior of the monkeys in the main text. These are really important things to consider in the context of the network solution after training. I couldn't actually find any figure that presents the target output for the network. Did I miss something key here?

In Result Section 1, we added a paragraph that describes in detail how the RNN is trained. We explained that the network is first simulated and then the choice outputs and reward outcomes are fed into the Bayesian model to infer the scheduled reversal trial. A few trials are added to the inferred reversal trial to obtain the behavioral reversal trial, as found in a previous study [Bartolo and Averbeck ‘20]. Then the network weights are updated by backpropagation-through-time via supervised learning. 

In the original manuscript, the target output for the network was described in Methods Section 2.5, Step 4. To make this information readily accessible, we added a schematic in Figure 1B that shows the scheduled, inferred and behavioral reversal trials. It also shows how the target choice ouputs are defined. They switch abruptly at the behavioral reversal trial.

(2) The role of block structure in the task is an important consideration. What are the statistics of block switches? The authors say on average the reversals are every 36 trials, but also say there are random block switches. The reviewer's notes suggest that both the networks and monkeys may be learning about the typical duration of blocks, which could influence their expectations of reversals. This aspect of the task design should be explained more thoroughly and considered in the context of Figure 1E and 5 results.

We provided more detailed description of the reversal learning task in Result Section 1. We clarified that (1) a task is completed by executing a block of fixed number of trials and (2) reversal of reward schedule occurrs at a random trial around the mid-trial in a block. The differences in the number of trials in a block that the RNNs (36) and the monkeys (80) perform are also explained. We also pointed out the differences in how the reversal trial is randomly sampled.

However, it is unclear what Reviewer 1 meant by random block switches. Our reversal learning task is completed when a block of fixed number of trials is executed. Reversal of reward schedule occurs only once on a randomly selected trial in the block, and the reversed reward schedule is maintained until the end of a block. It is different from other versions of reveral learning where the reward schedule switches multiple times across trials. We clarified this point in Result Section 1.

(3) The relationship between the supervised learning approach used in the RNNs and reinforcement learning was confused in the discussion. "Although RNNs in our study were trained via supervised learning, animals learn a reversal-learning task from reward feedback, making it into a reinforcement learning (RL) problem." This is fundamentally not true. In the case of this work, the outcome of the previous trial updates the target output, rather than the trial and error type learning as is typical in reinforcement learning. Networks are not learning by reinforcement learning and this statement is confusing.

We agree with Reviewer 1’s comment that the statement in the original manuscript is confusing. Our intention was to point out that our study used supervised learning, and this is different from animals learn by reinforcement learning in rea life. We revised the sentence in Discussion as follows:

“The RNNs in our study were trained via supervised learning. However, in real life, animals learn a reversal learning task via reinforcement learning (RL), i.e., learn the task from reward outcomes.”

(4) The distinction between line attractors and the dynamic trajectories described by the authors deserves further investigation. A significant concern arises from the authors' use of targeted dimensionality reduction (TDR), a form of regression, to identify the axis determining reversal probability. While this approach can reveal interesting patterns in the data, it may not necessarily isolate the dimension along which the RNN computes reversal probability. This limitation could lead to misinterpretation of the underlying neural dynamics.

a) This manuscript cites work described in "Prefrontal cortex as a meta-reinforcement learning system," which examined a similar task. In that study, the authors identified a v-shaped curve in the principal component space of network states, representing the probability of choosing left or right.

Importantly, this curve is topologically equivalent to a line and likely represents a line attractor. However, regressing against reversal probability in such a case would show that a single principal component (PC2) directly correlates with reversal probability.

b) The dynamics observed in the current study bear a striking resemblance to this structure, with the addition of intervening loops in the network state corresponding to within-trial state evolution. Crucially, these observations do not preclude the existence of a line attractor. Instead, they may reflect the network's need to produce fast timescale dynamics within each trial, superimposed on the slower dynamics of the line attractor.

c) This alternative interpretation suggests that reward signals could function as inputs that shift the network state along the line attractor, with information being maintained across trials. The fast "intervening behaviors" observed by the authors could represent faster timescale dynamics occurring on top of the underlying line attractor dynamics, without erasing the accumulated evidence for reversals.

d) Given these considerations, the authors' conclusion that their results are better described by separable dynamic trajectories rather than fixed points on a line attractor may be premature. The observed dynamics could potentially be reconciled with a more nuanced understanding of line attractor models, where the attractor itself may be curved and coexist with faster timescale dynamics.

We appreciate the insightful comments on (1) the similarity of the work by Wang et al ’18 with our findings and (2) an alternative interpretation that augments the line attractor with fast timescale dynamics. 

(1) We added a discussion of the work by Wang et al ’18 in Result Section 2 to point out the similarity of their findings in the principal component space with ours in the x_rev and x_choice space. We commented that such network dynamics could emerge when learning to perform the reversal learning the task, regardless of the training schemes. 

We also mention that the RL approach in Wang et al ’18 does not consider within-trial dynamics, therefore lacks the non-stationary activity observed during the trial in the PFC of monkeys and our trained RNNs.

(2) We revised our original manuscript substantially to reconcile the line attractor model with the nonstationary activity observed during a trial. 

Here are the highlights of the revised interpretation of the PFC and the RNN network activity

- The dynamics of x_rev consists of two activity modes, i.e., stationary activity at the start of a trial and non-stationary activity during the trial. Schematic of the augmented model that reconciles two activity modes is shown in Figure 4A. Analysis of the time derivative (dx_reverse / dt) and contractivity of the stationary state are shown in Figure 4B,C to demonstrate two activity modes.

- We discuss in Result Section 4 main text that the stationary activity is consistent with the line attractor model, but the non-stationary activity deviates from the model. 

- The two activity modes are linked dynamically. There is an underlying dynamics that can map the stationary state to the non-stationary trajectory. This is shown by predicting the nonstationary trajectory with the stationary state using a support vector regression model. The prediction results are shown in Figure 4D,E,F.

- We discuss in Result Section 4 an extension of the standard line attractor model: points on the line attractor can serve as initial states that launch non-stationary activity associated with taskrelated behavior.

- The separability of neural trajectories presented in Result Section 5 is framed as a property of the non-stationary dynamics associated with task-related behavior.

To strengthen their claims, the authors should:

(1) Provide a more detailed description of their RNN training paradigm and task structure, including clear illustrations of target outputs.

(2) Discuss how their findings relate to and potentially extend previous work on similar tasks, particularly addressing the similarities and differences with the v-shaped state organization observed in reinforcement learning contexts. (https://www.nature.com/articles/s41593-018-0147-8 Figure1).

(3) Explore whether their results could be consistent with a curved line attractor model, rather than treating line attractors and dynamic trajectories as mutually exclusive alternatives.

Our response to these three comments is described above.

Addressing these points would significantly enhance the impact of the study and provide a more nuanced understanding of how reversal probabilities are represented in neural circuits.

In conclusion, while this study provides interesting insights into the neural representation of reversal probability, there are several areas where the methodology and interpretations could be refined.

Additional Minor Concerns:

(1) Network Training and Reversal Timing: The authors mention that the network was trained to switch after a reversal to match animal behavior, stating "Maximum a Posterior (MAP) of the reversal probability converges a few trials past the MAP estimate." More explanation of how this training strategy relates to actual animal behavior would enhance the reader's understanding of the meaning of the model's similarity to animal behavior in Figure 1.

In Method Section 2.5, we described how our observation that the running estimate of MAP converges a few trials after the actual MAP is analogous to the animal’s reversal behavior.

“This observation can be interpreted as follows. If a subject performing the reversal learning task employs the ideal observer model to detect the trial at which reward schedule is reversed, the subject can infer the reversal of reward schedule a few trials past the actual reversal and then switch its preferred choice. This delay in behavioral reversal, relative to the reversal of reward schedule, is analogous to the monkeys switching their preferred choice a few trials after the reversal of reward schedule.”

In Step 4, we also mentioned that the target choice outputs are defined based on our observation in Step 3.

“We used the observation from Step 3 to define target choice outputs that switch abruptly a few trials after the reversal of reward schedule, denoted as $t^*$ in the following. An example of target outputs are shown in Fig.\,\ref{fig_behavior}B.”

(2) How is the network simulated in step 1 of training? Is it just randomly initialized? What defines this network structure?

The initial state at the start of a block was random. We think the initial state is less relevant as the external inputs (i.e., cue and feedback) are strong and drive the network dynamics. We mentioned these setup and observation in Step 1 of training.

“Step 1. Simulate the network starting from a random initial state, apply the external inputs, i.e., cue and feedback inputs, at each trial and store the network choices and reward outcomes at all the trials in a block. The network dynamics is driven by the external inputs applied periodically over the trials.”

(3) Clarification on Learning Approach: More description of the approach in the main text would be beneficial. The statement "Here, we trained RNNs that learned from a Bayesian inference model to mimic the behavioral strategies of monkeys performing the reversal learning task [2, 4]" is somewhat confusing, as the model isn't directly fit to monkey data. A more detailed explanation of how the Bayesian inference model relates to monkey behavior and how it's used in RNN training would improve clarity.

We described the learning approach in more detail, but also tried to be concise without going into technical details.

We revised the sentence in Introduction as follows:

“We sought to train RNNs to mimic the behavioral strategies of monkeys performing the reversal learning task. Previous studies \cite{costa2015reversal, bartolo2020prefrontal} have shown that a Bayesian inference model can capture a key aspect of the monkey's behavioral strategy, i.e., adhere to the preferred choice until the reversal of reward is detected and then switch abruptly. We trained the RNNs to replicate this behavioral strategy by training them on target behaviors generated from the Bayesian model.”

We also added a paragraph in Result Section 1 that explains in detail how the training approach works.

(4) In Figure 1B, it would be helpful to show the target output.

We added a figure in Fig1B that shows a schematic of how the target output is generated.

(5) An important point to consider is that a line attractor can be curved while still being topologically equivalent to a line. This nuance makes Figure 4A somewhat difficult to interpret. It might be helpful to discuss how the observed dynamics relate to potentially curved line attractors, which could provide a more nuanced understanding of the neural representations.

As discussed above, we interpret the “curved” activity during the trial as non-stationary activity. We do not think this non-stationary activity would be characterized as attractor. Attractor is (1) a minimal set of states that is (2) invariant under the dynamics and (3) attracting when perturbed into its neighborhood [Strogatz, Nonlinear dynamics and chaos]. If we consider the autonomous system without the behavior-related external input as the base system, then the non-stationary states could satisfy (2) and (3) but not (1), so they are not part of the attractor. If we include the behavior-related external input to the autonomous dynamics, then it may be possible that the non-stationary trajectories are part of the attractor. We adopted the former interpretation as the behavior-related inputs are external and transient.

(6) The results of the perturbation experiments seem to follow necessarily from the way x_rev was defined. It would be valuable to clarify if there's more to these results than what appears to be a direct consequence of the definition, or if there are subtleties in the experimental design or analysis that aren't immediately apparent.

The neural activity x_rev is correlated to the reversal probability, but it is unclear if the activity in this neural subspace is causally linked to behavioral variables, such as choice output. We added this explanation at the beginning of Results Section 7 to clarify the reason for performing the perturbation experiments.

“The neural activity $x_{rev}$ is obtained by identifying a neural subspace correlated to reversal probability. However, it remains to be shown if activity within this neural subspace is causally linked to behavioral variables, such as choice output.”

Reviewer #2:

Below is a list of things I have found difficult to understand, and been puzzled/concerned about while reading the manuscript:

(1) It would be nice to say a bit more about the dataset that has been used for PFC analysis, e.g. number of neurons used and in what conditions is Figure 2A obtained (one has to go to supplementary to get the reference).

We added information about the PFC dataset in the opening paragraph of Result Section 2 to provide an overview of what type of neural data we’ve analyzed. It includes information about the number of recorded neurons, recording method and spike binning process.

(2) It would be nice to give more detail about the monkey task and better explain its trial structure.

In Result Section 1 we added a description of the overall task structure (and its difference with other versions of revesal learning task), the RNN / monkey trial structure and differences in RNN and monkey tasks.

(3) In the introduction it is mentioned that during the hold period, the probability of reversal is represented. Where does this statement come from?

The fact that neural activity during a hold period, i.e., fixation period before presenting the target images, encodes the probability of reversal was demonstrated in a previous study (Bartolo and Averbeck ’20). 

We realize that our intention was to state that, during the hold period, the reversal probability activity is stationary as in the line attractor model, instead of focusing on that the probability of reversal is represented during this period. We revised the sentence to convey this message. In addition, we revised the entire paragraph to reinterpret our findings: there are two activity modes where the stationary activity is consistent with the line attractor model but the non-stationary activity deviates from it.

(4) "Around the behavioral reversal trial, reversal probabilities were represented by a family of rankordered trajectories that shifted monotonically". This sentence is confusing and hard to understand.

Thank you for point this out. We rewrote the paragraph to reflect our revised interpretation. This sentence was removed, as it can be considered as part of the result on separable trajectories.

(5) For clarity, in the first section, when it is written that "The reversal behavior of trained RNNs was similar to the monkey's behavior on the same task" it would be nice to be more precise, that this is to be expected given the strategy used to train the network.

We removed this sentence as it makes a blanket statement. Instead, we compared the behavioral outputs of the RNNs and the monkeys one by one.

We added a sentence in Result Section 1 that the RNN’s abrupt behavioral reversal is expected as they are trained to mimic the target choice outputs of the Bayesian model.

“Such abrupt reversal behavior was expected as the RNNs were trained to mimic the target outputs of the Bayesian inference model.”

(6) What is the value of tau used in eq (1), and how does it compare to trial duration?

We described the value of time constant tau in Eq (1) and also discussed in Result Section 1 that tau=20ms is much faster than trial duration 500ms, thus the persistent behavior seen in trained RNNs is due to learning.

(7) It would be nice to expand around the notion of « temporally flexible representation » to help readers grasp what this means.

Instead of stating that the separable dynamic trajectories have “temporally flexible representation”, we break down in what sense it is temporally flexible: separable dynamic trajectories can accommodate the effects that task-related behavior have on generating non-stationary neural dynamics.

“In sum, our results show that, in a probabilistic reversal learning task, recurrent neural networks encode reversal probability by adopting, not only stationary states as in a line attractor, but also separable dynamic trajectories that can represent distinct probabilistic values while accommodating non-stationary dynamics associated with task-related behavior.”

Reviewer #3:

(1) Data:

It would be useful to describe the experimental task, recording setup, and analyses in much more detail - both in the text and in the methods. What part of PFC are the recordings from? How many neurons were recorded over how many sessions? Which other papers have they been used in? All of these things are important for the reader to know, but are not listed anywhere. There are also some inconsistencies, with the main text e.g. listing the 'typical block length' as 36 trials, and the methods listing the block length as 24 trials (if this is a difference between the biological data and RNN, that should be more explicit and motivated).

We provided more detailed description of the monkey experimental task and PFC recordings in Result Section 1. We also added a new section in Methods 2.1 to describe the monkey experiment.

The experimental analyses should be explained in more detail in the methods. There is e.g. no detailed description of the analysis in Figure 6F.

We added a new section in Methods 6 to describe how the residual PFC activity is computed. It also describes the RNN perturbation experiments.

Finally, it would be useful for more analyses of monkey behaviour and performance, either in the main text or supplementary figures.

We did not pursue this comment as it is unclear how additional behavioral analyses would improve the manuscript.

(2) Model:

When fitting the network, 'step 1' of training in 2.3 seems superfluous. The posterior update from getting a reward at A is the same as that from not getting a reward at B (and vice versa), and it is therefore completely independent of the network choice. The reversal trial can therefore be inferred without ever simulating the network, simply by generating a sample of which trials have the 'good' option being rewarded and which trials have the 'bad' option being rewarded.

We respectfully disagree with Reviewer 3’s comment that the reversal trial can be inferred without ever simulating the network. The only way for the network to know about the underlying reward schedule is to perform the task by itself. By simulating the network, it can sample the options and the reward outcomes. 

Our understanding is that Review 3 described a strategy that a human would use to perform this task. Our goal was to train the RNN to perform the task.

Do the blocks always start with choice A being optimal? Is everything similar if the network is trained with a variable initial rewarded option? E.g. in Fig 6, would you see the appropriate swap in the effect of the perturbation on choice probability if choice B was initially optimal?

Thank you for pointing out that the initial high-value option can be random. When setting up the reward schedule, the initial high-value option was chosen randomly from two choice outputs and, at the scheduled reversal, it was switched to the other option. We did not describe this in the original manuscript.

We added a descrption in Training Scheme Step 4 that the the initial high-value option is selected randomly. This is also explained in Result Section 1 when we give an overview of the RNN training procedure.

(3) Content:

It is rarely explained what the error bars represent (e.g. Figures 3B, 4C, ...) - this should be clear in all figures.

We added that the error bars represent the standard error of mean.

Figure 2A: this colour scheme is not great. There are abrupt colour changes both before and after the 'reversal' trial, and both of the extremes are hard to see.

We changed the color scheme to contrast pre- and post-reversal trials without the abrupt color change.

Figure 3E/F: how is prediction accuracy defined?

We added that the prediction accuracy is based on Pearson correlation.

Figure 4B: why focus on the derivative of the dynamics? The subsequent plots looking at the actual trajectories are much easier to understand. Also - what is 'relative trial' relative to?

The derivative was analyzed to demonstrate stationarity or non-stationarity of the neural activity. We think it will be clearer in the revised manuscript that the derivative allows us to characterize those two activity modes.

Relative trial number indicate the trial position relative to the behavioral reversal trial. We added this description to the figures when “relative trial” is used.

Figure 4C: what do these analyses look like if you match the trial numbers for the shift in trajectories? As it is now, there will presumably be more rewarded trials early and late in each block, and more unrewarded trials around the reversal point. Does this introduce biases in the analysis? A related question is (i) why the black lines are different in the top and bottom plots, and (ii) why the ends of the black lines are discontinuous with the beginnings of the red/blue lines.

We could not understand what Reviewer 3 was asking in this comment. It’d help if Review 3 could clarify the following question:

“Figure 4C: what do these analyses look like if you match the trial numbers for the shift in trajectories?”

Question (i): We wanted to look at how the trajectory shifts in the subsequent trial if a reward is or is not received in the current trial. The top panel analyzed all the trials in which the subsquent trial did not receive a reward. The bottom panel analyzed all the trials in which the subsequent trial received a reward. So, the trials analyzed in the top and bottom panels are different, and the black lines (x_rev of “current” trial) in the top and bottom panels are different.

Question (ii): Black line is from the preceding trial of the red/blue lines, so if trials are designed to be continuous with the inter-trial-interval, then black and red/blue should be continuous. However, in the monkey experiment, the inter-trial-intervals were variable, so the end of current trial does not match with the start of next trial. The neural trajectories presented in the manuscript did not include the activity in this inter-trial-interval.

Figure 6C: are the individual dots different RNNs? Claiming that there is a decrease in Delta x_choice for a v_+ stimulation is very misleading.

Yes individual dots are different RNN perturbations. We added explanation about the dots in Figure7C caption. 

We agree with the comment that \Delta x_choice did not decrease. This sentence was removed. Instead, we revised the manuscript to state that x_choice for v_+ stimulation was smaller than the x_choice for v_- stimulation. We performed KS-test to confirm statistical significance.

Discussion: "...exhibited behaviour consistent with an ideal Bayesian observer, as found in our study". The RNN was explicitly trained to reproduce an ideal Bayesian observer, so this can only really be considered an assumption (not a result) in the present study.

We agree that the statement in the original manuscript is inaccurate. It was revised to reflect that, in the other study, behavior outputs similar to a Bayesian observer emerged by simply learning to do the task, intead of directly mimicking the outputs of Bayesian observer as done in our study.

“Authors showed that trained RNNs exhibited behavior outputs consistent with an ideal Bayesian observer without explicitly learning from the Bayesian observer. This finding shows that the behavioral strategies of monkeys could emerge by simply learning to do the task, instead of directly mimicking the outputs of Bayesian observer as done in our study.”

Methods: Would the results differ if your Bayesian observer model used the true prior (i.e. the reversal happens in the middle 10 trials) rather than a uniform prior? Given the extensive literature on prior effects on animal behaviour, it is reasonable to expect that monkeys incorporate some non-uniform prior over the reversal point.

Thank you for pointing out the non-uniform prior. We haven’t conducted this analysis, but would guess that the convergence to the posterior distribution would be faster. We’d have to perform further analysis, which is out of the scope of this paper, to investigate whether the posteior distribution would be different from what we obtained from uniform prior.

Making the code available would make the work more transparent and useful to the community.

The code is available in the following Github repository: https://github.com/chrismkkim/LearnToReverse

I wonder if there's anywhere where the urban housing supply isn't so terribly constrained where you could compare to see if it's just that the cost math is insane

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Reply to the reviewers

Reply to the reviewers

We would like to thank the reviewers for their comments, we see great value in the suggestions they made to strengthen our work. We are glad to see that they are in general positive about the manuscript. In the following, we include a point-by-point response to their comments, which are in general consistent with each other.

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

In this manuscript, Sanchez-Cisneros and colleagues, examine how tracheal cell adhesion to the ECM underneath the epidermis helps shape the tracheal system. They show that if cell-ECM adhesion is perturbed the development of the tracheal system and the epidermis is disrupted. They also detect protrusions extending from the dorsal trunk cells towards the ECM. The work is novel, the figures are clear, and the questions are well addressed. However, I find that some of the claims are not completely supported by the data presented. I have some suggestions that will, I believe, clarify certain points.

Major comments

At the beginning of the results section as in the introduction the authors claim that "It is generally assumed that trunk displacement occurs due to tip cells pulling on the trunks so that they follow their path dorsally." This sentence is not referenced, and I do not know where it has been shown or proposed to be like this. In addition, the comparison with the ventral branches is also not referenced and the movie does not really show this. Forces generated by tracheal branch migration have been shown to drive intercalation (Caussinus E, Colombelli J, Affolter M. Tip-cell migration controls stalk-cell intercalation during Drosophila tracheal tube elongation. Curr Biol. 2008;18(22):1727-1734. doi:10.1016/j.cub.2008.10.062), but not dorsal trunk (DT) displacement.

• *

We agree that dorsal trunk displacement has not been discussed in previous works, just the fact that tip-cell migration influences stalk cell intercalation. We will rephrase this sentence, stating that dorsal trunk displacement has not been studied.

However, to rule out the possibility that DT displacement and the phenotype observed in XXX is due to dorsal branch pulling forces, the authors should analyze what happens in the absence of dorsal branches (in condition of Dpp signalling inhibition as in punt mutants or Dad overexpression conditions).

This is a great idea, and we thank the reviewer for suggesting this. We tried to achieve a similar goal by expressing a Dominant Negative FGFR (Breathless-DN) in the tracheal system, since its expression under btl-gal4 affects tip cell migration. However, the phenotype arises too late to have an effect in dorsal branch migration during the stages we were interested in analyzing. The alternative proposed by the reviewer should be more efficient, as blocking Dpp signalling prevents the formation of dorsal branches completely. We have just received flies carrying the UAS-Dad construct. We will express Dad under btl-gal4 and see how this affects dorsal trunk displacement.

I am concerned about the TEM observations. The authors claim they can identify tracheal cells by their lumen (Fig. 2 C'). However, at stage 15, the tracheal lumen should be clearly identifiable, and the interluminal DT space should be wider relative to the size of the cells. In this case, there is nothing telling us that we are not looking at a dorsal branch or lateral trunk cell. Furthermore, at embryonic stage 15, the tracheal lumen is filled with a chitin filament, which is not visible in these micrographs. Also, there is quite a lot of tissue detachment and empty spaces between cells, which might be a sign of problems in sample fixing. Better images and more accurate identification of dorsal trunk cells is necessary to support the claim that "These experiments revealed a novel anatomical contact between the epidermis and tracheal trunks".

The protocol that we use for TEM involves performing 1-μm sections that allow us to stage embryos and to identify the anatomical regions using light microscopy and then switch to ultra-thin sections for electron microscopy once we have found the right position within the sample. This approach also allows us to determine the integrity of the sample. We attach here a micrograph of the last section we analyzed before we decided to do the EM analysis. The asterisk (*) points to a region where the multicellular lumen of the trunk is visible. Due to its proximity to the posterior spiracles, we are confident this is the dorsal trunk and not the lateral trunk. We realize now, after comparing this image with an atlas of development (Campos-Ortega and Hartenstein, 2013), that the stage we chose to illustrate the interaction is a stage 14 embryo instead of the stage 15 we indicated in the manuscript. We will change the stage but given that dorsal closure has already started by stage 14, this does not affect our analysis. Still, we apologize for the mis-staging of the embryo.

In the light-microscopy image, we have overlaid the EM section to the corresponding region of interest. We agree that the lumen should be thicker compared to the length of the cells, if the section would be cutting the trunk through its largest diameter. However, the protrusions we see do not emerge from the middle part of the trunk where the lumen is found but are seen towards the dorsal side of the trunk, where the lumen will no longer be visible in a longitudinal section as the ones we present. In the embryo shown in Figure 2A-C, our interpretation is that the section was done through a very shallow section of the lumen (represented below). We interpret this from the fact that we see abundant electron-dense areas which we think are adherens junctions from multiple cells. These junctions are visible in Figure 2C but are currently not labelled. We will add arrows to increase their visibility.

Given that protruding cells lie at the base of dorsal branches, it would be expected that in some sections we would find the protrusions close to the dorsal branches. This is in fact what we show in the micrograph shown in Figure 2D, with a lower magnification overview image shown in Figure S2D. In this case, we see a cell in close proximity to the tendon cells on one side (Figure 2D), which is connected to a dorsal branch on the opposite side (shown in Figure S2D). This dorsal branch is clearly autocellular and chitin deposition is visible as expected for the developmental stage. Again, in Figure S2E we see an electron-dense patch near the lumen that corresponds to the adherens junctions that seal the lumen. We see that all this needs to be better explained in the manuscript, so we will elaborate on the descriptions, and incorporate the light microscopy micrograph to the supplemental figures. This should also aid with the anatomical descriptions requested by Reviewer #3. Nevertheless, we think these observations confirm that what we are describing are the contact points between the dorsal trunk and tendon cells.

Timelapse imaging of the protrusions in DT cells is done with frames every 4 minutes (Video S3). This is not enough to properly show cellular protrusions and the images do not really show interaction with the epidermis. Video S4 has a better time resolution but it is very short and only shows the cut moment. Video S4, shows the cut, but the reported (and quantified recoil) is not clear. Nevertheless, the results are noteworthy and should be further analysed.

We will acquire high temporal resolution time-lapse images using E-Cadherin::GFP and btl-gal4, UAS-PH::mCherry to show the behaviour of the protrusions on a short time scale.

• *

Provided these embryos survive, would it be possible to check if embryos after laser cutting will develop wavy DTs?

We think it would be interesting to carry out this experiment, but the laser cut experiments were done under a collaborative visit and we would not be able to repeat it in a short-term period.

What happens to the larvae under the genetic conditions presented in Fig.S3? Do they reach pupal stages? Do these animals reach adult stages?

We have seen escapers out of these crosses, but we have not quantified the lethality of the experiment. We will analyse this and include it in the manuscript.

The kayak phenotypes are very interesting and perhaps the authors could explore them more. As in inhibition of adhesion to the ECM, kay mutants display wavy dorsal trunks. Do they have defective adhesion? Fos being a transcription factor, this is a possibility. The authors should at least discuss the kay phenotypes more extensively and present a suitable hypothesis for the phenotype.

We agree that the kayak experiments might bring more consequences than just preventing dorsal closure. We will complement this approach by blocking dorsal closure by other independent means. We will use pannier-gal4 (a lateral epidermis driver), engrailed-gal4 (a driver for epidermal posterior compartment), and 332-gal4 (an amnioserosa driver) to express dominant-negative Moesin. In our experience, this also delays dorsal closure and it should result in a similar tracheal phenotype as the one we see in kayak embryos.

Minor comments

Page 2 Line 9/10 The sentence "tracheal tubes branch and migrate over neighbouring tissues of different biochemical and mechanical properties to ventilate them." should be rewritten. Tracheal cells do not migrate over other tissues to ventilate them.

We meant to say that tracheal cells migrate over other tissues at the same time as they branch and interconnect to allow gas exchange in their surroundings after tracheal morphogenesis is completed. Ventilation is used here as a synonym for gas exchange or breathing. We will rephrase this if the reviewer considers it confusing.

Page 2 Line 24/25 The sentence "It has been generally assumed that trunks reach the dorsal side of the embryo because of the pulling forces of dorsal branch migration." needs to be backed up by a reference.

As explained above, we will rephrase this sentence.

Page 7 Line 32/23 In this sentence, the references are not related to dorsal closure "Similarly, the signals that regulate epidermal dorsal closure do not participate in tracheal development, or vice versa (Letizia et al., 2023; Reichman-Fried et al., 1994)."

Our goal in this sentence was to explain that while JNK is required for proper epidermal dorsal closure, loss of JNK signaling in the trachea does not affect tracheal development (as shown by Letizia et al., 2023). At the same time, Reichman-Fried et al., 1994 described the phenotypes of loss of breathless (btl). We will remove this last reference as the work does not study the epidermis. We will rephrase the sentence as: “Similarly, the signals that regulate epidermal dorsal closure do not participate in tracheal development; namely, JNK signaling (Letizia et al., 2023).”

Page 12 Line 1 "Muscles attach to epidermal tendon cells through a dense meshwork of ECM" this sentence must be referenced.

We will add the corresponding references for this statement: (Fogerty et al., 1994; Prokop et al., 1998; Urbano et al., 2009). We will change “dense” for “specialized”.

Fig. S1- Single channel images (A'-C' and A'-C') should be presented in grayscale.

Fig. S4- Single channel images (A'-D' and A'-D') should be presented in grayscale.

We will add the grayscale, single-channel images for these figures.

Reviewer #1 (Significance (Required)):

The findings shown in this manuscript shed light on the interactions and cooperation between two organs, the tracheal system and the epidermis. These interactions are mediated by cell-ECM contacts which are important for the correct morphogenesis of both systems. The strengths of the work lie on its novelty and live analysis of these interactions. However, its weaknesses are related to some claims not completely backed by the data, some technical issues regarding imaging and some over-interpreted conclusions.

This basic research work will be of interest to a broad cell and developmental biology community as they provide a functional advance on the importance of cell-ECM interactions for the morphogenesis of a tubular organ. It is of specific interest to the specialized field of tubulogenesis and tracheal morphogenesis.

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

Summary: In this paper, the authors explore the relationships between two Drosophila tissues - the epidermis and tracheal dorsal trunk (DT) - that get dorsally displaced during mid-late embryogenesis. The show a nice temporal correlation between the movements of the epithelia during dorsal closure and DT displacement. They also show a correlation between the movement of an endogenously tagged version of collagen and the DT, suggesting that the ECM may contribute to this coordinated movement. Through high magnification TEM, they show that tracheal cells make direct contact with the subset of epithelial cells, known as tendon cells, that also serve as muscle attachment sites. In between these contact sites, tracheae are separated from the epithelia by the muscles. Furthermore, the TEMs and confocal imaging of tracheal cells expressing a membrane marker at these contact sites show that the tracheal cells are extending filopodia toward the tendon cells. The authors then explore how a variety of perturbations to the ECM produced by the tendon and DT cells affect DT and epithelial movement. They find that expressing membrane-associated matrix metalloproteases (MMP1 or MMP2) in tendon cells as well as perturbations in integrin or integrin signaling components leads to delays in dorsal displacement as well as defective lengthening of the tracheal DT tubes. They find that defects in the association between the tracheal and epidermal ECM attachments affect dorsal displacement of the epidermis, disrupting dorsal closure.

Major comments: I like the goals of this paper testing the idea that the ECM plays important roles in the coordination of tissue placement, and I think they have good evidence of that from this study. However, I disagree with the conclusions of the authors that disrupting contact between DT and the tendon cells has no effect on DT dorsal displacement. DT tracheal positioning is clearly delayed; the fact that it takes a lot longer indicates that the ECM does affect the process. It's just that there are likely backup systems in place - clearly not as good since the tracheal tubes end up being the wrong length.

We agree with this view; in our deGradFP experiments we see a delayed DT displacement. We focused our analyses on the coordination with epidermal remodelling, which remained unaltered, but we in fact see a delayed progression in dorsal displacement of both tissues (Figure 5I-J). We will emphasize this in the corresponding section of the Results.

It also seems important that the parts of the DT where the dorsal branches (DB) emanate are moving dorsally ahead of the intervening portions of the trachea. This suggests to me that the DB normally does contribute to DT dorsal displacement and that this activity may be what helps the DT eventually get into its final position. The authors should test whether the portions of the DT that contact the DB are under tension. If the DB migration is providing some dorsal pulling force on the DT, this may also contribute to the observed increases in DT length observed with the perturbations of the ECM between the tendon cells and the trachea - if tube lengthening is a consequence of the pulling forces that would be created by parts of the trachea moving dorsally ahead of the other parts. Here again, it would be good to test if the DT itself is under additional tension when the ECM is disrupted.

• *

We thank the reviewer for the suggested experiments. We agree with the fact that the dorsal branches should pull on the dorsal trunk and that this interaction should generate tension. Unfortunately, we are unable to test this with the experiments proposed by the reviewer, but we propose an alternative strategy to overcome this. We understand that the reviewer suggests we do laser cut experiments in dorsal branches to see if there is a recoil in the opposite direction of dorsal branch migration. We carried out our laser cut experiments using a 2-photon laser through a visit to the EMBL imaging facility, using funds from a collaborative grant. Funding a second visit would require us to apply for extra funding, which would delay the preparation of the experiments. We are aware of UV-laser setups within our university, however, UV-laser cuts would also affect the epidermis above the dorsal branches, which we think might contribute to recoil we would expect to see.

Instead of doing laser cuts, we have designed an experiment based on the suggestion of reviewer #1 of blocking Dpp signaling (with UAS-Dad), which would prevent the formation of dorsal branches. We expect that in this experimental setup, the trunk will bend ventrally in response to thepulling forces of the ventral branches. We will also co-express UAS-Dad (to prevent dorsal branch formation) and UAS-Mmp2 (to ‘detach’ the dorsal trunk from the epidermis), and we would expect to at least partially rescue the wavy trunk phenotype.

Minor comments: The authors need to do a much better job in the intro and in the discussion of citing the work of the people who made many of the original findings that are relevant to this study. Many citations are missing (especially in the introduction) or the authors cite their own review (which most people will not have read) for almost everything (especially in the discussion). This fails to give credit to decades of work by many other groups and makes it necessary for someone who would want to see the original work to first consult the review before they can find the appropriate reference. I know it saves space (and effort) but I think citing the original work is important.

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The reviewer is right; we apologize for falling into this practice. We will reference the original works wherever it is needed.

Figure 7 is not a model. It is a cartoon depicting what they see with confocal and TEM images.

We will change the figure; we will include our interpretations of the phenotypes we observed under different experimental manipulations.

Reviewer #2 (Significance (Required)):

Overall, this study is one of the first to focus on how the ECM affects coordination of tissue placement. The coordination of tracheal movement with that of the epidermis is very nicely documented here and the observation that the trachea make direct contact with the tendon cells/muscle attachment sites is quite convincing. It is less clear from the data how exactly the cells of the trachea and the ECM are affected by the different perturbations of the ECM. It seems like this could be better done with immunostaining of ECM proteins (collagen-GFP?), cell type markers, and super resolution confocal imaging with combinations of these markers. What happens right at the contact site between the tendon cell and the trachea with the perturbation? I think that at the level of analysis presented here, this study would be most appropriate for a specialized audience working in the ECM or fly embryo development field.

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

Summary The manuscript by Sanchez-Cisneros et al provides a detailed description of the cellular interactions between cells of the Drosophila embryonic trachea and nearby tendon and epidermal cells. The researchers use a combination of genetic experiments, light sheet style live imaging and transmission electron microscopy. The live imaging is particularly clear and detailed, and reveals protruding cells. The results overall suggest that interactions mediated through the ECM contribute to development of trachea and dorsal closure of epidermis. One new aspect is the existence of dorsal trunk filipodia that are under tension and may impact tracheal morphogenesis through required integrin/ECM interactions.

Major comments: - Are the key conclusions convincing? Generally, the key conclusions are well supported by the data, and the movies are very impressive. Interactions between the cell types are clearly shown, as is the correlations in their development. However, some of the images are challenging to decipher for a non-expert in Drosophila trachea, especially the EM images, and some of the data is indirect or a bit weak.

We thank the reviewer for their observations. As mentioned above in response to Reviewer #1, we will add an overview image of the embryo we processed for TEM that is presented in Figure 2.

The data related to failure of dorsal closure affecting trachea relies on one homozygous allele of one gene (kayak), and so this is somewhat weak evidence. Even though kay is not detected in trachea, there could be secondary effects of the mutation or another lesion on the mutant chromosome. The segments look a bit uneven in the mutant examples.

• *

The reviewer is right; as we proposed before, we will complement the kayak experiments with independent approaches that will delay dorsal closure.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Some of the experiments have low n values, especially in imaging experiments, so these may be more preliminary, but they are in concordance with other data.

The problem we face in our live-imaging experiments is related to the probability of finding the experimental embryos. In most of our experiments we combine double-tissue labelling plus the expression of genetic tools. This generally corresponds to a very small proportion of the progeny. We will aim to have at least 4 embryos per condition.

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Higher n-values would substantiate the claims. To strengthen the argument that dorsal closure affects trachea morphogenesis mechanically, the authors might consider using of a combination of kay mutant alleles or other mutant genes in this pathway to provide stronger evidence. Or they could try a rescue experiment in epidermis and trachea separately for the kay mutants.

We think our experiments delaying dorsal closure using the Gal4/UAS system and a variety of drivers should address the point of the possible indirect effects of kay in tracheal development.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. Imaging data can take awhile to obtain, but the genetic experiments could be done in a couple of months, and the authors should be able to obtain any needed lines within a few weeks.

The reviewer is correct, we will be able to plan our crosses for the proposed experiments within a couple of months.

• Are the data and the methods presented in such a way that they can be reproduced? Generally, yes. For the deGrad experiments, it is not clear how the fluorescent intensity was normalized - was this against a reference marker?

Briefly, we used signals from within the embryo as internal controls. In the case of en-gal4, we normalized the signal to the sections of the embryo where en is not expressed and therefore, beta-integrin levels should not be affected. In the case of btl-gal4, we normalized against the signal surrounding the trunks which should also not be affected by the deGradFP system. We will elaborate on these analyses in the methods section.

Are the experiments adequately replicated and statistical analysis adequate? There are several experiments with low n values, so this could fall below statistical significance. For example, data shown in Fig 1G: n=3; Fig 4D n=4, n=3; Fig 6J n=4

As mentioned above, we will increase our sample sizes.

Minor comments: - Specific experimental issues that are easily addressable. To make the TEM images more easily interpreted, it would be helpful to provide a fluorescent image of all the relevant cell types (especially trachea, epidermis, muscle, and tendon cells, plus segmental boundaries) labelled accordingly, so that reader can correlate them more easily with the TEM images. They might also include a schematic of an embryo to show where the TEM field of view is.

We believe this should be addressed by adding the light microscopy section of the embryo with the TEM image overlaid as illustrated above.

It is hard to be confident that the EM images reflect the cells they claim and that the filopodia are in fact that, at least for people not used to looking at these types of images.

As we explained in the response to Reviewer #1, we will elaborate on the descriptions of our TEM data. We think that adding the reference micrograph will aid with the interpretations of the TEM images.

• Are prior studies referenced appropriately? yes

• Are the text and figures clear and accurate? yes

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? The writing could be revised to be a bit clearer. Since the results of the experiments do not support the initial hypothesis, I found it a bit confusing as I read along. It may help to introduce an alterative hypothesis earlier to make the paper more logical and easy to follow. To be more specific, On page 3, the authors say they "show that dorsal trunk displacement is mechanically coupled to the remodelling of the epidermis" and also in the results comment that "With two opposing forces pulling the trunks other factors likely participate in their dorsal displacement, but so far these have remained unstudied." But that doesn't end up being what they find. The results from figure 5 and related interpretation on page 17 says "cell-ECM interactions are important for proper trunk morphology, but not for its displacement." So this was confusing to read and I would encourage the authors to frame the issues a bit differently in terms of tube morphogenesis.

We see how this might be confusing. We will rewrite the introduction so that the work is easier to follow. To achieve this, we will state from the beginning the mechanisms we anticipate that regulate trunk displacement: 1) adhesion to the epidermis, 2) pulling forces from the dorsal branches and 3) a combination of both.

Some minor presentation issues: What orientation is the cross-sectional view in figure 1C and movie 1?

We will add a dotted box that indicates the region that we turned 90° to show the cross-section.

On page 12, the authors say the "Electron micrographs also suggested high filopodial activity" but activity suggests dynamics that are not clear from EM. This could be re-phrased.

As the reviewer indicates, we cannot conclude dynamics from a static image. We will replace “suggested high filopodial activity” with “revealed filopodial abundance”.

Reviewer #3 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. The results of the paper are significant in that they characterize a mechanical interaction between two tissue types in development, which are linked by the extracellular matrix that sits between them. It is not clear to me that this describes a "novel mechanism for tissue coordination" as stated in the abstract, but it does characterize this type of interaction in a detailed cellular way.

• Place the work in the context of the existing literature (provide references, where appropriate). For specialists, the work identifies a novel protruding cell type in the fly embryonic trachea, and provides beautiful and detailed imaging data on tracheal development. The "wavy" trachea phenotype is also uncommon and very interesting, so this result could be linked to the few papers that also describe this phenotype and be built up.

• State what audience might be interested in and influenced by the reported findings. As it stands, this is most interesting for a specialized audience because it requires some understanding of the development of this system in particular. As it characterizes this to a new level of detail, it could be influential to those in the field. Some addition clarification of the results and re-framing could make the manuscript more clear and interesting for non-specialists.

• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. I work with Drosophila and have studied embryonic and adult cell types, although not trachea specifically. I am familiar with all the genetic techniques and imaging techniques used here.

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Referee #2

Evidence, reproducibility and clarity

Summary: In this paper, the authors explore the relationships between two Drosophila tissues - the epidermis and tracheal dorsal trunk (DT) - that get dorsally displaced during mid-late embryogenesis. The show a nice temporal correlation between the movements of the epithelia during dorsal closure and DT displacement. They also show a correlation between the movement of an endogenously tagged version of collagen and the DT, suggesting that the ECM may contribute to this coordinated movement. Through high magnification TEM, they show that tracheal cells make direct contact with the subset of epithelial cells, known as tendon cells, that also serve as muscle attachment sites. In between these contact sites, tracheae are separated from the epithelia by the muscles. Furthermore, the TEMs and confocal imaging of tracheal cells expressing a membrane marker at these contact sites show that the tracheal cells are extending filopodia toward the tendon cells. The authors then explore how a variety of perturbations to the ECM produced by the tendon and DT cells affect DT and epithelial movement. They find that expressing membrane-associated matrix metalloproteases (MMP1 or MMP2) in tendon cells as well as perturbations in integrin or integrin signaling components leads to delays in dorsal displacement as well as defective lengthening of the tracheal DT tubes. They find that defects in the association between the tracheal and epidermal ECM attachments affect dorsal displacement of the epidermis, disrupting dorsal closure.

Major comments: I like the goals of this paper testing the idea that the ECM plays important roles in the coordination of tissue placement, and I think they have good evidence of that from this study. However, I disagree with the conclusions of the authors that disrupting contact between DT and the tendon cells has no effect on DT dorsal displacement. DT tracheal positioning is clearly delayed; the fact that it takes a lot longer indicates that the ECM does affect the process. It's just that there are likely backup systems in place - clearly not as good since the tracheal tubes end up being the wrong length. It also seems important that the parts of the DT where the dorsal branches (DB) emanate are moving dorsally ahead of the intervening portions of the trachea. This suggests to me that the DB normally does contribute to DT dorsal displacement and that this activity may be what helps the DT eventually get into its final position. The authors should test whether the portions of the DT that contact the DB are under tension. If the DB migration is providing some dorsal pulling force on the DT, this may also contribute to the observed increases in DT length observed with the perturbations of the ECM between the tendon cells and the trachea - if tube lengthening is a consequence of the pulling forces that would be created by parts of the trachea moving dorsally ahead of the other parts. Here again, it would be good to test if the DT itself is under additional tension when the ECM is disrupted.

Minor comments: The authors need to do a much better job in the intro and in the discussion of citing the work of the people who made many of the original findings that are relevant to this study. Many citations are missing (especially in the introduction) or the authors cite their own review (which most people will not have read) for almost everything (especially in the discussion). This fails to give credit to decades of work by many other groups and makes it necessary for someone who would want to see the original work to first consult the review before they can find the appropriate reference. I know it saves space (and effort) but I think citing the original work is important.

Figure 7 is not a model. It is a cartoon depicting what they see with confocal and TEM images.

Significance

Overall, this study is one of the first to focus on how the ECM affects coordination of tissue placement. The coordination of tracheal movement with that of the epidermis is very nicely documented here and the observation that the trachea make direct contact with the tendon cells/muscle attachment sites is quite convincing. It is less clear from the data how exactly the cells of the trachea and the ECM are affected by the different perturbations of the ECM. It seems like this could be better done with immunostaining of ECM proteins (collagen-GFP?), cell type markers, and super resolution confocal imaging with combinations of these markers. What happens right at the contact site between the tendon cell and the trachea with the perturbation? I think that at the level of analysis presented here, this study would be most appropriate for a specialized audience working in the ECM or fly embryo development field.

I never thought about how much of my design education has been focused on “how” to do things. This line helped me realize that asking “why” we design something gives it more meaning and direction. It’s not just about using tools it’s also about purpose.

Barthes says that reading the linguistic message mainly just needs language skills, like knowing how to read and understand French. But I think the look of the text—especially the font—also matters a lot. Different typefaces give off different vibes. For example, a handwritten font might feel friendly or personal, while a clean, modern font might feel professional or serious. So even though it’s still text, the style of the typography adds another layer of meaning. It’s not just what the words say, but also how they visually come across.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Flowers et al describe an improved version of qFit-ligand, an extension of qFit. qFit and qFit-ligand seek to model conformational heterogeneity of proteins and ligands, respectively, cryo-EM and X-ray (electron) density maps using multi-conformer models - essentially extensions of the traditional alternate conformer approach in which substantial parts of the protein or ligand are kept in place. By contrast, ensemble approaches represent conformational heterogeneity through a superposition of independent molecular conformations.

The authors provide a clear and systematic description of the improvements made to the code, most notably the implementation of a different conformer generator algorithm centered around RDKit. This approach yields modest improvements in the strain of the proposed conformers (meaning that more physically reasonable conformations are generated than with the "old" qFit-ligand) and real space correlation of the model with the experimental electron density maps, indicating that the generated conformers also better explain the experimental data than before. In addition, the authors expand the scope of ligands that can be treated, most notably allowing for multi-conformer modeling of macrocyclic compounds.

Strengths:

The manuscript is well written, provides a thorough analysis, and represents a needed improvement of our collective ability to model small-molecule binding to macromolecules based on cryo-EM and X-ray crystallography, and can therefore have a positive impact on both drug discovery and general biological research.

Weaknesses:

There are several points where the manuscript needs clarification in order to better understand the merits of the described work. Overall the demonstrated performance gains are modest (although the theoretical ceiling on gains in model fit and strain energy are not clear!).

We thank the reviewer for their thoughtful review. To address comments, we have added clarifying statements and discussion points around the extent of performance gains, our choice of benchmarking metrics, and the “standards” in the field for significance. We expanded our analysis to highlight how to use qFit ligand in “discovery” mode, which is aimed at supporting individual modeling efforts. As we now write in the discussion:

“It is advisable to employ qFit-ligand selectively, focusing on cases with a moderate correlation between your input model and the experimental data, strong visual density in the binding pocket, high map resolution, or when your single-conformer ligand model is strained.”

Additionally, we note in the discussion:

“qFit-ligand primarily serves as a “thought partner” for manual modeling. Modelers still must resolve many ambiguities, including initial ligand placement, to fully take advantage of qFit capabilities. In active modeling workflows or large scale analyses, the workflow would only accept the output of qFit-ligand when it improves model quality. In cases where qFit-ligand degrades map-to-model fit and/or strain, we can simply revert to the input model. In practice, users can easily remove poorly fitting conformations using molecular modeling software such as COOT, while keeping the well modeled conformations, which is an advantage of the multiconformer approach over ensemble refinement methods.”

Reviewer #2 (Public review):

Summary:

The manuscript by Flowers et al. aimed to enhance the accuracy of automated ligand model building by refining the qFit-ligand algorithm. Recognizing that ligands can exhibit conformational flexibility even when bound to receptors, the authors developed a bioinformatic pipeline to model alternate ligand conformations while improving fitting and more energetically favorable conformations.

Strengths:

The authors present a computational pipeline designed to automatically model and fit ligands into electron density maps, identifying potential alternative conformations within the structures.

Weaknesses:

Ligand modeling, particularly in cases of poorly defined electron density, remains a challenging task. The procedure presented in this manuscript exhibits clear limitations in low-resolution electron density maps (resolution > 2.0 Å) and low-occupancy scenarios, significantly restricting its applicability. Considering that the maps used to establish the operational bounds of qFit-ligand were synthetically generated, it's likely that the resolution cutoff will be even stricter when applied to real-world data.

We thank Reviewer #2 for their comments on the role of conformational flexibility and how our tool addresses the complexity involved in modeling alternative conformations. We agree that there are limitations at low resolution, limiting the application of our algorithm. That is the case with all structural biology tools. Automatically finding alternative conformations of ligands in high-resolution structures is an enhancement to the toolbox of ligand fitting. Expanding the algorithm to work with fragment screening data is important in this realm, as almost all of this data fits in the high-resolution range where qFit-ligand works best.

The reported changes in real-space correlation coefficients (RSCC) are not substantial, especially considering a cutoff of 0.1. Furthermore, the significance of improvements in the strain metric remains unclear. A comprehensive analysis of the distribution of this metric across the Protein Data Bank (PDB) would provide valuable insights.

We agree that the changes are small, partially because the baseline (manually modeled ligands) is very high. To provide additional evidence, we added evaluations using EDIAm, which is a more sensitive metric. In Figure 2 (page 10), representing the development dataset, we see more improvements above 0.1. With this being said, it is unclear what constitutes a ‘substantial’ improvement for either of these metrics, especially considering alternative conformations may only change the coordinates of a subset of ligands, just slightly improving the fit to density.

We agree that looking across the PDB on strain would provide valuable insight. To explore this, we looked to see how qFit-ligand could improve the fitting of deposited ligands with high strain (see section: Evaluating qFit-ligand on a set of structures known to be highly strained, Page 15). While only a subset of these structures had alternative conformers placed (24.6%), we observed that in this subset, the ligands often improved the RSCC and strain. This figure also demonstrates that while RSCC may not change much numerically, the alternative conformers explain previously unexplained density with lower energy conformers than what is currently deposited.

To mitigate the risk of introducing bias by avoiding real strained ligand conformations, the authors should demonstrate the effectiveness of the new procedure by testing it on known examples of strained ligand-substrate complexes.

See above.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

A - Specific comments:

(1) It appears necessary to provide qFit-ligand with an initial model with the ligand already placed. This is not clear from the start of the introduction on page 3. It appears that ligand position is only weakly adjusted fairly late in the process, in step F of Figure 1. It seems, therefore, that the accuracy of initial placement is rather critical (see the example discussed on page 21). At the same time, in my experience, ambiguous cases are quite common, for example with flat ligands with a few substituents sticking out or with ligands with highly mobile tails. It would be helpful for the authors to comment on the sensitivity to initial ligand placement, either in the discussion or, better yet, in the form of an analysis in which the starting model position is randomly perturbed.

In our revised version, we have modified the introduction to clarify the necessity of including an initial ligand model (page 4).

“The qFit-ligand algorithm takes as input a crystal or cryo-EM structure of an initial protein-ligand complex with a single conformer ligand in PDBx/mmCIF format, a density map or structure factors (encoded by a ccp4 formatted map or an MTZ), and a SMILES string for the ligand.”

We also describe our sampling algorithm more clearly (see: Biasing Conformer Generation, page 6). Steps A-E generate many conformations (using RDKit), which are then selected/fit into experimental density (using quadratic programming). To help with additional shifting issues in the input ligand, after the first selection, we do additional rotation/translation of the generated conformers that are kept. We then do another round of fitting to the density (quadratic programming followed by mixed integer quadratic programming).

Given this sampling, we have not elected to do an additional computational experiment to test the “radius of convergence” or dependence on initial conditions. However, we outline the fundamental procedure here so that someone can build on the work and test the idea:

- Create single conformer models as we currently do

- randomly perturb the coordinates of the ligand by 0.1-0.3Å

- refine to convergence, creating a series of “perturbed, modified true positives” for each dataset

- Run qFit ligand

- Evaluate the variability in the resulting multi-conformer models

(2) Top of page 6 ("Biasing Conformer Generation"): the authors say "as we only want to generate ligands that physically fit within the protein binding pocket, we bias conformation generation towards structures more likely to fit well within the receptor's binding site". Apart from the odd redundancy of this sentence, I am confused: at the stage that seems to be referred to here (A-C in Figure 1) is the fit to the electron density already taken into account, or does this only happen later (after step E)?

Thank you for pointing this out. We have edited the statement to clarify it:

“To guide the conformation generation from the Chem.rdDistGeom based on the ligand type and protein pocket, we developed a suite of specialized sampling functions to bias the conformational search towards structures more likely to fit well into the receptor’s binding site.”

We do not consider the electron density during conformer generation (only selection from the generated conformers). The sampling is additionally biased by the type of ligand and the size of the binding pocket.

(3) qFit-ligand appears to be quite slow. Are there prospects for speedup? Can the code take advantage of GPUs or multi-CPU environments?

We agree with this. We have made some algorithmic improvements, most notably removing duplicate conformers based on root mean squared distance. This, along with parallelization, decreased the average runtime from ~19 minutes to ~8 minutes (see additional details: qFit-ligand runtime, page 8). We do not currently take advantage of GPU specific code.

(4) Section: Detection of experimental true positive multi-conformer ligands:

a) Why are carbohydrate ligands excluded? This seems like an important class of ligands that one would like qFit to be able to treat! Which brings me to a related question: can covalently attached groups (e.g., glycosylation sites!) be modeled using qFit-ligand, or is qFit-ligand restricted to non-covalently bound groups?

Currently, qFit-ligand does not support covalently bound ligands, but this is an area of interest we are hoping to expand into. In the revised version, we added the non-covalently attached carbohydrates back into the true positive dataset. In Figure 4 (page 14), we show that qFit-ligand is able to improve fit to the experimental density in around 80% of structures, while also often reducing torsion strain (see additional details: qFit-ligand applied to unbiased dataset of experimental true positives, page 14).

b) "as well as 758 cases where the ligand model's deposited alternate conformations (altlocs) were not bound in the same chain and residue number" - I do not understand what this means, or why it leads to the exclusion of so many structures. Likewise, a number of additional exclusions are described in Figure S3. Some more background on why these all happened would be helpful. Are you just left with the "easy" cases?

Sometimes modelers will list the multiple conformations of a bound ligand as a separate residue within the PDB file, rather than as a single multiconformer model. For example, rather than writing a multiconformer LIG bound at A, 201 with altlocs ‘A’ and ‘B’, a modeler might write this instead as LIG, A, 201 and LIG A, 301. We initially excluded these kinds of structures. However, we agree that this choice resulted in the removal of many potentially valid true positives. We have since updated our data processing pipeline to include these cases, and they are examined in the updated manuscript.

c) I do not follow the argument made at the end of this section (last two paragraphs on page 9): "when using a single average conformation to describe density from multiple conformations, the true low-energy states may be ignored". I get that, but the conformations in the "modified true positives" dataset derive directly from models in which two conformations were modeled, so this cannot be the explanation for why qFit-ligand models result in somewhat lower average strain. It would seem that the paper could be served by providing examples where single conformations were modeled in deposited structures, but qFit detects multiple conformations.

We agree with this comment that the strain obtained from the modified true positives is likely higher than the deposited models. However, the modified structure is refined with a single conformation, and therefore changed from the deposited “A” conformation. Thus, the reduced strain observed in our qFit-ligand models relative to the modified true positives is not unexpected.

To expand our dataset, we also looked at deposited structures with high strain, all of which were modeled as single conformers. Here, we saw a decrease in strain when alternative conformers were placed (see section: Evaluating qFit-ligand on a set of structures known to be highly strained, page 15). Further, we provide an example from the XGen macrocycle dataset where a ligand initially modeled as a single conformer exhibited relatively high strain. After qFit‐ligand modeled a second conformation, the overall strain was reduced (Figure 6C, page 19; Figure 6—figure supplement 1C, page 59).

(5) Section: qFit-ligand applied to an unbiased dataset of experimental true positives Bottom of page 14: The paragraph starting with "qFit-ligand shows particular strength in scenarios with strong evidence..." is enigmatic: there's no illustration (unless it directly relates to the findings in Figure 4, in which case this should be more explicit). Since this points out when the reader will and will not benefit from using qFit-ligand, it should be clear what the authors are talking about.

This claim considers all the evidence presented in the manuscript, not necessarily one particular aspect of it. We advise using qFit-ligand when there is a moderate correlation between the input model and the experimental data, strong visual density in the binding pocket, high map resolution, and/or when your single conformer ligand model is strained. We have made all of these points clearer in the updated manuscript.

B  - Section: qFit-ligand can automatically detect and model multiple conformations of macrocycles:

This is an exciting extension of qFit-ligand, but some aspects of the analysis strike me as worrisome. Of the initial dataset of 150 structures, fewer than half make it all the way through analysis. It's hard to believe that this is a fully representative subset. Why, for example, could 29 structures not be refined against the deposited structure factors? Why does strain calculation (in RDKit?) fail on 30 ligands? What about the other 18 cases--why did these fail (in PHENIX?).

We agree that this is a striking number of failures, however, we note that they are not specific shortcomings of qFit-ligand (in fact, most are because standard structural biology and/or cheminformatics software fail on many PDB depositions). Therefore, these failures reflect broader limitations in standard bioinformatics and refinement restraint files when handling macrocycles. The strain calculator we used was not built for macrocycles, and after consulting with many experts in the field, the consensus was that no method works well with macrocycles. We discuss these issues in additional detail in the discussion (page 27):

“Additionally, our algorithm’s placement within the larger refinement and ligand modeling ecosystem highlighted other areas that need improvement. We note that macrocycles, due to their complicated and interconnected degrees of freedom, suffer acutely from the refinement issues, as demonstrated by the failure of approximately one-third of datasets in our standard preparation or post-refinement pipelines due to ligand parameterization issues. Many of these stemmed from problematic ligand restraint files, highlighting the difficulty of encoding the geometric constraints of macrocycles using standard restraint libraries. Improved force-field or restraints for macrocycles are desperately needed to improve their modeling.”

C  - Minor issues:

(1) "Fragment-soaked event maps" - this is a semantically strange section title!

We have updated the section title in our revised manuscript. The new title is ‘qFit-ligand recovers heterogeneity in fragment-soaked event maps’.

(2) Too many digits! All over the manuscript, percentages are displayed with 0.01% precision, while these mostly refer to datasets with ~150 structures. Shifting just one structure from one category to another changes these percentages by nearly 1%.

We have updated the sig figs in our revised manuscript.

(3) The authors are keen to classify decreases in RSCC as significant only when these changes exceed 0.1, but do not apply the same standard for increases. For instance, in Figure 4B if we were to classify improvements as significant if ΔRSCC > 0.1, there would be fewer significant improvements than decreases in performance (although it is visually clear that for most datasets things get better. Similarly, in Figure 5A if we were to classify improvements as significant if ΔRSCC > 0.1, qFit-ligand would only yield significant improvements for two out of 73 cases-not a lot).

We agree with the reviewer that there needs to be more consistency in our analysis of improvements/deteriorations. However, we note that operationally, when the decreases in model quality are observed, the modeler would simply reject the new model in favor of the input model. We have added to the discussion:

“In active modeling workflows or large scale analyses, the workflow would only accept the output of qFit-ligand when it improves model quality. In cases where qFit-ligand degrades map-to-model fit and/or strain, we can simply revert to the input model. In practice, users can easily remove poorly fitting conformations using molecular modeling software such as COOT, while keeping the well modeled conformations, which is an advantage of the multiconformer approach over ensemble refinement methods.”

There is generally no consensus in the field as to what might indicate a ‘significant’ change in RSCC, and any threshold we choose would be arbitrary. We note that in our manuscript, we had previously characterized a decrease in RSCC to be ‘significant’ if it exceeded 0.1. However, as there is no real scientific justification for this cutoff, or any cutoff, we moved away from this framing in the revised manuscript. Therefore, we just classify if we improve RSCC. For example, on page 9:

“qFit-ligand modeled an alternative conformation in 72.5% (n=98) of structures. Compared with the modified true positive models, 83.7% (n=113) of qFit-ligand models have a better RSCC and 77.0% (n=104) structures saw an improvement in EDIAm, representing an improved fit to experimental data in the vast majority of structures.”

In addition, we have conducted additional experiments using more sensitive metrics (EDIAm) to further illustrate qFit-ligand’s performance.

(4) Small peptides are not discussed as a class of ligands, although these are quite common.

Canonical peptides can be modeled with standard qFit. Non-canonical peptides present failure modes similar to the macrocycles discussed above, with a mix of ATOM and HETATM records and the need for custom cif definitions and link records. For these reasons we have not included an analysis outside of the macrocycle section. We have noted this caveat in the discussion:

“We note that even linear non-canonical peptides present similar failure modes to macrocycles, with a mix of ATOM and HETATM records and the need for custom cif definitions and link records. For these reasons, we did not include analysis on small peptide ligands; however, canonical peptides can be modeled with standard qFit [8].”

(5) Top of page 10: "while refinement improves": what kind of refinement does this refer to?

This refers to refinement with Phenix. We have updated this language to reflect this (page 8). “We refer to these altered structures as our ‘modified true positives’, which we use as input to qFit-ligand, and subsequent refinement using Phenix.”

(6) Bottom of page 11: "they often did" -> "it often did"

We have made this change in the revised version.

(7) Top of page 14: RMSDs and B factors do have units.

We have added the units in our revision.

(8) Top of page 24. In the generation of a composite omit map, why are new Rfree flags being generated? Did I misunderstand that?

r_free_flags.generate=True only creates R-free flags if they are not present in the input file as is the case for many (especially older) PDB depositions.

(9) Bottom of page 27: how large is the mask? Presumably when alt confs of the ligand are possible, it would be helpful for the mask to cover those?

We agree that this mask should be updated. In our revision, we define the mask around the coordinates of the full qFit-ligand ensemble. The same mask is used to calculate the RSCC of the input (single conformer) model versus the qFit-ligand model.

(10) Middle of page 29: "These structure factors are then used to compute synthetic electron density maps." - It is not clear whether the following three sentences are an explanation of the details of that statement or rather things that are done afterwards.

We clarify this in the manuscript (page 36).

“These structure factors are then used to compute synthetic electron density maps. To each of these maps, we generate and add random Gaussian noise values scaled proportionally to the resolution. This scaling reflects the escalation of experimental noise as resolution deteriorates, a common occurrence in real-life crystallographic data.”

(11) Chemical synthesis: I am not qualified to assess this and am surprised to see some much detail here rather than in some other manuscript. Are the corresponding structures deposited anywhere?

All of the structures we discuss in this manuscript are deposited in the PDB and listed in Supplementary Table 5.

Reviewer #2 (Recommendations for the authors):

The data should consistently present the number of structures that exhibit improvements or deterioration in particular metrics, like RSCC and strain, using a cutoff that should be significant. For instance, stating that "85.93% (n=116) of structures having a better RSCC in the qFit-ligand models compared to the modified true positive models" without clarifying the magnitude of improvement (e.g., a marginal increase of 0.01 in RSCC) lacks meaningful context. The figures should clearly indicate the specific cutoff values used for each metric. The accompanying text should provide a detailed explanation for the selection of these cutoff values, justifying their significance in the context of the study.

Currently, there is no established consensus within the field on what constitutes a 'significant' improvement in RSCC or strain values. As such, we chose not to impose an arbitrary cutoff and just look at which structures improve RSCC. We also removed all language stating significance, as there isn’t a good standard in the field to assess significance. This is especially important as only improvements would be considered in an active modeling project. In cases where qFit ligand degrades the RSCC (or strain) to a large extent, the modeler would simply revert to the input model.

In the first section of Results: "First, for all ligands, we perform an unconstrained search function allowing the generated conformers to only be constrained from the bounds matrix (Figure 1A). This is particularly advantageous for small ligands that benefit from less restriction to fully explore their conformational space. We then perform a fixed terminal atoms search function (Figure 1B)." It is unclear whether a fixed terminal atom search was conducted for each conformer generated in the initial step to further explore the conformational space. This aspect should be clarified to provide a more comprehensive understanding of the methodology.

Each independent conformer generation function (A-E) is initialized with only the input ligand model and runs in parallel with the other functions. These functions do not build on each other, but rather perturb the input molecule independently of one another. In our updated manuscript, we have clarified the methodology (page 6).

“First, in all cases, we perform an unconstrained search function (Figure 1A), a fixed terminal atoms search function (Figure 1B), and a blob search function (Figure 1C).”

Phrase: "We randomly sampled 150 structures and, after manual inspection of the fit of alternative conformations, chose 135 crystal structures as a development set for improving qFit-ligand." The authors should explain why they filtered 10% of the structures.

To develop qFit-ligand, we wanted to use a very high-quality dataset. We needed to know with some degree of certainty that if qFit-ligand failed to produce an alternate conformation (or generated conformations low in RSCC or high in strain), the failure was due to an algorithmic limitation rather than poor-quality input data. Therefore, after selection based on numerical metrics, we manually examined each ligand in Coot to observe if we believed the alternative conformers fit well into the density.

How do social media platforms balance helping users share authentically while also managing the risks of context collapse? It feels like this tension is at the heart of how we interact online—and a big reason why sometimes it’s easier just to post nothing at all.

I think users want to make bots because they can be a really helpful way to save time and make tasks easier. Instead of doing the same things over and over, like answering simple questions or organizing information, a bot can handle that automatically. In my opinion, bots also make online experiences better, whether it’s getting quick customer support or just having a fun, interactive tool to use. For businesses especially, I believe bots are a smart way to stay connected with people without needing someone to always be available.

This whole paragraph really hit the nail right on the head in my opinion. Those four terms cannot be used interchangeably because they all mean different things entirely. I believe that everyone loves to learn naturally, it's the vector through which their education is brought to them in that plays a key role in determining if the training or education was valuable.

This statement reminds us that the freedoms we enjoy today weren’t just handed to us—they were hard-won through generations of resistance against oppression and fear. It’s a powerful reminder that democracy must be actively protected and never taken for granted. The fight for justice and equality is ongoing.

Throughout this course (or experience), my views on ethics have been both reinforced and expanded. I’ve always believed that ethics involves doing the “right thing,” but I now see that ethical decisions are often complex, situational, and require balancing competing interests. What was reinforced for me is the importance of empathy and fairness—considering how my actions affect others and striving for transparency.

At the same time, my understanding of ethics has evolved. I used to think of ethical behavior as primarily personal—about individual honesty or integrity—but I now see how deeply ethics is tied to systems, power structures, and social responsibility. Learning about ethical dilemmas in business, technology, or healthcare (insert your relevant field) helped me understand that being ethical isn't just about avoiding harm; it's about actively promoting justice and accountability.

Overall, I now approach ethical questions more thoughtfully, with a greater awareness of nuance, context, and the need to ask deeper questions about who benefits and who may be harmed.

My views on ethics have deepened over time. I used to think ethics was just about knowing what’s right and wrong, but now I see it’s more complicated than that. Real-life situations often don’t have clear answers, and doing the “right” thing can still feel uncomfortable or messy. I’ve learned that empathy, listening, and understanding different perspectives matter just as much as the rules. It’s made me more thoughtful and careful about the choices I make and how they affect others.

Rashida’s words remind me that being asked to speak is not the same as being truly heard. Even when dialogue happens, students’ insights can be filtered or dismissed by adults who hold more power. Her hope that personal experience can move someone to take action reveals a quiet kind of strength. It’s thoughtful and brave—she’s using her voice not just to describe injustice, but to change who listens and how they respond

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

(1) I miss some treatment of the lack of behavioural correlate. What does it mean that metamine benefits EEG classification accuracy without improving performance? One possibility here is that there is an improvement in response latency, rather than perceptual sensitivity. Is there any hint of that in the RT results? In some sort of combined measure of RT and accuracy? 

First, we would like to thank the reviewer for their positive assessment of our work and for their extremely helpful and constructive comments that helped to significantly improve the quality of our manuscript.  

The reviewer rightly points out that, to our surprise, we did not obtain a correlate of the effect of memantine in our behavioral data, neither in the reported accuracy data nor in the RT data. We do not report RT results as participants were instructed to respond as accurately as possible, without speed pressure. We added a paragraph in the discussion section to point to possible reasons for this surprising finding:

“There are several possible reasons for this lack of behavioral correlate.  For example, EEG decoding may be a more sensitive measure of the neural effects of memantine, in particular given that perceptual sensitivity may have been at floor (masked condition, experiment 1) or ceiling (unmasked condition, experiment 1, and experiment 2). It is also possible that the present decoding results are merely epiphenomenal, not mapping onto functional improvements (e.g., Williams et al., 2007). However, given that we found a tight link between these EEG decoding markers and behavioral performance in our previous work (Fahrenfort et al., 2017; Noorman et al., 2023), it is possible that the effect of memantine was just too subtle to show up in changes in overt behavior.”

(2) An explanation is missing, about why memantine impacts the decoding of illusion but not collinearity. At a systems level, how would this work? How would NMDAR antagonism selectively impact long-range connectivity, but not lateral connectivity? Is this supported by our understanding of laminar connectivity and neurochemistry in the visual cortex?

We have no straightforward or mechanistic explanation for this finding. In the revised discussion, we are highlighting this finding more clearly, and included some speculative explanations:

“The present effect of memantine was largely specific to illusion decoding, our marker of feedback processing, while collinearity decoding, our marker of lateral processing, was not (experiment 1) or only weakly (experiment 2) affected by memantine. We have no straightforward explanation for why NMDA receptor blockade would impact inter-areal feedback connections more strongly than intra-areal lateral connections, considering their strong functional interdependency and interaction in grouping and segmentation processes (Liang et al., 2017). One possibility is that this finding reflects properties of our EEG decoding markers for feedback vs. lateral processing: for example, decoding of the Kanizsa illusion may have been more sensitive to the relatively subtle effect of our pharmacological manipulation, either because overall decoding was better than for collinearity or because NMDA receptor dependent recurrent processes more strongly contribute to illusion decoding than to collinearity decoding.”

(3) The motivating idea for the paper is that the NMDAR antagonist might disrupt the modulation of the AMPA-mediated glu signal. This is in line with the motivating logic for Self et al., 2012, where NMDAR and AMPAR efficacy in macacque V1 was manipulated via microinfusion. But this logic seems to conflict with a broader understanding of NMDA antagonism. NMDA antagonism appears to generally have the net effect of increasing glu (and ACh) in the cortex through a selective effect on inhibitory GABAergic cells (eg. Olney, Newcomer, & Farber, 1999). Memantine, in particular, has a specific impact on extrasynaptic NMDARs (that is in contrast to ketamine; Milnerwood et al, 2010, Neuron), and this type of receptor is prominent in GABA cells (eg. Yao et al., 2022, JoN). The effect of NMDA antagonists on GABAergic cells generally appears to be much stronger than the effect on glutamergic cells (at least in the hippocampus; eg. Grunze et al., 1996).

This all means that it's reasonable to expect that memantine might have a benefit to visually evoked activity. This idea is raised in the GD of the paper, based on a separate literature from that I mentioned above. But all of this could be better spelled out earlier in the paper, so that the result observed in the paper can be interpreted by the reader in this broader context.

To my mind, the challenging task is for the authors to explain why memantine causes an increase in EEG decoding, where microinfusion of an NMDA antagonist into V1 reduced the neural signal Self et al., 2012. This might be as simple as the change in drug... memantine's specific efficacy on extrasynaptic NMDA receptors might not be shared with whatever NMDA antagonist was used in Self et al. 2012. Ketamine and memantine are already known to differ in this way. 

We addressed the reviewer’s comments in the following way. First, we bring up our (to us, surprising) result already at the end of the Introduction, pointing the reader to the explanation mentioned by the reviewer:

“We hypothesized that disrupting the reentrant glutamate signal via blocking NMDA receptors by memantine would impair illusion and possibly collinearity decoding, as putative markers of feedback and lateral processing, but would spare the decoding of local contrast differences, our marker of feedforward processing. To foreshadow our results, memantine indeed specifically affected illusion decoding, but enhancing rather than impairing it. In the Discussion, we offer explanations for this surprising finding, including the effect of memantine on extrasynaptic NMDA receptors in GABAergic cells, which may have resulted in boosted visual activity.”

Second, as outlined in the response to the first point by Reviewer #2, we are now clear throughout the title, abstract, and paper that memantine “improved” rather than “modulated” illusion decoding.

Third, and most importantly, we restructured and expanded the Discussion section to include the reviewer’s proposed mechanisms and explanations for the effect. We would like to thank the reviewer for pointing us to this literature. We also discuss the results of Self et al. (2012), specifically the distinct effects of the two NMDAR antagonists used in this study, more extensively, and speculate that their effects may have been similar to ketamine and thus possibly opposite of memantine (for the feedback signal):

“Although both drugs are known to inhibit NMDA receptors by occupying the receptor’s ion channel and are thereby blocking current flow (Glasgow et al., 2017; Molina et al., 2020), the drugs have different actions at receptors other than NMDA, with ketamine acting on dopamine D2 and serotonin 5-HT2 receptors, and memantine inhibiting several subtypes of the acetylcholine (ACh) receptor as well as serotonin 5HT3 receptors. Memantine and ketamine are also known to target different NMDA receptor subpopulations, with their inhibitory action displaying different time courses and intensity (Glasgow et al., 2017; Johnson et al., 2015). Blockade of different NMDA receptor subpopulations can result in markedly different and even opposite results. For example, Self and colleagues (2012) found overall reduced or elevated visual activity after microinfusion of two different selective NMDA receptor antagonists (2-amino-5phosphonovalerate and ifendprodil) in macaque primary visual cortex. Although both drugs impaired the feedback-related response to figure vs. ground, similar to the effects of ketamine (Meuwese et al., 2013; van Loon et al., 2016) such opposite effects on overall activity demonstrate that the effects of NMDA antagonism strongly depend on the targeted receptor subpopulation, each with distinct functional properties.”

Finally, we link these differences to the potential mechanism via GABAergic neurons:

“As mentioned in the Introduction, this may be related to memantine modulating processing at other pre- or post-synaptic receptors present at NMDA-rich synapses, specifically affecting extrasynaptic NMDA receptors in GABAergic cells (Milnerwood et al, 2010; Yao et al., 2022). Memantine’s strong effect on extrasynaptic NMDA receptors in GABAergic cells leads to increases in ACh levels, which have been shown to increase firing rates and reduce firing rate variability in macaques (Herrero et al., 2013, 2008). This may represent a mechanism through which memantine (but not ketamine or the NMDA receptor antagonists used by Self and colleagues) could boost visually evoked activity.”

(4) The paper's proposal is that the effect of memantine is mediated by an impact on the efficacy of reentrant signaling in visual cortex. But perhaps the best-known impact of NMDAR manipulation is on LTP, in the hippocampus particularly but also broadly.

Perception and identification of the kanisza illusion may be sensitive to learning (eg. Maertens & Pollmann, 2005; Gellatly, 1982; Rubin, Nakayama, Shapley, 1997); what argues against an account of the results from an effect on perceptual learning? Generally, the paper proposes a very specific mechanism through which the drug influences perception. This is motivated by results from Self et al 2012 where an NMDA antagonist was infused into V1. But oral memantine will, of course, have a whole-brain effect, and some of these effects are well characterized and - on the surface - appear as potential sources of change in illusion perception. The paper needs some treatment of the known ancillary effects of diffuse NMDAR antagonism to convince the reader that the account provided is better than the other possibilities. 

We cannot fully exclude an effect based on perceptual learning but consider this possibility highly unlikely for several reasons. First, subjects have performed more than a thousand trials in a localizer session before starting the main task (in experiment 2 even more than two thousand) containing the drug manipulation. Therefore, a large part of putative perceptual learning would have already occurred before starting the main experiment. Second, the main experiment was counterbalanced across drug sessions, so half of the participants first performed the memantine session and then the placebo session, and the other half of the subjects the other way around. If memantine would have improved perceptual learning in our experiments, one may actually expect to observe improved decoding in the placebo session and not in the memantine session. If memantine would have facilitated perceptual learning during the memantine session, the effect of that facilitated perceptual learning would have been most visible in the placebo session following the memantine session. Because we observed improved decoding in the memantine session itself, perceptual learning is likely not the main explanation for these findings. Third, perceptual learning is known to occur for several stimulus dimensions (e.g., orientation, spatial frequency or contrast). If these findings would have been driven by perceptual learning one would have expected to see perceptual learning for all three features, whereas the memantine effects were specific to illusion decoding. Especially in experiment 2, all features were equally often task relevant and in such a situation one would’ve expected to observe perceptual learning effects on those other features as well.  

To further investigate any potential role of perceptual learning, we analyzed participants’ performance in detecting the Kanizsa illusion over the course of the experiments. To investigate this, we divided the experiments’ trials into four time bins, from the beginning until the end of the experiment. For the first experiment’s first target (T1), there was no interaction between the factors bin and drug (memantine/placebo; F_3,84=0.89, P\=0.437; Figure S6A). For the second target (T2), we performed a repeatedmeasures ANOVA with the factors bin, drug, T1-T2 lag (short/long), and masks (present/absent). There was only a trend towards a bin by drug interaction (F_3,84=2.57, P\=0.064; Figure S6B), reflecting worse performance under memantine in the first three bins and slightly better performance in the fourth bin. The other interactions that include the factors bin and drug factors were not significant (all P>0.117). For the second experiment, we performed a repeated-measures ANOVA with the factors bin, drug, masks, and task-relevant feature (local contrast/collinearity/illusion). None of the interactions that included the bin and drug factors were significant (all P>0.219; Figure S6C). Taken together, memantine does not appear to affect Kanizsa illusion detection performance through perceptual learning. Finally, there was no interaction between the factors bin and task-relevant feature (F_6,150=0.76, P\=0.547; Figure S6D), implying there is no perceptual learning effect specific to Kanizsa illusion detection. We included these analyses in our revised Supplement as Fig. S6.

(5) The cross-decoding approach to data analysis concerns me a little. The approach adopted here is to train models on a localizer task, in this case, a task where participants matched a kanisza figure to a target template (E1) or discriminated one of the three relevant stimuli features (E2). The resulting model was subsequently employed to classify the stimuli seen during separate tasks - an AB task in E1, and a feature discrimination task in E2. This scheme makes the localizer task very important. If models built from this task have any bias, this will taint classifier accuracy in the analysis of experimental data. My concern is that the emergence of the kanisza illusion in the localizer task was probably quite salient, respective to changes in stimuli rotation or collinearity. If the model was better at detecting the illusion to begin with, the data pattern - where drug manipulation impacts classification in this condition but not other conditions - may simply reflect model insensitivity to non-illusion features.

I am also vaguely worried by manipulations implemented in the main task that do not emerge in the localizer - the use of RSVP in E1 and manipulation of the base rate and staircasing in E2. This all starts to introduce the possibility that localizer and experimental data just don't correspond, that this generates low classification accuracy in the experimental results and ineffective classification in some conditions (ie. when stimuli are masked; would collinearity decoding in the unmasked condition potentially differ if classification accuracy were not at a floor? See Figure 3c upper, Figure 5c lower).

What is the motivation for the use of localizer validation at all? The same hypotheses can be tested using within-experiment cross-validation, rather than validation from a model built on localizer data. The argument may be that this kind of modelling will necessarily employ a smaller dataset, but, while true, this effect can be minimized at the expense of computational cost - many-fold cross-validation will mean that the vast majority of data contributes to model building in each instance. 

It would be compelling if results were to reproduce when classification was validated in this kind of way. This kind of analysis would fit very well into the supplementary material.

We thank the reviewer for this excellent question. We used separate localizers for several reasons, exactly to circumvent the kind of biases in decoding that the reviewer alludes to. Below we have detailed our rationale, first focusing on our general rationale and then focusing on the decisions we made in designing the specific experiments.  

Using a localizer task in the design of decoding analysis offers several key advantages over relying solely on k-fold cross-validation within the main task:

(1) Feature selection independence and better generalization: A separate localizer task allows for independent feature selection, ensuring that the features used for decoding are chosen without bias from the main task data. Specifically, the use of a localizer task allows us to determine the time-windows of interest independently based on the peaks of the decoding in the localizer. This allows for a better direct comparison between the memantine and placebo conditions because we can isolate the relevant time windows outside a drug manipulation. Further, training a classifier on a localizer task and testing it on a separate experimental task assesses whether neural representations generalize across contexts, rather than simply distinguishing conditions within a single dataset. This supports claims about the robustness of the decoded information.

(2) Increased sensitivity and interpretability: The localizer task can be designed specifically to elicit strong, reliable responses in the relevant neural patterns. This can improve signal-to-noise ratio and make it easier to interpret the features being used for decoding in the test set. We facilitate this by having many more trials in the localizer tasks (1280 in E1 and 5184 in E2) than in the separate conditions of the main task, in which we would have to do k-folding (e.g., 2, mask, x 2 (lag) design in E1 leaves fewer than 256 trials, due to preprocessing, for specific comparisons) on very low trial numbers. The same holds for experiment 2 which has a 2x3 design, but also included the base-rate manipulation. Finally, we further facilitate sensitivity of the model by having the stimuli presented at full contrast without any manipulations of attention or masking during the localizer, which allows us to extract the feature specific EEG signals in the most optimal way.

(3) Decoupling task-specific confounds: If decoding is performed within the main task using k-folding, there is a risk that task-related confounds (e.g., motor responses, attention shifts, drug) influence decoding performance. A localizer task allows us to separate the neural representation of interest from these taskrelated confounds.

Experiment 1 

In experiment 1, the Kanizsa was always task relevant in the main experiment in which we employed the pharmacological manipulation. To make sure that the classifiers were not biased towards Kanizsa figures from the start (which would be the case if we would have done k-folding in the main task), we used a training set in which all features were equally relevant for task performance. As can be seen in figure 1E, which plots the decoding accuracies of the localizer task, illusion decoding as well as rotation decoding were equally strong, whereas collinearity decoding was weaker. It may be that the Kanizsa illusion was quite salient in the localizer task, which we can’t know at present, but it was at least less salient and relevant than in the main task (where it was the only task-relevant feature). Based on the localizer decoding results one could argue that the rotation dimension and illusion dimension were most salient, because the decoding was highest for these dimensions. Clearly the model was not insensitive to nonillusory features. The localizer task of experiment 2 reveals that collinearity decoding tends to be generally lower, even when that feature is task relevant.  

Experiment 2 

In experiment 2, the localizer task and main task were also similar, with three exceptions: during the localizer task no drug was active, and no masking and no base rate manipulation were employed. To make sure that the classifier was not biased towards a certain stimulus category (due to the bias manipulation), e.g. the stimulus that is presented most often, we used a localizer task without this manipulation. As can be seen in figure 4D decoding of all the features was highly robust, also for example for the collinearity condition. Therefore the low decoding that we observe in the main experiment cannot be due to poor classifier training or feature extraction in the localizer. We believe this is actually an advantage instead of a disadvantage of the current decoding protocol.

Based on the rationale presented above we are uncomfortable performing the suggested analyses using a k-folding approach in the main task, because according to our standards the trial numbers are too low and the risk that these results are somehow influenced by task specific confounds cannot be ruled out.  

Line 301 - 'Interestingly, in both experiments the effect of memantine... was specific to... stimuli presented without a backward mask.' This rubs a bit, given that the mask broadly disrupted classification. The absence of memantine results in masked results may simply be a product of the floor ... some care is needed in the interpretation of this pattern. 

In the results section of experiment 1, we added:

“While the interaction between masking and memantine only approached significance (P\=0.068), the absence of an effect of memantine in the masked condition could reflect a floor effect, given that illusion decoding in the masked condition was not significantly better than chance.”

While floor is less likely to account for the absence of an effect in the masked condition in experiment 2, where illusion decoding in the masked condition was significantly above chance, it is still possible that to obtain an effect of memantine, decoding accuracy needed to be higher. We therefore also added here:

“For our time window-based analyses of illusion decoding, the specificity of the memantine effect to the unmasked condition was supported by a significant interaction between drug and masking (note, however, given overall much lower decoding accuracy in the masked condition, the lack of a memantine effect could reflect a floor effect).”

In the discussion, we changed the sentence to read “…the effect of memantine on illusion decoding tended to be specific to attended, task-relevant stimuli presented without a backward mask.”

Line 441 - What were the contraindications/exclusion parameters for the administration of memantine? 

Thanks for spotting this. We have added the relevant exclusion criteria in the revised version of the supplement. See also below.

– Allergy for memantine or one of the inactive ingredients of these products;

– (History of) psychiatric treatment;

– First-degree relative with (history of) schizophrenia or major depression;

– (History of) clinically significant hepatic, cardiac, obstructive respiratory, renal, cerebrovascular, metabolic or pulmonary disease, including, but not limited to fibrotic disorders;

– Claustrophobia;

–  Regular usage of medicines (antihistamines or occasional use of paracetamol);

– (History of) neurological disease;

–  (History of) epilepsy;

–  Abnormal hearing or (uncorrected) vision;

–  Average use of more than 15 alcoholic beverages weekly;

– Smoking

– History of drug (opiate, LSD, (meth)amphetamine, cocaine, solvents, cannabis, or barbiturate) or alcohol dependence;

– Any known other serious health problem or mental/physical stress;

– Used psychotropic medication, or recreational drugs over a period of 72 hours prior to each test session,  

– Used alcohol within the last 24 hours prior to each test session;

– (History of) pheochromocytoma.

– Narrow-angle glaucoma;

– (History of) ulcer disease;

– Galactose intolerance, Lapp lactase deficiency or glucose­galactose malabsorption.

– (History of) convulsion;

Line 587 - The localizer task used to train the classifier in E2 was collected in different sessions. Was the number of trials from separate sessions ultimately equal? The issue here is that the localizer might pick up on subtle differences in electrode placement. If the test session happens to have electrode placement that is similar to the electrode placement that existed for a majority of one condition of the localizer... this will create bias. This is likely to be minor, but machine classifiers really love this kind of minor confound.

Indeed, the trial counts in the separate sessions for the localizer in E2 were equal. We have added that information to the methods section.  

Experiment 1: 1280 trials collected during the intake session.

In experiment 2: 1728 trials were collected per session (intake, and 2 drug sessions), so there were 5184 trials across three sessions.

Reviewer #2:

To start off, I think the reader is being a bit tricked when reading the paper. Perhaps my priors are too strong, but I assumed, just like the authors, that NMDA-receptors would disrupt recurrent processing, in line with previous work. However, due to the continuous use of the ambiguous word 'affected' rather than the more clear increased or perturbed recurrent processing, the reader is left guessing what is actually found. That's until they read the results and discussion finding that decoding is actually improved. This seems like a really big deal, and I strongly urge the authors to reword their title, abstract, and introduction to make clear they hypothesized a disruption in decoding in the illusion condition, but found the opposite, namely an increase in decoding. I want to encourage the authors that this is still a fascinating finding.

We thank the reviewer for the positive assessment of our manuscript, and for many helpful comments and suggestions.  

We changed the title, abstract, and introduction in accordance with the reviewer’s comment, highlighting that “memantine […] improves decoding” and “enhances recurrent processing” in all three sections. We also changed the heading of the corresponding results section to “Memantine selectively improves decoding of the Kanizsa illusion”.

Apologies if I have missed it, but it is not clear to me whether participants were given the drug or placebo during the localiser task. If they are given the drug this makes me question the logic of their analysis approach. How can one study the presence of a process, if their very means of detecting that process (the localiser) was disrupted in the first place? If participants were not given a drug during the localiser task, please make that clear. I'll proceed with the rest of my comments assuming the latter is the case. But if the former, please note that I am not sure how to interpret their findings in this paper.

Thanks for asking this, this was indeed unclear. In experiment 1 the localizer was performed in the intake session in which no drugs were administered. In the second experiment the localizer was performed in all three sessions with equal trial numbers. In the intake session no drugs were administrated. In the other two sessions the localizer was performed directly after pill intake and therefore the memantine was not (or barely) active yet. We started the main task four hours after pill intake because that is the approximate peak time of memantine. Note that all three localizer tasks were averaged before using them as training set. We have clarified this in the revised manuscript.

The main purpose of the paper is to study recurrent processing. The extent to which this study achieves this aim is completely dependent to what extent we can interpret decoding of illusory contours as uniquely capturing recurrent processing. While I am sure illusory contours rely on recurrent processing, it does not follow that decoding of illusory contours capture recurrent processing alone. Indeed, if the drug selectively manipulates recurrent processing, it's not obvious to me why the authors find the interaction with masking in experiment 2. Recurrent processing seems to still be happening in the masked condition, but is not affected by the NMDA-receptor here, so where does that leave us in interpreting the role of NMDA-receptors in recurrent processing? If the authors can not strengthen the claim that the effects are completely driven by affecting recurrent processing, I suggest that the paper will shift its focus to making claims about the encoding of illusory contours, rather than making primary claims about recurrent processing.

We indeed used illusion decoding as a marker of recurrent processing. Clearly, such a marker based on a non-invasive and indirect method to record neural activity is not perfect. To directly and selectively manipulate recurrent processing, invasive methods and direct neural recordings would be required. However, as explained in the revised Introduction,

“In recent work we have validated that the decoding profiles of these features of different complexities at different points in time, in combination with the associated topography, can indeed serve as EEG markers of feedforward, lateral and recurrent processes (Fahrenfort et al., 2017; Noorman et al., 2023).”  

The timing and topography of the decoding results of the present study were consistent with our previous EEG decoding studies (Fahrenfort et al., 2017; Noorman et al., 2023). This validates the use of these EEG decoding signatures as (imperfect) markers of distinct neural processes, and we continue to use them as such. However, we expanded the discussion section to alert the reader to the indirect and imperfect nature of these EEG decoding signatures as markers of distinct neural processes: “Our approach relied on using EEG decoding of different stimulus features at different points in time, together with their topography, as markers of distinct neural processes. Although such non-invasive, indirect measures of neural activity cannot provide direct evidence for feedforward vs. recurrent processes, the timing, topography, and susceptibility to masking of the decoding signatures obtained in the present study are consistent with neurophysiology (e.g., Bosking et al., 1997; Kandel et al., 2000; Lamme & Roelfsema, 2000; Lee & Nguyen, 2001; Liang et al., 2017; Pak et al., 2020), as well as with our previous work (Fahrenfort et al., 2017; Noorman et al., 2023).” 

The reviewer is also concerned about the lack of effect of memantine on illusion decoding in the masked condition in experiment 2. In our view, the strong effect of masking on illusion decoding (both in absolute terms, as well as when compared to its effect on local contrast decoding), provides strong support for our assumption that illusion decoding represents a marker of recurrent processing. Nevertheless, as the reviewer points out, weak but statistically significant illusion decoding was still possible in the masked condition, at least when the illusion was task-relevant. As the reviewer notes, this may reflect residual recurrent processing during masking, a conclusion consistent with the relatively high behavioral performance despite masking (d’ > 1). However, rather than invalidating the use of our EEG markers or challenging the role of NMDA-receptors in recurrent processing, this may simply reflect a floor effect. As outlined in our response to reviewer #1 (who was concerned about floor effects), in the results section of experiment 1, we added:

“While the interaction between masking and memantine only approached significance (P\=0.068), the absence of an effect of memantine in the masked condition could reflect a floor effect, given that illusion decoding in the masked condition was not significantly better than chance.”

And for experiment 1:

“For our time window-based analyses of illusion decoding, the specificity of the memantine effect to the unmasked condition was supported by a significant interaction between drug and masking (note, however, given overall much lower decoding accuracy in the masked condition, the lack of a memantine effect could reflect a floor effect).”

An additional claim is being made with regards to the effects of the drug manipulation. The authors state that this effect is only present when the stimulus is 1) consciously accessed, and 2) attended. The evidence for claim 1 is not supported by experiment 1, as the masking manipulation did not interact in the cluster-analyses, and the analyses focussing on the peak of the timing window do not show a significant effect either. There is evidence for this claim coming from experiment 2 as masking interacts with the drug condition. Evidence for the second claim (about task relevance) is not presented, as there is no interaction with the task condition. A classical error seems to be made here, where interactions are not properly tested. Instead, the presence of a significant effect in one condition but not the other is taken as sufficient evidence for an interaction, which is not appropriate. I therefore urge the authors to dampen the claim about the importance of attending to the decoded features. Alternatively, I suggest the authors run their interactions of interest on the time-courses and conduct the appropriate clusterbased analyses.

We thank the reviewer for pointing out the importance of key interaction effects. Following the reviewer’s suggestion, we dampened our claims about the role of attention. For experiment 1, we changed the heading of the relevant results section from “Memantine’s effect on illusion decoding requires attention” to “The role of consciousness and attention in memantine’s effect on illusion decoding”, and we added the following in the results section:

“Also our time window-based analyses showed a significant effect of memantine only when the illusion was both unmasked and presented outside the AB (t_28\=-2.76, _P\=0.010, BF₁₀=4.53; Fig. 3F). Note, however, that although these post-hoc tests of the effect of memantine on illusion decoding were significant, for our time window-based analyses we did not obtain a statistically significant interaction between the AB and memantine, and the interaction between masking and memantine only approached significance (P\= 0.068). Thus, although these memantine effects were slightly less robust than for T1, probably due to reduced trial counts, these results point to (but do not conclusively demonstrate) a selective effect of memantine on illusion-related feedback processing that depends on the availability of attention. In addition to the lack of the interaction effect, another potential concern…”

For experiment 2, we added the following in the results section:

“Note that, for our time window-based analyses of illusion decoding, although the specificity of the memantine effect to the unmasked condition was supported by a significant interaction between drug and masking, we did not obtain a statistically significant interaction between memantine and task-relevance. Thus, although the memantine effect was significant only when the illusion was unmasked and taskrelevant, just like for the effect of temporal attention in experiment 1, these results do not conclusively demonstrate a selective effect of memantine that depends attention (task-relevance).”

In the discussion, we toned down claims about memantine’s effects being specific to attended conditions, we are highlighting the “preliminary” nature of these findings, and we are now alerting the reader explicitly to be careful with interpreting these effects, e.g.:

“Although these results have to be interpreted with caution because the key interaction effects were not statistically significant, …”

How were the length of the peak-timing windows established in Figure 1E? My understanding is that this forms the training-time window for the further decoding analyses, so it is important to justify why they have different lengths, and how they are determined. The same goes for the peak AUC time windows for the interaction analyses. A number of claims in the paper rely on the interactions found in these posthoc analyses, so the 223- to 323 time window needs justification.

Thanks for this question. The length of these peak-timing windows is different because the decoding of rotation is temporarily very precise and short-lived, whereas the decoding of the other features last much longer and is more temporally variable. In fact, we have followed the same procedure as in a previously published study (Noorman et al., elife 2025) for defining the peak-timing and length of the windows. We followed the same procedure for both experiments reported in this paper, replicating the crucial findings and therefore excluding the possibility that these findings are in any way dependent on the time windows that are selected. We have added that information to the revised version of the manuscript.

Reviewer #3:

First, despite its clear pattern of neural effects, there is no corresponding perceptual effect. Although the manipulation fits neatly within the conceptual framework, and there are many reasons for not finding such an effect (floor and ceiling effects, narrow perceptual tasks, etc), this does leave open the possibility that the observation is entirely epiphenomenal, and that the mechanisms being recorded here are not actually causally involved in perception per se.

We thank the reviewer for the positive assessment of our work. The reviewer rightly points out that, to our surprise, we did not obtain a correlate of the effect of memantine in our behavioral data. We agree with the possible reasons for the absence of such an effect highlighted by the reviewer, and expanded our discussion section accordingly:

“There are several possible reasons for this lack of behavioral correlate.  For example, EEG decoding may be a more sensitive measure of the neural effects of memantine, in particular given that perceptual sensitivity may have been at floor (masked condition, experiment 1) or ceiling (unmasked condition, experiment 1, and experiment 2). It is also possible that the present decoding results are merely epiphenomenal, not mapping onto functional improvements (e.g., Williams et al., 2007). However, given that in our previous work we found a tight link between these EEG decoding markers and behavioral performance (Fahrenfort et al., 2017; Noorman et al., 2023), it is possible that the effect of memantine in the present study was just too subtle to show up in changes in overt behavior.”

Second, although it is clear that there is an effect on decoding in this particular condition, what that means is not entirely clear - particularly since performance improves, rather than decreases. It should be noted here that improvements in decoding performance do not necessarily need to map onto functional improvements, and we should all be careful to remain agnostic about what is driving classifier performance. Here too, the effect of memantine on decoding might be epiphenomenal - unrelated to the information carried in the neural population, but somehow changing the balance of how that is electrically aggregated on the surface of the skull. *Something* is changing, but that might be a neurochemical or electrical side-effect unrelated to actual processing (particularly since no corresponding behavioural impact is observed.)

We would like to refer to our reply to the previous point, and we would like to add that in our previous work (Fahrenfort et al., 2017; Noorman et al., 2023) similar EEG decoding markers were often tightly linked to changes in behavioral performance. This indicates that these particular EEG decoding markers do not simply reflect some sideeffect not related to neural processing. However, as stated in the revised discussion section, “it is possible that the effect of memantine in the present study was just too subtle to show up in changes in overt behavior.”

This article is about how Facebook offers a free, tester-version of the internet to people around the world. It is criticized and called digital colonialism the service it only provides certain info and restricts its users access. Meta already has a huge hold on the digital market and this is just another way of exploiting people that can't afford to know better.

You can only win if you can focus on what matters most

It's sad to learn that people who want to learn how to program have to learn english since the majority of programming is in english. It is just another way of making minorities stay in a box and adds a layer of struggle in gaining an education in programming. Its truly unfortunate and unfair, I understand that some common words stay in english in order to keep some things consistent but it gets to a point.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

(1) The data are generated using ATP read-out (CTG assay). For any inhibitor of mitochondrial function, ATP assays are highly sensitive reflecting metabolic stress, yet these do not necessarily translate into cell growth inhibition using standard Trypan blue assays and tend to overestimate the effects. Please show orthogonal more robust assays of cell growth or proliferation.

We acknowledge the sensitivity of the ATP read-out assay in reflecting metabolic stress. While additional cell growth assays such as Trypan blue exclusion could provide further insights, we believe that the current ATP assay data robustly demonstrate the effect of the IMT and venetoclax combination on cellular metabolism, which is a critical aspect of our study. The scope of our current work focused on metabolic inhibition, and we suggest that future studies could further explore cell proliferation assays to complement these findings.

(2) It is concluded that AML cells do not utilize glucose for ATP production. Please provide formal measurements of glycolysis/lactate upon combinatorial treatment.

We appreciate the reviewer’s suggestion to include glycolysis and lactate measurements, which could indeed add further granularity to our metabolic analysis. However, the primary focus of our study is on mitochondrial function and oxidative phosphorylation (OXPHOS) in AML cells treated with IMT and venetoclax. We believe the data presented in Figure 3 provide strong support for the conclusion that glycolysis is not a major energy source in these cells.

Specifically, in Figure 3C, we demonstrate that AML cells maintain ATP levels and viability when cultured in galactose, a condition that restricts ATP production through glycolysis and forces cells to rely on OXPHOS. This result strongly suggests that these AML cells are not dependent on glycolysis for ATP production. Furthermore, in Supplementary Figure S3B, we show that oxygen consumption rate (OCR) measurements remain stable in the presence of excess glucose, further supporting our conclusion that the cells do not switch to glycolysis when OXPHOS is inhibited.

These findings collectively indicate a primary reliance on OXPHOS for energy generation in AML cells, consistent with our study’s objectives to explore mitochondrial dependency and the therapeutic potential of targeting mitochondrial transcription in AML. Future studies could certainly expand on these insights by incorporating a more detailed analysis of glycolytic flux and lactate production under combinatorial treatment, but we believe the current data are sufficient to support our main conclusions.

(3) The transcriptome data are shown without any analysis of pathways. The conclusion from this data beyond the higher number of genes impacted in the combination arm is unclear. Please provide analysis for example GO pathways and interpret in the context of the drugs' mechanism of action.

In response to the reviewer’s question, we have added gene ontology (GO) pathway analysis to clarify the transcriptomic impact of our combination treatment with IMT and venetoclax. Functional annotation identified significant enrichment in pathways relevant to innate immune response, mitochondrial function, and cellular signaling processes. Specifically, pathways associated with immune defense, mitochondrial signaling, and intracellular signaling were notably affected. These findings suggest that the combination treatment not only disrupts cellular energy metabolism but also potentially primes immune signaling mechanisms. This aligns with the proposed mechanism, where IMT targets mitochondrial transcription and venetoclax induces apoptosis, together enhancing sensitivity in AML cells. The enriched pathways, therefore, support the mechanism of action of both drugs, showing how the combined inhibition of BCL-2 and mitochondrial transcription creates a compounded cellular disruption that enhances the therapeutic effect.

(4) Please demonstrate (could be in supplement) matrix of combination to support the statement that the combination is synergistic using Bliss index. The actual Bliss values are missing.

For the revision, we have now included a matrix of combination treatment effects with the corresponding Bliss synergy index values to substantiate our claim of synergy between IMT and venetoclax. This analysis, provided in the supplement, demonstrates that the observed effects exceed the expected additive impact of each drug alone, as calculated by the Bliss independence model. Specifically, the Bliss values confirm a synergistic interaction in venetoclax-sensitive AML cell lines, highlighting that the combined treatment significantly enhances inhibition of cell viability and apoptosis induction compared to single treatments. This data supports our interpretation of synergy and strengthens the mechanistic conclusions drawn from our findings on the combination therapy’s efficacy.

(5) Please show KG1 data (OCR), here or in Supplement.

In response to the reviewer’s request to include OCR data for the KG-1 cell line, we would like to clarify that OCR measurements were attempted; however, they did not yield conclusive results. This is noted in the revised manuscript (Results section), where we explain that the KG-1 cell line did not provide usable OCR data, likely due to limitations in detecting reliable mitochondrial respiration in this particular line under our experimental conditions. Therefore, we were unable to include KG-1 OCR data in the main figures or the supplement.

Reviewer #2:

(1) It's important that the authors show that the drug's effects in AML are due to on-target inhibition. It's critical that they show that IMT actually inhibits the mito polymerase in the AML cells in the dose range employed.

We appreciate the importance of demonstrating on-target inhibition of mitochondrial RNA polymerase by IMT1, especially in light of the detailed characterization of IMT1b, a closely related compound, as presented in Bonekamp et al., Nature 2020. The work by Bonekamp et al. established the specificity and efficacy of IMT1b in targeting mitochondrial RNA polymerase across various tumor models. Building on these findings, we designed our study to primarily evaluate the combinatorial efficacy of IMT1 with venetoclax in AML models, assuming a similar mechanism of action as described for IMT1b. While direct confirmation of on-target inhibition in AML cells by IMT1 would undoubtedly provide additional mechanistic insight, we focused on translational aspects in this study. We believe that the foundational work provided by Bonekamp et al. supports the assumption of on-target effects by IMT1, and we suggest that future studies could explicitly verify this in the context of AML.

(2) For Fig 1, the stated synergism between Venetoclax (Vex) and IMT in p53 mutant THP1 cells is really not evident, despite what the statistical analysis says. In some ways, the more interesting conclusion is that inhibiting mitochondrial transcription does NOT potentiate the efficacy of Bcl2 inhibition in TP53 mutant AML.

We appreciate the reviewer’s observation regarding the lack of evident synergy between IMT and venetoclax in TP53 mutant THP-1 cells. In line with this comment, we have now expanded the discussion to emphasize that, while statistical analysis suggested a potential interaction, the biological response in TP53 mutant cells was minimal. This contrasts with the strong synergy observed in TP53 wild-type cell lines, such as MV4-11 and MOLM-13. We have now highlighted that TP53 mutation status may limit the effectiveness of mitochondrial transcription inhibition in potentiating BCL-2 inhibition. This addition underscores the importance of mutation profiles, such as TP53 status, in predicting response to combination therapies in AML and is now clearly addressed in the revised discussion.

(3) They combine IMT with Vex, but Vex plus azacytidine or decitabine is the approved therapy for AML. Any clinical trial would likely start with this backbone (like Vex+Aza). They should test combinations of IMT with Vex/Aza or Vex/Dec.

While we recognize the importance of testing IMT in combination with clinically approved therapies like Vex+Aza, our current study was designed to explore the potential of IMT in combination with venetoclax alone. Expanding to other combinations would be an excellent direction for future research but is beyond the scope of our current investigation.

(4) It's interesting that AML cell lines do not show any reliance on ATP generation from glycolysis, but would this still be the case when OxPhos is inhibited with IMT? Such a simple experiment would be much more interesting and could help them better understand the mechanism of IMT efficacy.

We thank the reviewer for highlighting this point regarding the reliance of AML cell lines on glycolysis under OxPhos inhibition. In our study, we observed that AML cells predominantly rely on OxPhos, and we did test for ATP production in conditions that favored glycolysis by growing AML cells with galactose instead of glucose in the medium. As described in the manuscript, we did not observe significant ATP production or cell viability from glycolysis, even under these conditions. This finding suggests that AML cells have a low capacity to adapt to glycolytic ATP generation when OxPhos is disrupted by IMT, reinforcing the view that they are highly dependent on mitochondrial function for energy production. We agree that this adaptation—or lack thereof—is an intriguing aspect of IMT efficacy in targeting energy metabolism in AML cells, and we have clarified this point in the discussion.

(5) OxPhos measurements need statistical analyses.

We appreciate the reviewer’s suggestion to include statistical analyses for the OXPHOS measurements. We would like to clarify that statistical analyses were included in the initial submission. These are detailed in Figure 3 and its legend, as well as in the Statistical Analysis section, where we specify methods such as the calculation of standard error across replicates. This approach was implemented to ensure the rigor of our OCR data and its conclusions on OXPHOS inhibition in AML cells.

(6) Given that the combo-treated mice do not exhibit much leukemia in the blood through ~180 days, and yet start dying after 100 days, the authors should comment on this, given that the bone marrow has been shown to be a refuge that protects leukemia cells from various therapies.

We thank the reviewer for highlighting the observed discrepancy between peripheral blood leukemia levels and survival in combo-treated mice. While leukemic cells were minimally detected in the blood up to approximately 180 days, treated mice began to show signs of disease progression and reduced survival around 100 days. This may suggest that residual leukemic cells persist within the bone marrow, which has been established as a sanctuary site for leukemic cells, providing protection from various therapies. The bone marrow environment likely supports a survival niche, enabling these residual cells to evade treatment effects and potentially initiate disease relapse. We have added this interpretation to the discussion to acknowledge the possibility of bone marrow as a protective refuge, which may limit the full eradication of leukemia in these models despite apparent peripheral blood clearance.

(7) For Fig 5C, the authors should statistically compare the Combo with Vex alone.

We have now included statistical comparisons between the combination treatment and venetoclax alone in Fig 5C to provide a clearer interpretation of the data.

(8) The analyses of gene expression using RNAseq of harvested leukemia cells from the PDX model (Table S2), some more discussion of these results would be helpful, particularly given that neither drug is directly targeting nuclear gene expression.

We thank the reviewer for their suggestion to discuss the RNAseq findings in more detail. In the revised manuscript, we have expanded on the functional annotation of the gene expression changes observed in leukemia cells from the PDX model following combination treatment (Table S2). The enriched pathways include innate immune involvement, mitochondrial function and immune signaling, and intracellular signaling. This suggests that while neither IMT nor venetoclax directly targets nuclear gene expression, the combined treatment induces secondary effects that alter these pathways, potentially contributing to the treatment’s efficacy in AML. This expanded discussion provides greater insight into how the drug combination impacts gene expression and cellular pathways.

(9) We need more information on the PDX models, in terms of the classification (M1 to M6) of the patient AMLs and genetics (specific mutations, not just the genes mutated, and chromosomal alterations).

Additional details regarding the classification and genetic background of the PDX models have been included in the manuscript to better contextualize our findings.

(10) The authors should discuss whether or not IMT represents an improvement over other therapies intended to target Oxphos in AML (clearly, the low toxicity of IMT is a plus, at least in mice).

We appreciate the reviewer’s suggestion to discuss IMT in comparison with other OXPHOS-targeting therapies for AML. In the revised discussion, we highlight IMT’s unique properties, particularly its low toxicity profile, which may offer advantages over other OXPHOS inhibitors. This low toxicity, demonstrated in preclinical studies, suggests that IMT might improve patient tolerability compared to existing therapies that target mitochondrial function.

(11) The authors examined toxicity by weighing the mice and performing CBCs. Measurements of liver and kidney toxicity will be necessary for further clinical development.

We thank the reviewer for the suggestion to further investigate liver and kidney toxicity. In our study, we assessed toxicity through regular weight monitoring and complete blood counts (CBCs) to evaluate overall health status. While additional liver and kidney toxicity measurements will indeed be important in future studies, resource limitations currently prevent us from performing these additional analyses in this model. We agree that these assessments will be essential as we progress towards clinical development, and we plan to address them in upcoming preclinical studies.

Really though, who doesn't do that. I am not totally sure that sometimes it's an exaggeration or something that we think we know subconsciously or if we just want to feel like we know it all. Who can relate to this?

Author response:

The following is the authors’ response to the previous reviews

General Response to Reviewers:

We thank the Reviewers for their comments, which continue to substantially improve the quality and clarity of the manuscript, and therefore help us to strengthen its message while acknowledging alternative explanations.

All three reviewers raised the concern that we have not proven that Rab3A is acting on a presynaptic mechanism to increase mEPSC amplitude after TTX treatment of mouse cortical cultures.  The reviewers’ main point is that we have not shown a lack of upregulation of postsynaptic receptors in mouse cortical cultures. We want to stress that we agree that postsynaptic receptors are upregulated after activity block in neuronal cultures.  However, the reviewers are not acknowledging that we have previously presented strong evidence at the mammalian NMJ that there is no increase in AChR after activity blockade, and therefore the requirement for Rab3A in the homeostatic increase in quantal amplitude points to a presynaptic contribution. We agree that we should restrict our firmest conclusions to the data in the current study, but in the Discussion we are proposing interpretations. We have added the following new text:

“The impetus for our current study was two previous studies in which we examined homeostatic regulation of quantal amplitude at the NMJ.  An advantage of studying the NMJ is that synaptic ACh receptors are easily identified with fluorescently labeled alpha-bungarotoxin, which allows for very accurate quantification of postsynaptic receptor density. We were able to detect a known change due to mixing 2 colors of alpha-BTX to within 1% (Wang et al., 2005).  Using this model synapse, we showed that there was no increase in synaptic AChRs after TTX treatment, whereas miniature endplate current increased 35% (Wang et al., 2005). We further showed that the presynaptic protein Rab3A was necessary for full upregulation of mEPC amplitude (Wang et al., 2011). These data strongly suggested Rab3A contributed to homeostatic upregulation of quantal amplitude via a presynaptic mechanism.  With the current study showing that Rab3A is required for the homeostatic increase in mEPSC amplitude in cortical cultures, one interpretation is that in both situations, Rab3A is required for an increase in the presynaptic quantum.”

The point we are making is that the current manuscript is an extension of that work and interpretation of our findings regarding the variability of upregulation of postsynaptic receptors in our mouse cortical cultures further supports the idea that there is a Rab3Adependent presynaptic contribution to homeostatic increases in quantal amplitude.

Public Reviews:

Reviewer #1 (Public review):

Koesters and colleagues investigated the role of the small GTPase Rab3A in homeostatic scaling of miniature synaptic transmission in primary mouse cortical cultures using electrophysiology and immunohistochemistry. The major finding is that TTX incubation for 48 hours does not induce an increase in the amplitude of excitatory synaptic miniature events in neuronal cortical cultures derived from Rab3A KO and Rab3A Earlybird mutant mice. NASPM application had comparable effects on mEPSC amplitude in control and after TTX, implying that Ca2+-permeable glutamate receptors are unlikely modulated during synaptic scaling. Immunohistochemical analysis revealed no significant changes in GluA2 puncta size, intensity, and integral after TTX treatment in control and Rab3A KO cultures. Finally, they provide evidence that loss of Rab3A in neurons, but not astrocytes, blocks homeostatic scaling. Based on these data, the authors propose a model in which neuronal Rab3A is required for homeostatic scaling of synaptic transmission, potentially through GluA2-independent mechanisms.

The major finding - impaired homeostatic up-scaling after TTX treatment in Rab3A KO and Rab3 earlybird mutant neurons - is supported by data of high quality. However, the paper falls short of providing any evidence or direction regarding potential mechanisms. The data on GluA2 modulation after TTX incubation are likely statistically underpowered, and do not allow drawing solid conclusions, such as GluA2-independent mechanisms of up-scaling.

The study should be of interest to the field because it implicates a presynaptic molecule in homeostatic scaling, which is generally thought to involve postsynaptic neurotransmitter receptor modulation. However, it remains unclear how Rab3A participates in homeostatic plasticity.

Major (remaining) point:

(1) Direct quantitative comparison between electrophysiology and GluA2 imaging data is complicated by many factors, such as different signal-to-noise ratios. Hence, comparing the variability of the increase in mini amplitude vs. GluA2 fluorescence area is not valid. Thus, I recommend removing the sentence "We found that the increase in postsynaptic AMPAR levels was more variable than that of mEPSC amplitudes, suggesting other factors may contribute to the homeostatic increase in synaptic strength." from the abstract.

We have not removed the statement, but altered it to soften the conclusion. It now reads, “We found that the increase in postsynaptic AMPAR levels in wild type cultures was more variable than that of mEPSC amplitudes, which might be explained by a presynaptic contribution, but we cannot rule out variability in the measurement.”.

Similarly, the data do not directly support the conclusion of GluA2-independent mechanisms of homeostatic scaling. Statements like "We conclude that these data support the idea that there is another contributor to the TTX- induced increase in quantal size." should be thus revised or removed.

This particular statement is in the previous response to reviewers only, we deleted the sentence that starts, “The simplest explanation Rab3A regulates a presynaptic contributor….”. and “Imaging of immunofluorescence more variable…”. We deleted “ our data suggest….consistently leads to an increase in mEPSC amplitude and sometimes leads to….” We added “…the lack of a robust increase in receptor levels leaves open the possibility that there is a presynaptic contributor to quantal size in mouse cortical cultures. However, the variability could arise from technical factors associated with the immunofluorescence method, and the mechanism of Rab3A-dependent plasticity could be presynaptic for the NMJ and postsynaptic for cortical neurons.”

Reviewer #2 (Public review):

I thank the authors for their efforts in the revision. In general, I believe the main conclusion that Rab3A is required for TTX-induced homeostatic synaptic plasticity is wellsupported by the data presented, and this is an important addition to the repertoire of molecular players involved in homeostatic compensations. I also acknowledge that the authors are more cautious in making conclusions based on the current evidence, and the structure and logic have been much improved.

The only major concern I have still falls on the interpretation of the mismatch between GluA2 cluster size and mEPSC amplitude. The authors argue that they are only trying to say that changes in the cluster size are more variable than those in the mEPSC amplitude, and they provide multiple explanations for this mismatch. It seems incongruous to state that the simplest explanation is a presynaptic factor when you have all these alternative factors that very likely have contributed to the results. Further, the authors speculate in the discussion that Rab3A does not regulate postsynaptic GluA2 but instead regulates a presynaptic contributor. Do the authors mean that, in their model, the mEPSC amplitude increases can be attributed to two factors- postsynaptic GluA2 regulation and a presynaptic contribution (which is regulated by Rab3A)? If so, and Rab3A does not affect GluA2 whatsoever, shouldn't we see GluA2 increase even in the absence of Rab3A? The data in Table 1 seems to indicate otherwise.

The main body of this comment is addressed in the General Response to Reviewers. In addition, we deleted text “current data, coupled with our previous findings at the mouse neuromuscular junction, support the idea that there are additional sources contributing to the homeostatic increase in quantal size.” We added new text, so the sentence now reads: “Increased receptors likely contribute to increases in mESPC amplitudes in mouse cortical cultures, but because we do not have a significant increase in GluA2 receptors in our experiments, it is impossible to conclude that the increase is lacking in cultures from Rab3A^-/- neurons.”

I also question the way the data are presented in Figure 5. The authors first compare 3 cultures and then 5 cultures altogether, if these experiments are all aimed to answer the same research question, then they should be pooled together. Interestingly, the additional two cultures both show increases in GluA2 clusters, which makes the decrease in culture #3 even more perplexing, for which the authors comment in line 261 that this is due to other factors. Shouldn't this be an indicator that something unusual has happened in this culture?

Data in this figure is sufficient to support that GluA2 increases are variable across cultures, which hardly adds anything new to the paper or to the field. 

A major goal of performing the immunofluorescence measurements in the same cultures for which we had electrophysiological results was to address the common impression that the homeostatic effect itself is highly variable, as the reviewer notes in the comment “…GluA2 increases are variable across cultures…” Presumably, if GluA2 increases are the mechanism of the mEPSC amplitude increases, then variable GluA2 increases should correlate with variable mEPSC amplitude increases, but that is not what we observed. We are left with the explanation that the immunofluorescence method itself is very variable. We have added the point to the Discussion, which reads, “the variability could arise from technical factors associated with the immunofluorescence method, and the mechanism of Rab3A-dependent homeostatic plasticity could be presynaptic for the NMJ and postsynaptic for cortical neurons.”

Finally, the implication of “Shouldn’t this be an indicator that something unusual has happened in this culture?” if it is not due to culture to culture variability in the homeostatic response itself, is that there was a technical problem with accurately measuring receptor levels. We have no reason to suspect anything was amiss in this set of coverslips (the values for controls and for TTX-treated were not outside the range of values in other experiments). In any of the coverslips, there may be variability in the amount of primary anti-GluA2 antibody, as this was added directly to the culture rather than prepared as a diluted solution and added to all the coverslips. But to remove this one experiment because it did not give the expected result is to allow bias to direct our data selection.

The authors further cite a study with comparable sample sizes, which shows a similar mismatch based on p values (Xu and Pozzo-Miller 2007), yet the effect sizes in this study actually match quite well (both ~160%). P values cannot be used to show whether two effects match, but effect sizes can. Therefore, the statement in lines 411-413 "... consistently leads to an increase in mEPSC amplitudes, and sometimes leads to an increase in synaptic GluA2 receptor cluster size" is not very convincing, and can hardly be used to support "the idea that there are additional sources contributing to the homeostatic increase in quantal size.”

We have the same situation; our effect sizes match (19.7% increase for mEPSC amplitude; 18.1% increase for GluA2 receptor cluster size, see Table 1), but in our case, the p value for receptors does not reach statistical significance. Our point here is that there is published evidence that the variability in receptor measurements is greater than the variability in electrophysiological measurements. But we have softened this point, removing the sentences containing “…consistently leads and sometimes...” and “……additional sources contributing…”.

I would suggest simply showing mEPSC and immunostaining data from all cultures in this experiment as additional evidence for homeostatic synaptic plasticity in WT cultures, and leave out the argument for "mismatch". The presynaptic location of Rab3A is sufficient to speculate a presynaptic regulation of this form of homeostatic compensation.

We have removed all uses of the word “mismatch,” but feel the presentation of the 3 matched experiments, 23-24 cells (Figure 5A, D), and the additional 2 experiments for a total of 5 cultures, 48-49 cells (Figure 5C, F), is important in order to demonstrate that the lack of statistically significant receptor response is due neither to a variable homeostatic response in the mEPSC amplitudes, nor to a small number of cultures.

Minor concerns:

(1) Line 214, I see the authors cite literature to argue that GluA2 can form homomers and can conduct currents. While GluA2 subunits edited at the Q/R site (they are in nature) can form homomers with very low efficiency in exogenous systems such as HEK293 cells (as done in the cited studies), it's unlikely for this to happen in neurons (they can hardly traffic to synapses if possible at all).

We were unable to identify a key reference that characterized GluA2 homomers vs. heteromers in native cortical neurons, but we have rewritten the section in the manuscript to acknowledge the low conductance of homomers:

“…to assess whether GluA2 receptor expression, which will identify GluA2 homomers and GluA2 heteromers (the former unlikely to contribute to mEPSCs given their low conductance relative to heteromers (Swanson et al., 1997; Mansour et al., 2001)…”

(2) Lines 221-222, the authors may have misinterpreted the results in Turrigiano 1998. This study does not show that the increase in receptors is most dramatic in the apical dendrite, in fact, this is the only region they have tested. The results in Figures 3b-c show that the effect size is independent of the distance from soma.

Figure 3 in Turrigiano et al., shows that the increase in glutamate responsiveness is higher at the cell body than along the primary dendrite. We have revised our description to indicate that an increase in responsiveness on the primary dendrite has been demonstrated in Turrigiano et al. 1998.

“We focused on the primary dendrite of pyramidal neurons as a way to reduce variability that might arise from being at widely ranging distances from the cell body, or, from inadvertently sampling dendritic regions arising from inhibitory neurons. In addition, it has been shown that there is a clear increase in response to glutamate in this region (Turrigiano et al., 1998).”

“…synaptic receptors on the primary dendrite, where a clear increase in sensitivity to exogenously applied glutamate was demonstrated (see Figure 3 in (Turrigiano et al., 1998)).

(3) Lines 309-310 (and other places mentioning TNFa), the addition of TNFa to this experiment seems out of place. The authors have not performed any experiment to validate the presence/absence of TNFa in their system (citing only 1 study from another lab is insufficient). Although it's convincing that glia Rab3A is not required for homeostatic plasticity here, the data does not suggest Rab3A's role (or the lack of) for TNFa in this process.

We have modified the paragraph in the Discussion that addresses the glial results, to describe more clearly the data that supported an astrocytic TNF-alpha mechanism: “TNF-alpha accumulates after activity blockade, and directly applied to neuronal cultures, can cause an increase in GluA1 receptors, providing a potential mechanism by which activity blockade leads to the homeostatic upregulation of postsynaptic receptors (Beattie et al., 2002; Stellwagen et al., 2005; Stellwagen and Malenka, 2006).”

We have also acknowledged that we cannot rule out TNF-alpha coming from neurons in the cortical cultures: “…suggesting the possibility that neuronal Rab3A can act via a non-TNF-alpha mechanism to contribute to homeostatic regulation of quantal amplitude, although we have not ruled out a neuronal Rab3A-mediated TNF-alpha pathway in cortical cultures.”

Reviewer #3 (Public review):

This manuscript presents a number of interesting findings that have the potential to increase our understanding of the mechanism underlying homeostatic synaptic plasticity (HSP). The data broadly support that Rab3A plays a role in HSP, although the site and mechanism of action remain uncertain.

The authors clearly demonstrate that Rab3A plays a role in HSP at excitatory synapses, with substantially less plasticity occurring in the Rab3A KO neurons. There is also no apparent HSP in the Earlybird Rab3A mutation, although baseline synaptic strength is already elevated. In this context, it is unclear if the plasticity is absent, already induced by this mutation, or just occluded by a ceiling effect due to the synapses already being strengthened. Occlusion may also occur in the mixed cultures when Rab3A is missing from neurons but not astrocytes. The authors do appropriately discuss these options. The authors have solid data showing that Rab3A is unlikely to be active in astrocytes, Finally, they attempt to study the linkage between changes in synaptic strength and AMPA receptor trafficking during HSP, and conclude that trafficking may not be solely responsible for the changes in synaptic strength during HSP.

Strengths:

This work adds another player into the mechanisms underlying an important form of synaptic plasticity. The plasticity is likely only reduced, suggesting Rab3A is only partially required and perhaps multiple mechanisms contribute. The authors speculate about some possible novel mechanisms, including whether Rab3A is active pre-synaptically to regulate quantal amplitude.

As Rab3A is primarily known as a pre-synaptic molecule, this possibility is intriguing. However, it is based on the partial dissociation of AMPAR trafficking and synaptic response and lacks strong support. On average, they saw a similar magnitude of change in mEPSC amplitude and GluA2 cluster area and integral, but the GluA2 data was not significant due to higher variability. It is difficult to determine if this is due to biology or methodology - the imaging method involves assessing puncta pairs (GluA2/VGlut1) clearly associated with a MAP2 labeled dendrite. This is a small subset of synapses, with usually less than 20 synapses per neuron analyzed, which would be expected to be more variable than mEPSC recordings averaged across several hundred events. However, when they reduce the mEPSC number of events to similar numbers as the imaging, the mESPC amplitudes are still less variable than the imaging data. The reason for this remains unclear. The pool of sampled synapses is still different between the methods and recent data has shown that synapses have variable responses during HSP. Further, there could be variability in the subunit composition of newly inserted AMPARs, and only assessing GluA2 could mask this (see below). It is intriguing that pre-synaptic changes might contribute to HSP, especially given the likely localization of Rab3A. But it remains difficult to distinguish if the apparent difference in imaging and electrophysiology is a methodological issue rather than a biological one. Stronger data, especially positive data on changes in release, will be necessary to conclude that pre-synaptic factors are required for HSP, beyond the established changes in post-synaptic receptor trafficking.

Regarding the concern that the lack of increase in receptors is due to a technical issue, please see General Response to Reviewers, above. We have also softened our conclusions throughout, acknowledging we cannot rule out a technical issue.

Other questions arise from the NASPM experiments, used to justify looking at GluA2 (and not GluA1) in the immunostaining. First, there is a strong frequency effect that is unclear in origin. One would expect NASPM to merely block some fraction of the post-synaptic current, and not affect pre-synaptic release or block whole synapses. But the change in frequency seems to argue (as the authors do) that some synapses only have CP-AMPARs, while the rest of the synapses have few or none. Another possibility is that there are pre-synaptic NASPM-sensitive receptors that influence release probability. Further, the amplitude data show a strong trend towards smaller amplitude following NASPM treatment (Fig 3B). The p value for both control and TTX neurons was 0.08 - it is very difficult to argue that there is no effect. The decrease on average is larger in the TTX neurons, and some cells show a strong effect. It is possible there is some heterogeneity between neurons on whether GluA1/A2 heteromers or GluA1 homomers are added during HSP. This would impact the conclusions about the GluA2 imaging as compared to the mEPSC amplitude data.

The key finding in Figure 3 is that NASPM did not eliminate the statistically significant increase in mEPSC amplitude after TTX treatment (Fig 3A).  Whether or not NASPM sensitive receptors contribute to mESPC amplitude is a separate question (Fig 3B). We are open to the possibility that NASPM reduces mEPSC amplitude in both control and TTX treated cells (p = 0.08 for both), but that does not change our conclusion that NASPM has no effect on the TTX-induced increase in mEPSC amplitude. The mechanism underlying the decrease in mEPSC frequency following NASPM is interesting, but does not alter our conclusions regarding the role of Rab3A in homeostatic synaptic plasticity of mEPSC amplitude. In addition, the Reviewer does not acknowledge the Supplemental Figure #1, which shows a similar lack of correspondence between homeostatic increases in mEPSC amplitude and GluA1 receptors in two cultures where matched data were obtained. Therefore, we do not think our lack of a robust increase in receptors can be explained by our failing to look at the relevant receptor.

To understand the role of Rab3A in HSP will require addressing two main issues:

(1) Is Rab3A acting pre-synaptically, post-synaptically or both? The authors provide good evidence that Rab3A is acting within neurons and not astrocytes. But where it is acting (pre or post) would aid substantially in understanding its role. The general view in the field has been that HSP is regulated post-synaptically via regulation of AMPAR trafficking, and considerable evidence supports this view. More concrete support for the authors' suggestion of a pre-synaptic site of control would be helpful.

We agree that definitive evidence for a presynaptic role of Rab3A in homeostatic plasticity of mEPSC amplitudes in mouse cortical cultures requires demonstrating that loss of Rab3A in postsynaptic neurons does not disrupt the plasticity, whereas loss in presynaptic neurons does. Without these data, we can only speculate that the Rab3A-dependence of homeostatic plasticity of quantal size in cortical neurons may be similar to that of the neuromuscular junction, where it cannot be receptors. We have added to the Discussion that the mechanism of Rab3A regulation of homeostatic plasticity of quantal amplitude could different between cortical neurons and the neuromuscular junction (lines 448-450 in markup,). Establishing a way to co-culture Rab3A-/- and Rab3A+/+ neurons in ratios that would allow us to record from a Rab3A-/- neuron that has mainly Rab3A+/+ inputs (or vice versa) is not impossible, but requires either transfection or transgenic expression with markers that identify the relevant genotype, and will be the subject of future experiments.

(2): Rab3A is also found at inhibitory synapses. It would be very informative to know if HSP at inhibitory synapses is similarly affected. This is particularly relevant as at inhibitory synapses, one expects a removal of GABARs or a decrease in GABA release (ie the opposite of whatever is happening at excitatory synapses). If both processes are regulated by Rab3A, this might suggest a role for this protein more upstream in the signaling; an effect only at excitatory synapses would argue for a more specific role just at those synapses.

We agree with the Reviewer, that it is important to determine the generality of Rab3A function in homeostatic plasticity. Establishing the homeostatic effect on mIPSCs and then examining them in Rab3A-/- cultures is a large undertaking and will be the subject of future experiments.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Minor (remaining) points:

(1) The figure referenced in the first response to the reviewers (Figure 5G) does not exist.

We meant Figure 5F, which has been corrected in the current response.

(2) I recommend showing the data without binning (despite some overlap).

The box plot in Origin will not allow not binning, but we can make the bin size so small that for all intents and purposes, there is close to 1 sample in each bin. When we do this, the majority of data are overlapped in a straight vertical line. Previously described concerns were regarding the gaps in the data, but it should be noted that these are cell means and we are not depicting the distributions of mEPSC amplitudes within a recording or across multiple recordings.

(3) Please auto-scale all axes from 0 (e.g., Fig 1E, F).

We have rescaled all mEPSC amplitude axes in box plots to go from 0 (Figures 1, 2 and 6).

(4) Typo in Figure legend 3: "NASPM (20 um)" => uM

Fixed.

Reviewer #2 (Recommendations for the authors):

(1) Line 140, frequencies are reported in Hz while other places are in sec-1, while these are essentially the same, they should be kept consistent in writing.

All mEPSC frequencies have been changed to sec^-1, except we have left “Hz” for repetitive stimulation and filtering.

(2) Paragraph starting from line 163 (as well as other places where multiple groups are compared, such as the occlusion discussion), the authors assessed whether there was a change in baseline between WT and mutant group by doing pairwise tests, this is not the right test. A two-way ANOVA, or at least a multivariant test would be more appropriate.

We have performed a two-way ANOVA, with genotype as one factor, and treatment as the other factor. The p values in Figures 1 and 2 have been revised to reflect p values from the post-hoc Tukey test on the specific interactions (for each particular genotype, TTX vs CON effects). The difference in the two WT strains, untreated, was not significant in the Post-Hoc Tukey test, and we have revised the text. The difference between the untreated WT from the Rab3A+/Ebd colony and the untreated Rab3AEbd/Ebd mutant was still significant in the Post-Hoc Tukey test, and this has replaced the Kruskal-Wallis test. The two-way ANOVA was also applied to the neuron-glia experiments and p values in Figure 6 adjusted accordingly.

(3) Relevant to the second point under minor concerns, I suggest this sentence be removed, as reducing variability and avoiding inhibitory projects are reasons good enough to restrict the analysis to the apical dendrites.

We have revised the description of the Turrigiano et al., 1998 finding from their Figure 3 and feel it still strengthens the justification for choosing to analyze only synapses on the apical dendrite.

Reviewer #3 (Recommendations for the authors):

Minor points:

The comments on lines 256-7 could seem misleading - the NASPM results wouldn't rule out contribution of those other subunits, only non-GluA2 containing combinations of those subunits. I would suggest revising this statement. Also, NASPM does likely have an effect, just not one that changes much with TTX treatment.

At new line 213 (markup) we have added the modifier “homomeric” to clarify our point that the lack of NASPM effect on the increase in mEPSC amplitude after TTX indicates that the increase is not due to more homomeric Ca²⁺-permeable receptors. We have always stated that NASPM reduces mEPSC amplitude, but it is in both control and treated cultures.

Strong conclusions based on a single culture (lines 314-5) seem unwarranted.

We have softened this statement with a “suggesting that” substituted for the previous “Therefore,” but stand by our point that the mEPSC amplitude data support a homeostatic effect of TTX in Culture #3, so the lack of increase in GluA2 cluster size needs an explanation other than variability in the homeostatic effect itself.

Saying (line 554) something is 'the only remaining possibility' also seems unwarranted.

We have softened this statement to read, “A remaining possibility…”.

Beattie EC, Stellwagen D, Morishita W, Bresnahan JC, Ha BK, Von Zastrow M, Beattie MS, Malenka RC (2002) Control of synaptic strength by glial TNFalpha. Science 295:2282-2285.

Mansour M, Nagarajan N, Nehring RB, Clements JD, Rosenmund C (2001) Heteromeric AMPA receptors assemble with a preferred subunit stoichiometry and spatial arrangement. Neuron 32:841-853. Stellwagen D, Malenka RC (2006) Synaptic scaling mediated by glial TNF-alpha. Nature 440:1054-1059.

Stellwagen D, Beattie EC, Seo JY, Malenka RC (2005) Differential regulation of AMPA receptor and GABA receptor trafficking by tumor necrosis factor-alpha. J Neurosci 25:3219-3228.

Swanson GT, Kamboj SK, Cull-Candy SG (1997) Single-channel properties of recombinant AMPA receptors depend on RNA editing, splice variation, and subunit composition. J Neurosci 17:5869.

Turrigiano GG, Leslie KR, Desai NS, Rutherford LC, Nelson SB (1998) Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391:892-896.

Wang X, Wang Q, Yang S, Bucan M, Rich MM, Engisch KL (2011) Impaired activity-dependent plasticity of quantal amplitude at the neuromuscular junction of Rab3A deletion and Rab3A earlybird mutant mice. J Neurosci 31:3580-3588.

Wang X, Li Y, Engisch KL, Nakanishi ST, Dodson SE, Miller GW, Cope TC, Pinter MJ, Rich MM (2005) Activity-dependent presynaptic regulation of quantal size at the mammalian neuromuscular junction in vivo. J Neurosci 25:343-351.

I love how the creative process is accessible to all learners no matter what. This is great for students who struggle to learn. Being creative is one of the best ways to learn and this highlighted text says just that!

I think public shaming is bad because it's done in front of strangers which leads to one person's low self esteem. Especially, if if the person didn’t do something that bad, but people act like it’s huge that’s not fair. Majority of the time people are just being mean or trying to feel better than someone which is just embarrassing.

The most important factors that I think makes public shaming bad is the fact that it's public. There's a saying "correct in private" that I think is really important. Not many people learn when they are just humiliated in public.

The idea of shame being a tool the weak can use against the strong is rather interesting- I can see examples of this in how minorities make a point to speak out against the parts of history that are hidden away for the sake of making history more sanitized for the children of the majority. It's just a shame that the strength of the majority, hubris, pride, prejudice, and quite a lot of other qualities is rather good at drowning out their shame

What I found particularly compelling was the idea that shame can be “stored” in the body, and unless it’s addressed through safe relational repair or therapeutic processing, it can remain stuck and continue to influence behavior in harmful ways. This resonates with some of the course themes about the role of community in healing. It makes me think we need more conversations not just about individual recovery, but about how groups and institutions can either perpetuate shame or help alleviate it.

Add before this section as a reminder. Could be in it's own separate box. Add a graphic, too.

Active Listening Tips Pay Attention: Minimize distractions, make eye contact, and use open body language to show you are engaged. Show You're Listening: Use verbal cues ("yes," "I see") and nonverbal cues (nodding) to encourage the speaker. Provide Feedback: Paraphrase the speaker's points to confirm understanding and ask clarifying questions. Defer Judgment: Avoid interrupting or forming rebuttals while the speaker is talking. Respond Appropriately: Validate the speaker's emotions and respond thoughtfully, not just with advice.

Examples of Active Listening Responses

“I see, go on.”

“Can you explain that part again?”

“So, you’re saying…”

“That must have been difficult for you.”

“What happened next?”

“I understand how you feel.”

“It sounds like you’re upset.”

“Can you give me an example?”

“I appreciate your point of view.”

“Let me make sure I got that right.”

Some helpful strategies would fit well here: Working through conflict with families can be challenging, but it's essential for fostering a positive and productive partnership that ultimately benefits the child. Here are some comprehensive tips:

Tips for Working Through a Conflict with Families 1.Prioritize Relationship Building (Proactive Strategy): Start Positive: Don't let the first communication with a family be negative. Reach out early in the year with positive news about their child. Share successes, no matter how small. This builds a bank of goodwill that you can draw on during difficult conversations. Be Accessible and Approachable: Make it easy for families to connect with you. Offer various communication channels (email, phone, in-person, class app) and be responsive. A warm and welcoming demeanor can significantly de-escalate potential conflict before it even begins. Be Culturally Responsive: Understand and respect diverse family values, communication styles, and cultural norms. What might seem like a conflict could be a misunderstanding rooted in different cultural expectations.

1. Prepare for the Conversation: Gather Facts and Documentation: Base your concerns on objective observations and specific examples, not emotions or assumptions. Document dates, times, and details of relevant incidents or academic patterns. "Shira hit another child after they took the toy she was playing with" is more helpful than "Shira was aggressive today." Identify the Core Issue: What is the specific problem you need to address? Be clear in your own mind. Anticipate Family Concerns: Put yourself in their shoes. What questions might they have? What might make them defensive? Think About Solutions: Don't just present a problem; come with potential solutions or ideas for how to move forward. This shifts the conversation from blame to problem-solving. Choose the Right Time and Place: Schedule a dedicated time for the conversation when you can minimize interruptions and all parties can be fully present. Avoid impromptu discussions at drop-off or pick-up times. Ensure privacy. Consider a Third Party: If emotions are high or the situation is particularly complex, consider having another school administrator or colleague present as a neutral witness or mediator.

3.During the Conversation: Stay Calm and Professional: Your demeanor can significantly influence the tone of the conversation. Speak in a calm, even tone, and maintain open body language. Avoid fidgeting or crossing your arms. If you feel yourself getting emotional, take a deep breath or ask for a brief pause. Listen Actively and Empathetically: This is perhaps the most crucial step. Give the family your full attention. Let them speak without interruption, even if they're emotional or angry. Listen for the underlying concerns or feelings. Use phrases like, "It sounds like you're feeling frustrated about..." or "I hear that you're concerned about..." Validating their feelings doesn't mean you agree with their perspective, but it shows you're listening and respect their emotions. Focus on the Child's Best Interest (Common Goal): Frame the discussion around the child's well-being and success. Remind the family that you both share a common goal: supporting their child. Use "we" language: "How can we work together to help [child's name] with this?" Stick to Facts, Not Personalities or Assumptions: Present your observations objectively. Avoid assigning blame or making assumptions about the family's intentions or home life. Use "I" Statements: Instead of "Your child is always disruptive," try "I've observed [child's name] frequently talking during instruction time, which is making it difficult for them to complete their work." This focuses on your experience and observations. Avoid Jargon: Speak in clear, accessible language, avoiding educational acronyms or terminology that families might not understand. Be Prepared to Apologize (When Appropriate): If you or the school made a mistake, acknowledge it and apologize sincerely. This can significantly de-escalate tension and rebuild trust. An apology for how a situation made them feel can also be powerful, even if you don't agree with their interpretation of events. Set Clear Boundaries (If Necessary): If a family member becomes verbally abusive or disrespectful, calmly but firmly state your boundaries. "I understand you're upset, but I need you to speak to me respectfully for us to continue this conversation." You can offer to reschedule if they cannot maintain a respectful tone. "Sandwich" Difficult Information: Start with a positive comment about the child, introduce the concern, and end with another positive comment or a collaborative plan for support.

1. Develop a Plan and Follow Up: Collaborate on Solutions: Work with the family to develop an action plan. What steps will you take? What steps can they take at home? This fosters a sense of partnership and shared responsibility. Set Measurable Goals: Agree on specific, achievable goals and a timeline for checking progress. Schedule a Follow-Up: This demonstrates commitment and allows both parties to assess progress and make adjustments. Document the Conversation: Keep a professional record of the date, attendees, topics discussed, agreed-upon actions, and follow-up plan. This is crucial for future reference and accountability.

By approaching conflicts with empathy, clear communication, and a focus on collaboration, you can navigate challenging family conversations more effectively and strengthen the home-school partnership.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This work studies representations in a network with one recurrent layer and one output layer that needs to path-integrate so that its position can be accurately decoded from its output. To formalise this problem, the authors define a cost function consisting of the decoding error and a regularisation term. They specify a decoding procedure that at a given time averages the output unit center locations, weighted by the activity of the unit at that time. The network is initialised without position information, and only receives a velocity signal (and a context signal to index the environment) at each timestep, so to achieve low decoding error it needs to infer its position and keep it updated with respect to its velocity by path integration.

The authors take the trained network and let it explore a series of environments with different geometries while collecting unit activities to probe learned representations. They find localised responses in the output units (resembling place fields) and border responses in the recurrent units. Across environments, the output units show global remapping and the recurrent units show rate remapping. Stretching the environment generally produces stretched responses in output and recurrent units. Ratemaps remain stable within environments and stabilise after noise injection. Low-dimensional projections of the recurrent population activity forms environment-specific clusters that reflect the environment's geometry, which suggests independent rather than generalised representations. Finally, the authors discover that the centers of the output unit ratemaps cluster together on a triangular lattice (like the receptive fields of a single grid cell), and find significant clustering of place cell centers in empirical data as well.

The model setup and simulations are clearly described, and are an interesting exploration of the consequences of a particular set of training requirements - here: path integration and decodability. But it is not obvious to what extent the modelling choices are a realistic reflection of how the brain solves navigation. Therefore it is not clear whether the results generalize beyond the specifics of the setup here.

Strengths:

The authors introduce a very minimal set of model requirements, assumptions, and constraints. In that sense, the model can function as a useful 'baseline', that shows how spatial representations and remapping properties can emerge from the requirement of path integration and decodability alone. Moreover, the authors use the same formalism to relate their setup to existing spatial navigation models, which is informative.

The global remapping that the authors show is convincing and well-supported by their analyses. The geometric manipulations and the resulting stretching of place responses, without additional training, are interesting. They seem to suggest that the recurrent network may scale the velocity input by the environment dimensions so that the exact same path integrator-output mappings remain valid (but maybe there are other mechanisms too that achieve the same).

The clustering of place cell peaks on a triangular lattice is intriguing, given there is no grid cell input. It could have something to do with the fact that a triangular lattice provides optimal coverage of 2d space? The included comparison with empirical data is valuable, although the authors only show significant clustering - there is no analysis of its grid-like regularity.

First of all, we would like to thank the reviewer for their comprehensive feedback, and their insightful comments. Importantly, as you point out, our goal with this model was to build a minimal model of place cell representations, where representations were encouraged to be place-like, but free to vary in tuning and firing locations. By doing so, we could explore what upstream representations facilitate place-like representations, and even remapping (as it turned out) with minimal assumptions. However, we agree that our task does not capture some of the nuances of real-world navigation, such as sensory observations, which could be useful extensions in future work. Then again, the simplicity of our setup makes it easier to interpret the model, and makes it all the more surprising that it learns many behaviors exhibited by real world place cells.

As to the distribution of phases - we also agree that a hexagonal arrangement likely reflects some optimal configuration for decoding of location.

And we agree that the symmetry within the experimental data is important; we have revised analyses on experimental phase distributions, and included an analysis of ensemble grid score, to quantify any hexagonal symmetries within the data.

Weaknesses:

The navigation problem that needs to be solved by the model is a bit of an odd one. Without any initial position information, the network needs to figure out where it is, and then path-integrate with respect to a velocity signal. As the authors remark in Methods 4.2, without additional input, the only way to infer location is from border interactions. It is like navigating in absolute darkness. Therefore, it seems likely that the salient wall representations found in the recurrent units are just a consequence of the specific navigation task here; it is unclear if the same would apply in natural navigation. In natural navigation, there are many more sensory cues that help inferring location, most importantly vision, but also smell and whiskers/touch (which provides a more direct wall interaction; here, wall interactions are indirect by constraining velocity vectors). There is a similar but weaker concern about whether the (place cell like) localised firing fields of the output units are a direct consequence of the decoding procedure that only considers activity center locations.

Thank you for raising this point; we absolutely agree that the navigation task is somewhat niche. However, this was a conscious decision, to minimize any possible confounding from alternate input sources, such as observations. In part, this experimental design was inspired by the suggestion that grid cells support navigation/path integration in open-field environments with minimal sensory input (as they could, conceivably do so with no external input). This also pertains to your other point, that boundary interactions are necessary for navigation. In our model, using boundaries is one solution, but there is another way around this problem, which is conceivably better: to path integrate in an egocentric frame, starting from your initial position. Since the locations of place fields are inferred only after a trajectory has been traversed, the network is free to create a new or shifted representation every time, independently of the arena. In this case, one might have expected generalized solutions, such as grid cells to emerge. That this is not the case, seems to suggest that grid cells may somehow not be optimal for pure path integration, or at the very least, hard to learn (but may still play a part, as alluded to by place field locations). We have tried to make these points more evident in the revised manuscript.

As for the point that the decoding may lead to place-like representations, this is a fair point. Indeed, we did choose this form of decoding, inspired by the localized firing of place cells, in the hope that it would encourage minimally constrained, place-like solutions. However, compared to other works (Sorscher and Xu) hand tuning the functional form of their place cells, our (although biased towards centralized tuning curves) allows for flexible functional forms such as the position of the place cell centers, their tuning width, whether or not it is center-surround activity, and how they should tune to different environments/rooms. This allows us to study several features of the place cell system, such as remapping and field formation. We have revised to make this more clear in the model description.

The conclusion that 'contexts are attractive' (heading of section 2) is not well-supported. The authors show 'attractor-like behaviour' within a single context, but there could be alternative explanations for the recovery of stable ratemaps after noise injection. For example, the noise injection could scramble the network's currently inferred position, so that it would need to re-infer its position from boundary interactions along the trajectory. In that case the stabilisation would be driven by the input, not just internal attractor dynamics. Moreover, the authors show that different contexts occupy different regions in the space of low-dimensional projections of recurrent activity, but not that these regions are attractive.

We agree that boundary interactions could facilitate the convergence of representations after noise injection. We did try to moderate this claim by the wording “attractor-like”, but we agree that boundaries could confound this result. We have therefore performed a modified noise injection experiment, where we let the network run for an extended period of time, before noise injection (and no velocity signal), see Appendix Velocity Ablation in the revised text. Notably, representations converge to their pre-scrambled state after noise injection, even without a velocity signal. However, place-like representations do not converge for all noise levels in this case, possibly indicating that boundary interactions do serve an error-correcting function, also. Thank you for pointing this out.

As for the attractiveness of contexts, we agree that more analyses were required to demonstrate this. We have therefore conducted a supplementary analysis where we run the trained network with a mismatch in context/geometry, and demonstrate that the context signal fixes the representation, up to geometric distortions.

The authors report empirical data that shows clustering of place cell centers like they find for their output units. They report that 'there appears to be a tendency for the clusters to arrange in hexagonal fashion, similar to our computational findings'. They only quantify the clustering, but not the arrangement. Moreover, in Figure 7e they only plot data from a single animal, then plot all other animals in the supplementary. Does the analysis of Fig 7f include all animals, or just the one for which the data is plotted in 7e? If so, why that animal? As Appendix C mentions that the ratemap for the plotted animal 'has a hexagonal resemblance' whereas other have 'no clear pattern in their center arrangements', it feels like cherrypicking to only analyse one animal without further justification.

Thank you for pointing this out; we agree that this is not sufficiently explained and explored in the current version. We have therefore conducted a grid score analysis of the experimental place center distributions, to uncover possible hexagonal symmetries. The reason for choosing this particular animal was in part because it featured the largest number of included cells, while also demonstrating the most striking phase distribution, while including all distributions in the supplementary. Originally, this was only intended as a preliminary analysis, suggesting non-uniformity in experimental place field distributions, but we realize that these may all provide interesting insight into the distributional properties of place cells.

We have explained these choices in the revised text, and expanded analyses on all animals to showcase these results more clearly.

Reviewer #2 (Public Review):

Summary:

The authors proposed a neural network model to explore the spatial representations of the hippocampal CA1 and entorhinal cortex (EC) and the remapping of these representations when multiple environments are learned. The model consists of a recurrent network and output units (a decoder) mimicking the EC and CA1, respectively. The major results of this study are: the EC network generates cells with their receptive fields tuned to a border of the arena; decoder develops neuron clusters arranged in a hexagonal lattice. Thus, the model accounts for entorhinal border cells and CA1 place cells. The authors also suggested the remapping of place cells occurs between different environments through state transitions corresponding to unstable dynamical modes in the recurrent network.

Strengths:

The authors found a spatial arrangement of receptive fields similar to their model's prediction in experimental data recorded from CA1. Thus, the model proposes a plausible mechanisms to generate hippocampal spatial representations without relying on grid cells. This result is consistent with the observation that grid cells are unnecessary to generate CA1 place cells.

The suggestion about the remapping mechanism shows an interesting theoretical possibility.

We thank the reviewer for their kind feedback.

Weaknesses:

The explicit mechanisms of generating border cells and place cells and those underlying remapping were not clarified at a satisfactory level.

The model cannot generate entorhinal grid cells. Therefore, how the proposed model is integrated into the entire picture of the hippocampal mechanism of meｍory processing remains elusive.

We appreciate this point, and hope to clarify: From a purely architectural perspective, place-like representations are generated by linear combinations of recurrent unit representations, which, after training, appear border-like. During remapping, the network is simply evaluated/run in different geometries/contexts, which, it turns out, causes the network to exhibit different representations, likely as solutions to optimally encoding position in the different environments. We have attempted to revise the text to make some of these interpretations more clear. We have also conducted a supplementary analysis to demonstrate how representations are determined by the context signal directly, which helps to explain how recurrent and output units form their representations.

We also agree that our model does not capture the full complexity of the Hippocampal formation. However, we would argue that its simplicity (focusing on a single cell type and a pure path integration task), acts as a useful baseline for studying the role of place cells during spatial navigation. The fact that our model captures a range of place cell behaviors (field formation, remapping and geometric deformation) without grid cells also point to several interesting possibilities, such that grid cells may not be strictly necessary for place cell formation and remapping, or that border cells may account for many of the peculiar behaviors of place cells. However, we wholeheartedly agree that including e.g. sensory information and memory storage/retrieval tasks would prove a very interesting extension of our model to more naturalistic tasks and settings. In fact, our framework could easily accommodate this, e.g. by decoding contexts/observations/memories from the network state, alongside location.

Reviewer #3 (Public Review):

Summary:

The authors used recurrent neural network modelling of spatial navigation tasks to investigate border and place cell behaviour during remapping phenomena.

Strengths:

The neural network training seemed for the most part (see comments later) well-performed, and the analyses used to make the points were thorough.

The paper and ideas were well explained.

Figure 4 contained some interesting and strong evidence for map-like generalisation as environmental geometry was warped.

Figure 7 was striking, and potentially very interesting.

It was impressive that the RNN path-integration error stayed low for so long (Fig A1), given that normally networks that only work with dead-reckoning have errors that compound. I would have loved to know how the network was doing this, given that borders did not provide sensory input to the network. I could not think of many other plausible explanations... It would be even more impressive if it was preserved when the network was slightly noisy.

Thank you for your insightful comments! Regarding the low path integration error, there is a slight statistical signal from the boundaries, as trajectories tend to turn away from arena boundaries. However, we agree, that studying path integration performance in the face of noise would make for a very interesting future development.

Weaknesses:

I felt that the stated neuroscience interpretations were not well supported by the presented evidence, for a few reasons I'll now detail.

First, I was unconvinced by the interpretation of the reported recurrent cells as border cells. An equally likely hypothesis seemed to be that they were positions cells that are linearly encoding the x and y position, which when your environment only contains external linear boundaries, look the same. As in figure 4, in environments with internal boundaries the cells do not encode them, they encode (x,y) position. Further, if I'm not misunderstanding, there is, throughout, a confusing case of broken symmetry. The cells appear to code not for any random linear direction, but for either the x or y axis (i.e. there are x cells and y cells). These look like border cells in environments in which the boundaries are external only, and align with the axes (like square and rectangular ones), but the same also appears to be true in the rotationally symmetric circular environment, which strikes me as very odd. I can't think of a good reason why the cells in circular environments should care about the particular choice of (x,y) axes... unless the choice of position encoding scheme is leaking influence throughout. A good test of these would be differently oriented (45 degree rotated square) or more geometrically complicated (two diamonds connected) environments in which the difference between a pure (x,y) code and a border code are more obvious.

Thank you for pointing this out. This is an excellent point, that we agree could be addressed more rigorously. Note that there is no position encoding in our model; the initial state of the network is a vector of zeros, and the network must infer its location from boundary interactions and context information alone. So there is no way for positional information to leak through to the recurrent layer directly. However, one possible reason for the observed symmetry breaking, is the fact that the velocity input signal is aligned with the cardinal directions. To investigate this, we trained a new model, wherein input velocities are rotated 45 degrees relative to the horizontal, as you suggest. The results, shown and discussed in appendix E (Learned recurrent representations align with environment boundaries), do indicate that representations are tuned to environment boundaries, and not the cardinal directions, which hopefully improves upon this point.

Next, the decoding mechanism used seems to have forced the representation to learn place cells (no other cell type is going to be usefully decodable?). That is, in itself, not a problem. It just changes the interpretation of the results. To be a normative interpretation for place cells you need to show some evidence that this decoding mechanism is relevant for the brain, since this seems to be where they are coming from in this model. Instead, this is a model with place cells built into it, which can then be used for studying things like remapping, which is a reasonable stance.

This is a great point, and we agree. We do write that we perform this encoding to encourage minimally constrained place-like representations (to study their properties), but we have revised to make this more evident.

However, the remapping results were also puzzling. The authors present convincing evidence that the recurrent units effectively form 6 different maps of the 6 different environments (e.g. the sparsity of the code, or fig 6a), with the place cells remapping between environments. Yet, as the authors point out, in neural data the finding is that some cells generalise their co-firing patterns across environments (e.g. grid cells, border cells), while place cells remap, making it unclear what correspondence to make between the authors network and the brain. There are existing normative models that capture both entorhinal's consistent and hippocampus' less consistent neural remapping behaviour (Whittington et al. and probably others), what have we then learnt from this exercise?

Thanks for raising this point! We agree that this finding is surprising, but we hold that it actually shows something quite important: that border-type units are sufficient to create place-like representations, and learns several of the behaviors associated with place cells and remapping (including global remapping and field stretching). In other words, a single cell type known to exist upstream of place cells is sufficient to explain a surprising range of phenomena, demonstrating that other cell types are not strictly necessary. However, we agree that understanding why the boundary type units sometimes rate remap, and whether that can be true for some border type cells in the brain (either directly, or through gating mechanisms) would be important future developments. Related to this point, we also expanded upon the influence of the context signal for representation selection (appendix F)

Concerning the relationship to other models, we would argue that the simplicity of our model is one of its core strengths, making it possible to disentangle what different cell types are doing. While other models, including TEM, are highly important for understanding how different cell types and brain regions interact to solve complex problems, we believe there is a need for minimal, understandable models that allows us to investigate what each cell type is doing, and this is where we believe our work is important. As an example, our model not only highlights the sufficiency of boundary-type cells as generators of place cells, its lack of e.g. grid cells also suggest that grid cells may not be strictly necessary for e.g. open-field/sensory-deprived navigation, as is often claimed.

One striking result was figure 7, the hexagonal arrangement of place cell centres. I had one question that I couldn't find the answer to in the paper, which would change my interpretation. Are place cell centres within a single clusters of points in figure 7a, for example, from one cell across the 100 trajectories, or from many? If each cluster belongs to a different place cell then the interpretation seems like some kind of optimal packing/coding of 2D space by a set of place cells, an interesting prediction. If multiple place cells fall within a single cluster then that's a very puzzling suggestion about the grouping of place cells into these discrete clusters. From figure 7c I guess that the former is the likely interpretation, from the fact that clusters appear to maintain the same colour, and are unlikely to be co-remapping place cells, but I would like to know for sure!

This is a good point, and you are correct: one cluster tends to correspond to one unit. To make this more clear, we have revised Fig. 7, so that each decoded center is shaded by unit identity, which makes this more evident. And yes, this is, seemingly in line with some form of optimal packing/encoding of space, yes!

I felt that the neural data analysis was unconvincing. Most notably, the statistical effect was found in only one of seven animals. Random noise is likely to pass statistical tests 1 in 20 times (at 0.05 p value), this seems like it could have been something similar? Further, the data was compared to a null model in which place cell fields were randomly distributed. The authors claim place cell fields have two properties that the random model doesn't (1) clustering to edges (as experimentally reported) and (2) much more provocatively, a hexagonal lattice arrangement. The test seems to collude the two; I think that nearby ball radii could be overrepresented, as in figure 7f, due to either effect. I would have liked to see a computation of the statistic for a null model in which place cells were random but with a bias towards to boundaries of the environment that matches the observed changing density, to distinguish these two hypotheses.

Thanks for raising this point. We agree that we were not clear enough in our original manuscript. We included additional analyses in one animal, to showcase one preliminary case of non-uniform phases. To mitigate this, we have performed the same analyses for all animals, and included a longer discussion of these results (included in the supplementary material). We have also moderated the discussion on Ripley’s H to encompass only non-uniformity, and added a grid score analysis to showcase possible rotational symmetries in the data. We hope this gets our findings across more clearly

Some smaller weaknesses:

- Had the models trained to convergence? From the loss plot it seemed like not, and when including regularisors recent work (grokking phenomena, e.g. Nanda et al. 2023) has shown the importance of letting the regularisor minimise completely to see the resulting effect. Else you are interpreting representations that are likely still being learnt, a dangerous business.

Longer training time did not seem to affect representations. However, due to the long trajectories and statefulness involved, training was time-intensive and could become unstable for very long training. We therefore stopped training at the indicated time.

- Since RNNs are nonlinear it seems that eigenvalues larger than 1 doesn't necessarily mean unstable?

This is a good point; stability is not guaranteed. We have updated the text to reflect this.

- Why do you not include a bias in the networks? ReLU networks without bias are not universal function approximators, so it is a real change in architecture that doesn't seem to have any positives?

We found that bias tended to have a detrimental effect on training, possibly related to the identity initialization used (see e.g. Le et al. 2015), and found that training improved when biases were fixed to zero.

- The claim that this work provided a mathematical formalism of the intuitive idea of a cognitive map seems strange, given that upwards of 10 of the works this paper cite also mathematically formalise a cognitive map into a similar integration loss for a neural network.

We agree that other works also provide ways of formalizing this concepts. However, our goal by doing so was to elucidate common features across these seemingly disparate models. We also found that the concept of a learned and target map made it easier to come up with novel models, such as one wherein place cells are constructed to match a grid cell label.

Aim Achieved? Impact/Utility/Context of Work

Given the listed weaknesses, I think this was a thorough exploration of how this network with these losses is able to path-integrate its position and remap. This is useful, it is good to know how another neural network with slightly different constraints learns to perform these behaviours. That said, I do not think the link to neuroscience was convincing, and as such, it has not achieved its stated aim of explaining these phenomena in biology. The mechanism for remapping in the entorhinal module seemed fundamentally different to the brain's, instead using completely disjoint maps; the recurrent cell types described seemed to match no described cell type (no bad thing in itself, but it does limit the permissible neuroscience claims) either in tuning or remapping properties, with a potentially worrying link between an arbitrary encoding choice and the responses; and the striking place cell prediction was unconvincingly matched by neural data. Further, this is a busy field in which many remapping results have been shown before by similar models, limiting the impact of this work. For example, George et al. and Whittington et al. show remapping of place cells across environments; Whittington et al. study remapping of entorhinal codes; and Rajkumar Vasudeva et al. 2022 show similar place cell stretching results under environmental shifts. As such, this papers contribution is muddied significantly.

Thank you for this perspective; we agree that all of these are important works that arrive at complementary findings. We hold that the importance of our paper lies in its minimal nature, and its focus on place cells, via a purpose-built decoding that enables place-like representations. In doing so, we can point to possibly under explored relationships between cell types, in particular place cells and border cells, while challenging the necessity of other cell types for open-field navigation (i.e. grid cells). In addition, our work points to a novel connection between grid cells, place cells and even border cells, by way of the hexagonal arrangement of place unit centers. However, we agree that expanding our model to include more biologically plausible architectures and constraints would make for a very interesting extension in the future.

Thank you again for your time, as well as insightful comments.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Even after reading Methods 5.3, I found it hard to understand how the ratemap population vectors that produce Fig 3e and Fig 5 are calculated. It's unclear to me how there can be a ratemap at a single timestep, because calculating a ratemap involves averaging the activity in each location, which would take a whole trajectory and not a single timestep. But I think I've understood from Methods 5.1 that instead the ratemap is calculated by running multiple 'simultaneous' trajectories, so that there are many visited locations at each timestep. That's a bit confusing because as far as I know it's not a common way to calculate ratemaps in rodent experiments (probably because it would be hard to repeat the same task 500 times, while the representations remain the same), so it might be worth explaining more in Methods 5.3.

We understand the confusion, and have attempted to make this more clear in the revised manuscript. We did indeed create ratemaps over many trajectories for time-dependent plots, for the reasons you mentioned. We also agree that this would be difficult to do experimentally, but found it an interesting way to observe convergence of representations in our simulated scenario.

Fig 3b-d shows multiple analyses to support output unit global remapping, but no analysis to support the claim that recurrent units remap by rate changes. The examples in Fig 3ai look pretty convincing, but it would be useful to also have a more quantitative result.

We agree, and only showed that units turn off/become silent using ratemaps. We have therefore added an explicit analysis, showcasing rate remapping in recurrent units (see appendix G; Recurrent units rate remap)

Reviewer #2 (Recommendations For The Authors):

Some parts of the current manuscript are hard to follow. Particularly, the model description is not transparent enough. See below for the details.

Major comments:

(1) Mathematical models should be explained more explicitly and carefully. I had to guess or desperately search for the definitions of parameters. For instance, define the loss function L in eq.(1). Though I can assume L represents the least square error (in A.8), I could not find the definition in Model & Objective. N should also be defined explicitly in equation (3). Is this the number of output cells?

Thank you for pointing this out, we have revised to make it more clear.

(2) In Fig. 1d, how were the velocity and context inputs given to individual neurons in the network? The information may be described in the Methods, but I could not identify it.

This was described in the methods section (Neural Network Architecture and Training), but we realize that we used confusing notation, when comparing with Fig. 1d. We have therefore changed the notation, and it should hopefully be clearer now. Thanks for pointing out this discrepancy.

(3) I took a while to understand equations (3) and (4) (for instance, t is not defined here). The manuscript would be easier to read if equations (5) and (6) are explained in the main text but not on page 18 (indeed, these equations are just copies of equations 3 and 4). Otherwise, the authors may replace equations (3) and (4) with verbal explanations similar to figure legend for Fig. 1b.

(4) Is there any experimental evidence for uniformly strong EC-to-CA1 projections assumed in the non-trainable decoder? This point should be briefly mentioned.

Thank you for raising this point. The decoding from EC (the RNN) to CA1 (the output layer) consists of a trainable weight matrix, and may thus be non-uniform in magnitude. The non-trainable decoding acts on the resulting “CA1” representation only. We hope that improvements to the model description also makes this more evident.  

(5) The explanation of Fig. 3 in the main text is difficult to follow because subpanels are explained in separate paragraphs, some of which are very short, as short as just a few lines.

This presentation style makes it difficult to follow the logical relationships between the subpanels. This writing style is obeyed throughout the manuscript but is not popular in neuroscience.

Thanks for pointing this out, we have revised to accommodate this.

(6) Why do field centers cluster near boundaries? No underlying mechanisms are discussed in the manuscript.

This is a good point; we have added a note on this; it likely reflects the border tuning of upstream units.

(7) In Fig. 4, the authors presented how cognitive maps may vary when the shape and size of open arenas are modified. The results would be more interesting if the authors explained the remapping mechanism. For instance, on page 8, the authors mentioned that output units exhibit global remapping between contexts, whereas recurrent units mainly rate remapping.

Why do such representational differences emerge?

We agree! Thanks for raising this point. We have therefore expanded upon this discussion in section 2.4.

(8) In the first paragraph of page 10, the authors stated ".. some output units display distinct field doubling (see both Fig. 4c), bottom right, and Fig. 4d), middle row)". I could not understand how Fig. 4d, middle row supports the argument. Similarly, they stated "..some output units reflect their main boundary input (with greater activity near one boundary)." I can neither understand what the authors mean to say nor which figures support the statement. Please clarify.

This is a good point, there was an identifier missing; we have updated to refer to the correct “magnification”. Thanks!

(9) The underlying mechanism of generating the hexagonal representation of output cells remains unclear. The decoder network uses a non-trainable decoding scheme based on localized firing patterns of output units. To what extent does the hexagonal representation depend on the particular decoding scheme? Similarly, how does the emergence of the hexagonal representation rely on the border representation in the upstream recurrent network? Showing several snapshots of the two place representations during learning may answer these questions.

This is an interesting point, and we have added some discussion on this matter. In particular, we speculate whether it’s an optimal configuration for position reconstruction, which is demanded by the task and thus highly likely dependent on the decoding scheme. We have not reached a conclusive method to determine the explicit dependence of the hexagonal arrangement on the choice of decoding scheme. Still, it seems this would require comparison with other schemes. In our framework, this would require changing the fundamental operation of the model, which we leave as inspiration for future work. We have also added additional discussion concerning the relationship between place units, border units, and remapping in our model. As for exploring different training snapshots, the model is randomly initialized, which suggests that earlier training steps should tend to reveal unorganized/uninformative phase arrangements, as phases are learned as a way of optimizing position reconstruction. However, we do call for more analysis of experimental data to determine whether this is true in animals, which would strongly support this observation. We also hope that our work inspires other models studying the formation and remapping of place cells, which could serve as a starting point for answering this question in the future.

(10) Figure 7 requires a title including the word "hexagonal" to make it easier to find the results demonstrating the hexagonal representations. In addition, please clarify which networks, p or g, gave the results shown here.

We agree, and have added it!

Minor comments:

(11) In many paragraphs, conclusions appear near their ends. Stating the conclusion at the beginning of each paragraph whenever possible will improve the readability.

We have made several rewrites to the manuscript, and hope this improves readability.

(12) Figure A4 is important as it shows evidence of the CA1 spatial representation predicted by the model. However, I could not find where the figure is cited in the manuscript. The authors can consider showing this figure in the main text.

We agree, and we have added more references to the experimental data analyses in the main text, as well as expanded this analysis.

(13) The main text cites figures in the following format: "... rate mapping of Fig. 3a), i), boundary ...." The parentheses make reading difficult.

We have removed the overly stringent use of double parentheses, thanks for letting us know.

(14) It would be nice if the authors briefly explained the concept of Ripley's H function on page 14.

Yes, we have added a brief descriptor.

This explanation really helped me see why some stories are boring and others are gripping. Without tension, it’s just a list of facts, not something that makes you want to keep reading. I want to remember to always add some kind of conflict or challenge when I tell stories, even in professional settings

I think this is a good line about how stories are more than just different statements about something that happened. You need a lot more emotion, adjectives, and a lot of other things in a story to make it memorable or engaging. I think this is very similar to the way we talk as well. When we talk, especially when we are trying to teach something, stating facts is enough. Adding emotion, character, and a lot of other elements make it actually interesting.

Whoa. So in order to win a race, Google will kill off the web. Just so it can win the race. Boy, it really shows that this isn't just moving from stage to the next stage. Or a new medium developing while the old medium survives. But it's the old medium being killed off so the new medium can reign supreme.

https://www.reddit.com/r/typewriters/comments/1kunlxr/the_rules_of_typewriter_club/

Just like most areas of life relating to expertise, it's nice to have a broad set of rules when you start out. Then as your knowledge of the arts and sciences grow, you can begin to "paint outside the lines."

Once you've used, tinkered on, collected, repaired, or restored more machines than there are rules, then you can consider them more like guidelines and feel free to experiment more freely. By that point you'll have enough experience to be a true typewriter artist. ⛵🧑‍🎨🎨🏴‍☠️

I appreciate the idea of this, I just don't agree that it's a stopgap I think AI used in this way could instead be a gateway for non-offenders to become exposed to CSAM, and for offenders to seek out contact offenses.

The modern incarnation of the fallacy of bulk-is-better is judging the quality of a project (esp. a component) by being overly concerned with whether it is more or less "active" than some other project.

The question posed here is an obvious response for when you encounter instances of the bulk-is-better mindset (although it's not as easy to automate as Cox suggests here).

I've often mused about the thought of pulling a Reddit* and automating some level of churn within a codebase for a project that's hosted on e.g. GitHub for the sole purpose of making sure that it appears "active".

* A story about the early days of Reddit has become well-known after the creators volunteered some information about the early days when they made use of a bunch of "fake" accounts to submit links that the creators had aggregated/curated for the purpose of seeding the site.

Interpolation - it's a function f(P1, P2, t) that gives you a point between 2 points for given t ∈ <0, 1> ( imagine you walk on path between point A and point B and you stop after you finished exactly 34,2353% of the whole road. To know what is your current point on path, you calculate interpolation f(A, B, 0.342353) )

Polynomial interpolation - it's just a interpolation of two interpolations which are interpolations of two interpolations and on and on.... So quadratic interpolation is just a interpolation of two interpolations. (it's a situation when we have 3 points - cubic interpolation it's when we have 4 points)

Quadratic Bezier curve - it's a curve drawn using interpolation of two interpolations (A -> B, A -> C)

Cubic Bezier curve - it's a curve drawn using interpolation of two interpolations of two inteprolations 1st interpolations: A->B, B->C, C->D 2nd inteprolations: (A->B)->(B->C), (B->C)->(C->D) 3rd (final) interpolation: ((A->B)->(B->C))->((B->C)->(C->D))

cubicTo(x1,x2,x3) - it's just drawing a cubic Bezier curve. You might ask, where is the fourth point? The fourth point is where you currently are on Path, it's a starting point.

"Jay Z tha mera Badshah, Nelly tha mera king"

Literal Meaning

• "Jay Z tha mera Badshah" Jay-Z was Raftaar's ultimate idol and "Emperor" (Badshah) in hip-hop – the undisputed master of the craft, business, and empire-building.
• "Nelly tha mera king": Nelly was Raftaar's "King," a major inspiration for his more melodic flows, catchy delivery, and mainstream appeal.

The Subtler Meaning within DHH

• "Jay Z tha mera Badshah" - By saying Jay-Z was his "Badshah" (Emperor), Raftaar is making a nuanced statement about the Indian rapper Badshah. It subtly implies that in Raftaar's hierarchy of hip-hop, Jay-Z holds the ultimate position of "Badshah," implicitly placing any other "Badshah(rapper)" in a secondary or lesser role in his personal pantheon of greatness. It's a way of saying, "My 'Badshah' is a global legend, not just a peer from the Indian scene."

• "Nelly tha mera king" - Listeners often speculate this also refers to the Indian rapper King (Rocco), as a subtle double entendre or competitive nod.

"Meri puri mob deep 'cause it's tough down there"

1. Literally means to have a large, loyal crew or entourage. This meaning fits perfectly with the latter part of the line, "'cause it's tough down there," implying that a strong, numerous crew is necessary for protection and support in a difficult or dangerous environment.

2. Name-dropping "Mobb Deep", Mobb Deep was an American hip hop duo

By using Mobb Deep, KR$NA isn't just describing the size of their crew; they are also evoking the specific gritty, street-hardened image and reputation associated with the legendary hip-hop group. This adds a layer of cultural resonance and meaning for listeners familiar with Mobb Deep's work. The phrase "it's tough down there" directly mirrors the kind of lyrical content Mobb Deep was famous for, making the double entendre even more potent.

Poochhe mujhse label, what I brought to the table

“What I brought to the table” is a common idiom that means what contributions, skills, ideas, or value a person brings to a project, organisation or Business.

Jawab hoga kewal, I brought the fuckin’ table

This is the powerful punchline. Instead of listing contributions on the table, KR$NA declares he brought the entire table itself.

KR$NA has a history of being an independent artist who later signed with Kalamkaar. This line directly addresses the music industry or record labels that often seek to define an artist’s worth or dictate their path. For KR$NA, it’s a defiant statement that he doesn’t just contribute to their system; he defines the system itself.

This shows that annotation isn’t just something new or academic. Instead, it’s something people have been doing for centuries to make sense of what they read and remember it. When we annotate today, we’re really just continuing a long tradition of thinking deeply and staying connected to the text.

reply to u/highspeed_steel at https://reddit.com/r/typewriters/comments/1krspvh/im_totally_blind_and_new_to_typewriters_wax/

Your question is a great one, but I'll go another direction since I'd dug into some of the history and details of Helen Keller's mid-century typewriters a while back. You can find some details and descriptions here (and in the associated links which includes an accessible video of Ms. Keller using a solid and sexy black Remington Noiseless standard typewriter): https://www.reddit.com/r/typewriters/comments/1ihot96/helen_kellers_typewriters/

She managed on both her Remington as well as her brailler as well as any sighted person, though obviously had someone to check her printed work.

I recently saw another heavily modified midcentury typewriter for someone who, if I recall correctly was not only blind, but had no arms. It was set up so that they could move a selector and type using a custom chin rest. Sadly, I didn't index it at the time, but it's interesting to know that such things existed for accessibility reasons.

As for Braillers, you might appreciate this recent article about a repairman in Britain who was retiring: https://www.theguardian.com/society/2025/jan/02/wed-be-stuck-alarm-as-uks-last-braille-typewriter-repairer-ponders-retirement

I've got my own brailler, which is a sleek-looking art-deco industrial piece of art with the loveliest shade of dark shiny gray paint I've ever seen on a typewriter. (I'm both a mathematician and information theorist into the areas of coding and cryptography, so Morse code, Braille, etc. are professionally fascinating to me.) I still need to take it apart and repair a few portions to get it back to perfection, but it generally works well.

As for the aesthetics, I personally enjoy the solid industrial look and feel of the machines from the 1930s-1960s. The early 30s and some 40s have glossy black enamel and machines like the Corona Standard/Silent from the 30s are low slung with flat tops that sort of resemble small pianos and just scream out "I'm a writer" with a flair for dark academia and just a hint of classical Roman design. Many of these machines come with gold tinged water-slide decals which really set themselves off against the black enamel, though on the majority of machines the gold is beginning to dim from time, wear, and uncareful application of cleaning solutions.

I love the Royal KMM, KMG, and the Remington 17, Standard, and Super-Riter for their industrial chonkiness and (usually) their glass keytops. One of my favorites is the Henry Dreyfuss designed Royal Quiet De Luxe from 1948 which always gives me the feel of what it would look like if a typewriter wore a tuxedo or the 1948 gray and chrome model which is similar but has the feel of a sleek gray flannel suit on a 1950s advertising executive prone to wearing dapper hats, smoking cigarettes, and always with a cocktail in his hand. Into the 50s and 60s almost everyone had moved to plastic keytops which I don't think are as pretty as the older glass keytops with the polished metal rings around them.

At the opposite end of that spectrum are the late 50s Royal FP and Futura 800s which have some colorful roundness which evokes the aesthetic of the coming space age. They remind me of the modern curves and star shapes of the television show The Jetsons. Similarly space-aged are the sexy curves of the silver metalic spray paint on wooden cases for the Olympia SM3 from the same period. These to me are quintessential typewriter industrial design. In gray, green, maroon, brown, and sometimes yellow crinkle paint with just a hint of sparkle in their keytops I really love the combination of roundedness and slight angularity these German designed machines provide. They have a definite understated sort of elegance most other typewriters just miss. I suspect that late-in-life Steve Jobs would have had an Olympia SM3.

There's something comforting about the 40s and 50s sports-car vibe of the smaller Smith-Corona portables of the 5 series machines in the 1950s with their racing stripes on the hood. They feel like the sort of typewriter James Dean would have used as a student—just hip enough to be cool while still be solid and functional.

Sadly into the 70s, while machines typically got a broader range of colors outside of the typical black, gray, and browns things became more plastic and angular. They also begin to loose some of the industrial mid-century aesthetic that earlier machines had. They often feel very 70s in an uncomplimentary way without the fun color combinations or whimsy that art and general design of of that period may have had in the music or fashion spaces. They make me think of politics and war rather than the burgeoning sexual revolution of the time period.

Interestingly, for me, I feel like most typewriter design was often 10-20 years behind the general design aesthetic/zeitgeist for the particular decades in which they were made.

Good luck in your search for the right typewriter(s) for your own collection.

The Wikipedia page on doxing defines it as the act of publicly releasing previously private personal information about someone, sometimes with hostile intent. One element that struck me was how doxing is sometimes utilized as a type of online vigilantism, with people believing they are "serving justice" but actually causing genuine harm. This adds to the chapter's thesis that individual harassment has evolved—now it's not just private messages or threats, but a coordinated public shaming that can have severe offline implications like job loss, safety hazards, or mental health crises.

This sentence really resonated with me because even though I’m not trans, I’ve still experienced how rigid systems can make people feel “risky” just for being different. As a person of color, I’ve noticed that certain spaces whether it’s airport security or job interviews automatically treat me with more suspicion. I believe it’s unacceptable that these systems are designed in ways that don’t account for the diversity of real human bodies and identities. In my opinion, design should always prioritize inclusion and comfort, especially in places like airports where people already feel vulnerable.

This reminded me of when I had to take my little brother, who’s in a wheelchair, to this one older building. The front entrance had a big staircase, and we had to go around the back just to find a ramp. It felt like he wasn’t really supposed to be there, like the design forgot people like him exist. I realize that it's hard to accomodate everyone, but it's important that every design tries its best to do that because the feeling of being an afterthought is very unpleasant and even humiliating.

This statement perfectly summarizes the heart of multicultural education. Banks emphasizes that true multicultural literacy goes beyond awareness—it involves cultivating empathy (to care) and encouraging civic engagement (to act). The goal isn't just academic—it’s deeply ethical and political. Multicultural education should prepare students to participate in building a society where all cultural groups are respected, heard, and empowered. “Cultural democracy” means not just inclusion, but shared ownership of the national narrative and equal opportunity to shape it.

This defines the heart of multicultural curriculum transformation: a shift in worldview. It’s not just about what we teach, but how we frame it. For example, teaching Columbus as a “discoverer” ignores the perspectives of Indigenous peoples. True transformation requires rethinking the foundational narratives that shape knowledge.

This passage emphasizes the power of representation and advocacy in education reform. As people of color gain leadership roles in academia, they are pushing to ensure that the curriculum reflects the diverse realities of U.S. society—not just the dominant narratives. I found the statistic that over 50% of public school students are students of color especially compelling; it shows that the demand for inclusive curricula isn't just idealistic—it's necessary. The call for experimental schools for Black males also shows a willingness to rethink educational structures entirely to address long-standing disparities.

This part reveals how even with funding and systems in place, bureaucracy and technical failure can still prevent students from receiving the support they need. It’s especially frustrating to see how statistics like on-time graduation rates show just how far behind students with disabilities are. Promises of reform sound good, but for families, the day-to-day experience is still exhausting and

This closing quote is both devastating and deeply revealing. It points to the widespread and largely invisible crisis within the special education system: children like T.J., who are lost not because of a lack of effort from their families, but because of systemic neglect, procedural failure, and institutional inertia. The fact that even engaged, persistent parents—those who are “pushing and fighting”—still face dead ends underscores just how broken the system truly is.

By stating that “nobody knows” how many students have been failed in this way, the article calls attention to the lack of accountability and transparency in public education systems. This is not a rare outlier; it’s a recurring pattern. Lori Podvesker’s observation that it’s “the same story” across many families turns T.J.’s experience from a personal tragedy into a collective systemic indictment. If parents who advocate persistently are still ignored, what does that say about access to justice for those with fewer resources or less familiarity with the system?

I think this sentence is particularly sad but also very true. At school, sometimes we can see that some students clearly need more help, but they are ignored due to insufficient system resources or teachers being too busy. Just like T.J., he clearly has his own study plan (I.E.P.), but no one really follows up or cares whether it has been implemented. This feeling of being pushed along by the system, I think, is actually experienced by many students. It's not because we don't work hard, but because no one really asks, "What do you need now?" This feeling of being ignored will make people lose confidence more and more and also make learning very lonely.

This introduction makes it clear that understanding gender and sexuality in schools isn't just about identity—it’s also about power and systems. I think it's really important how the chapter connects homophobia with broader structures like sexism and racism. It shows that these biases aren’t isolated but deeply connected, and understanding them requires looking at how identities intersect.

It really is concerning how normalized gender dynamics are, especially amongst younger children, such as splitting teams up by gender or just overall establishing this sense of competition between them. Considering just how young these children are, it's clear that it would impact their development and how they think, allowing them to think that these gender roles or stereotypes are normal and okay.

This paragraph made me think more deeply about how much pressure transgender students face just to stay safe or be accepted. It’s painful that some feel forced to either hide who they are or try hard to match strict gender expectations. It shows how school environments can make identity feel like a risk, not a right. It’s not just about fitting in—it’s about avoiding harm.

Thorne’s statement, as well as the studies mentioned above, remind us that gender-based traditional expectations, especially when it comes to conformity, can place an additional socialization burden on girls, just as dress codes often unfairly target women. It’s not just about what to wear; it’s about the control of gender by social rules and everyday activities. This is evident in the broader school environment, where gender-specific power relations influence students’ daily lives, often in subtle but harmful ways.

This section stated that multicultural education is not an extra- it’s a basic education. So, I asked, and this text was very important for me that the author agrees to make this clear: the “canon” of knowledge leads to just a narrow strip of society (European, male, upper class) See, it is so sad what we do to our children by keeping them away from different voices, their knowledge out of reach (and thus making them feel as if their knowledge is not important now) and the exclusion of anything else than what naturally occurs in the world only allowing a single story to be told by so-called important others whose voices have been amplified above those of the other races makes a commitment to both of them, to all the children to say an understanding of themselves, to say the world is about something much larger, democratic in that sense. And so, it becomes the important thing to make multicultural literacy a basic.

I agree with this idea. It’s easy to overlook how many decisions we make in a day, but once you start paying attention, it’s overwhelming. From deciding what to wear, to figuring out what to eat, to even choosing which email to reply to first and it all adds up. By the end of the day, I can feel mentally drained, like my brain has just run out of steam. I definitely notice that when I’ve been making decisions non-stop, I become more prone to making mistakes or even procrastinating because I just don’t have the energy to decide anymore. It's like my brain is out of gas.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Using a cross-modal sensory selection task in head-fixed mice, the authors attempted to characterize how different rules reconfigured representations of sensory stimuli and behavioral reports in sensory (S1, S2) and premotor cortical areas (medial motor cortex or MM, and ALM). They used silicon probe recordings during behavior, a combination of single-cell and population-level analyses of neural data, and optogenetic inhibition during the task.

Strengths:

A major strength of the manuscript was the clarity of the writing and motivation for experiments and analyses. The behavioral paradigm is somewhat simple but well-designed and wellcontrolled. The neural analyses were sophisticated, clearly presented, and generally supported the authors' interpretations. The statistics are clearly reported and easy to interpret. In general, my view is that the authors achieved their aims. They found that different rules affected preparatory activity in premotor areas, but not sensory areas, consistent with dynamical systems perspectives in the field that hold that initial conditions are important for determining trial-based dynamics.

Weaknesses:

The manuscript was generally strong. The main weakness in my view was in interpreting the optogenetic results. While the simplicity of the task was helpful for analyzing the neural data, I think it limited the informativeness of the perturbation experiments. The behavioral read-out was low dimensional -a change in hit rate or false alarm rate- but it was unclear what perceptual or cognitive process was disrupted that led to changes in these read-outs. This is a challenge for the field, and not just this paper, but was the main weakness in my view. I have some minor technical comments in the recommendations for authors that might address other minor weaknesses.

I think this is a well-performed, well-written, and interesting study that shows differences in rule representations in sensory and premotor areas and finds that rules reconfigure preparatory activity in the motor cortex to support flexible behavior.

Reviewer #2 (Public Review):

Summary:

Chang et al. investigate neuronal activity firing patterns across various cortical regions in an interesting context-dependent tactile vs visual detection task, developed previously by the authors (Chevee et al., 2021; doi: 10.1016/j.neuron.2021.11.013). The authors report the important involvement of a medial frontal cortical region (MM, probably a similar location to wM2 as described in Esmaeili et al., 2021 & 2022; doi: 10.1016/j.neuron.2021.05.005; doi: 10.1371/journal.pbio.3001667) in mice for determining task rules.

Strengths:

The experiments appear to have been well carried out and the data well analysed. The manuscript clearly describes the motivation for the analyses and reaches clear and well-justified conclusions. I find the manuscript interesting and exciting!

Weaknesses:

I did not find any major weaknesses.

Reviewer #3 (Public Review):

This study examines context-dependent stimulus selection by recording neural activity from several sensory and motor cortical areas along a sensorimotor pathway, including S1, S2, MM, and ALM. Mice are trained to either withhold licking or perform directional licking in response to visual or tactile stimulus. Depending on the task rule, the mice have to respond to one stimulus modality while ignoring the other. Neural activity to the same tactile stimulus is modulated by task in all the areas recorded, with significant activity changes in a subset of neurons and population activity occupying distinct activity subspaces. Recordings further reveal a contextual signal in the pre-stimulus baseline activity that differentiates task context. This signal is correlated with subsequent task modulation of stimulus activity. Comparison across brain areas shows that this contextual signal is stronger in frontal cortical regions than in sensory regions. Analyses link this signal to behavior by showing that it tracks the behavioral performance switch during task rule transitions. Silencing activity in frontal cortical regions during the baseline period impairs behavioral performance.

Overall, this is a superb study with solid results and thorough controls. The results are relevant for context-specific neural computation and provide a neural substrate that will surely inspire follow-up mechanistic investigations. We only have a couple of suggestions to help the authors further improve the paper.

(1) We have a comment regarding the calculation of the choice CD in Fig S3. The text on page 7 concludes that "Choice coding dimensions change with task rule". However, the motor choice response is different across blocks, i.e. lick right vs. no lick for one task and lick left vs. no lick for the other task. Therefore, the differences in the choice CD may be simply due to the motor response being different across the tasks and not due to the task rule per se. The authors may consider adding this caveat in their interpretation. This should not affect their main conclusion.

We thank the Reviewer for the suggestion. We have discussed this caveat and performed a new analysis to calculate the choice coding dimensions using right-lick and left-lick trials (Fig. S3h) on page 8. 

“Choice coding dimensions were obtained from left-lick and no-lick trials in respond-to-touch blocks and right-lick and no-lick trials in respond-to-light blocks. Because the required lick directions differed between the block types, the difference in choice CDs across task rules (Fig. S4f) could have been affected by the different motor responses. To rule out this possibility, we did a new version of this analysis using right-lick and left-lick trials to calculate the choice coding dimensions for both task rules. We found that the orientation of the choice coding dimension in a respond-to-touch block was still not aligned well with that in a respond-to-light block (Fig. S4h;  magnitude of dot product between the respond-to-touch choice CD and the respond-to-light choice CD, mean ± 95% CI for true vs shuffled data: S1: 0.39 ± [0.23, 0.55] vs 0.2 ± [0.1, 0.31], 10 sessions; S2: 0.32 ± [0.18, 0.46] vs 0.2 ± [0.11, 0.3], 8 sessions; MM: 0.35 ± [0.21, 0.48] vs 0.18 ± [0.11, 0.26], 9 sessions; ALM: 0.28 ± [0.17, 0.39] vs 0.21 ± [0.12, 0.31], 13 sessions).”

We also have included the caveats for using right-lick and left-lick trials to calculate choice coding dimensions on page 13.

“However, we also calculated choice coding dimensions using only right- and left-lick trials. In S1, S2, MM and ALM, the choice CDs calculated this way were also not aligned well across task rules (Fig. S4h), consistent with the results calculated from lick and no-lick trials (Fig. S4f). Data were limited for this analysis, however, because mice rarely licked to the unrewarded water port (# of licksunrewarded port  / # of lickstotal , respond-to-touch: 0.13, respond-to-light: 0.11). These trials usually came from rule transitions (Fig. 5a) and, in some cases, were potentially caused by exploratory behaviors. These factors could affect choice CDs.”

(2) We have a couple of questions about the effect size on single neurons vs. population dynamics. From Fig 1, about 20% of neurons in frontal cortical regions show task rule modulation in their stimulus activity. This seems like a small effect in terms of population dynamics. There is somewhat of a disconnect from Figs 4 and S3 (for stimulus CD), which show remarkably low subspace overlap in population activity across tasks. Can the authors help bridge this disconnect? Is this because the neurons showing a difference in Fig 1 are disproportionally stimulus selective neurons?

We thank the Reviewer for the insightful comment and agree that it is important to link the single-unit and population results. We have addressed these questions by (1) improving our analysis of task modulation of single neurons  (tHit-tCR selectivity) and (2) examining the relationship between tHit-tCR selective neurons and tHit-tCR subspace overlaps.  

Previously, we averaged the AUC values of time bins within the stimulus window (0-150 ms, 10 ms bins). If the 95% CI on this averaged AUC value did not include 0.5, this unit was considered to show significant selectivity. This approach was highly conservative and may underestimate the percentage of units showing significant selectivity, particularly any units showing transient selectivity. In the revised manuscript, we now define a unit as showing significant tHit-tCR selectivity when three consecutive time bins (>30 ms, 10ms bins) of AUC values were significant. Using this new criterion, the percentage of tHittCR selective neurons increased compared with the previous analysis. We have updated Figure 1h and the results on page 4:

“We found that 18-33% of neurons in these cortical areas had area under the receiver-operating curve (AUC) values significantly different from 0.5, and therefore discriminated between tHit and tCR trials (Fig. 1h; S1: 28.8%, 177 neurons; S2: 17.9%, 162 neurons; MM: 32.9%, 140 neurons; ALM: 23.4%, 256 neurons; criterion to be considered significant: Bonferroni corrected 95% CI on AUC did not include 0.5 for at least 3 consecutive 10-ms time bins).”

Next, we have checked how tHit-tCR selective neurons were distributed across sessions. We found that the percentage of tHit-tCR selective neurons in each session varied (S1: 9-46%, S2: 0-36%, MM:25-55%, ALM:0-50%). We examined the relationship between the numbers of tHit-tCR selective neurons and tHit-tCR subspace overlaps. Sessions with more neurons showing task rule modulation tended to show lower subspace overlap, but this correlation was modest and only marginally significant (r= -0.32, p= 0.08, Pearson correlation, n= 31 sessions). While we report the percentage of neurons showing significant selectivity as a simple way to summarize single-neuron effects, this does neglect the magnitude of task rule modulation of individual neurons, which may also be relevant. 

In summary, the apparent disconnect between the effect sizes of task modulation of single neurons and of population dynamics could be explained by (1) the percentages of tHit-tCR selective neurons were underestimated in our old analysis, (2) tHit-tCR selective neurons were not uniformly distributed among sessions, and (3) the percentages of tHit-tCR selective neurons were weakly correlated with tHit-tCR subspace overlaps. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

For the analysis of choice coding dimensions, it seems that the authors are somewhat data limited in that they cannot compare lick-right/lick-left within a block. So instead, they compare lick/no lick trials. But given that the mice are unable to initiate trials, the interpretation of the no lick trials is a bit complicated. It is not clear that the no lick trials reflect a perceptual judgment about the stimulus (i.e., a choice), or that the mice are just zoning out and not paying attention. If it's the latter case, what the authors are calling choice coding is more of an attentional or task engagement signal, which may still be interesting, but has a somewhat different interpretation than a choice coding dimension. It might be worth clarifying this point somewhere, or if I'm totally off-base, then being more clear about why lick/no lick is more consistent with choice than task engagement.

We thank the Reviewer for raising this point. We have added a new paragraph on page 13 to clarify why we used lick/no-lick trials to calculate choice coding dimensions, and we now discuss the caveat regarding task engagement.  

“No-lick trials included misses, which could be caused by mice not being engaged in the task. While the majority of no-lick trials were correct rejections (respond-to-touch: 75%; respond-to-light: 76%), we treated no-licks as one of the available choices in our task and included them to calculate choice coding dimensions (Fig. S4c,d,f). To ensure stable and balanced task engagement across task rules, we removed the last 20 trials of each session and used stimulus parameters that achieved similar behavioral performance for both task rules (Fig. 1d; ~75% correct for both rules).”

In addition, to address a point made by Reviewer 3 as well as this point, we performed a new analysis to calculate choice coding dimensions using right-lick vs left-lick trials. We report this new analysis on page 8:

“Choice coding dimensions were obtained from left-lick and no-lick trials in respond-to-touch blocks and right-lick and no-lick trials in respond-to-light blocks. Because the required lick directions differed between the block types, the difference in choice CDs across task rules (Fig. S4f) could have been affected by the different motor responses. To rule out this possibility, we did a new version of this analysis using right-lick and left-lick trials to calculate the choice coding dimensions for both task rules. We found that the orientation of the choice coding dimension in a respond-to-touch block was still not aligned well with that in a respond-to-light block (Fig. S4h;  magnitude of dot product between the respond-to-touch choice CD and the respond-to-light choice CD, mean ± 95% CI for true vs shuffled data: S1: 0.39 ± [0.23, 0.55] vs 0.2 ± [0.1, 0.31], 10 sessions; S2: 0.32 ± [0.18, 0.46] vs 0.2 ± [0.11, 0.3], 8 sessions; MM: 0.35 ± [0.21, 0.48] vs 0.18 ± [0.11, 0.26], 9 sessions; ALM: 0.28 ± [0.17, 0.39] vs 0.21 ± [0.12, 0.31], 13 sessions).” 

We added discussion of the limitations of this new analysis on page 13:

“However, we also calculated choice coding dimensions using only right- and left-lick trials. In S1, S2, MM and ALM, the choice CDs calculated this way were also not aligned well across task rules (Fig. S4h), consistent with the results calculated from lick and no-lick trials (Fig. S4f). Data were limited for this analysis, however, because mice rarely licked to the unrewarded water port (# of licksunrewarded port  / # of lickstotal , respond-to-touch: 0.13, respond-to-light: 0.11). These trials usually came from rule transitions (Fig. 5a) and, in some cases, were potentially caused by exploratory behaviors. These factors could affect choice CDs.”

The authors find that the stimulus coding direction in most areas (S1, S2, and MM) was significantly aligned between the block types. How do the authors interpret that finding? That there is no major change in stimulus coding dimension, despite the change in subspace? I think I'm missing the big picture interpretation of this result.

That there is no significant change in stimulus coding dimensions but a change in subspace suggests that the subspace change largely reflects a change in the choice coding dimensions.

As I mentioned in the public review, I thought there was a weakness with interpretation of the optogenetic experiments, which the authors generally interpret as reflecting rule sensitivity. However, given that they are inhibiting premotor areas including ALM, one might imagine that there might also be an effect on lick production or kinematics. To rule this out, the authors compare the change in lick rate relative to licks during the ITI. What is the ITI lick rate? I assume pretty low, once the animal is welltrained, in which case there may be a floor effect that could obscure meaningful effects on lick production. In addition, based on the reported CI on delta p(lick), it looks like MM and AM did suppress lick rate. I think in the future, a task with richer behavioral read-outs (or including other measurements of behavior like video), or perhaps something like a psychological process model with parameters that reflect different perceptual or cognitive processes could help resolve the effects of perturbations more precisely.

Eighteen and ten percent of trials had at least one lick in the ITI in respond-to-touch and  respond-tolight blocks, respectively. These relatively low rates of ITI licking could indeed make an effect of optogenetics on lick production harder to observe. We agree that future work would benefit from more complex tasks and measurements, and have added the following to make this point (page 14):

“To more precisely dissect the effects of perturbations on different cognitive processes in rule-dependent sensory detection, more complex behavioral tasks and richer behavioral measurements are needed in the future.”

Reviewer #2 (Recommendations For The Authors):

I have the following minor suggestions that the authors might consider in revising this already excellent manuscript :

(1) In addition to showing normalised z-score firing rates (e.g. Fig 1g), I think it is important to show the grand-average mean firing rates in Hz.

We thank the Reviewer for the suggestion and have added the grand-average mean firing rates as a new supplementary figure (Fig. S2a). To provide more details about the firing rates of individual neurons, we have also added to this new figure the distribution of peak responses during the tactile stimulus period (Fig. S2b).

(2) I think the authors could report more quantitative data in the main text. As a very basic example, I could not easily find how many neurons, sessions, and mice were used in various analyses.

We have added relevant numbers at various points throughout the Results, including within the following examples:

Page 3: “To examine how the task rules influenced the sensorimotor transformation occurring in the tactile processing stream, we performed single-unit recordings from sensory and motor cortical areas including S1, S2, MM and ALM (Fig. 1e-g, Fig. S1a-h, and Fig. S2a; S1: 6 mice, 10 sessions, 177 neurons, S2: 5 mice, 8 sessions, 162 neurons, MM: 7 mice, 9 sessions, 140 neurons, ALM: 8 mice, 13 sessions, 256 neurons).”

Page 5: “As expected, single-unit activity before stimulus onset did not discriminate between tactile and visual trials (Fig. 2d; S1: 0%, 177 neurons; S2: 0%, 162 neurons; MM: 0%, 140 neurons; ALM: 0.8%, 256 neurons). After stimulus onset, more than 35% of neurons in the sensory cortical areas and approximately 15% of neurons in the motor cortical areas showed significant stimulus discriminability (Fig. 2e; S1: 37.3%, 177 neurons; S2: 35.2%, 162 neurons; MM: 15%, 140 neurons; ALM: 14.1%, 256 neurons).”

Page 6: “Support vector machine (SVM) and Random Forest classifiers showed similar decoding abilities

(Fig. S3a,b; medians of classification accuracy [true vs shuffled]; SVM: S1 [0.6 vs 0.53], 10 sessions, S2

[0.61 vs 0.51], 8 sessions, MM [0.71 vs 0.51], 9 sessions, ALM [0.65 vs 0.52], 13 sessions; Random

Forests: S1 [0.59 vs 0.52], 10 sessions, S2 [0.6 vs 0.52], 8 sessions, MM [0.65 vs 0.49], 9 sessions, ALM [0.7 vs 0.5], 13 sessions).”

Page 6: “To assess this for the four cortical areas, we quantified how the tHit and tCR trajectories diverged from each other by calculating the Euclidean distance between matching time points for all possible pairs of tHit and tCR trajectories for a given session and then averaging these for the session (Fig. 4a,b; S1: 10 sessions, S2: 8 sessions, MM: 9 sessions, ALM: 13 sessions, individual sessions in gray and averages across sessions in black; window of analysis: -100 to 150 ms relative to stimulus onset; 10 ms bins; using the top 3 PCs; Methods).” 

Page 8: “In contrast, we found that S1, S2 and MM had stimulus CDs that were significantly aligned between the two block types (Fig. S4e; magnitude of dot product between the respond-to-touch stimulus CDs and the respond-to-light stimulus CDs, mean ± 95% CI for true vs shuffled data: S1: 0.5 ± [0.34, 0.66] vs 0.21 ± [0.12, 0.34], 10 sessions; S2: 0.62 ± [0.43, 0.78] vs 0.22 ± [0.13, 0.31], 8 sessions; MM: 0.48 ± [0.38, 0.59] vs 0.24 ± [0.16, 0.33], 9 sessions; ALM: 0.33 ± [0.2, 0.47] vs 0.21 ± [0.13, 0.31], 13 sessions).”  Page 9: “For respond-to-touch to respond-to-light block transitions, the fractions of trials classified as respond-to-touch for MM and ALM decreased progressively over the course of the transition (Fig. 5d; rank correlation of the fractions calculated for each of the separate periods spanning the transition, Kendall’s tau, mean ± 95% CI: MM: -0.39 ± [-0.67, -0.11], 9 sessions, ALM: -0.29 ± [-0.54, -0.04], 13 sessions; criterion to be considered significant: 95% CI on Kendall’s tau did not include 0).

Page 11: “Lick probability was unaffected during S1, S2, MM and ALM experiments for both tasks, indicating that the behavioral effects were not due to an inability to lick (Fig. 6i, j; 95% CI on Δ lick probability for cross-modal selection task: S1/S2 [-0.18, 0.24], 4 mice, 10 sessions; MM [-0.31, 0.03], 4 mice, 11 sessions; ALM [-0.24, 0.16], 4 mice, 10 sessions; Δ lick probability for simple tactile detection task: S1/S2 [-0.13, 0.31], 3 mice, 3 sessions; MM [-0.06, 0.45], 3 mice, 5 sessions; ALM [-0.18, 0.34], 3 mice, 4 sessions).”

(3) Please include a clearer description of trial timing. Perhaps a schematic timeline of when stimuli are delivered and when licking would be rewarded. I may have missed it, but I did not find explicit mention of the timing of the reward window or if there was any delay period.

We have added the following (page 3): 

“For each trial, the stimulus duration was 0.15 s and an answer period extended from 0.1 to 2 s from stimulus onset.”

(4) Please include a clear description of statistical tests in each figure legend as needed (for example please check Fig 4e legend).

We have added details about statistical tests in the figure legends:

Fig. 2f: “Relationship between block-type discriminability before stimulus onset and tHit-tCR discriminability after stimulus onset for units showing significant block-type discriminability prior to the stimulus. Pearson correlation: S1: r = 0.69, p = 0.056, 8 neurons; S2: r = 0.91, p = 0.093, 4 neurons; MM: r = 0.93, p < 0.001, 30 neurons; ALM: r = 0.83, p < 0.001, 26 neurons.” 

Fig. 4e: “Subspace overlap for control tHit (gray) and tCR (purple) trials in the somatosensory and motor cortical areas. Each circle is a subspace overlap of a session. Paired t-test, tCR – control tHit: S1: -0.23, 8 sessions, p = 0.0016; S2: -0.23, 7 sessions, p = 0.0086; MM: -0.36, 5 sessions, p = <0.001; ALM: -0.35, 11 sessions, p < 0.001; significance: ** for p<0.01, *** for p<0.001.”  

Fig. 5d,e: “Fraction of trials classified as coming from a respond-to-touch block based on the pre-stimulus population state, for trials occurring in different periods (see c) relative to respond-to-touch → respondto-light transitions. For MM (top row) and ALM (bottom row), progressively fewer trials were classified as coming from the respond-to-touch block as analysis windows shifted later relative to the rule transition. Kendall’s tau (rank correlation): MM: -0.39, 9 sessions; ALM: -0.29, 13 sessions. Left panels: individual sessions, right panels: mean ± 95% CI. Dash lines are chance levels (0.5). e, Same as d but for respond-to-light → respond-to-touch transitions. Kendall’s tau: MM: 0.37, 9 sessions; ALM: 0.27, 13 sessions.”

Fig. 6: “Error bars show bootstrap 95% CI. Criterion to be considered significant: 95% CI did not include 0.”

(5) P. 3 - "To examine how the task rules influenced the sensorimotor transformation occurring in the tactile processing stream, we performed single-unit recordings from sensory and motor cortical areas including S1, S2, MM, and ALM using 64-channel silicon probes (Fig. 1e-g and Fig. S1a-h)." Please specify if these areas were recorded simultaneously or not.

We have added “We recorded from one of these cortical areas per session, using 64-channel silicon probes.”  on page 3.  

(6) Figure 4b - Please describe what gray and black lines show.

The gray traces are the distance between tHit and tCR trajectories in individual sessions and the black traces are the averages across sessions in different cortical areas. We have added this information on page 6 and in the Figure 4b legend. 

Page 6: “To assess this for the four cortical areas, we quantified how the tHit and tCR trajectories diverged from each other by calculating the Euclidean distance between matching time points for all possible pairs of tHit and tCR trajectories for a given session and then averaging these for the session (Fig. 4a,b; S1: 10 sessions, S2: 8 sessions, MM: 9 sessions, ALM: 13 sessions, individual sessions in gray and averages across sessions in black; window of analysis: -100 to 150 ms relative to stimulus onset; 10 ms bins; using the top 3 PCs; Methods).

Fig. 4b: “Distance between tHit and tCR trajectories in S1, S2, MM and ALM. Gray traces show the time varying tHit-tCR distance in individual sessions and black traces are session-averaged tHit-tCR distance (S1:10 sessions; S2: 8 sessions; MM: 9 sessions; ALM: 13 sessions).”

(7) In addition to the analyses shown in Figure 5a, when investigating the timing of the rule switch, I think the authors should plot the left and right lick probabilities aligned to the timing of the rule switch time on a trial-by-trial basis averaged across mice.

We thank the Reviewer for suggesting this addition. We have added a new figure panel to show the probabilities of right- and left-licks during rule transitions (Fig. 5a).

Page 8: “The probabilities of right-licks and left-licks showed that the mice switched their motor responses during block transitions depending on task rules (Fig. 5a, mean ± 95% CI across 12 mice).” 

(8) P. 12 - "Moreover, in a separate study using the same task (Finkel et al., unpublished), high-speed video analysis demonstrated no significant differences in whisker motion between respond-to-touch and respond-to-light blocks in most (12 of 14) behavioral sessions.". Such behavioral data is important and ideally would be included in the current analysis. Was high-speed videography carried out during electrophysiology in the current study?

Finkel et al. has been accepted in principle for publication and will be available online shortly. Unfortunately we have not yet carried out simultaneous high-speed whisker video and electrophysiology in our cross-modal sensory selection task.

Reviewer #3 (Recommendations For The Authors):

(1) Minor point. For subspace overlap calculation of pre-stimulus activity in Fig 4e (light purple datapoints), please clarify whether the PCs for that condition were constructed in matched time windows. If the PCs are calculated from the stimulus period 0-150ms, the poor alignment could be due to mismatched time windows.

We thank the Reviewer for the comment and clarify our analysis here. We previously used timematched windows to calculate subspace overlaps. However, the pre-stimulus activity was much weaker than the activity during the stimulus period, so the subspaces of reference tHit were subject to noise and we were not able to obtain reliable PCs. This caused the subspace overlap values between the reference tHit and control tHit to be low and variable (mean ± SD, S1:  0.46± 0.26, n = 8 sessions, S2: 0.46± 0.18, n = 7 sessions, MM: 0.44± 0.16, n = 5 sessions, ALM: 0.38± 0.22, n = 11 sessions).  Therefore, we used the tHit activity during the stimulus window to obtain PCs and projected pre-stimulus and stimulus activity in tCR trials onto these PCs. We have now added a more detailed description of this analysis in the Methods (page 32). 

“To calculate the separation of subspaces prior to stimulus delivery, pre-stimulus activity in tCR trials (100 to 0 ms from stimulus onset) was projected to the PC space of the tHit reference group and the subspace overlap was calculated. In this analysis, we used tHit activity during stimulus delivery (0 to 150 ms from stimulus onset) to obtain reliable PCs.”   

We acknowledge this time alignment issue and have now removed the reported subspace overlap between tHit and tCR during the pre-stimulus period from Figure 4e (light purple). However, we think the correlation between pre- and post- stimulus-onset subspace overlaps should remain similar regardless of the time windows that we used for calculating the PCs. For the PCs calculated from the pre-stimulus period (-100 to 0 ms), the correlation coefficient was 0.55 (Pearson correlation, p <0.01, n = 31 sessions). For the PCs calculated from the stimulus period (0-150 ms), the correlation coefficient was 0.68 (Figure 4f, Pearson correlation, p <0.001, n = 31 sessions). Therefore, we keep Figure 4f.  

(2) Minor point. To help the readers follow the logic of the experiments, please explain why PPC and AMM were added in the later optogenetic experiment since these are not part of the electrophysiology experiment.

We have added the following rationale on page 9.

“We recorded from AMM in our cross-modal sensory selection task and observed visually-evoked activity (Fig. S1i-k), suggesting that AMM may play an important role in rule-dependent visual processing. PPC contributes to multisensory processing51–53 and sensory-motor integration50,54–58.  Therefore, we wanted to test the roles of these areas in our cross-modal sensory selection task.”

(3) Minor point. We are somewhat confused about the timing of some of the example neurons shown in figure S1. For example, many neurons show visually evoked signals only after stimulus offset, unlike tactile evoked signals (e.g. Fig S1b and f). In addition, the reaction time for visual stimulus is systematically slower than tactile stimuli for many example neurons (e.g. Fig S1b) but somehow not other neurons (e.g. Fig S1g). Are these observations correct?

These observations are all correct. We have a manuscript from a separate study using this same behavioral task (Finkel et al., accepted in principle) that examines and compares (1) the onsets of tactile- and visually-evoked activity and (2) the reaction times to tactile and visual stimuli. The reaction times to tactile stimuli were slightly but significantly shorter than the reaction times to visual stimuli (tactile vs visual, 397 ± 145 vs 521 ± 163 ms, median ± interquartile range [IQR], Tukey HSD test, p = 0.001, n =155 sessions). We examined how well activity of individual neurons in S1 could be used to discriminate the presence of the stimulus or the response of the mouse. For discriminability for the presence of the stimulus, S1 neurons could signal the presence of the tactile stimulus but not the visual stimulus. For discriminability for the response of the mouse, the onsets for significant discriminability occurred earlier for tactile compared with visual trials (two-sided Kolmogorov-Smirnov test, p = 1x10-16, n = 865 neurons with DP onset in tactile trials, n = 719 neurons with DP onset in visual trials).

Author Response

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

eLife assessment

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

The paragraph now reads:

Lines 740 – 751:

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

Summary:

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

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

Strengths:

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

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

Weaknesses:

My concerns are:

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

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

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

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

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

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

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

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

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

We have improved the discussion accordingly.

Recommendations for the authors:

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

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

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

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

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

We have done this.

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

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

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

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

We have improved the discussion accordingly.

Reviewer #1 (Recommendations For The Authors):

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

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

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

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

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

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

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

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

Reviewer #2 (Recommendations For The Authors):

Abstract

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In lines 102 - 110 we wrote:

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

We think this sufficiently introduces the cell body axes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This had already been stated in lines 331 – 336:

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

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

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

We have clarified this in lines 397 – 400:

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

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

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

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

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

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

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

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

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

We do not think that this is constructive feedback.

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

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

Reviewer #3 (Recommendations For The Authors):

Line 81 - delete 's

Done.

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

We have removed this paragraph.

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

We have included the numbers.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are instances where this can help and ways that it can do more harm than good. Intentions here were good but with joint witch hunting, it seems that folks online can get blinded from the possible repercussions of identifying a criminal incorrectly. It sure does happen already in the justice system but obviously can extend the harm due to human error in the online space. I still do think it's a good thing such as locating a vehicle using a license plate number in an Amber alert but when it comes to just a person looks, it can get hairy.

I hadn’t really thought about how many dimensions go into communication until I read this. The breakdown—especially things like synchronicity and archiving—made me reflect on my own habits. For example, I love texting because I can respond when it’s convenient, but I never considered how helpful it is that messages are automatically saved. That’s really different from face-to-face conversations, where things can be easily forgotten or misunderstood. I also realized how often I choose communication tools based on how “safe” or anonymous I feel in the moment. This made me more aware of how tech shapes not just how we talk, but how we relate to people.

This sentence really stood out to me because it puts a human face on the statistics about racial disparities in infant mortality. Instead of just giving us numbers, Bridges shares a story that makes the issue feel real and emotional. It made me think about how often data is used without considering the people behind it. I wonder if public health would get more attention or funding if more stories like this were highlighted. It’s a heartbreaking reminder of the impact of systemic inequality in health care.

So from the start he’s telling us that he’s writing during a time where faith is shaky. That kind of explains why he’s writing the way he is , it’s not just a story, it’s a defense of belief.

It almost feels like some people think Islam, the way it’s been practiced or understood, needs to be torn down completely. Like, not just changed around the edges, but fully rethought. Either rebuilt into something that fits better with the modern world or replaced with a whole new way of thinking and living that speaks more to where people are now, socially, spiritually, intellectually.

Basically, Yazdi’s saying his rise to power was fate. He didn’t just take control. He was always meant to rule. It’s a way of justifying everything he did before the story even gets going.

This line is dramatic on purpose. It sets Tamerlane up as more than just a person. It’s like the text is saying his existence changed the world from the moment he arrived.

Silva might seem super routine on the outside, just going through the motions, but once he gets into a manuscript, his imagination kicks in heavy. It’s like whatever he’s reading starts to shape how he sees the world around him. This time, he’s proofreading a history book about something that actually happened right where he lives, the siege of Lisbon in 1147.

You could say that what really makes Jerusalem important, especially outside of its actual location, isn’t just the physical city itself, it’s how people all over the world imagine it. Muslims, Jews, and Christians have each built deep connections to Jerusalem, and a lot of that comes from religious meaning, not just geography. Kudus (the way Muslims refer to it) shows this really well. It’s less about what’s physically there and more about what the city represents spiritually and emotionally.

Joseph was tested by Yahweh through the pharoah’s wife to see if he would sin, and while Joseph stayed true to his beliefs, he was later on accused of advancing on her. Joseph was tested and put through suffering and almost faced exile and death but justice prevailed in the end. Joseph ended up rising up to a high position of power in Egypt, and saved many people from famine. From a theological perspective, Yahweh in the Old Testament is hard, but fair. This teaches that Yahweh isn't a cruel God, but one that allows tests of faith and if you're like Joseph, you will be redeemed for having that faith. Yahweh teaches a just world, where justice is always served, even if it’s delayed sometimes.

Canada’s two official languages are English and French. As such, it’s not only important that an official Canadian government website provides the option for both languages, but having that option to toggle between them higher up on the page greatly benefits those who might require text-to-speech or who are unable to use a mouse as they wouldn’t need to navigate to somewhere like the bottom of the page just to change it.

I now wonder if I can build a powerful CRM on Django.

This quote stands out because it presents a more critical lens on educational technology, one that challenges the common belief that more tech always means better learning. Cuban’s phrase “oversold and underused” reflects the reality that many tools don’t live up to their promises in the classroom. Feenberg’s warning about inequality and democracy highlights the broader social impact of tech use. As a future teacher, this pushes me to think more deeply about why I use certain technologies, not just how. It’s a reminder that I need to be thoughtful, inclusive, and critical in my approach to ed tech.

This powerful quote from Kat Holmes emphasizes that design is not neutral, it directly impacts inclusion, opportunity, and equity. When applied to education, it reminds me that the way we design learning environments, tools, and systems can either support or hinder students' ability to thrive. As a future educator, this encourages me to be intentional about creating accessible, inclusive, and participatory learning experiences. Good design isn’t just about aesthetics, it’s about justice and belonging in the classroom.

This quote highlights the long-term impact of students’ digital behavior and the importance of teaching responsible online habits. It reminds me that digital citizenship is more than just avoiding harm, it’s about actively building a positive presence online. As a future educator, I want to help students understand that what they post, share, and engage with online can follow them into college and careers. Teaching students to manage their digital footprint thoughtfully is just as important as teaching traditional academic skills in today’s world.

This quote highlights how dramatically our expectations of privacy have changed in the digital age. In the past, there were clear legal protections around private communication, like phone calls. But now, with digital tools and platforms, those lines are much blurrier, especially when it comes to what companies and governments can access. As a future educator, this reminds me that teaching digital literacy isn’t just about using tools, it’s also about helping students understand their rights, how data is collected, and how to protect their privacy online.

This quote highlights the critical responsibility educators have to ensure equity in learning by making environments accessible to all students. It reminds me that inclusivity isn’t just a nice-to-have; it’s essential for fair education. The emphasis on evaluating digital tools before using them is especially important in today’s tech-driven classrooms, where some apps or resources may unintentionally exclude learners with disabilities. This reinforces the need for me, as a future teacher, to be diligent about choosing tools that support diverse learning needs and to advocate for accessibility in all aspects of teaching.

This quote stands out because it expands the idea of a Personal Learning Network beyond just the people we connect with. It emphasizes that the environments we engage in and the tools we use are equally important. As a future teacher, this reminds me that professional growth doesn't happen in isolation. I need to be intentional about the spaces I frequent (like online forums or conferences) and the tools I rely on (like note-taking apps or blogs). It’s also a good reminder that building a PLN isn’t just about networking, it’s about continuously seeking resources and environments that push my thinking, reflect diverse perspectives, and keep me engaged in lifelong learning.

Rather than truly being out of "concern" for the safety of children, it feels like the inability to discuss the LGBTQ+ community with children really just feels like a desire to continue perpetuating a heteronormative society. This may in turn cause a lack of acceptance when they encounter those that are different later on, or self resentment if they believe themselves to be "different" from others.

This statement is very relatable to many people, including myself. Many kids have trouble being themselves over fear of how others will react. Will they lose their friends? lose their social status? It can be very scary for anyone to be themselves. The easiest way is to find a group of people who you trust and have similar interests, like a team.

This sentence is simple yet profound. Starting from a personal reflection, it exquisitely shows the state of mind of the LGBTQ+ community as they struggle between self - identification and external perceptions, and reflects the intangible pressure exerted on individuals by social concepts.

I’ve heard classmates say almost the same thing—that they thought they were “quiet,” but really they were just hiding parts of themselves to avoid judgment. It shows how quick labels at school can push people to shrink back. When peers and teachers welcome different personalities, students feel safer showing who they really are.

This sentence left a particularly deep impression on me because it made me think that many times, a family's definition of "success" is actually conditional. It's not just about having good grades, but also about conforming to a certain "normal" appearance, such as cisgender or heterosexuality. I also feel this hidden pressure. It seems that once I'm different from others, I'll be regarded as "inattentive" or "not good enough". This made me start to reflect on whether it is possible for schools and families to challenge this single "success" standard, so that more students of different identities can be seen and supported.

This sentence left a particularly deep impression on me because it made me think that many times, a family's definition of "success" is actually conditional. It's not just about having good grades, but also about conforming to a certain "normal" appearance, such as cisgender or heterosexuality. I also feel this hidden pressure. It seems that once I'm different from others, I'll be regarded as "inattentive" or "not good enough". This made me start to reflect on whether it is possible for schools and families to challenge this single "success" standard, so that more students of different identities can be seen and supported.

Thi Ngo challenges the mainstream, often romanticized idea of "coming out" as a single, dramatic, once-in-a-lifetime event. This perception—reinforced by media and cultural narratives—fails to reflect the lived reality of LGBTQ+ individuals, who must navigate visibility and disclosure repeatedly across different settings, relationships, and stages of life.

By describing coming out as a recurring process, Ngo emphasizes how exhausting and emotionally taxing it can be to continually assess safety and acceptance in new social contexts. This quote also sheds light on the emotional labor queer people endure—deciding when, how, and if they should reveal parts of themselves to others. It’s not just about a one-time declaration; it’s a constant negotiation of identity and safety.

This quote powerfully captures the emotional and social toll of invisibility that many LGBTQ+ youth experience. Ngo’s reflection highlights a common misunderstanding: what might appear as introversion or social withdrawal can actually stem from a deeper, internal conflict—the fear of judgment for simply being oneself. In environments where queer and non-binary identities are viewed as “weird” or abnormal, students like Ngo often feel forced to suppress their personality in order to stay safe or accepted.

Rather than a lack of sociability, this is a survival strategy. It underscores the importance of affirming environments where youth can express their identity without fear. When Ngo says he “just wanted to be [his] true and genuine self,” it reminds us that authenticity isn't possible without cultural and institutional support. This makes me wonder, how many students are mislabeled as shy, antisocial, or unmotivated when in reality they’re struggling to feel seen and safe? What can educators do to create spaces where being different isn’t treated as “weird,” but as valuable?

I found this really interesting, and I agree with the author. My brother in 8th grade recently had a sex ed class that included conversations on transgender individuals. The parents were unaware of this new addition of information and were outraged in the community boards on Facebook. I remember a lot of the comments were saying that what the students learned were completely inappropriate, that kids were too young to learn about "men being able to be pregnant," and that the material should've been kept for the SexEd classes in high school. From seeing the comments and the discourse, what really stood out to me was the personal discomfort and misunderstanding rather than concern for the students. I agree with this statement from the author in that often what is deemed age-inappropriate is associated with parents protecting their own beliefs or avoiding conversations that they are just not ready for or do not wish to have. This also made me realize the role parents have in limiting what a kid is allowed or able to learn.

This list makes it clear that overrepresentation isn’t just about numbers—it’s about outcomes. The pattern described here reflects deep-rooted structural racism that affects how CLD students are perceived, placed, treated, and disciplined in schools. How might these patterns be challenged through inclusive practices, culturally responsive training, and community advocacy?

This passage introduces the central issue of the article: the longstanding systemic inequality in how students of color are identified and served in special education. The concern isn’t just about numbers—it’s about misidentification and inequitable treatment. Students of color are often either over-identified for special education (particularly in subjective categories like emotional or intellectual disabilities) or under-identified when they genuinely need support. Even when placed, the quality of the programs they enter is often subpar, reinforcing a cycle of educational marginalization.

This statement offers a crucial reminder: school climates are not fixed—they are changeable. While the chapter outlines the many forms of bias and harm LGBTQ+ students face, this quote shifts the focus toward collective responsibility and the potential for transformation. By emphasizing the role of not just students, but also teachers, administrators, and broader school communities, Mayo reinforces that change doesn't rest on one person or policy alone. It’s a collaborative effort that requires commitment across all levels of a school system.

The reference to the 2019 GLSEN survey provides data-backed hope—evidence that inclusive curricula, GSAs, and active adult intervention do lead to better outcomes: less harassment, more feelings of safety, and stronger student connections to their schools. This makes the case for proactive, rather than reactive, strategies to support LGBTQ+ students.

This passage underscores a painful but essential truth: for LGBTQ+ youth, the threat of violence isn’t hypothetical—it’s a real and structuring force in their lives. The example of Sakia Gunn’s murder and the school district’s delayed, symbolic response highlights how institutions often fail to acknowledge queer lives until forced to do so—and even then, often through surface-level gestures like “No Name Calling Day” instead of deeper structural change.

Despite these threats, the passage also honors the resilience and agency of queer youth. Students like Gunn not only assert their identities in dangerous environments, but they also work to create communities of care and resistance. This challenges narratives that frame LGBTQ+ youth solely as victims; instead, it centers their creativity, resistance, and leadership in shaping more inclusive futures. What would it look like if schools responded to violence not just with symbolic recognition, but with lasting structural support for LGBTQ+ youth?

Which is such a hopeful sign that things can change for the better. With data showing that inclusive curriculum correlates with less harassment and more connection between students, the importance of schools actively including LGBTQ+ in their curriculum cannot be overemphasized. It's not just about representation; it's more so about supporting environments where students feel safest, most valued, and allowed to be their true selves. It really brings out how much education can change more than minds, but also inclusive, respectful communities.

This section emphasizes that while laws (like Title IX or state anti-discrimination statutes) provide a foundation for inclusive practice, laws alone aren’t enough. Even educators who want to support LGBTQ students often feel blocked—not by a lack of policy, but by the unspoken biases and pressures within their school communities. These biases may come from colleagues, parents, or administrators, creating an environment where silence and neutrality feel safer than advocacy.

Mayo’s point here is crucial: homophobia and transphobia are institutional, not just interpersonal. They shape what gets taught, what gets ignored, and who feels safe to speak up. The implication is that real change must involve both top-down (policy) and bottom-up (cultural) efforts. It’s not just about knowing what’s right—it’s about having the support and freedom to act on it.

This sentence provides strong evidence for the real-world impact of inclusive education. It shows that LGBTQ representation in the curriculum isn't just about visibility—it's about creating safer, more affirming learning environments. When LGBTQ students see themselves reflected in what they’re taught, they feel a greater sense of belonging and are less likely to face harassment. This supports the idea that inclusion isn’t merely symbolic; it directly improves students' mental health, safety, and academic engagement.

Importantly, this doesn’t just benefit LGBTQ students—it fosters a more respectful school culture for everyone. By normalizing diverse identities through curriculum, schools send a message that all students belong, which reduces the “othering” that often fuels bullying.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This paper is an elegant, mostly observational work, detailing observations that polysome accumulation appears to drive nucleoid splitting and segregation. Overall I think this is an insightful work with solid observations.

Thank you for your appreciation and positive comments. In our view, an appealing aspect of this proposed biophysical mechanism for nucleoid segregation is its self-organizing nature and its ability to intrinsically couple nucleoid segregation to biomass growth, regardless of nutrient conditions.

Strengths:

The strengths of this paper are the careful and rigorous observational work that leads to their hypothesis. They find the accumulation of polysomes correlates with nucleoid splitting, and that the nucleoid segregation occurring right after splitting correlates with polysome segregation. These correlations are also backed up by other observations:

(1) Faster polysome accumulation and DNA segregation at faster growth rates.

(2) Polysome distribution negatively correlating with DNA positioning near asymmetric nucleoids.

(3) Polysomes form in regions inaccessible to similarly sized particles.

These above points are observational, I have no comments on these observations leading to their hypothesis.

Thank you!

Weaknesses:

It is hard to state weaknesses in any of the observational findings, and furthermore, their two tests of causality, while not being completely definitive, are likely the best one could do to examine this interesting phenomenon.

It is indeed difficult to prove causality in a definitive manner when the proposed coupling mechanism between nucleoid segregation and gene expression is self-organizing, i.e., does not involve a dedicated regulatory molecule (e.g., a protein, RNA, metabolite) that we could have eliminated through genetic engineering to establish causality. We are grateful to the reviewer for recognizing that our two causality tests are the best that can be done in this context.

Points to consider / address:

Notably, demonstrating causality here is very difficult (given the coupling between transcription, growth, and many other processes) but an important part of the paper. They do two experiments toward demonstrating causality that help bolster - but not prove - their hypothesis. These experiments have minor caveats, my first two points.

(1) First, "Blocking transcription (with rifampicin) should instantly reduce the rate of polysome production to zero, causing an immediate arrest of nucleoid segregation". Here they show that adding rifampicin does indeed lead to polysome loss and an immediate halting of segregation - data that does fit their model. This is not definitive proof of causation, as rifampicin also (a) stops cell growth, and (b) stops the translation of secreted proteins. Neither of these two possibilities is ruled out fully.

That’s correct; cell growth also stops when gene expression is inhibited, which is consistent with our model in which gene expression within the nucleoid promotes nucleoid segregation and biomass growth (i.e., cell growth), inherently coupling these two processes. This said, we understand the reviewer’s point: the rifampicin experiment doesn’t exclude the possibility that protein secretion and cell growth drive nucleoid segregation. We are assuming that the reviewer is envisioning an alternative model in which sister nucleoids would move apart because they would be attached to the membrane through coupled transcription-translation-protein secretion (transertion) and the membrane would expand between the separating nucleoids, similar to the model proposed by Jacob et al in 1963 (doi:10.1101/SQB.1963.028.01.048). There are several observations arguing against cell elongation/transertion acting a predominant mechanism of nucleoid segregation.

(1) For this alternative mechanism to work, membrane growth must be localized at the middle of the splitting nucleoids (i.e., midcell position for slow growth and ¼ and ¾ cell positions for fast growth) to create a directional motion. To our knowledge, there is no evidence of such localized membrane incorporation. Furthermore, even if membrane growth was localized at the right places, the fluidity of the cytoplasmic membrane (PMID: 6996724, 20159151, 24735432, 27705775) would be problematic. To circumvent the membrane fluidity issue, one could potentially evoke an additional connection to the rigid peptidoglycan, but then again, peptidoglycan growth would have to be localized at the middle of the splitting nucleoid. However, peptidoglycan growth is dispersed early in the cell division cycle when the nucleoid splitting happens in fast growing cells and only appears to be zonal after the onset of cell constriction (PMID: 35705811, 36097171, 2656655).

(2) Even if we ignore the aforementioned caveats, Paul Wiggins’s group ruled out the cell elongation/transertion model by showing that the rate of cell elongation is slower than the rate of chromosome segregation (PMID: 23775792). In our revised manuscript, we clarify this point and provide confirmatory data showing that the cell elongation rate is indeed slower than the nucleoid segregation rate (Figure 1H and Figure 1 - figure supplement 5A), indicating that it cannot be the main driver.

(3) The asymmetries in nucleoid compaction that we described in our paper are predicted by our model. We do not see how they could be explained by cell growth or protein secretion.

(4) We also show that polysome accumulation at ectopic sites (outside the nucleoid) results in correlated nucleoid dynamics, consistent with our proposed mechanism. It is not clear to us how such nucleoid dynamics could be explained by cell growth or protein secretion (transertion).

(1a) As rifampicin also stops all translation, it also stops translational insertion of membrane proteins, which in many old models has been put forward as a possible driver of nucleoid segregation, and perhaps independent of growth. This should at last be mentioned in the discussion, or if there are past experiments that rule this out it would be great to note them.

It is not clear to us how the attachment of the DNA to the cytoplasmic membrane could alone create a directional force to move the sister nucleoids. We agree that old models have proposed a role for cell elongation (providing the force) and transertion (providing the membrane tether). Please see our response above for the evidence (from the literature and our work) against it. This was mentioned in the Introduction and Results section, but we agree that this was not well explained. We have now put emphasis on the related experimental data (Figure 1H, Figure 1 – figure supplement 5A, ) and revised the text (lines 199 - 210) to clarify these points.

(1b) They address at great length in the discussion the possibility that growth may play a role in nucleoid segregation. However, this is testable - by stopping surface growth with antibiotics. Cells should still accumulate polysomes for some time, it would be easy to see if nucleoids are still segregated, and to what extent, thereby possibly decoupling growth and polysome production. If successful, this or similar experiments would further validate their model.

We reviewed the literature and could not find a drug that stops cell growth without stopping gene expression. Any drug that affects the integrity or potential of the membrane depletes cells of ATP; without ATP, gene expression is inhibited. However, our experiment in which we drive polysome accumulation at ectopic sites decouples polysome accumulation from cell growth. In this experiment, by redirecting most of chromosome gene expression to a single plasmid-encoded gene, we reduce the rate of cell growth but still create a large accumulation of polysomes at an ectopic location. This ectopic polysome accumulation is sufficient to affect nucleoid dynamics in a correlated fashion. In the revised manuscript, we have clarified this point and added model simulations (Figure 7 – figure supplement 2) to show that our experimental observations are predicted by our model.

(2) In the second experiment, they express excess TagBFP2 to delocalize polysomes from midcell. Here they again see the anticorrelation of the nucleoid and the polysomes, and in some cells, it appears similar to normal (polysomes separating the nucleoid) whereas in others the nucleoid has not separated. The one concern about this data - and the differences between the "separated" and "non-separated" nuclei - is that the over-expression of TagBFP2 has a huge impact on growth, which may also have an indirect effect on DNA replication and termination in some of these cells. Could the authors demonstrate these cells contain 2 fully replicated DNA molecules that are able to segregate?

We have included new flow cytometry data of fluorescently labeled DNA to show that DNA replication is not impacted.

(3) What is not clearly stated and is needed in this paper is to explain how polysomes do (or could) "exert force" in this system to segregate the nucleoid: what a "compaction force" is by definition, and what mechanisms causes this to arise (what causes the "force") as the "compaction force" arises from new polysomes being added into the gaps between them caused by thermal motions.

They state, "polysomes exert an effective force", and they note their model requires "steric effects (repulsion) between DNA and polysomes" for the polysomes to segregate, which makes sense. But this makes it unclear to the reader what is giving the force. As written, it is unclear if (a) these repulsions alone are making the force, or (b) is it the accumulation of new polysomes in the center by adding more "repulsive" material, the force causes the nucleoids to move. If polysomes are concentrated more between nucleoids, and the polysome concentration does not increase, the DNA will not be driven apart (as in the first case) However, in the second case (which seems to be their model), the addition of new material (new polysomes) into a sterically crowded space is not exerting force - it is filling in the gaps between the molecules in that region, space that needs to arise somehow (like via Brownian motion). In other words, if the polysome region is crowded with polysomes, space must be made between these polysomes for new polysomes to be inserted, and this space must be made by thermal (or ATP-driven) fluctuations of the molecules. Thus, if polysome accumulation drives the DNA segregation, it is not "exerting force", but rather the addition of new polysomes is iteratively rectifying gaps being made by Brownian motion.

We apologize for the understandable confusion. In our picture, the polysomes and DNA (conceptually considered as small plectonemic segments) basically behave as dissolved particles. If these particles were noninteracting, they would simply mix. However, both polysomes and DNA segments are large enough to interact sterically. So as density increases, steric avoidance implies a reduced conformational entropy and thus a higher free energy per particle. We argue (based on Miangolarra et al. 2021 PMID: 34675077 and Xiang et al. 2021 PMID: 34186018) that the demixing of polysomes and DNA segments occurs because DNA segments pack better with each other than they do with polysomes. This raises the free energy cost associated with DNA-polysome interactions compared to DNA-DNA interactions. We model this effect by introducing a term in the free energy χ_np, which refers to as a repulsion between DNA and polysomes, though as explained above it arises from entropic effects. At realistic cellular densities of DNA and polysomes, this repulsive interaction is strong enough to cause the DNA and polysomes to phase separate.

This same density-dependent free energy that causes phase separation can also give rise to forces, just in the way that a higher pressure on one side of a wall can give rise to a net force on the wall. Indeed, the “compaction force” we refer to is fundamentally an osmotic pressure difference. At some stages during nucleoid segregation, the region of the cell between nucleoids has a higher polysome concentration, and therefore a higher osmotic pressure, than the regions near the poles. This results in a net poleward force on the sister nucleoids that drives their migration toward the poles. This migration continues until the osmotic pressure equilibrates. Therefore, both phase separation (due to the steric repulsion described above) and nonequilibrium polysome production and degradation (which creates the initial accumulation of polysomes around midcell) are essential ingredients for nucleoid segregation.

This has been clarified in the revised text, with the support of additional simulation results showing how the asymmetry in polysome distribution causes a compaction force (Figure 4A).

The authors use polysome accumulation and phase separation to describe what is driving nucleoid segregation. Both terms are accurate, but it might help the less physically inclined reader to have one term, or have what each of these means explicitly defined at the start. I say this most especially in terms of "phase separation", as the currently huge momentum toward liquid-liquid interactions in biology causes the phrase "phase separation" to often evoke a number of wider (and less defined) phenomena and ideas that may not apply here. Thus, a simple clear definition at the start might help some readers.

In our case, phase separation means that the DNA-polysome steric repulsion is strong enough to drive their demixing, which creates a compact nucleoid. As mentioned in a previous point, this effect is captured in the free energy by the χ_np term, which is an effective repulsion between DNA and polysomes, though it arises from entropic effects.

In the revised manuscript, we now illustrate this with our theoretical model by initializing a cell with a diffuse nucleoid and low polysome concentration. For the sake of simplicity, we assume that the cell does not elongate. We observe that the DNA-polysome steric repulsion is sufficient to compact the nucleoid and place it at mid-cell (new Figure 4A).

(4) Line 478. "Altogether, these results support the notion that ectopic polysome accumulation drives nucleoid dynamics". Is this right? Should it not read "results support the notion that ectopic polysome accumulation inhibits/redirects nucleoid dynamics"?

We think that the ectopic polysome accumulation drives nucleoid dynamics. In our theoretical model, we can introduce polysome production at fixed sources to mimic the experiments where ectopic polysome production is achieved by high plasmid expression. The model is able to recapitulate the two main phenotypes observed in experiments (Figure 7). These new simulation results have been added to the revised manuscript (Figure 7 – figure supplement 2).

(5) It would be helpful to clarify what happens as the RplA-GFP signal decreases at midcell in Figure 1- is the signal then increasing in the less "dense" parts of the cell? That is, (a) are the polysomes at midcell redistributing throughout the cell? (b) is the total concentration of polysomes in the entire cell increasing over time?

It is a redistribution—the RplA-GFP signal remains constant in concentration from cell birth to division (Figure 1 – Figure Supplement 1E). This is now clarified in the revised text.

(6) Line 154. "Cell constriction contributed to the apparent depletion of ribosomal signal from the mid-cell region at the end of the cell division cycle (Figure 1B-C and Movie S1)" - It would be helpful if when cell constriction began and ended was indicated in Figures 1B and C.

Good idea. We have added markers in Figure 1C to indicate the average start of cell constriction. This relative time from birth to division was estimated as described in the new Figure 1 – figure supplement 2. We have also indicated that cell birth and division correspond to the first and last images/timepoint in Figure 1B and C, respectively. The two-imensional average cell projections presented in Figure 3D also indicate the average timing of cell constriction, consistent with our analysis in Figure 1 – figure supplement 2.

(7) In Figure 7 they demonstrate that radial confinement is needed for longitudinal nucleoid segregation. It should be noted (and cited) that past experiments of Bacillus l-forms in microfluidic channels showed a clear requirement role for rod shape (and a given width) in the positing and the spacing of the nucleoids.

Wu et al, Nature Communications, 2020. "Geometric principles underlying the proliferation of a model cell system" https://dx.doi.org/10.1038/s41467-020-17988-7

Good point! We have revised the text to mention this work. Thank you.

(8) "The correlated variability in polysome and nucleoid patterning across cells suggests that the size of the polysome-depleted spaces helps determine where the chromosomal DNA is most concentrated along the cell length. This patterning is likely reinforced through the displacement of the polysomes away from the DNA dense region"

It should be noted this likely functions not just in one direction (polysomes dictating DNA location), but also in the reverse - as the footprint of compacted DNA should also exclude (and thus affect) the location of polysomes

We agree that the effects could go both ways at this early stage of the story. We have revised the text accordingly.

(9) Line 159. Rifampicin is a transcription inhibitor that causes polysome depletion over time. This indicates that all ribosomal enrichments consist of polysomes and therefore will be referred to as polysome accumulations hereafter". Here and throughout this paper they use the term polysome, but cells also have monosomes (and 2 somes, etc). Rifampicin stops the assembly of all of these, and thus the loss of localization could occur from both. Thus, is it accurate to state that all transcription events occur in polysomes? Or are they grouping all of the n-somes into one group?

In the original discussion, we noted that our term “polysomes” also includes monosomes for simplicity, but we agree that the term should have been defined much earlier. The manuscript has been revised accordingly. Furthermore, in the revised manuscript, we have included additional simulation results with three different diffusion coefficients that reflect different polysome sizes to show that different polysome species with less or more ribosomes give similar results (Figure 4 – figure supplement 4). This shows that the average polysome description in our model is sufficient.

Thank you for the valuable comments and suggestions!

Reviewer #2 (Public review):

Summary:

The authors perform a remarkably comprehensive, rigorous, and extensive investigation into the spatiotemporal dynamics between ribosomal accumulation, nucleoid segregation, and cell division. Using detailed experimental characterization and rigorous physical models, they offer a compelling argument that nucleoid segregation rates are determined at least in part by the accumulation of ribosomes in the center of the cell, exerting a steric force to drive nucleoid segregation prior to cell division. This evolutionarily ingenious mechanism means cells can rely on ribosomal biogenesis as the sole determinant for the growth rate and cell division rate, avoiding the need for two separate 'sensors,' which would require careful coupling.

Terrific summary! Thank you for your positive assessment.

Strengths:

In terms of strengths; the paper is very well written, the data are of extremely high quality, and the work is of fundamental importance to the field of cell growth and division. This is an important and innovative discovery enabled through a combination of rigorous experimental work and innovative conceptual, statistical, and physical modeling.

Thank you!

Weaknesses:

In terms of weaknesses, I have three specific thoughts.

Firstly, my biggest question (and this may or may not be a bona fide weakness) is how unambiguously the authors can be sure their ribosomal labeling is reporting on polysomes, specifically. My reading of the work is that the loss of spatial density upon rifampicin treatment is used to infer that spatial density corresponds to polysomes, yet this feels like a relatively indirect way to get at this question, given rifampicin targets RNA polymerase and not translation. It would be good if a more direct way to confirm polysome dependence were possible.

The heterogeneity of ribosome distribution inside E. coli cells has been attributed to polysomes by many labs (PMID: 25056965, 38678067, 22624875, 31150626, 34186018, 10675340). The attribution is also consistent with single-molecule tracking experiments showing that slow-moving ribosomes (polysomes) are excluded by the nucleoid whereas fast-diffusing ribosomes (free ribosomal subunits) are distributed throughout the cytoplasm (PMID: 25056965, 22624875). These points are now mentioned in the revised manuscript.

Second, the authors invoke a phase separation model to explain the data, yet it is unclear whether there is any particular evidence supporting such a model, whether they can exclude simpler models of entanglement/local diffusion (and/or perhaps this is what is meant by phase separation?) and it's not clear if claiming phase separation offers any additional insight/predictive power/utility. I am OK with this being proposed as a hypothesis/idea/working model, and I agree the model is consistent with the data, BUT I also feel other models are consistent with the data. I also very much do not think that this specific aspect of the paper has any bearing on the paper's impact and importance.

We appreciate the reviewer’s comment, but the output of our reaction-diffusion model is a bona fide phase separation (spinodal decomposition). So, we feel that we need to use the term when reporting the modeling results. Inside the cell, the situation is more complicated. As the reviewer points out, there are likely entanglements (not considered in our model) and other important factors (please see our discussion on the model limitations). This said, we have revised our text to clarify our terms and proposed mechanism.

Finally, the writing and the figures are of extremely high quality, but the sheer volume of data here is potentially overwhelming. I wonder if there is any way for the authors to consider stripping down the text/figures to streamline things a bit? I also think it would be useful to include visually consistent schematics of the question/hypothesis/idea each of the figures is addressing to help keep readers on the same page as to what is going on in each figure. Again, there was no figure or section I felt was particularly unclear, but the sheer volume of text/data made reading this quite the mental endurance sport! I am completely guilty of this myself, so I don't think I have any super strong suggestions for how to fix this, but just something to consider.

We agree that there is a lot to digest. We could not come up with great ideas for visuals others than the schematics we already provide. However, we have revised the text to clarify our points and added a simulation result (Figure 4A) to help explain biophysical concepts.

Reviewer #3 (Public review):

Summary:

Papagiannakis et al. present a detailed study exploring the relationship between DNA/polysome phase separation and nucleoid segregation in Escherichia coli. Using a combination of experiments and modelling, the authors aim to link physical principles with biological processes to better understand nucleoid organisation and segregation during cell growth.

Strengths:

The authors have conducted a large number of experiments under different growth conditions and physiological perturbations (using antibiotics) to analyse the biophysical factors underlying the spatial organisation of nucleoids within growing E. coli cells. A simple model of ribosome-nucleoid segregation has been developed to explain the observations.

Weaknesses:

While the study addresses an important topic, several aspects of the modelling, assumptions, and claims warrant further consideration.

Thank you for your feedback. Please see below for a response to each concern.

Major Concerns:

Oversimplification of Modelling Assumptions:

The model simplifies nucleoid organisation by focusing on the axial (long-axis) dimension of the cell while neglecting the radial dimension (cell width). While this approach simplifies the model, it fails to explain key experimental observations, such as:

(1) Inconsistencies with Experimental Evidence:

The simplified model presented in this study predicts that translation-inhibiting drugs like chloramphenicol would maintain separated nucleoids due to increased polysome fractions. However, experimental evidence shows the opposite-separated nucleoids condense into a single lobe post-treatment (Bakshi et al 2014), indicating limitations in the model's assumptions/predictions. For the nucleoids to coalesce into a single lobe, polysomes must cross the nucleoid zones via the radial shells around the nucleoid lobes.

We do not think that the results from chloramphenicol-treated cells are inconsistent with our model. Our proposed mechanism predicts that nucleoids will condense in the presence of chloramphenicol, consistent with experiments. It also predicts that nucleoids that were still relatively close at the time of chloramphenicol treatment could fuse if they eventually touched through diffusion (thermal fluctuation) to reduce their interaction with the polysomes and minimize their conformational energy. Fusion is, however, not expected for well-separated nucleoids since their diffusion is slow in the crowded cytoplasm. This is consistent with our experimental observations: In the presence of a growth-inhibitory concentration of chloramphenicol (70 μg/mL), nucleoids in relatively close proximity can fuse, but well-separated nucleoids condense and do not fuse. Since the growth rate inhibition is not immediate upon chloramphenicol treatment, many cells with well-separated condensed nucleoids divide during the first hour. As a result, the non-fusion phenotype is more obvious in non-dividing cells, achieved by pre-treating cells with the cell division inhibitor cephalexin (50μg/mL). In these polyploid elongated cells, well-separated nucleoids condensed but did not fuse, not even after an hour in the presence of chloramphenicol. We have revised the manuscript to add these data (illustrative images + a quantitative analysis) in Figure 4 – figure supplement 1.

(2) The peripheral localisation of nucleoids observed after A22 treatment in this study and others (e.g., Japaridze et al., 2020; Wu et al., 2019), which conflicts with the model's assumptions and predictions. The assumption of radial confinement would predict nucleoids to fill up the volume or ribosomes to go near the cell wall, not the nucleoid, as seen in the data.

The reviewer makes a good point that DNA attachment to the membrane through transertion could contribute to the nucleoid being peripherally localized in A22 cells. We have revised the text to add this point. However, we do not think that this contradicts the proposed nucleoid segregation mechanism described in our model. On the contrary, by attaching the nucleoid to the cytoplasmic membrane along the cell width, transertion might help reduce the diffusion and thus exchange of polysomes across nucleoids. We have revised the text to discuss transertion over radial confinement.

(3) The radial compaction of the nucleoid upon rifampicin or chloramphenicol treatment, as reported by Bakshi et al. (2014) and Spahn et al. (2023), also contradicts the model's predictions. This is not expected if the nucleoid is already radially confined.

We originally evoked radial confinement to explain the observation that polysome accumulations do not equilibrate between DNA-free regions. We agree that transertion is an alternative explanation. Thank you for bringing it to our attention. However, please note that this does not contradict the model. In our view, it actually supports the 1D model by providing a reasonable explanation for the slow exchange of polysomes across DNA-free regions. The attachment of the nucleoid to the membrane along the cell width may act as diffusion barrier. We have revised the text and the title of the manuscript accordingly.

(4) Radial Distribution of Nucleoid and Ribosomal Shell:

The study does not account for well-documented features such as the membrane attachment of chromosomes and the ribosomal shell surrounding the nucleoid, observed in super-resolution studies (Bakshi et al., 2012; Sanamrad et al., 2014). These features are critical for understanding nucleoid dynamics, particularly under conditions of transcription-translation coupling or drug-induced detachment. Work by Yongren et al. (2014) has also shown that the radial organisation of the nucleoid is highly sensitive to growth and the multifork nature of DNA replication in bacteria.

We have revised the manuscript to discuss the membrane attachment. Please see the previous response.

The omission of organisation in the radial dimension and the entropic effects it entails, such as ribosome localisation near the membrane and nucleoid centralisation in expanded cells, undermines the model's explanatory power and predictive ability. Some observations have been previously explained by the membrane attachment of nucleoids (a hypothesis proposed by Rabinovitch et al., 2003, and supported by experiments from Bakshi et al., 2014, and recent super-resolution measurements by Spahn et al.).

We agree—we have revised the text to discuss membrane attachment in the radial dimension. See previous responses.

Ignoring the radial dimension and membrane attachment of nucleoid (which might coordinate cell growth with nucleoid expansion and segregation) presents a simplistic but potentially misleading picture of the underlying factors.

Please see above.

This reviewer suggests that the authors consider an alternative mechanism, supported by strong experimental evidence, as a potential explanation for the observed phenomena:

Nucleoids may transiently attach to the cell membrane, possibly through transertion, allowing for coordinated increases in nucleoid volume and length alongside cell growth and DNA replication. Polysomes likely occupy cellular spaces devoid of the nucleoid, contributing to nucleoid compaction due to mutual exclusion effects. After the nucleoids separate following ter separation, axial expansion of the cell membrane could lead to their spatial separation.

This “membrane attachment/cell elongation” model is reminiscent to the hypothesis proposed by Jacob et al in 1963 (doi:10.1101/SQB.1963.028.01.048). There are several lines of evidence arguing against it as the major driver of nucleoid segregation:

(Below is a slightly modified version of our response to a comment from Reviewer 1—see page 3)

(1) For this alternative model to work, axial membrane expansion (i.e., cell elongation) would have to be localized at the middle of the splitting nucleoids (i.e., midcell position for slow growth and ¼ and ¾ cell positions for fast growth) to create a directional motion. To our knowledge, there is no evidence of such localized membrane incorporation. Furthermore, even if membrane growth was localized at the right places, the fluidity of the cytoplasmic membrane (PMID: 6996724, 20159151, 24735432, 27705775) would be problematic. To go around this fluidity issue, one could potentially evoke a potential connection to the rigid peptidoglycan, but then again, peptidoglycan growth would have to be localized at the middle of the splitting nucleoid to “push” the sister nucleoid apart from each other. However, peptidoglycan growth is dispersed prior to cell constriction (PMID: 35705811, 36097171, 2656655).

(2) Even if we ignore the aforementioned caveats, Paul Wiggins’s group ruled out the cell elongation/transertion model by showing that the rate of cell elongation is slower than the rate of chromosome segregation (PMID: 23775792). In the revised manuscript, we confirm that the cell elongation rate is indeed overall slower than the nucleoid segregation rate (see Figure 1 - figure supplement 5A where the subtraction of the cell elongation rate to the nucleoid segregation rate at the single-cell level leads to positive values).

(3) Furthermore, our correlation analysis comparing the rate of nucleoid segregation to the rate of either cell elongation or polysome accumulation argues that polysome accumulation plays a larger role than cell elongation in nucleoid segregation. These data were already shown in the original manuscript (Figure 1I and Figure 1 – figure supplement 5B) but were not highlighted in this context. We have revised the text to clarify this point.

(4) The membrane attachment/cell elongation model does not explain the nucleoid asymmetries described in our paper (Figure 3), whereas they can be recapitulated by our model.

(5) The cell elongation/transertion model cannot predict the aberrant nucleoid dynamics observed when chromosomal expression is largely redirected to plasmid expression (Figure 7). In the revised manuscript, we have added simulation results showing that these nucleoid dynamics are predicted by our model (Figure 7 – figure supplement 2).

Based on these arguments, we do not believe that a mechanism based on membrane attachment and cell elongation is the major driver of nucleoid segregations. However, we do believe that it may play a complementary role (see “Nucleoid segregation likely involves multiple factors” in the Discussion). We have revised the text to clarify our thoughts and mention the potential role of transertion.

Incorporating this perspective into the discussion or future iterations of the model may provide a more comprehensive framework that aligns with the experimental observations in this study and previous work.

As noted above, we have revised the text to mention transertion.

Simplification of Ribosome States:

Combining monomeric and translating ribosomes into a single 'polysome' category may overlook spatial variations in these states, particularly during ribosome accumulation at the mid-cell. Without validating uniform mRNA distribution or conducting experimental controls such as FRAP or single-molecule measurements to estimate the proportions of ribosome states based on diffusion, this assumption remains speculative.

Indeed, for simplicity, we adopt an average description of all polysomes with an average diffusion coefficient and interaction parameters, which is sufficient for capturing the fundamental mechanism underlying nucleoid segregation. To illustrate that considering multiple polysome species does not change the physical picture, we have considered an extension of our model, which contains three polysome species, each with a different diffusion coefficient (D_P = 0.018, 0.023, or 0.028 μm²/s), reflecting that polysomes with more ribosomes will have a lower diffusion coefficient. Simulation of this model reveals that the different polysome species have essentially the same concentration distribution, suggesting that the average description in our minimal model is sufficient for our purposes. We present these new simulation results in Figure 4 – figure supplement 4 of the revised manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Does the polysome density correlate with the origins? If the majority of ribosomal genes are expressed near the origins,

This is indeed an interesting point that we mention in the discussion. The fact that the chromosomal origin is surrounded by highly expressed genes (PMID: 30904377) and is located near the middle of the nucleoid prior to DNA replication (PMID: 15960977, 27332118, 34385314, 37980336) can only help the model that we propose by increasing the polysome density at the mid-nucleoid position.

(2) Red lines in 3C are hard to resolve - can the authors make them darker?

Absolutely. Sorry about that.

Reviewer #2 (Recommendations for the authors):

The authors use rifampicin treatment as a mechanism to trigger polysome disassembly and show this leads to homogenous RplA distribution. This is a really important experiment as it is used to link RplA localization to polysomes, and tp argue that RplA density is reporting on polysomes. Given rifampicin inhibits RNA polymerase, and given the only reference of the three linking rifampicin to polysome disassembly is the 1971 Blundell and Wild ref), it would perhaps be useful to more conclusively show that polysome depletion (as opposed to inhibition of mRNA synthesis, which is upstream of polysome assembly) by using an alternative compound more commonly linked to polysome disassembly (e.g., puromycin) and show timelapse loss of density as a function of treatment time. This is not a required experiment, but given the idea that RplA density reports on polysomes is central to the authors' interpretation, it feels like this would be a thing worth being certain of. An alternative model is that ribosomes undergo self-assembly into local storage depots when not being used, but those depots are not translationally active/lack polysomes. I don't know if I think this is likely, but I'm not convinced the rifampicin treatment + waiting for a relatively long period of time unambiguously excludes other possible mechanisms given the large scale remodeling of the intracellular environment upon mRNA inhibition. I 100% buy the relationship between ribosomal distribution and nucleoid segregation (and the ectopic expression experiments are amazing in this regard), so my own pause for thought here is "do we know those ribosomes are in polysomes in the ribosome-dense regions". I'm not sure the answer to this question has any bearing on the impact and importance of this work (in my mind, it doesn't, but perhaps there's a reason it does?). The way to unambiguously show this would really be to do CryoET and show polysomes in the dense ribosomal regions, but I would never suggest the authors do that here (that's an entire other paper!).

We agree that mRNAs play a role, as mRNAs are major components of polysomes and most mRNAs are expected to be in the form of polysomes (i.e., in complex with ribosomes). In addition, as mentioned above, the enrichments of ribosome distribution are known to be associated with polysomes (PMID: 25056965, 38678067, 22624875, 31150626, 34186018, 10675340). The attribution is consistent with single-molecule tracking experiments showing that slow-moving ribosomes (polysomes) are excluded by the nucleoid whereas fast-diffusing ribosomes (free ribosomal subunits) are distributed throughout the cytoplasm (PMID: 25056965, 22624875). This is also consistent with cryo-ET results that we actually published (see Figure S5, PMID: 34186018). We have added this information to the revised manuscript. Thank you for alerting us of this oversight.

On line 320 the authors state "Our single-cell studies provided experimental support that phase separation between polysomes and DNA contributes to nucleoid segregation." - this comes pretty out of left field? I didn't see any discussion of this hypothesis leading up to this sentence, nor is there evidence I can see that necessitates phase separation as a mechanistic explanation unless we are simply using phase separation to mean cellular regions with distinct cellular properties (which I would advise against). If the authors really want to pursue this model I think much more support needs to be provided here, including (1) defining what the different phases are, (2) providing explicit description of what the attractive/repulsive determinants of these different phases could be/are, and (3) ruling out a model where the behavior observed is driven by a combination of DNA / polysome entanglement + steric exclusion; if this is actually the model, then being much more explicit about this being a locally arrested percolation phenomenon would be essential. Overall, however, I would probably dissuade the authors from pursuing the specific underlying physics of what drives the effects they're seeing in a Results section, solely because I think ruling in/out a model unambiguously is very difficult. Instead, this would be a useful topic for a Discussion, especially couched under a "our data are consistent with..." if they cannot exclude other models (which I think is unreasonably difficult to do).

Thank you for your advice. We have revised the text to more carefully choose our words and define our terms.

Minor comments:

The results in "Cell elongation may also contribute to sister nucleoid migration near the end of the division cycle" are really interesting, but this section is one big paragraph, and I might encourage the authors to divide this paragraph up to help the reader parse this complex (and fascinating) set of results!

We have revised this section to hopefully make it more accessible.

Reviewer #3 (Recommendations for the authors):

Technical Controls:

The authors should conduct a photobleaching control to confirm that the perceived 'higher' brightness of new ribosomes at the mid-cell position is not an artefact caused by older ribosomes being photobleached during the imaging process. Comparing results at various imaging frequencies and intensities is necessary to address this issue.

The ribosome localization data across 30 nutrient conditions (Figure 2, Figure 1 – figure supplement 6, Figure 2 – Figure supplement 1, Figure 2 – Figure supplement 3 and Figure 5) are from snapshot images, which do not have any photobleaching issue. They confirm the mid-cell accumulation seen by time-lapse microscopy. We have revised the text to clarify this point.

Novelty of Experimental Measurements:

While the scale of the study is unprecedented, claims of novelty (e.g., line 142) regarding ribosome-nucleoid segregation tracking are overstated. Similar observations have been made previously (e.g., Bakshi et al., 2012; Bakshi et al., 2014; Chai et al., 2014).

Our apologies. The text in line 142 oversimplified our rationale. This has been corrected in the revised manuscript.

The at-oddsness of the two things mentioned here—spewing bullshit and private beliefs that someone could correct you about—is hard to skip over.

Ciaran Feedback: Emphasis of this analysis should be on new business. It's where our growth is. CS escalations were just looking for the same pricing on the same deal. And now we have that so the CS escalations in relation to pricing should drop significantly

Here, the differences in weaponry highlight the overall differences between the groups. It's not just about using particular tools, it's about what those tools say about a culture. From an Athenian perspective, a bow may seem like a more cowardly weapon. Calling attention to this perception could be a way of disparaging the foreign Persians while also building a sense of national pride around the Athenians’ own choice of weapon. CC BY-NC-SA

Sita cries due to being captured by the evil "Other". It's made clear that it’s not just the king that's evil, but also the subjects that are called "wild she-demons". This is a very direct example of othering a foreign people by perceiving them as uncivilized. This lays the groundwork for the judgment of others’ unique customs, which in turn, builds an identity around one’s own customs. CC BY-NC-SA

Reviewer #1 (Public review):

Summary

In their paper Zhan et al. have used Pf genetic data from simulated data and Ghanaian field samples to elucidate a relationship between multiplicity of infection (MOI) (the number of distinct parasite clones in a single host infection) and force of infection (FOI). Specifically, they use sequencing data from the var genes of Pf along with Bayesian modeling to estimate MOI individual infections and use these values along with methods from queueing theory that rely on various assumptions to estimate FOI. They compare these estimates to known FOIs in a simulated scenario and describe the relationship between these estimated FOI values and another commonly used metric of transmission EIR (entomological inoculation rate).

This approach does fill an important gap in malaria epidemiology, namely estimating force of infection, which is currently complicated by several factors including superinfection, unknown duration of infection, and highly genetically diverse parasite populations. The authors use a new approach borrowing from other fields of statistics and modeling and make extensive efforts to evaluate their approach under a range of realistic sampling scenarios. However, the write-up would greatly benefit from added clarity both in the description of methods, and in the presentation of the results. Without these clarifications, rigorously evaluating whether the author's proposed method of estimating FOI is sound remains difficult. Additionally, there are several limitations that call into question the stated generalizability of this method that should at minimum be further discussed by authors and in some cases require a more thorough evaluation.

Major comments:

(1) Description and evaluation of FOI estimation procedure.

a. The methods section describing the two-moment approximation and accompanying appendix is lacking several important details. Equations on line 891 and 892 are only a small part of the equations in Choi et al. and do not adequately describe the procedure notably several quantities in those equations are never defined some of them are important to understand the method (e.g. A, S as the main random variables for inter-arrival times and service times, aR and bR which are the known time average quantities, and these also rely on the squared coefficient of variation of the random variable which is also never introduced in the paper). Without going back to the Choi paper to understand these quantities, and to understand the assumptions of this method it was not possible to follow how this works in the paper. At minimum, all variables used in the equations should be clearly defined.

b. Additionally, the description in the main text of how queueing procedure can be used to describe malaria infections would benefit from a diagram currently as written it's very difficult to follow.

c. Just observing the box plots of mean and 95% CI on a plot with the FOI estimate (Figures 1, 2 and 10-14) is not sufficient to adequately assess the performance of this estimator. First, it is not clear whether authors are displaying the bootstrapped 95%Cis or whether they are just showing the distribution of the mean FOI taken over multiple simulations, and then it seems that they are also estimating mean FOI per host on an annual basis. Showing a distribution of those per host estimates would also be helpful. Second, a more quantitative assessment of the ability of the estimator to recover the truth across simulations (e.g. proportion of simulations where the truth is captured in the 95% CI or something like this) is important in many cases it seems that the estimator is always underestimating the true FOI and may not even contain the true value in the FOI distribution (e.g. figure 10, figure 1 under the mid IRS panel). But it's not possible to conclude on way or the other based on this visualization. This is a major issue since it calls into question whether there is in fact data to support that these methods give good and consistent FOI estimates.

d. Furthermore authors state in the methods that the choice of mean and variance (and thus second moment) parameters for inter arrival times are varied widely, however, it's not clear what those ranges are there needs to be a clear table or figure caption showing what combinations of values were tested and which results are produced from them, this is an essential component of the method and it's impossible to fully evaluate its performance without this information. This relates to the issue of selecting the mean and variance values that maximize the likelihood of observing a given distribution of MOI estimates, this is very unclear since no likelihoods have been written down in the methods section of the main text, which likelihood are the authors referring to, is this the probability distribution of the steady state queue length distribution? At other places the authors refer to these quantities as Maximum Likelihood estimators, how do they know they have found the MLE? There are no derivations in the manuscript to support this. The authors should specify and likelihood and include in an appendix why their estimation procedure is in fact maximizing this likelihood preferably with evidence of the shape of the likelihood, and how fine the grid of values they tested are for their mean and variance since this could influence the overall quality of the estimation procedure.

(2) Limitation of FOI estimation procedure.

a. The authors discuss the importance of duration of infection to this problem. While I agree that empirically estimating this is not possible, there are other options besides assuming that all 1-5 year olds have the same duration of infection distribution as naïve adults co-infected with syphilis. E.g. it would be useful to test a wide range of assumed infection duration and assess their impact on the estimation procedure. Furthermore, if the authors are going to stick to the described method for duration of infection, the potentially limited generalizability of this method needs to be further highlighted in both the introduction, and the discussion. In particular, for an estimated mean FOI of about 5 per host per year in the pre-IRS season as estimated in Ghana (Figure 3) it seems that this would not translate to 4 year old being immune naïve, and certainly this would not necessarily generalize well to a school-aged child population or an adult population.

b. The evaluation of the capacity parameter c seems to be quite important, and is set at 30, however, the authors only describe trying values of 25 and 30, and claim that this does not impact FOI inference, however it is not clear that this is the case. What happens if carrying capacity is increased substantially? Alternatively, this would be more convincing if the authors provided a mathematical explanation of why the carrying capacity increasing will not influence the FOI inference, but absent that, this should be mentioned and discussed as a limitation.

Comments on revisions:

The authors have adequately responded to all comments.

I have reported a post before, but honestly not often. It was a comment on TikTok that was clearly racist and got a lot of likes, which made it worse. I don’t usually report things unless they’re really bad, because most of the time I assume nothing will happen. But that one just felt too much to ignore. Reading this chapter made me think more about how the report systems are kind of hidden or hard to trust. It feels like people don’t know what counts as “bad enough” to report unless it’s super obvious.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

I can find no problems with the experiments performed in this study, but there are several results that are not easily explained. I would like to see more consideration of possible explanations. For example, one of the major differences between the the CESA structure from primary and secondary cell walls is the displacement of TM7 in the primary cell wall CESAs that leads to the formation of lipid exposed channel. Why does this vary between primary and secondary cell wall CESA proteins? Could it explain differences in the properties, such as crystallinity between primary and secondary cell wall cellulose?

At this time, the different position of TM helix 7 observed in our GmCesA structures is just an observation. We have some emerging evidence that this helix is also flexible in POCesA8 under certain conditions; however, we do not know whether this affects catalytic activity or cellulose coalescence. We have revised the text to avoid the interpretation that TM 7 repositioning is a characteristic feature of primary cell wall CesAs only.

Similarly, regarding the formation of the larger structures from mixtures of different CESA trimers. Why do they not form roseOes? Par;cularly as these appear to be forming 2-dimensional structures.

We have included additional data on the interaction between different CesA isoform trimers (Figure 6). To answer the reviewer’s ques;on, the most likely reasons for not observing closely packed roseOe-like structures are (a) steric interferences between the micelles harboring the individual CesA trimers, and (b) the lack of a stabilizing cellulose fiber.  This interpretation is supported by 2D class averages of dimers of CesA1 and CesA3 trimers (now shown in Fig. 6). The class averages show an ‘upside-down and side-by-side’ orientation of the two trimers, consistent with interferences between the solubilizing detergent micelles. The implica;ons of this non-physiological arrangement are discussed in the revised manuscript. In a biological membrane, the CesA trimers are confined to the same plane in the same orientation, which is likely necessary to form ordered arrangements.

What role does the NTD play in trimer formation given its apparent very high class specificity?

We have no data suggesting any contribution of the NTD to trimer formation. Recent work on moss CesA5 and similar AlphaFold predic;ons suggest that, for some CesAs, an extreme Nterminal region can interact with the beta sheet of the catalytic domain via beta-strand augmentation. Whether this interaction can contribute to CesA-CesA interactions remains unknown.

Reviewer #2 (Recommendations For The Authors):

The authors provide PDB codes but not EMDB codes for the EM maps, also I would encourage the authors to upload the raw micrographs to the EMPIAR database.

The EMDB codes are shown in Table 1 and data transfer to EMPIAR is ongoing.  

Page 6 line 144, the statement "All CesA isoforms show greatest catalytic activity at neutral pH" seems to contradict the data in Figure 1e and the subsequent statements. This sentence should be removed.

The text has been revised to indicate that CesA1 and CesA6 show highest activity under mild alkaline conditions.  

Page 6, line 150, the authors state "The affinities for substrate binding range from 1.4 mM for CesA1 to 0.6 and 2.4 mM for CesA3 and CesA6, respectively." How were the affinities determined? Is this the affinities or the Michaelis constants? Is it known whether CesAs are rapid equilibrium enzymes? This should be clarified.

The text now states that we performed Michaelis Menten kine;cs using the ‘UDP-Glo’ glycosyltransferase assay kit. We are uncertain about whether CesAs can be classified as rapid equilibrium enzymes. The rate-limiting step of cellulose biosynthesis has been proposed to be glycosyl transfer, rather than cellulose transloca;on.  To avoid any confusion, we changed the text from '…reveals Michaelis Menten constants for substrate binding of CesA1 and CesA3' to '…reveals Michaelis Menten constants for CesA1 and CesA3 with respect to UDP-Glc'.

Page 6, line 153, the authors state "CesA1's apparent Ki for UDP is roughly 0.8 mM, whereas this concentration is increased to about 1.2 to 1.5 mM for CesA6 and CesA3, respectively." From the Figure 1g legend, it appears that the authors performed additional experiments at different UDP-Glc concentrations in order to determine Ki that are not shown. This data should be included as a figure supplement as the data presented are insufficient to determine Ki (only IC50).

The UDP inhibition data show apparent IC50 values, and this has been corrected in the text. For each CesA isoform, the titration was done at one UDP-Glc concentration only.    

Page 8, line 202, the authors state that TM helix 7 of the primary cell wall CesAs is more flexible "as evidenced by weaker density." The density for the TM helix 7 should be shown. If the density shown in Supplementary Figure 3 corresponds to TM helices the number of the helices should be indicated as it is not immediately obvious from the amino acid residue numbers.

The densities for TM helix 7 of all CesA isoforms are shown in Supplemental Figure 3. The helices are now labeled to orient the reader.  

Reviewer #2 (Public Review)

The authors demonstrate via truncation that the N-terminus of the CesA is not involved in the interactions between the isoforms and propose that the CSR hook-like extensions are the primary mediator of trimer-trimer interactions. This argument would be strengthened by equivalent truncation experiments in which the CSR region is removed.

We performed the suggested experiment. We replaced the CSR in N-terminally truncated GmCesA1 and GmCesA3 with a 20-residue long linker. The resulting constructs assemble into homotrimeric complexes as observed for the wild type and only N-terminally truncated versions. However, the CSR-truncated constructs of the different isoforms do not interact with each other in vitro. Further, CSR-deleted GmCesA3 also does not interact with full-length CesA1, suggesting that two CSR domains of different isoforms are necessary for homotrimer interaction. This data is now shown as Fig. 5.  

Reviewer #3 (Recommendations For The Authors):

Major Points

(1) The authors state on Line 354 that they were unable to isolate heterotrimers, but they need to provide the data to support this claim; for example, it is important for readers to understand whether co-expression of all three CESAs leads to only homotrimers or only monomers. This information is essential to exclude model C in Figure 6.

We have revised the corresponding discussion and toned down the statement that heterotrimeric complexes did not form in our recombinant expression system. Co-expression of differently tagged secondary or primary cell wall CesAs in Sf9 cells has consistently resulted in negligible amounts of material that can be purified sequentially over different affinity matrices (corresponding to the tags on the recombinantly expressed CesAs – His, Strep, Flag). While this does not exclude the formation of a small fraction of hetero-oligomeric complexes (which could be trimers as observed in the structures or monomers interacting via their CSR regions), it demonstrates that CesAs favor the same isoform for trimer formation, rather than partnering with other isoforms. An example of such a purification is now shown as Supplemental Figure 8.

Determining whether heterotrimers are formed upon co-expression of different CesA isoforms requires high resolution structural analysis because co-purification of different isoforms can also be due to interactions between different homo-trimeric complexes, as demonstrated in this study.

While we cannot exclude that factors exist in planta that may prevent the formation of homotrimers and favor the formation of hetero-trimers, it is important to keep in mind that currently no experimental data supports the formation of hetero-trimeric complexes. Instead, our work demonstrates that existing data on CesA isoform interactions can be explained by the interaction of homotrimers of different isoforms.

(2) The evidence that the products of GmCEA1, GmCESA3, and GmCESA6 homotrimers are cellulose is that they consume UDP-glucose and produce a beta-glucanase-sensitive product. Other beta-glucans synthesized by similar GT2 family proteins (e.g. CSLDs, Yang et al., 2020 Plant Cell or CSLCs, Kim et al., 2020 PNAS) would be sensitive to this enzyme, and the product cannot truly be called cellulose unless it forms microfibrils. Previous reports of CESA activity in vitro have demonstrated that the products form genuine cellulose microfibrils rather than amorphous beta-glucan (via electron microscopy); extensively documented that the product is sensitive to beta-glucanase, but not other enzymes (e.g., callose or MLG degrading enzymes); provided linkage analysis of the product to conclusively demonstrate that it is a beta1,4-linked glucan; and documented a loss of activity when key catalytic residues were mutated (Purushotham et al., 2016 PNAS; Cho et al., 2017 Plant Phys; Purushotham et al., 2020 Science).

Other GT2 characterization efforts have documented activity to similar standards (e.g. CSLDs, Yang et al., 2020 Plant Cell or CSLFs, Purushotham et al., 2022 Science Advances). At least one independent method should be provided, and the TEM of the product is necessary for readers to appreciate whether the product forms true cellulose microfibrils.

There may be some confusion regarding the nomenclature. Therefore, we revised the second sentence of the Introduction to define ‘cellulose’ as a beta-1,4 linked glucose polymer, in accordance with the ‘Essentials of Glycobiology’. This is also consistent with enzyme nomenclature as the primary product of cellulose synthase is a single glucose polymer, and not a fibril. For example, most bacterial cellulose synthases only produce amorphous (single chain) cellulose. 

We show that the GmCesA products can be degraded with a beta-1,4 specific glucanase (cellulase), which demonstrates the formation of authentic cellulose. This study does not focus on the formation of fibrillar cellulose apart from suggesting a revised model for a microfibrilforming CSC.       

(3) The position of isoxaben-resistant mutations implies that primary cell wall CESAs form heterotrimers (Shim et al., 2018 Frontiers in Plant Biology). Indeed, in their previous description of the POCESA8 structure (Purushotham et al., 2020 Science), the authors discussed the position of isoxaben-resistant mutations as a way to justify the way that TM7 of one CESA can contribute to forming the cellulose translocation pore in the neighbouring CESA within a heterotrimer. However, in this manuscript, the authors document a different location for TM7 in the GmCEA1, GmCESA3, and GmCESA6 homotrimers, which would change the position of these resistance mutations. Please discuss.

As stated in the manuscript, we do not know what the functional implication of the TM7 flexibility may be, but we speculate that it could affect the alignment of the synthesized cellulose polymers. Regarding the previously reported POCesA8 structure, the mapping of one of the reported isoxaben resistance mutants to the C-terminus of TM7 was not used to justify the structure; the structure with its position of TM7 stands on its own.  Considering recent observations suggesting that isoxaben may affect cellulose biosynthesis via secondary effects, we prefer not to speculate on the mechanism by which these mutations cause the apparent resistance to isoxaben (PMID: 37823413).

(4) The authors present no evidence that GmCESA1/3/6 are involved in primary cell wall synthesis. Please include gene expression information (documenting widespread expression consistent with primary CESAs) and rigorous molecular phylogenetic analysis (or references to these published data) to clarify that these are indeed primary cell wall CESAs.

This has been addressed. We have included additional figures (Fig. 1 and S1B) that show the strong and wide distribution of the selected CesAs in soybean leaves, their co-expression with primary cell wall markers, and their phylogenetic clustering with Arabidopsis primary cell wall CesAs.  

(5) Several small changes need to be made to the abstract to ensure that it aligns with the data: Line 28: add "in vitro" arer "their assembly into homotrimeric complexes" Line 28: change "stabilized by the PCR" to "presumably stabilized by the PCR".

We inserted ‘in vitro’ as requested. We did not insert the second modification as requested since CesA trimers are stabilized by the PCR. This is a fact arising from several experimentally determined CesA trimer structures.  

(6) In all graphs in all figures it is unclear what the sample size is and what the bars represent. These must be stated in the figure legends. It is best practice to plot individual data points so that readers can easily interpret both the sample size and the variation.

The sample sizes and error bars are now defined in the relevant figure legends.

(7) The methods need to unambiguously define GmCESA1, GmCESA3, GmCESA6 protein identities using appropriate accession numbers.

The accession codes are now provided in the Methods.

Minor Points

(1) Does CESA1 have higher activity in Figure 1D because of the pH at which the assay was conducted (see Figure 1E)? Could this difference in activity or pH preference have also affected their capacity to resolve TM7 of CESA1?

We consistently observe higher in vitro catalytic activity of CesA1, compared to CesA3 and CesA6. Activity assays are performed at a pH of 7.5, roughly halfway between the activity maxima of CesA3 and CesA1/6. At this pH, we expect activity differences to arise from factors other than the buffer pH. As detailed above, we do not know whether the conformational flexibility of TM helix 7 affects catalytic activity.

(2) Line 55: The authors should cite additional papers that also provide insight into CESA structure (e.g. Qiao et al 2021 PNAS).

A recent publication on moss CesA5 has been included. Qiao et al unfortunately report on a dimeric assembly of a fragment of Arabidopsis thaliana’s CesA3 catalytic domain, which we consider non-physiological. We added a brief statement in the Discussion explaining that our GmCesA3 structure is inconsistent with the dimeric arrangement reported by Qiao et al.

(3) Line 95: these references are about secondary cell wall CESA isoforms, but there are more appropriate references for the primary CESAs that should be included in place of these papers.

Fagard et al report on growth defects in roots and dark-grown hypocotyls linked to Arabidopsis CesA 1 and CesA6, which are primary cell wall CesAs. Nevertheless, we have included two additional recent publications from the Meyerowitz and Persson labs.

(4) Line 121-122: Please cite a specific figure that supports this claim, since the (Purushotham et al., 2020) reference refers to POCESA8 enrichment results, but the claims are about the GmCESA1/3/6 enrichment.

The POCesA8 reference has been removed. The classification into monomers and trimers arises from the data processing described in this manuscript and is consistent with similar results obtained for POCesA8.

(5) Line 314: It is more appropriate to use "enzyme activity" rather than "cellulose synthesis".

We prefer to use cellulose biosynthesis since the enzyme produces cellulose.

(6) Figure 1: please add colour to the graphs to clarify which trend lines belong to which data series (especially Figure 1G).

The figure (now Fig. 2) has been revised as suggested.