Author response:

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

Reviewer 1:

Comment 1: Indirect Estimates of White Matter Connections: While dMRI is a valuable tool, it inherently provides indirect and inferred information about neural pathways. The accuracy and specificity of tractography can be influenced by various factors, including fiber crossing, partial volume effects, and algorithmic assumptions. A potential limitation in the accuracy of indirect estimates might affect the precision of spatial extent measurements, introducing uncertainty in the interpretation of cortico-thalamic connectivity patterns. Addressing the methodological limitations associated with indirect estimates and considering complementary approaches could strengthen the overall robustness of the findings.

We appreciate the reviewer’s comment and agree tractography is an indirect estimate and subject to limitations. Regarding this manuscript, the key question is not whether the anatomical tracts are without false positives or negatives, and in fact we argue that this question is outside the scope of this manuscript and has been addressed in several previous studies (e.g. Thomas et al. 2015, Schilling et al., 2020, Grisot et al. 2021, and many others). Instead, the key question for this manuscript is whether the focality of termination patterns within the thalamus is systematically biased in a way that the observation of a hierarchy effect is artifactual. The many supplementary analyses in this manuscript do help address this question and increase our confidence that the indirect nature of tractography does not systematically bias the EDpc1 measure such that association areas only appear to have more diffuse connectivity patterns relative to sensorimotor areas.

Comment 2: An over-arching theme of my review is that, each time I found myself wondering about a detail, a null, or a reference, I had only to read the next sentence or paragraph to find my concern handled in a clear and concise fashion. This is, in my opinion, the mark of work of the highest order. I congratulate the authors on their excellent work, which I believe will be impactful and well-received.

I have no notes that I feel can help improve what is already an impeccable piece of work.

We thank the reviewer for the kind comment.

Reviewer #2:

Comment 1: Structural thalamocortical connectivity was estimated from diffusion imaging data obtained from the HCP dataset. Consequently, the robustness and accuracy of the results depend on the suitability of this data for such a purpose. Conducting tractography on the cortical-thalamic system is recognized as a challenging endeavor for several reasons. First, diffusion directions lose their clearly defined principal orientations once they reach the deep thalamic nuclei, rendering the tracking of structures on the medial side, such as the medial dorsal (MD) and pulvinar nuclei difficult. Somewhat concerning is those are regions that authors found to show diffuse connectivity patterns. Second, the thalamic radiata diverge into several directions, and routes to the lateral surface often lack the clarity necessary for successful tracking. It is unclear if all cortical regions have similar levels of accuracy, and some of the lateral associative regions might have less accurate tracking, making them appear to be more diffuse, biasing the results.

As mentioned in the weakness section, it is crucial to address the need for better validation or the inclusion of control analyses to ensure that the results are not systematically biased due to known issues, such as the difficulty in tracking the medial thalamus and the potential for higher false positives when tracking the lateral frontal cortex.

We thank that reviewer for bringing up an important point. To determine if some areas of the thalamus were more difficult to track and, in turn, biased the EDpc1 measure we added an additional supplemental figure (S31). In this figure, shown below, we calculate the total SC of all ipsilateral cortical areas to each thalamic voxel. We show that, indeed, medial thalamic voxels have a lower total streamline count to ipsilateral cortex, and we see reduced total streamline counts to lateral thalamic areas and the very posterior end of the thalamus. We determined if some cortical areas preferentially projected to parts of the thalamus with lower ipsilateral total SC (i.e. by calculating the overlap between SC and total cortical SC for each thalamic voxel) and found only a weak relationship with our measure. Furthermore, we regressed each voxel’s mean ipsilateral cortical SC from streamline count matrix. We found that the EDpc1 measure didn’t significantly change after the regression.

Additionally, we note that this analysis assumes that all thalamic voxels should have equal strength of connectivity (i.e., total SC) to the ipsilateral cortex and that such a measure is a proxy for “accuracy.” While both of these assumptions may not be entirely valid, this figure does demonstrate that potential reductions in tracking from the medial thalamus does not significantly affect the EDpc1 measure.

Comment 2: While the methodology employed by the authors appears to be state-of-the-art, there exists uncertainty regarding its appropriateness for validation, given the well-documented issues of false positives and false negatives in probabilistic diffusion tractography, as discussed by Thomas et al. 2014 PNAS. Although replicating the results in both humans and non-human primates strengthens the study, a more compelling validation approach would involve demonstrating the method's ability to accurately trace known tracts from established tracing studies or, even better, employing phantom track data. Many of the control analyses the authors presented, such as track density, do not speak to accuracy.

In addition to or response to Reviewer 1 Comment 1, we would like to add the following:

We agree with the reviewer that tractography methods have known limitations. We would also like to point out that several studies have already performed the studies suggested by the reviewer. Many studies have compared tracts reconstructed from diffusion data using tractography methods to tracer-derived connections (eg. Thomas et al., 2014, as mentioned by the reviewer; Donahue et al., 2016, J Neurosci; Dauguet et al., 2007 NeuroImage; Gao et al., 2013 PloS One; van den Heuvel et al., 2015, Hum Brain Map; Azadbakht et al., 2015 Cereb Cortex; Ambrosen et al., 2020 NeuroIamge). Notably, studies comparing tractography and tracer-derived white matter tracts in the same animal (e.g. Grisot et al., 2021; Gao et al., 2013 PloS One) have demonstrated that tractography errors may be inflated in studies comparing tractography and tracer-derived connections in different animals.

Additionally, others have employed phantoms to assess the validity of tractography methods (e.g. Drobnjak et al., 2021). For the purposes of this manuscript, phantom data would not be an adequate control because phantom data would likely not capture the biological complexities of tracking subcortical white matter tracts and identifying projections within subcortical grey matter.

While a comparison of our tractography-derived ED measure to ED calculated on terminations from tracer studies within the thalamus from several somatomotor and associative regions in macaques would provide additional confidence for our results, such a control is certainly outside the scope of this study. Additionally, such a study would not provide a ground truth comparison for the human data. Even if this hypothetical experiment was performed, a negative finding would not refute our results, as any differences could be attributed to evolutionary differences. Unfortunately, there exists no ground truth to compare human white matter connectivity patterns to, which is why we stress-tested our results in as many ways as possible. These stress tests revealed that our main findings are very robust.

Specifically, as the key validity question of our study was whether there was a confound that systematically biased the ED measure as to make the hierarchy effect artifactual, the control analyses we performed to determine if track density, cortical geometry, bundle integrity, etc in fact do speak the robustness of the results. Regarding the track density analyses we argue that these control analyses do speaks to accuracy. The reviewer mentioned above that some cortical areas may be biased because their anatomical tracts may be more difficult to reconstruct using tractography. The mean streamline count is meant to reflect the density of a fiber bundle, but corticothalamic tracts that are more difficult to track will, by nature, have fewer streamline counts. So, the mean streamline not only reflects the density of a fiber bundle but also how easily that tract is to reconstruct. Therefore, if it was the case that cortical areas with more difficult to reconstruct white matter tracts to the thalamus are also more diffuse, then we should observe a strong positive correlation between the ED measure and the mean streamline count, which we tested directly and found only a weak correlation (Fig. S11). This is true for tracking to the entire thalamus, and the additional supplemental Figure S31 shows that reduced tracking to specific parts of the thalamus (e.g. the medial portion) also does not strongly relate to the ED measure. So, tracts that are more difficult to reconstruct may also be more diffuse, but this seems to add only a little noise and does not account for the strong relationship between the ED measure and T1w/T2w and RSFCpc1 measures the reflect the cortical hierarchy.

Comment 3: If tracking the medial thalamus is indeed less accurate, characterized by higher false positives and false negatives, it could potentially lead to increased variability among individual subjects. In cases where results are averaged across subjects, as the authors have apparently done, this could inadvertently contribute to the emergence of the "diffuse" motif, as described in the context of the associative cortex. This presents a critical issue that requires a more thorough control analysis and validation process to ensure that the main results are not artifacts resulting from limitations in tractography.

Additionally, conducting a control analysis to demonstrate that individual variability in tracking endpoints within the thalamus, when averaged across subjects, does not artificially generate a more diffuse connectivity pattern, is essential.

We thank the reviewer for bringing up this point, and the reviewer is correct that a simple group average of streamline counts across that thalamus could make some thalamic patterns appear more diffuse if those patterns vary slightly in location across people. The simplest way to address this concern is to show that diffuse patterns are present in individual subjects. Fig. 2 panels B, C, H, and I are all subject-level figures, which show that we can replicate the group level findings in Fig. 2 panels F, G. Specifically, Fig 2. Panels H and I show that the effect of association areas exhibiting more diffuse connectivity patterns within the thalamus relative to sensorimotor areas is generalizable across subjects.

To the reviewer’s point, the other way that averaged streamline counts could make focal connections seem diffuse is by averaging within cortical areas (e.g. to test the possibility that association areas may have highly variability focal patterns, and when averaged within the cortical area it makes these focal patterns appear more diffuse). To test this, we show that we can replicate the hierarchy effect at the vertex level, by calculating the extent of connectivity patterns for every cortical vertex and correlated vertex-level EDpc1 values to vertex-level T1w/T2w and RSFC_pc1 values (Fig S20).

Hopefully the data shown in Fig. 2 (replication at the individual level) and Fig. S20 (replication at the vertex level) ameliorate the reviewer’s concerns that averaging highly variable focal connectivity patterns within the thalamus (either across people or across vertices) does not artifactually produce diffuse thalamic connectivity patterns for associative cortical areas.

Comment 4: Because the authors included data from all thresholds, it seems likely that false positive tracks were included in the results. The methodology described seems to unavoidably include anatomically implausible pathways in the spatial extent analyses.

The thresholding approach taken in the manuscript aimed to control for inter-areal differences in anatomical connection strength that could confound the ED estimates. Here I am not quite clear why inter-areal differences in anatomical connection strength have to be controlled. A global threshold applied on all thalamic voxels might kill some connections that are weak but do exist. Those weak pathways are less likely to survive at high thresholds. In the meantime, the mean ED is weighted, with more conservative thresholds having higher weights. That being said, isn't it possible that more robust pathways might contribute more to the mean ED than weaker pathways?

This is a good point from the reviewer, and we appreciate them bringing up these points about our thresholding rationale. We would like to clarify two points: why it was appropriate for our question to threshold thalamic voxels for each cortical area separately and why we iteratively thresholded thalamic voxels.

Regarding thalamic connectivity differences between cortical areas: a global threshold would indeed exclude weak, but potentially true, connections. This was part of our rationale for thresholding thalamic voxels for each cortical area separately. Too conservative of a global threshold would exclude all thalamic voxels for some cortical areas and too liberal of a threshold would include many potentially false positive connections for other cortical areas. Our method of thresholding each cortical area’s thalamic voxels separately ensured that we were sampling thalamic voxels in an equitable manner across cortical areas. We updated the text to clarify this:

Methods section, pg. 11, section Framework to quantify the extent of thalamic connectivity patterns via Euclidean distance (ED)

“We used Euclidean distance (ED) to quantify the extent of each cortical area's thalamic connectivity patters. Probabilistic tractography data require thresholding before the ED calculation. To avoid the selection of an arbitrary threshold (Sotiropoulos et al., 2019, Zhang et al., 2022), we calculated ED for a range of thresholds (Figure 1a). Our thresholding framework uses a tractography-derived connectivity matrix as input. We iteratively excluded voxels with lower streamline counts for each cortical parcel such that the same number of voxels was included at each threshold. At each threshold, ED was calculated between the top x\% of thalamic voxels with the highest streamline counts. This produced a matrix of ED values (360 cortical parcels by 100 thresholds). This matrix was used as input into a PCA to derive a single loading for each cortical parcel. While alternative thresholding approaches have been proposed, this framework optimizes the examination of spatial patterns by proportionally thresholding the data, enabling equitable sampling of each cortical parcel's streamline counts within the thalamus.

This approach controlled for inter-areal differences in anatomical connection strength that could confound the ED estimates. In contrast, a global threshold, which is applied to all cortical areas, may exclude all thalamic streamline counts for some cortical areas that are more difficult to reconstruct, thus making it impossible to calculate ED for that cortical area, as there are no surviving thalamic voxels from which to calculate ED. This would be especially problematic for white matter tracts are more difficult to reconstruct (e.g. the auditory radiation), and cortical areas connected to the thalamus by those white matter tracts would have a disproportionate number of thalamic voxels excluded when using a global threshold.”

Regarding thalamic connectivity differences across the thalamus for a given cortical area, the thresholding method we use does include anatomically implausible connections in the ED calculation because we sample voxels iteratively, and as more and more thalamic voxels are included in the ED analysis the likelihood that they reflect spurious connections increases. This approach made the most sense to us, because there is no way to identify a threshold that only includes true positive connections. And since this method does not exist, we sampled all thresholds and leveraged the behavior of the ED metric across thresholds to quantify the spread of a connectivity pattern. As the reviewer points out, since the measure is effectively “weighted,” more “robust” or anatomically plausible pathways should contribute more to the EDpc1 rather than weaker pathways. This is exactly the balanced approach we aimed for: a measure that is driven by connections that have the highest likelihood of being a true positive but does not rely on an arbitrary threshold.

We did also replicate our main findings after thresholding and binarizing the data for separate thresholds, which show that our main effect was strongest only when thalamic voxels with the highest streamline counts (which are assumed to have a lower chance of being false positives) are included in the ED calculation (Fig. S5). This more traditional method of thresholding also supported our results, and increases our overall confidence that associative cortical areas have more diffuse connectivity patterns within the thalamus relative to somatomotor areas.

Comment 5: In the introduction, there is a bit of ambiguity that needs clarification. The overall goal of the study appears to be the examination of anatomical connectivity from the cortex to the thalamus, specifically whether a cortical region projects to a single thalamic subregion or multiple thalamic subregions. However, certain parts of the introduction also suggest an exploration of the concept of thalamic integration, which typically means a single thalamic region integrating input from multiple cortical regions (converging input). These two patterns, many cortical regions to one thalamic region versus one cortical region to many different thalamic regions, represent distinct and fundamentally different concepts that should be clarified in the manuscript.

We thank the reviewer for pointing out this ambiguity and have edited the introduction to clarify this point:

Our argument for a potential mechanism for integration is the following: because corticothalamic connectivity is topographically organized, if a cortical area has a more diffuse anatomical projection across the thalamus that means its connections overlap with more cortical areas. To the reviewer’s point, our argument is simply that one cortical area targeting multiple thalamic nuclei inherently suggests that such a cortical area has overlapping connectivity patterns with many other cortical areas in the same thalamic subregion. We have updated the introduction to clarify this further.

Intro, pg 1.

“Studies of cortical-thalamic connectivity date back to the early 19th century, yet we still lack a comprehensive understanding of how these connections are organized (see 13 and 14 for review). The traditional view of the thalamus is based on its histologically-defined nuclear structure (6). This view was originally supported by evidence that cortical areas project to individual thalamic nuclei, suggesting that the thalamus primarily relays information (15). However, several studies have demonstrated that cortical connectivity within the thalamus is topographically organized and follows a smooth gradient across the thalamus (16–21). Additionally, some cortical areas exhibit extensive connections within the thalamus, which target multiple thalamic nuclei (22? ). These extensive connections may enable information integration within the thalamus through overlapping termination patterns from different cortical areas, a key mechanism for higher-order associative thalamic computations (23– 25). However, our knowledge of how thalamic connectivity patterns vary across cortical areas, especially in humans, remains incomplete. Characterizing cortical variation in thalamic connectivity patterns may offer insights into the functional roles of distinct cortico-thalamic loops (6, 7).”

Discussion, pg 9. Section: The spatial properties of thalamic connectivity pat- terns provide insight into the role of the thalamus in shaping brain-wide information flow.

“In this study, we demonstrate that association cortical areas exhibit diffuse anatomical connections within the thalamus. This may enable these cortical areas to integrate information from distributed areas across the cortex, a critical mechanism supporting higher-order neural computations. Specifically, because thalamocortical connectivity is organized topographically, a cortical area that projects to a larger set of thalamic subregions has the potential to communicate with many other cortical areas. We observed that anterior cingulate cortical areas had some of the most diffuse thalamic connections. This observation aligns with findings from Phillips et al. that area 24 exhibited the most diffuse anatomical terminations across the mediodorsal nucleus of the thalamus relative to other prefrontal cortical area…”

Reviewer 3:

Comment 1: Potential weaknesses of the study are that it seems to largely integrate aspects of the thalamus that have been already described before. The differentiation between sensory and association systems across thalamic subregions is something that has been described before (see: Oldham and Ball, 2023; Zheng et al., 2023; Yang et al., 2020 Mueller, 2020; Behrens, 2003).

It is true that previous studies have shown that corticothalamic systems vary between sensory and associative cortical areas. Furthermore, there is much evidence that indicates that the sensory-association hierarchy is a major principle of brain organization in general. However, how and why these circuits are different is still not fully known, both across the whole brain and in corticothalamic circuits specifically.

Our study is the first to compare patterns of anatomical connectivity within the thalamus and determine if cortical areas vary in the extent of those patterns. So our main finding isn't that sensory and association cortical areas show differences in thalamic connectivity, it is that they specifically show differences in their pattern of connectivity within the thalamus. This provides a unique insight into how sensory and associative systems differ in their thalamic connectivity in primates.

Additionally, we show evidence that provides some insight into why these differences may exist. Although we cannot provide causal evidence, our data suggest that differences in patterns of anatomical connectivity within the thalamus were related to how different cortical areas process information via the thalamus, which aligns with speculations from Phillips et al 2021.

So our main finding isn't that sensory and association cortical areas show differences in thalamic connectivity, is it that they specifically show differences in their pattern of connectivity within the thalamus and these differences may help us understand how these cortical areas process information and, in turn, how they may support different types of computations, both of which are major goals in neuroscience. To better clarify this in the manuscript, we made the following changes:

Discussion, Paragraph 1, pg 8:

“This study contributes to the rich body of literature investigating the organization of cortico-thalamic systems in human and non-human primates. Prior research has shown that features of thalamocortical connectivity differ between sensory and association systems, and our work advances this understanding by demonstrating that these systems also differ in the pattern and spatial extent of their anatomical connections within the thalamus. Using dMRI-derived tractography across species, we show that these connectivity patterns vary systematically along the cortical hierarchy in both humans and macaques. These findings are critical for establishing the anatomical architecture of how information flows within distinct cortico-thalamic systems. Specifically, we identify reproducible tractography motifs that correspond to sensorimotor and association circuits, which were consistent across individuals and generalize across species. Collectively, this study offers convergent evidence that the spatial pattern of anatomical connections within the thalamus differs between sensory and association cortical areas, which may support distinct computations across cortico-thalamic systems.”

Comment 2: (1) Why not formally test the association between humans and macaques by bringing the brains to the same space?

We thank the reviewer for this query. We were primarily interested in using the macaque data as a validation of the human data, because it was acquired at a much higher resolution, there are no motion confounds, and it provides a bridge with the tract tracing literature in macaques. We are currently studying interspecies differences in patterns of thalamic connectivity, as well as extensions of our approach into structure-function coupling, and we believe these topics warrant their own paper.

Comment 3: (2) Possibly flesh out the differences between this study and other studies with related approaches a bit further.

We updated the discussion section to better clarify the differences in this study from previous research. See response to Reviewer 3 Comment 1 for text changes.

Comment 4: (3) The current title entails 'cortical hierarchy' but would 'differentiation between sensory and association regions' not be more correct? Or at least a reflection on how cortical hierarchy can be perceived?

We treat these phrases as synonymous terms. Our definition of cortical hierarchy is a smooth transition in features between sensory and motor areas to higher-order associative areas. The use of cortical hierarchy is meant to reflect that our measure continuously varies across the cortex. We updated the manuscript to make this clearer:

Abstract, pg 1.

“Additionally, we leveraged resting-state functional MRI, cortical myelin, and human neural gene expression data to test if the extent of anatomical connections within the thalamus varied along the cortical hierarchy, from sensory and motor to multimodal associative cortical areas.”

Comment 5: (4) For the core-matrix map, there is a marked left-right differences and also there are only two donors in the right hemisphere, possibly note this as a limitation?

We thank the reviewer for this observation. We updated Fig. S28 Panel D to show that the correspondence between EDpc1 and the Core-Matrix (CPc) cortical maps holds when the correlation was done for left and right cortex, separately.

Author response:

We would like to thank the reviewers. They have raised some points that we will address in a revised manuscript.

Reviewer #2 (Public review):

The work has significant implications for understanding immune evasion and nutrient uptake mechanisms in trypanosomes.

While the experimental rigor is commendable, revisions are needed to clarify methodological limitations and to broaden the discussion of functional consequences.

The authors argue that prior studies missed surface-localized TfR due to harsh washing/fixation (e.g., methanol). While this is plausible, additional evidence would strengthen the claim.

It remains unclear how centrifugation steps of various lengths (as in previous publications) can equally and quantitatively redistribute TfR into the flagellar pocket. If this were the case, it should be straightforward for the authors to test this experimentally.

If TfR is distributed over the cell surface, live-cell imaging with fluorescent transferrin should be performed as a control. Modern detection limits now reach the single-molecule level, and transient immobilization of live trypanosomes has been established, which would exclude hydrodynamic surface clearance as a confounding factor.

In most images, TfR is not evenly distributed on the surface but rather appears punctate. Could this reflect localization to membrane domains? Immuno-EM with high-pressure frozen parasites could resolve this question and is relatively straightforward.

The authors might consider discussing whether differences in parasite life cycle stages (procyclic versus bloodstream forms) or culture conditions (e.g., cell density) affect localization. The developmentally regulated retention of GPI-anchored procyclin in the flagellar pocket might be worth mentioning.

eLife Assessment

This valuable manuscript investigates the localisation of nutrient receptors in bloodstream stage trypanosomes, with implications for both nutrient uptake and immune evasion. Results after direct fixation of the cells in culture medium provide convincing evidence that the amounts of receptors on the surface of the cell, as opposed to the flagellar pocket, have previously been severely underestimated. Some results were essentially confirmatory, and there are questions regarding the quantitation of ligand binding by transferring receptors.

Reviewer #1 (Public review):

Summary:

An interesting manuscript from the Carrington lab is presented investigating the behavior of single vs double GPI-anchored nutrient receptors in bloodstream form (BSF) T. brucei. These include the transferrin receptor (TfR), the HpHb receptor (HpHbR), and the factor H receptor (FHR). The central question is why these critical proteins are not targeted by host-acquired immunity. It has generally been thought that they are sequestered in the flagellar pocket (FP), where they are subject to rapid endocytosis - any Ab:receptor complexes would be rapidly removed from the cell surface. This manuscript challenges that assumption by showing that these receptors can be found all over the outer cell body and flagella surfaces, if one looks in an appropriate manner (rapid direct fixation in culture media).

The main part of the manuscript focuses on TfR, typically a GPI1 heterodimer of very similar E6 (GPI anchored) and E7 (truncated, no GPI) subunits. These are expressed coordinately from 15 telomeric expression sites (BES), of which only one can be transcribed at a time. The authors identify a native E6:E7 pair in BES7 in which E7 is not truncated and therefore forms a GPI2 heterodimer. By in situ genetic manipulation, they generate two different sets of GPI1:GPI2 TfR combinations expressed from two different BESs (BES1 and BES7). Comparative analyses of these receptors form the bulk of the data.

The main findings are:

(1) Both GPI1 and GPI2 TfR can be found on the cell body/flagellar surface. (2) Both are functional for Tf binding and uptake. (3) GPI2 TfR is expressed at ~1.5x relative to GPI1 TfR. (4) Ultimate TfR expression level (protein) is dependent on the BES from which it is expressed.

Most of these results are quite reasonably explained in light of the hydrodynamic flow model of the Engstler lab and the GPI valence model of the Bangs lab. Additional experiments, again by rapid fixation, with HpHbR and FHR, show that these GPI1 receptors can also be seen on the cell surface, in contrast to published localizations.

It is quite interesting that the authors have identified a native GPI2 TfR. However, essentially all of the data with GPI2 TfR are confirmatory for the prior, more detailed studies of Tiengwe et al. (2017). That said, the suggestion that GPI2 was the ancestral state makes good evolutionary sense, and begs the question of why trypanosomes prefer GPI1 TfR in 14 of 15 ESs (i.e., what is the selection pressure?).

Strengths and weaknesses:

(1) BES7 TfR subunit genes (BES7_Tb427v10): There are actually three (in order 5'-3'): E7gpi, E6.1 and E6.2. E6.1 and E6.2 have a single nucleotide difference. This raises the issue of coordinate expression. If overall levels of E6 (2 genes) are not down-regulated to match E7 (1 gene), this will result in a 2x excess of E6 subunits. The most likely fate of these is the formation of non-functional GPI2 homodimers on the cell surface, as shown in Tiengwe et al. (2017), which will contribute to the elevated TfR expression seen in BES7.

(2) Surface binding studies: This is the most puzzling aspect of the entire manuscript. That surface GPI2 TfR should be functional for Tf binding and uptake is not surprising, as this has already been shown by Tiengwe et al. (2017), but the methodology for this assay raises important questions. First, labeled Tf is added at 500 nM to live cells in complete media containing 2.5 uM unlabeled Tf - a 5x excess. It is difficult to see how significant binding of labeled TfR could occur in as little as 15 seconds under these conditions. Second, Tiengwe et al. (2017) found that trypanosomes taken directly from culture could not bind labeled Tf in direct surface labeling experiments. To achieve binding, it was necessary to first culture cells in serum-free media for a sufficient time to allow new unligated TfR to be synthesized and transported to the surface. This result suggests that essentially all surface TfR is normally ligated and unavailable to the added probe. Third, the authors have themselves argued previously, based on binding affinities, that all surface-exposed TfR is likely ligated in a natural setting (DOI: 10.1002/bies.202400053). Could the observed binding actually be non-specific due to the high levels of fixative used?

(3) Variable TfR expression in different BESs: It appears that native TfR is expressed at higher levels from BES7 compared to BES1, and even more so when compared to BES3. This raises the possibility that the anti-TfR used in these experiments has differential reactivity with the three sets of TfRs. The authors discount this possibility due to the overall high sequence similarities of E6s and E7s from the various ESs. However, their own analyses show that the BES1, BES3, and BES7 TfRs are relatively distal to each other in the phylogenetic trees, and this Reviewer strongly suspects that the apparent difference in expression is due to differential reactivity with the anti-TfR used in this work. In the grand scheme, this is a minor issue that does not impact the other major conclusions concerning TfR localization and function, nor the behavior of HpHbR and FHR. However, the authors make very strong conclusions about the role of BESs in TfR expression levels, even claiming that it is the 'dominant determinant' (line 189).

(4) Surface immuno-localization of receptors: These experiments are compelling and useful to the field. To explain the difference with essentially all prior studies, the authors suggest that typical fixation procedures allow for clearance of receptor:ligand complexes by hydrodynamic flow due to extended manipulation prior to fixation (washing steps). Despite the fact that these protocols typically involve ice-cold physiological buffers that minimize membrane mobility, this is a reasonable possibility. Have the authors challenged their hypothesis by testing more typical protocols themselves? Other contributing factors that could play a role are the use of deconvolution, which tends to minimize weak signals, and also the fact that investigators tend to discount weak surface signals as background relative to stronger internal signals.

(5) Shedding: A central aspect of the GPI valence model (Schwartz et al., 2005, Tiengwe et al., 2017) is that GPI1 reporters that reach the cell body surface are shed into the media because a single dimyristoylglycerol-containing GPI anchor does not stably associate with biological membranes. As the authors point out, this is a major factor contributing to higher steady-state levels of cell-associated GPI2 TfR relative to GPI1 TfR. Those studies also found that the size/complexity of the attached protein correlated inversely with shedding, suggesting exit from the flagellar pocket as a restricting factor in cell body surface localization. The amount of newly synthesized TfR shed into the media was ~5%, indicating that very little actually exits the FP to the outer surface. In this regard, is it possible to know the overall ratio of cell surface:FP:endosomal localized receptors? Could these data not be 'harvested' from the 3D structural illumination imaging?

eLife Assessment

The manuscript reports fundamental findings supported by convincing data that supports the biological mechanism for optimal nodulation in soybean. The results are of relevance to understanding the signaling pathways (specifically those dependent on RIN4/RPM1-interacting protein 4) underpinning beneficial rhizobia symbiosis, while repressing the immune response.

Reviewer #1 (Public review):

The authors set out to illuminate how legumes promote symbiosis with beneficial nitrogen fixing bacteria while maintaining a general defensive posture towards the plethora of potentially pathogenic microbes in their environment. Intriguingly, a protein involved in plant defence signalling, RIN4, is implicated as a type of 'gatekeeper' for the symbiosis, connecting symbiosis signalling with defence signalling. Although questions remain about how exactly RIN4 enables the symbiosis, the work opens an important door to new discoveries in this area.

Strengths:

The study uses a multidisciplinary, state-of-the-art approach to implicate RIN4 in soybean nodulation and symbiosis development. The results support the authors' conclusions.

Weaknesses:

None after thoughtful revision.

Reviewer #3 (Public review):

Summary:

This manuscript by Toth et al reveals a conserved phosphorylation site within the RIN4 (RPM1-interacting protein 4) R protein that is exclusive to two of the four nodulating clades, Fabales and Rosales. The authors present persuasive genetic and biochemical evidence that phosphorylation at the serine residue 143 of GmRIN4b, located within a 15-aa conserved motif with a core five amino acids 'GRDSP' region, by SymRK, is essential for optimal nodulation in soybean. The experimental design and results are robust, the manuscript's discussion has been satisfactorily updated. Results described here are important to understand how the symbiosis signaling pathway prioritizes associations with beneficial rhizobia, while repressing immunity-related signals.

Strengths:

The manuscript asks an important question in plant-microbe interaction studies with interesting findings.

Overall, the experiments are detailed, thorough and very well-designed. The findings appear to be robust.

The authors provide results that are not overinterpreted and are instead measured and logical.

Weaknesses:

No major weaknesses.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The authors set out to illuminate how legumes promote symbiosis with beneficial nitrogen-fixing bacteria while maintaining a general defensive posture towards the plethora of potentially pathogenic bacteria in their environment. Intriguingly, a protein involved in plant defence signalling, RIN4, is implicated as a type of 'gatekeeper' for symbiosis, connecting symbiosis signalling with defence signalling. Although questions remain about how exactly RIN4 enables symbiosis, the work opens an important door to new discoveries in this area.

Strengths:

The study uses a multidisciplinary, state-of-the-art approach to implicate RIN4 in soybean nodulation and symbiosis development. The results support the authors' conclusions.

Weaknesses:

No serious weaknesses, although the manuscript could be improved slightly from technical and communication standpoints.

Reviewer #2 (Public Review):

Summary:

The study by Toth et al. investigates the role of RIN4, a key immune regulator, in the symbiotic nitrogen fixation process between soybean and rhizobium. The authors found that SymRK can interact with and phosphorylate GmRIN4. This phosphorylation occurs within a 15 amino acid motif that is highly conserved in Nfixation clades. Genetic studies indicate that GmRIN4a/b play a role in root nodule symbiosis. Based on their data, the authors suggest that RIN4 may function as a key regulator connecting symbiotic and immune signaling pathways.

Overall, the conclusions of this paper are well supported by the data, although there are a few areas that need clarification.

Strengths:

This study provides important insights by demonstrating that RIN4, a key immune regulator, is also required for symbiotic nitrogen fixation.

The findings suggest that GmRIN4a/b could mediate appropriate responses during infection, whether it is by friendly or hostile organisms.

Weaknesses:

The study did not explore the immune response in the rin4 mutant. Therefore, it remains unknown how GmRIN4a/b distinguishes between friend and foe.

Reviewer #3 (Public Review):

Summary:

This manuscript by Toth et al reveals a conserved phosphorylation site within the RIN4 (RPM1-interacting protein 4) R protein that is exclusive to two of the four nodulating clades, Fabales and Rosales. The authors present persuasive genetic and biochemical evidence that phosphorylation at the serine residue 143 of GmRIN4b, located within a 15-aa conserved motif with a core five amino acids 'GRDSP' region, by SymRK, is essential for optimal nodulation in soybean. While the experimental design and results are robust, the manuscript's discussion fails to clearly articulate the significance of these findings. Results described here are important to understand how the symbiosis signaling pathway prioritizes associations with beneficial rhizobia, while repressing immunity-related signals.

Strengths:

The manuscript asks an important question in plant-microbe interaction studies with interesting findings.

Overall, the experiments are detailed, thorough, and very well-designed. The findings appear to be robust.

The authors provide results that are not overinterpreted and are instead measured and logical.

Weaknesses:

No major weaknesses. However, a well-thought-out discussion integrating all the findings and interpreting them is lacking; in its current form, the discussion lacks 'boldness'. The primary question of the study - how plants differentiate between pathogens and symbionts - is not discussed in light of the findings. The concluding remark, "Taken together, our results indicate that successful development of the root nodule symbiosis requires cross-talk between NF-triggered symbiotic signaling and plant immune signaling mediated by RIN4," though accurate, fails to capture the novelty or significance of the findings, and left me wondering how this adds to what is already known. A clear conclusion, for eg, the phosphorylation of RIN4 isoforms by SYMRK at S143 modulates immune responses during symbiotic interactions with rhizobia, or similar, is needed.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I have no major criticism of the work, although it could be improved by addressing the following minor points:

(1) Page 8, Figure 2 legend. Consider changing "proper symbiosis formation" to "normal nodulation" or something that better reflects control of nodule development/number.

We thank you for the suggestion, the legend was changed to “...required for normal nodule formation” (see Page 10, revised manuscript)

(2) Page 9. Cut "newly" from the first sentence of paragraph 2, as S143 phosphorylation was identified previously.

Thank you for the suggestion, we removed “newly” from the sentence.

(3) Page 10, Figure 3. Panels B showing green-fluorescent nodules are unnecessary given the quantitative data presented in the accompanying panel A. This goes for similar supplemental figures later.

We appreciate the comment; regarding Figure 3 (complementing rin4b mutant, we updated the figures according to the other reviewer’s comment) and Suppl Figure 6 (OE phenotype of phospho-mimic/negative mutants), we removed the panels showing the micrographs. At the same time, we did not modify Figure 2 (where micrographs showing transgenic roots carrying the silencing constructs) for the sake of figure completeness. (See Page 10, revised manuscript)

(4) Consider swapping Figure 3 for Supplemental Figure S7, which I think shows more clearly the importance of RIN4 phosphorylation in nodulation.

We appreciate the comment and have swapped the figures according to the reviewer’s suggestion. Legend, figure description, and manuscript text have been updated accordingly. (See page 12 and 38, revised manuscript)

(5) Page 10. Replace "it will be referred to S143..." with "we refer to S143 instead of ....".

We replaced it according to the comment.

(6) Page 11, delete "While" from "While no interactions could be observed...".

We deleted it according to the suggestion.

(7) Page 33, Fig S5. How many biological replicates were performed to produce the data presented in panel C and what do the error bar and asterisk indicate? Check that this information is provided in all figures that show errors and statistical significance.

Thank you for the remark. The experiment was repeated three times, and this note was added to the figure description. All the other figure legends with error bar(s) were checked whether replicates are indicated accordingly.

(8) Page 37, Fig S11, panel B. Are averages of data from the 2 biological and 3 technical replicates shown? Add error bars and tests of significant difference.

Averages of a total of 6 replicates (from 2 biological replicates, each run in triplicates) are shown. We thank the reviewer for pointing out the missing error bars and statistical test, we have updated the figure accordingly.

(9) Fig S12. Why are panels A, C, E, and G presented? The other panels seem to show the same data more clearly- showing the linear relationship between peak area ratio and protein concentration.

We have taken the reviewer’s comment into consideration and revised the figure, removing the calibration curves and showing only four panels. The figure legend has been corrected accordingly. (Please see page 43, revised masnuscript). The original figure (unlike other revised figures) had to be deleted from the revised manuscript,as it caused technical issues when converting the document into pdf.

Reviewer #2 (Recommendations For The Authors):

Some small suggestions:

(1) It's good to include a protein schematic for RIN4 in Figure 1.

We appreciate the reviewer’s suggestion and we have drawn a protein schematic and added it to Figure 1. The figure legend was updated accordingly.

(2) There appears to be incorrect labeling in Figure 2c; please double-check and make the necessary corrections.

With respect, we do not understand the comment about incorrect labeling. Would the reviewer please help us out and give more explanation? In Figure 2C, RIN4a and RIN4b expression was checked in transgenic roots expressing either EV (empty vector) or different silencing constructs targeting RIN4a/b.

Reviewer #3 (Recommendations For The Authors):

I enjoyed the level of detail and precision in experimental design.

A discussion point could be - What does it mean that nodule number but not fixation is affected? Is RIN4 only involved in the entry stage of infection but not in nodules during N-fixation?

Current/Our data suggest that RIN4 does indeed appear to be involved in infection. This hypothesis is supported by the findings that RIN4a/b was found phosphorylated in root hairs but not in root (or it was not detected in the root). The interaction with the early signaling RLKs also suggests that RIN4 is likely involved in the early stage of symbiosis formation.

How would the authors explain their observation "However, the motif is retained in non-nodulating Fabales (such as C. canadensis, N. schottii; SI Appendix, Figure S2) and Rosales species as well." What does this imply about the role in symbiosis that the authors propose?

We appreciate the reviewer’s question. The motif seems to be retained, however, it might be not only the motif but also the protein structure that in case of nodulating plants might be different. We have not investigated the structure of RIN4, how it would look based on certain features/upon interaction with another protein and/or post-translational modification(s). Griesman et al, (2018) showed the absence of certain genes within Fabales in non-nodulating species, we can speculate that these absent genes can’t interact with RIN4 in those species, therefore the lack of downstream signaling could be possible (in spite of the retained motif in non-nodulating species). At this point, there is not enough data or knowledge to further speculate.

qPCR analysis of symbiotic pathway genes showed that both NIN-dependent and NIN-independent branches of the symbiosis signaling pathway were negatively affected in the rin4b mutant. Please derive a conclusion from this.

We appreciate the comment, it also prompted us to correct the following sentence; original: “Since NIN is responsible for induction of NF-YA and ERN1 transcription factors, their reduced expression in rin4b plants was not unexpected (Fig. 5). “As ERN1 expression is independent of NIN (Kawaharada et al, 2017). The following sentences were also deleted as it represented a repetition of a statement above these sentences: “Soybean NF-YA1 homolog responded significantly to rhizobial treatment in rin4b plants, whereas NF-YA3 induction did not show significant induction (Fig. 5).“

We added the following conclusion/hypothesis: “Based on the results of the expression data presented above, it seems that both NIN-dependent and NINindependent branches of the symbiotic signaling pathways are affected in the rin4b mutant background. This indicates that the role of RIN4 protein in the symbiotic pathway can be placed upstream of CYCLOPS, as the CYCLOPS transcription activating complex is responsible (directly or indirectly) for the activation of all TFs tested in our expression analysis (Singh et al, 2014/47, 48).” (Please see Page 16, revised manuscript)

The authors are highly encouraged to write a thoughtful discussion that would accompany the detailed experimental work performed in this manuscript.

We appreciate the comment, and we did some work on the discussion part of the document. (Please see Pages 17-19, revised manuscript)

Some minor suggestions for overall readability are below.

What about immune signaling genes? Given that authors hypothesize that "Absence of AtRIN4 leads to increased PTI responses and, therefore, it might be that GmRIN4b absence also causes enhanced PTI which might have contributed to significantly fewer nodules." Could check marker immune signaling gene expression FLS2 and others.

We appreciate the reviewer’s comment, and while we believe those are very interesting questions/suggestions, answering them is out of the scope of the current manuscript. Partially because it has been shown that several defenseresponsive genes that were described in leaf immune responses could not be confirmed to respond in a similar manner in root (Chuberre et al., 2018). It was also shown that plant immune responses are compartmentalized and specialized in roots (Chuberre et al., 2018). If we were looking at immune-responsive genes, the signal might be diluted because of its specialized and compartmentalized nature. Another reason why these questions cannot be answered as a part of the current manuscript is because finding a suitable immune responsive gene would require rigorous experiments (not only in root, but also in root hair (over a timecourse) which would be a ground work for a separate study (root hair isolation is not a trivial experiment, it requires at least 250-300 seedlings per treatment/per time-point).

Regarding FLS2, it is known in Arabidopsis that its expression is tissue-specific within the root, and it seems that FLS2 expression is restricted to the root vasculature (Wyrsch et al, 2015). In our manuscript, we showed that RIN4a/b is highly expressed in root hairs, as well as RIN4 phosphorylation was detectable in root hair but not in the root; therefore, we do not see the reason to investigate FLS2 expression.

"in our hands only ERN1a could be amplified. One possible explanation for this observation is that primers were designed based on Williams 82 reference genome, while our rin4b mutant was generated in the Bert cultivar background." Is the sequence between the two cultivars and the primers that bind to ERN1b in both cultivars so different? If not, this explanation is not very convincing.

At the time of performing the experiment the genomic sequence of the Bert cultivar (used for generating rin4b edited lines) was not publicly available. In accordance with the reviewer’s comment, we removed the explanation, as it does not seem to be relevant. (See page 16, revised manuscript)

The figures are clear and there is a logical flow. The images of fluorescing nodules in Figure 2,3 panels with nodules are not informative or unbiased .

We appreciate the comment, as for Figure 3 (complementing rin4b mutant), we updated the figures according to the other reviewer’s comment and Suppl. Figure 6 (OE phenotype of phospho-mimic/negative mutants) we removed the panels showing the micrographs. At the same time, we did not modify Figure 2 (where micrographs showing transgenic roots carrying the silencing constructs) for the sake of figure completeness. (See pages 10, 12 and 38, revised manuscript)

What does the exercise in isolation of rin4 mutants in lotus tell us? Is it worth including?

Isolation of the Ljrin4 mutant suggests that RIN4 carries such an importance that the mutant version of it is lethal for the plant (as in Arabidospis, where most of the evidence regarding the role of RIN4 has been described), and an additional piece of evidence that RIN4 is similarly crucial across most land plant species.

Sentence ambiguous. "Co-expression of RIN4a and b with SymRKßΔMLD and NFR1α _resulted in YFP fluorescence detected by Confocal Laser Scanning Microscopy (SI Appendix, Figure S8) suggesting that RIN4a and b proteins closely associate with both RLKs." Were all 4 expressed together?

Thank you for the remark. Not all 4 proteins were co-expressed together. We adjusted the sentence as follows: “Co-expression of RIN4a/ and b with SymRKßΔMLD as well as and NFR1α resulted in YFP fluorescence…” I hope it is phrased in a clearer way. (See page 13, revised manuscript)

Minor spelling errors throughout.. Costume-made (custom made?)

Thank you for noticing. According to the Cambridge online dictionary, it is written with a hyphen, therefore, we added a hyphen and corrected the manuscript accordingly.

CRISPR-cas9 or CRISPR/Cas9? Keep it consistent throughout. CRISPR-cas9 is the latest consensus.

We corrected it to “CRISPR-Cas9” throughout the manuscript.

References are missing for several 'obvious statements' but please include them to reach a broader audience. For example the first 5 sentences of the introduction. Also, statements such as 'Root hairs are the primary entry point for rhizobial infection in most legumes.'.

Thank you for the comment. To make it clearer, we also added reference #1, after the third sentence of the introduction, as well as we added an additional review as reference. This additional review was also cited as the source for the sentence “Root hairs are the primary…” (Please see page 2, revised manuscript)

Can you provide a percent value? Silencing of RIN4a and RIN4b resulted in significantly reduced nodule numbers on soybean transgenic roots in comparison to transgenic roots carrying the empty vector control. Also, this wording suggests it was a double K.D. but from the images, it appears they were individually silenced.

We appreciate the reviewer's comment. We observed a 50-70% reduction in the number of nodules. We adjusted the text according to the reviewer's remark. (See page 9, revised manuscript)

eLife Assessment

This paper shows that it is possible to optogenetically activate single retinal ganglion cells in vivo in monkeys. This is an important step towards towards causal tests of the role of specific ganglion cell types in visual perception. The paper presents convincing evidence for the promise of the approach but further work will be needed to full explore its limitations and specificity.

Reviewer #1 (Public review):

Summary

This manuscript reports preliminary evidence of successful optogenetic activation of single retinal ganglion cells (RGCs) through the eye of a living monkey using adaptive optics (AO).

Strengths

The eventual goals of this line of research have an enormous potential impact in that they will probe the perceptual impact of activating single RGCs. While I think more data should be included, the four examples shown look quite convincing.

Weaknesses

While this is undoubtedly a technical achievement and an important step along this group's stated goal to measure the perceptual consequences of single-RGC activations, the presentation lacks the rigor that I would expect from what is really a methods paper. In my view, it is perfectly reasonable to publish the details of a method before it has yielded any new biological insights, but in those publications, there is a higher burden to report the methodological details, full data sets, calibrations, and limitations of the method. There is considerable room for improvement in reporting those aspects. Specifically, more raw data should be shown for activations of neighboring RGCs to pinpoint the actual resolution of the technique, and more than two cells (one from each field of view) should be tested. Some information about the density of labeled RGCs in these animals would also be helpful to provide context for how many well-isolated target cells exist per animal.

Reviewer #2 (Public review):

Murphy et al. expressed ChrimsonR and GCaMP6s in retinal ganglion cells of a living macaque. They recorded calcium responses and stimulated individual cells, optically. Neurons targeted for stimulation were activated strongly whereas neighboring neurons were not.

The ability to record from neuronal populations while simultaneously stimulating a subset in controlled way is a high priority for systems neuroscience, and this has been particularly challenging in primates. This study marks an important milestone in the journey towards this goal.

Reviewer #3 (Public review):

This paper reports a considerable technical achievement: the optogenetic activation of single retinal ganglion cells in vivo in monkeys. As clearly specified in the paper, this is an important step towards causal tests of the role of specific ganglion cell types in visual perception. The paper is brief, and it will be important to follow this work with a more detailed methodological description to guide related work, to explore limitations, and to build confidence in the specificity of the approach.

Author response:

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

Reviewer #1 (Public Review):

Summary

This manuscript reports preliminary evidence of successful optogenetic activation of single retinal ganglion cells (RGCs) through the eye of a living monkey using adaptive optics (AO).

Strengths

The eventual goals of this line of research have enormous potential impact in that they will probe the perceptual impact of activating single RGCs. While I think more data should be included, the four examples shown look quite convincing. Weaknesses

While this is undoubtedly a technical achievement and an important step along this group's stated goal to measure the perceptual consequences of single-RGC activations, the presentation lacks the rigor that I would expect from what is really a methods paper. In my view, it is perfectly reasonable to publish the details of a method before it has yielded any new biological insights, but in those publications, there is a higher burden to report the methodological details, full data sets, calibrations, and limitations of the method. There is considerable room for improvement in reporting those aspects. Specifically, more raw data should be shown for activations of neighboring RGCs to pinpoint the actual resolution of the technique, and more than two cells (one from each field of view) should be tested.

We have expanded sections discussing both the methodology and limitations of this technique via a rewrite of the results and discussion section. The data used in the paper is available online via the link provided in the manuscript. We agree that a more detailed investigation of the strengths and limitations of the approach would have been a laudable goal. However, before returning to more detailed studies, we have shifted our effort to developing the monkey psychophysical performance we need to combine with the single cell stimulation approach described here. In addition, the optogenetic ChrimsonR used in this study is not the best choice for this experiment because of its poor sensitivity. We are currently exploring the use of ChRmine (as described in lines 93-97), which is roughly 2 orders of magnitude more sensitive. We have also been working on methods to improve probe stabilization to reduce tracking errors during eye movements. Once these improvements have been implemented, we will undertake the more detailed studies suggested here. Nonetheless, as a pragmatic matter, we submit that it is valuable to document proof-of-concept with this manuscript.

Some information about the density of labeled RGCs in these animals would also be helpful to provide context for how many well-isolated target cells exist per animal.

We agree. Getting reliable information about labeled cell density would be difficult without detailed histology of the retina, which we are reluctant to do because it would require sacrificing these precious and expensive monkeys from which we continue to get valuable information. We are actively exploring methods to reduce the cell density to make isolation easier including the use of the CAMKII promoter as well as the use of intracranial injections via AAV.retro that would allow calcium indicator expression in the peripheral retina where RGCs form a monolayer. It may be that the rarity of isolated RGCS will not be a fundamental limitation of the approach in the future.

Reviewer #2 (Public Review):

This proof-of-principle study lays important groundwork for future studies. Murphy et al. expressed ChrimsonR and GCaMP6s in retinal ganglion cells of a living macaque. They recorded calcium responses and stimulated individual cells, optically. Neurons targeted for stimulation were activated strongly whereas neighboring neurons were not.

The ability to record from neuronal populations while simultaneously stimulating a subset in a controlled way is a high priority for systems neuroscience, and this has been particularly challenging in primates. This study marks an important milestone in the journey towards this goal.

The ability to detect stimulation of single RGCs was presumably due to the smallness of the light spot and the sparsity of transduction. Can the authors comment on the importance of the latter factor for their results? Is it possible that the stimulation protocol activated neurons nearby the targeted neuron that did not express GCaMP? Is it possible that off-target neurons near the targeted neuron expressed GCaMP, and were activated, but too weakly to produce a detectable GCaMP signal? In general, simply knowing that off-target signals were undetectable is not enough; knowing something about the threshold for the detection of off-target signals under the conditions of this experiment is critical.

We agree with these points. We cannot rule out the possibility that some nearby cells were activated but we could not detect this because they did not express GCaMP. We also do not know whether cells responded but our recording methods were not sufficiently sensitive to detect them. A related limitation is that we do not know of course what the relationship is between the threshold for detection with calcium imaging and what the psychophysical detection threshold would have been an awake behaving monkey. Nonetheless, the data show that we can produce a much larger response in the target cell than in nearby cells whose response we can measure, and we suggest that that is a valuable contribution even if we can’t argue that the isolation is absolute. We’ve acknowledged these important limitations in the revised manuscript in lines 66-77.

Minor comments:

Did the lights used to stimulate and record from the retina excite RGCs via the normal lightsensing pathway? Were any such responses recorded? What was their magnitude?

The recording light does activate the normal light-sensing pathway to some extent, although it does not fall upon the RGC receptive fields directly. There was a 30 second adaptation period at the beginning of each trial to minimize the impact of this on the recording of optogeneticallymediated responses, as described in lines 222-224. The optogenetic probe does not appear to significantly excite the cone pathway, and we do not see the expected off-target excitations that would result from this.

The data presented attest to a lack of crosstalk between targeted and neighboring cells. It is therefore surprising that lines 69-72 are dedicated to methods for "reducing the crosstalk problem". More information should be provided regarding the magnitude of this problem under the current protocol/instrumentation and the techniques that were used to circumvent it to obtain the data presented.

The “crosstalk problem” referred to in this quote refers to crosstalk caused by targeting cells at higher eccentricities that are more densely packed, which are not represented in the data. The data presented is limited to the more isolated central RGCs.

Optical crosstalk could be spatial or spectral. Laying out this distinction plainly could help the reader understand the issues quickly. The Methods indicate that cells were chosen on the basis that they were > 20 µm from their nearest (well-labeled) neighbor to mitigate optical crosstalk, but the following sentence is about spectral overlap.

We have added a clearer explanation of what precisely we mean by crosstalk in lines 213-221.

Figure 2 legend: "...even the nearby cell somas do not show significantly elevated response (p >> 0.05, unpaired t-test) than other cells at more distant locations." This sentence does not indicate how some cells were classified as "nearby" whereas others were classified as being "at more distant locations". Perhaps a linear regression would be more appropriate than an unpaired t-test here.

The distinction here between “nearby” and “more distant” is 50 µm. We have clarified this in the figure caption. Performing a linear regression on cell response over distance shows a slight downward trend in two of the four cells shown here, but this trend does not reach the threshold of significance.

Line 56: "These recordings were... acquired earlier in the session where no stimulus was present." More information should be provided regarding the conditions under which this baseline was obtained. I assume that the ChrimsonR-activating light was off and the 488 nmGCaMP excitation light was on, but this was not stated explicitly. Were any other lights on (e.g. room lights or cone-imaging lights)? If there was no spatial component to the baseline measurement, "where" should be "when".

Your assumptions are correct. There was no spatial component to the baseline measurement, and these measurements are explained in more detail in lines 240-243.

Please add a scalebar to Figure 1a to facilitate comparison with Figure 2.

This has been done.

Lines 165-173: Was the 488 nm light static or 10 Hz-modulated? The text indicates that GCaMP was excited with a 488 nm light and data were acquired using a scanning light ophthalmoscope, but line 198 says that "the 488 nm imaging light provides a static stimulus".

The 488nm is effectively modulated at 25 Hz by the scanning action of the system. I believe the 10 Hz modulated you speak of is the closed-loop correction rate of the adaptive optics. The text has been updated in lines 217-219 to clarify this.

A potential application of this technology is for the study of visually guided behavior in awake macaques. This is an exciting prospect. With that in mind, a useful contribution of this report would be a frank discussion of the hurdles that remain for such application (in addition to eye movements, which are already discussed).

Lines 109-130 now offer an expanded discussion of this topic.

Reviewer #3 (Public Review):

This paper reports a considerable technical achievement: the optogenetic activation of single retinal ganglion cells in vivo in monkeys. As clearly specified in the paper, this is an important step towards causal tests of the role of specific ganglion cell types in visual perception. Yet this methodological advance is not described currently in sufficient detail to replicate or evaluate. The paper could be improved substantially by including additional methodological details. Some specific suggestions follow.

The start of the results needs a paragraph or more to outline how you got to Figure 1. Figure 1 itself lacks scale bars, and it is unclear, for example, that the ganglion cells targeted are in the foveal slope.

The results have been rewritten with additional explanation of methodology and the location of the RGCs has been clarified.

The text mentions the potential difficulties targeting ganglion cells at larger eccentricities where the soma density increases. If this is something that you have tried it would be nice to include some of that data (whether or not selective activation was possible). Related to this point, it would be helpful to include a summary of the ganglion cell density in monkey retina.

This is not something we tried, as we knew that the axial resolution allowed by the monkey’s eye would result in an axial PSF too large to only hit a single cell. The overall ganglion cell density is less relevant than the density of cells expressing ChrimsonR/GCaMP, which we only have limited info about without detailed histology.

Related to the point in the previous paragraph - do you have any experiments in which you systematically moved the stimulation spot away from the target ganglion cell to directly test the dependence of stimulation on distance? This would be a valuable addition to the paper.

We agree that this would have been a valuable addition to the paper, but we are reluctant to do them now. We are implementing an improved method to track the eye and a better optogenetic agent in an entirely new instrument, and we think that future experiments along these lines would be best done when those changes are completed.

The activity in Figure 1 recovers from activation very slowly - much more slowly than the light response of these cells, and much more slowly than the activity elicited in most optogenetic studies. Can you quantify this time course and comment on why it might be so slow?

We attribute the slow recovery to the calcium dynamics of the cell, and this slow recovery time is consistent with calcium responses seen in our lab elicited via the cone pathway. Similar time courses can be seen in Yin (2013) for RGCs excited via their cone inputs.

Traces from non-targeted cells should be shown in Figure 1 along with those of targeted cells.

We have added this as part of Figure 2.

eLife Assessment

The study reports an important finding on the role of the global metabolic regulator Crp/cAMP in the formation of antibiotic persister Escherichia coli. The evidence supporting the claims is solid including metabolomic analysis and characterization of many mutant strains.

Reviewer #1 (Public review):

The authors set out to understand the role played by a key global metabolic regulator called Crp/cAMP in the formation of persister Escherichia coli that survive antibiotic treatment without acquiring genetic mutations.

In order to achieve this aim, the authors employ an interdisciplinary approach integrating standard microbiology assays with cutting-edge genomic, metabolomic and proteomics screening.

The data presented by the authors convincingly demonstrate that the deletion of two key genes that are part of the Crp/cAMP complex (i.e. crp and cyaA) leads to a significant decrease in the number of E. coli.

The authors have carried out additional experiments to further validate this point by using the well characterised hipA7 E. coli mutant.

The data presented also demonstrate that deletion of the crp gene leads to an overall decrease in energy metabolism and an overall increase in anabolic metabolism at the population level. The deletion of cyaA has an opposite effect on cAMP concentration compared to crp deletion, the authors presented a possible hypotheses but did not test it.

The authors have now explicitly acknowledged in their discussion that the data presented in this study are obtained at the whole population level rather than at the level of the persister subpopulation and therefore should be considered with caution.

Finally, the authors convincingly show that the persisters they investigated are non-growing and have a higher redox activity and that the deletion of key genes involved in energy metabolism leads to a decrease in the number of persisters.

These data will be important for future investigations on the biochemical mechanisms that allow bacteria to adapt to stressors such as nutrient depletion or exposure to antibiotics. As such this work will likely have an impact in a variety of fields such as bacterial biochemistry, antimicrobial resistance research and environmental microbiology.

Strengths:

Interdisciplinary approach.

Excellent use of replication and ensuring reproducibility.

Excellent understanding and presentation of the biochemical mechanisms underpinning bacterial physiology via an integrated genomic, metabolomic and proteomic screening.

Weaknesses:

There is no tested mechanisms explaining why the deletion of cyaA has an opposite effect on cAMP concentration compared to crp deletion.

Metabolomics, proteomics and metabolic activity data are obtained at the whole population level rather than at the level of the persister sub-population.

eLife Assessment

This study introduces a valuable spring-bead model for epithelial cell layers, designed to improve previous cell-resolved approaches and to understand the connection between the biophysics of cell-cell contacts and the tissue mechanics. While the model is not entirely new and does not fully settle open questions such as the role of adhesion in tissue fluidity, it provides solid evidence and stands out as simple and efficient. A more comprehensive comparison with previous cell-revolved approaches and, in particular, with experimental data, would further strengthen the proposed model as a conceptual and practical tool.

Reviewer #1 (Public review):

Summary:

The manuscript by Ray et al. provides a theoretical framework to study tissue mechanics and the solid-to-fluid transition phenomenon observed in many tissues. The authors advanced previous models by directly incorporating cell-cell adhesion in force calculation with flexible cell geometries. They performed an in-depth analysis of the model and found that reducing cell-cell adhesion in near-confluent tissues can result in spontaneous cell rearrangements and transition to tissue fluidity. This is in contrast with previous predictions of Vertex models, which require higher adhesion for solid-to-fluid transition.

Strengths:

The authors provided a more general formulation of a 2D active foam model by directly incorporating cell-cell adhesion and performed a careful analysis of cell dynamics and cell shape in their simulations. They measured various quantities such as the mean-squared displacement of the cell center and shape index, which was introduced in previous studies to analyze jamming transition in tissues. By careful analysis of their simulations, they found a universal length scale in their simulations, explaining the observed heterogeneity. They provided a qualitative connection to previous experimental observations, where a reduction in cell adhesion caused tissue fluidity.

Weaknesses:

The phenomenon of tissue fluidity is an important and open question in biology. While theoretical models provide guidance to study such complex phenomena, the details in these models should go hand-in-hand with quantitative comparison with experiments. The study by Ray et al. indeed provided a more detailed description of deformable and adhesive cell collectives, but without a quantitative comparison with experiment, it is not clear if one needs all these details, or maybe more is needed. For example, do we need a more detailed mechanical model of the vertices, how the friction with substrate should be incorporated in such models, and is there a feedback between cell dynamics and its internal cytoskeleton organization?

While the manuscript by Ray et al. is an interesting theoretical study, without a quantitative comparison with experiments, it is not clear if it truly advances our understanding of tissue mechanics.

Reviewer #2 (Public review):

Summary:

Ray and coworkers introduce a discrete model of cellular layers aimed at investigating the role of inter-cellular adhesion in collective cell migration. The model combines aspects of particle-based models, in which cells are treated as simple point-particles with pair-interactions, and "morphological models", where interactions primarily depend on the cellular shape. In this case, cells are modeled as rings of beads connected by springs, thus allowing for exploration of the role of cell morphology while treating intercellular interactions as particle-like. Upon exploring the parameter space of this model, the authors recover physical behaviors reminiscent of reconstituted cell layers, including the onset of collective cell migration, when the forces leading to cell propulsion overweight inter-cellular adhesion, and various signatures of glassy dynamics.

Strengths:

The model presented in the article is simple, easy to implement, and scalable. The analysis appears solid and delivers a number of clear physical properties that could be tested in more depth in experiments and future numerical studies (e.g., distribution of displacements, etc.). The authors make an appreciable effort to make contact with other models and share their ideas for further investigations.

Weaknesses:

I found two main weaknesses in the original version of this manuscript, which I strongly encourage the authors to address.

(1) The manuscript explicitly aims at resolving an apparent contradiction of tessellation-based models, such as the Vertex and the Voronoi model. Both models used the so-called shape index p0 - i.e. the ratio between the preferential perimeter and the preferential area of the cells - to drive a solid/liquid phase transition in the presence of Brownian and/or rotational noise. Specifically, for sufficiently large p0 values, these in silico cell layers undergo a transition to a state of collective migration, where a rigid junction network becomes unstable to T1 events. Because p0 is often interpreted as "adhesion strength", this leads to the paradoxical conclusion that cell intercalation is favored by intercellular adhesion. The paradox, however, only lies in this interpretation, which assigns to the shape index p0 a biophysical role that is too specific. To illustrate this concept, let us consider the energy of an individual cell of area A and perimeter P: i.e. e = (a-1)^2+c*(p-p0)^2, where a=A/A_0, with A_0 the preferred area, p=P/sqrt(A_0) and p_0 = P_0/sqrt(A_0), with P_0 the preferred perimeter. Expanding the square in the second term gives e ~ p^2 - 2p_0 p. Thus, increasing p_0, favors longer cell junctions, from which it appears reasonable to interpret p0 as a dimensionless measure of intercellular adhesion. Such an increase in the length of the junctions is, however, only a byproduct of the effect of p0 on the overall shape of the cell, which becomes progressively less rounded as p0 is increased (e.g., for a circle, p0≈3.55, for an equilateral triangle, p0≈4.56). The roundness of an individual cell, on the other hand, cannot single-handedly be ascribed to intercellular adhesion, despite intercellular adhesion being undoubtedly one of the biophysical properties affecting this geometrical feature. Moreover, the shape index p0 ​enters the energy functional at the single-cell level, implying that even in isolation, without intercellular adhesion, an increase in p0 leads to a less rounded cell morphology. These peculiarities of the Vertex/Voronoi model do raise questions about its accuracy and validity, thus justify seeking for alternative cell-resolved models such as that introduced here by Ray et al., but, on the other hand, make the interpretation of p0 as an exclusive measure of adhesion evidently dubious.

(2) The spring-bead model by Ray and coworkers has at least two predecessors in the recent literature, none of which have been cited in the present manuscript. These are Boromand et al., Phys. Rev. Lett. 121, 248003 (2018) and Pasupalak et al. arXiv:2409.16128 (2024). The former paper investigates the packing of flexible polygons and is not specific to epithelial layers, while the latter is specifically designed to address various outstanding problems in tissue mechanics, including collective migration and wound healing. While none of these models is identical to that by Ray et al., it would be fair to present the latter as a member of the family rather than the first one of its kind and possibly comment about the differences and similarities with these previous models.

Reviewer #3 (Public review):

Summary:

This is a very focused and well-performed study that uses a somewhat less common approach in the field of tissue mechanics, a deformable particle model, to propose a solution to some important phenomenological inconsistencies between the standard vertex- and SPV-model approaches and experiments. The authors' focus in their study is on the role of adhesion in glassy dynamics and solid-fluid transition of epithelia.

Strengths:

It is a carefully performed study with an important technical edge compared to "mainstream" vertex and SPV models: the ability to describe cell-cell boundaries with two distinct membranes. This may have an important implication for the phenomenology, like the role of adhesion in solid-fluid transition.

Weaknesses:

Apart from some specific suggestions for improvement and clarification, I believe the authors could do a better job in comparing their results and their approach to other similar models, such as the one by Kim et al (Reference 7).

eLife Assessment

This important study utilizes the nematode C. elegans and mammalian cell culture to investigate the role of MML-1/Mondo in conserved regulation of metabolism and aging. The evidence supporting the conclusions is convincing and covers a range of areas including localization, upstream pathways, and conservation. The paper will be of interest to a broad range of biologists studying aging, metabolism, and transcriptional regulation.

Reviewer #1 (Public Review):

In this manuscript, Laboy and colleagues investigated upstream regulators of MML-1/Mondo, a key transcription factor that regulates aging and metabolism, using the nematode C. elegans and cultured mammalian cells. By performing a targeted RNAi screen for genes encoding enzymes in glucose metabolism, the authors found that two hexokinases, HXK-1 and HXK-2, regulate nuclear localization of MML-1 in C. elegans. The authors showed that knockdown of hxk-1 and hxk-2 suppressed longevity caused by germline-deficient glp-1 mutations. The authors demonstrated that genetic or pharmacological inhibition of hexokinases decreased nuclear localization of MML-1, via promoting mitochondrial β-oxidation of fatty acids. They found that genetic inhibition of hxk-2 changed the localization of MML-1 from the nucleus to mitochondria and lipid droplets by activating pentose phosphate pathway (PPP). The authors further showed that the inhibition of PPP increased the nuclear localization of mammalian MondoA in cultured human cells under starvation conditions, suggesting the underlying mechanism is evolutionarily conserved. This paper provides compelling evidence for the mechanisms by which novel upstream metabolic pathways regulate MML-1/Mondo, a key transcription factor for longevity and glucose homeostasis, through altering organelle communications, using two different experimental systems, C. elegans and mammalian cells. This paper will be of interest to a broad range of biologists who work on aging, metabolism, and transcriptional regulation.

Reviewer #2 (Public Review):

Raymond Laboy et.al explored how transcriptional Mondo/Max-like complex (MML-1/MXL-2) is regulated by glucose metabolic signals using germ-line removal longevity model. They believed that MML-1/MXL-2 integrated multiple longevity pathways through nutrient sensing and therefore screened the glucose metabolic enzymes that regulated MML-1 nuclear localization. Hexokinase 1 and 2 were identified as the most vigorous regulators, which function through mitochondrial beta-oxidation and the pentose phosphate pathway (PPP), respectively. MML-1 localized to mitochondria associated with lipid droplets (LD), and MML-1 nuclear localization was correlated with LD size and metabolism. Their findings are interesting and may help us to further explore the mechanisms in multiple longevity models. The data support their proposed working model.

Comments on Revised Version (from the Reviewing Editor):

The authors have addressed the remaining concerns from both reviewers, adding textual information for reviewer 1 and testing the roles of hxk-1 and lipid oxidation in regulating lipid droplets for reviewer 2. Specifically, they find that knockdown of acs-2 and hxk-1 acs-2 double knockdown each result in a mild but significant increase in LD size. This result supports that the two hexokinases regulate MML-1 via distinct mechanisms, and is reflected in the updated model.

Author response:

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

Recommendations for the authors:

Reviewer #1:

The authors addressed my previous concerns successfully. However, some critiques are addressed only in the response letter but not in the text (major comment 3, minor point 2). It will be great if they mention these in some parts of their manuscript.

Major 3: We now mention the effect of acs-2i on life span in the discussion, lines 475-480:

“Interestingly, acs-2 knockdown abolished glp-1 longevity (data not shown), consistent with previous work showing that NHR-49, a transcription factor that drives acs-2 expression, is required for glp-1 longevity (Ratnappan et al., 2014). Thus, inhibiting fatty acid β-oxidation promotes MML-1 nuclear localization under hxk-1i but abolishes lifespan extension, potentially due to epistatic effects on other transcription factors or processes.”

Minor 2: We now speculate on the differences concerning hxk-3 knockdown on MML-1 nuclear localization resulting from the low expression of hxk-3 in adults, lines 99-102:

“Among the three C. elegans hexokinase genes, hxk-1 and hxk-2 more strongly affected MML 1 nuclear localization in two independent MML-1::GFP reporter strains (Figure 1B, Supplementary Figure 1A), while hxk-3 had just a small effect on MML-1 nuclear localization, probably due to its low expression in adult worms (Hutter & Suh, 2016).”

Reviewer #2:

The authors have adequately addressed my previous concerns in their revised manuscript. However, I have one remaining minor concern regarding the link between lipid metabolism and MML-1 regulation. As proposed by the authors, HXKs modulate MML-1 localization between LD/mito and the nucleus. They have provided evidence supporting the roles of hxk-2 and the PPP in this regulatory process. Nonetheless, the involvement of hxk-1 and fatty acid oxidation (FAO) within this proposed framework remains unclear. Although FAO is generally believed to affect LD size, the potential effects of hxk-1 and FAO on LD should be investigated within the current study to further substantiate their model.

We thank the reviewer for this comment. We now examine how hxk-1 and acs-2 affect lipid droplet size. Interestingly, we found that knockdown of acs-2 and hxk-1 acs-2 double knockdown resulted in a mild but significant increase in LD size (Supplementary Figure 4I), supporting the notion that the two hexokinases regulate MML-1 via distinct mechanisms, reflected in the updated model (Figure 5E).

eLife Assessment

This Research Advance manuscript further elucidates the roles of SMC5/6 loader proteins and associated factors in the silencing of extrachromosomal circular DNA by the SMC5/6 complex. While the findings are largely in line with expectations, they are useful, representing a meaningful advance beyond the recent study (reference 33), contributing to a growing foundation for further comparative and mechanistic investigations. Solid evidence is presented for a role for SIMC1/SLF2 in the localization of the SMC5/6 complex to plasmid DNA, and the distinct requirements, as compared to the recruitment of SMC5/6 to chromosomal DNA lesions.

Reviewer #1 (Public review):

SMC5/6 is a highly conserved complex able to dynamically alter chromatin structure, playing in this way critical roles in genome stability and integrity that include homologous recombination and telomere maintenance. In the last years, a number of studies have revealed the importance of SMC5/6 in restricting viral expression, which is in part related to its ability to repress transcription from circular DNA. In this context, Oravcova and colleagues recently reported how SMC5/6 is recruited by two mutually exclusive complexes (orthologs of yeast Nse5/6) to SV40 LT-induced PML nuclear bodies (SIMC/SLF2) and DNA lesions (SLF1/2). In this current work, the authors extend this study, providing some new results. However, as a whole, the story lacks unity and does not delve into the molecular mechanisms responsible for the silencing process. One has the feeling that the story is somewhat incomplete, putting together not directly connected results.

(1) In the first part of the work, the authors confirm previous conclusions about the relevance of a conserved domain defined by the interaction of SIMC and SLF2 for their binding to SMC6, and extend the structural analysis to the modelling of the SIMC/SLF2/SMC complex by AlphaFold. Their data support a model where this conserved surface of SIMC/SLF2 interacts with SMC at the backside of SMC6's head domain, confirming the relevance of this interaction site with specific mutations. These results are interesting but confirmatory of a previous and more complete structural analysis in yeast (Li et al. NSMB 2024). In any case, they reveal the conservation of the interaction. My major concern is the lack of connection with the rest of the article. This structure does not help to understand the process of transcriptional silencing reported later beyond its relevance to recruit SMC5/6 to its targets, which was already demonstrated in the previous study.

(2) In the second part of the work, the authors focus on the functionality of the different complexes. The authors demonstrate that SMC5/6's role in transcription silencing is specific to its interaction with SIMC/SLF2, whereas SMC5/6's role in DNA repair depends on SLF1/2. These results are quite expected according to previous results. The authors already demonstrated that SLF1/2, but not SIMC/SLF2, are recruited to DNA lesions. Accordingly, they observe here that SMC5/6 recruitment to DNA lesions requires SLF1/2 but not SIMC/SLF2. Likewise, the authors already demonstrated that SIMC/SLF2, but not SLF1/2, targets SMC5/6 to PML NBs. Taking into account the evidence that connects SMC5/6's viral resistance at PML NBs with transcription repression, the observed requirement of SIMC/SLF2 but not SLF1/2 in plasmid silencing is somehow expected. This does not mean the expectation has not to be experimentally confirmed. However, the study falls short in advancing the mechanistic process, despite some interesting results as the dispensability of the PML NBs or the antagonistic role of the SV40 large T antigen. It had been interesting to explore how LT overcomes SMC5/6-mediated repression: Does LT prevent SIMC/SLF2 from interacting with SMC5/6? Or does it prevent SMC5/6 from binding the plasmid? Is the transcription-dependent plasmid topology altered in cells lacking SIMC/SLF2? And in cells expressing LT? In its current form, the study is confirmatory and preliminary. In agreement with this, the cartoons modelling results here and in the previous work look basically the same.

(3) There are some points about the presented data that need to be clarified.

Reviewer #2 (Public review):

Oracová et al. present data supporting a role for SIMC1/SLF2 in silencing plasmid DNA via the SMC5/6 complex. Their findings are of interest, and they provide further mechanistic detail of how the SMC5/6 complex is recruited to disparate DNA elements. In essence, the present report builds on the author's previous paper in eLife in 2022 (PMID: 36373674, "The Nse5/6-like SIMC1-SLF2 complex localizes SMC5/6 to viral replication centers") by showing the role of SIMC1/SLF2 in localisation of the SMC5/6 complex to plasmid DNA, and the distinct requirements as compared to recruitment to DNA damage foci. Although the findings of the manuscript are of interest, we are not yet convinced that the new data presented here represents a compelling new body of work and would better fit the format of a "research advance" article. In their previous paper, Oracová et al. show that the recruitment of SMC5/6 to SV40 replication centres is dependent on SIMC1, and specifically, that it is dependent on SIMC1 residues adjacent to neighbouring SLF2.

Other comments

(1) The mutations chosen in Figure 1 are quite extensive - 5 amino acids per mutant. In addition, they are in many cases 'opposite' changes, e.g., positive charge to negative charge. Is the effect lost if single mutations to an alanine are made?

(2) In Figure 2c, it isn't clear from the data shown that the 'SLF2-only' mutations in SMC6 result in a substantial reduction in SIMC1/SLF2 binding.

(3) In the GFP reporter assays (e.g. Figure 3), median fluorescence is reported - was there any observed difference in the percentage of cells that are GFP positive?

(4) The potential role of the large T antigen as an SMC5/6 evasion factor is intriguing. However, given the role of the large T antigen as a transcriptional activator, caution is required when interpreting enhanced GFP fluorescence. Antagonism of the SMC5/6 complex in this context might be further supported by ChIP experiments in the presence or absence of large T. Can large T functionally substitute for HBx or HIV-Vpr?

(5) In Figure 5c, the apparent molecular weight of large T and SMC6 appears to change following transfection of GFP-SMC5 - is there a reason for this?

Reviewer #3 (Public review):

Summary:

This study by the Boddy and Otomo laboratories further characterizes the roles of SMC5/6 loader proteins and related factors in SMC5/6-mediated repression of extrachromosomal circular DNA. The work shows that mutations engineered at an AlphaFold-predicted protein-protein interface formed between the loader SLF2/SIMC1 and SMC6 (similar to the interface in the yeast counterparts observed by cryo-EM) prevent co-IP of the respective proteins. The mutations in SLF2 also hinder plasmid DNA silencing when expressed in SLF2-/- cell lines, suggesting that this interface is needed for silencing. SIMC1 is dispensable for recruitment of SMC5/6 to sites of DNA damage, while SLF1 is required, thus separating the functions of the two loader complexes. Preventing SUMOylation (with a chemical inhibitor) increases transcription from plasmids but does not in SLF2-deleted cell lines, indicating the SMC5/6 silences plasmids in a SUMOylation dependent manner. Expression of LT is sufficient for increased expression, and again, not additive or synergistic with SIMC1 or SLF2 deletion, indicating that LT prevents silencing by directly inhibiting 5/6. In contrast, PML bodies appear dispensable for plasmid silencing.

Strengths:

The manuscript defines the requirements for plasmid silencing by SMC5/6 (an interaction of Smc6 with the loader complex SLF2/SIMC1, SUMOylation activity) and shows that SLF1 and PML bodies are dispensable for silencing. Furthermore, the authors show that LT can overcome silencing, likely by directly binding to (but not degrading) SMC5/6.

Weaknesses:

(1) Many of the findings were expected based on recent publications.

(2) While the data are consistent with SIMC1 playing the main function in plasmid silencing, it is possible that SLF1 contributes to silencing, especially in the absence of SIMC1. This would potentially explain the discrepancy with the data reported in ref. 50. SLF2 deletion has a stronger effect on expression than SIMC1 deletion in many but not all experiments reported in this manuscript. A double mutant/deletion experiments would be useful to explore this possibility.

(3) SLF2 is part of both types of loaders, while SLF1 and SIMC1 are specific to their respective loaders. Did the authors observe differences in phenotypes (growth, sensitivities to DNA damage) when comparing the mutant cell lines or their construction? This should be stated in the manuscript.

(4) It would be desirable to have control reporter constructs located on the chromosome for several experiments, including the SUMOylation inhibition (Figures 5A and 5-S2) and LT expression (Figure 5D) to exclude more general effects on gene expression.

(5) Figure 5A: There appears to be an increase in GFP in the SLF2-/- cells with SUMOi? Is this a significant increase?

(6) The expression level of SFL2 mut1 should be tested (Figure 3B).

Author response:

This study builds on, extends, and experimentally validates results/models from our previous study. Our and others’ data implicated SMC5/6, PML nuclear bodies (PML NBs), and SUMOylation in the transcriptional repression of extrachromosomal circular DNA (ecDNA). Moreover, multiple viruses were found to express early genes that combat SMC5/6-based repression through targeted proteasomal degradation (e.g. Hepatitis B virus HBx and HIV-1 Vpr). Thus, our analysis of the roles of the foregoing in plasmid repression yields a coherent set of results for the field to build on.

In our previous study we presented a model, but no supportive ecDNA silencing data, suggesting that distinct SMC5/6 subcomplexes, SIMC1-SLF2 and SLF1/2, separately control its transcriptional repression and DNA repair activities. In this study we experimentally validate that prediction using an ecDNA silencing assay and SMC5/6 localization analysis following DNA damage.

Our study further reveals the unexpected dispensability of PML NBs in the silencing of simple plasmid DNA, a departure from current dogma. This raises important questions for the field to address in terms of the silencing mechanisms for different ecDNAs across different cell types. Despite the dispensability of SUMO-rich PML NBs, SUMOylation is required for ecDNA repression. Lastly, the SV40 LT antigen early gene product counteracts ecDNA silencing. These results used genetic epistasis arguments to implicate SUMO and LT in SMC5/6-based transcriptional silencing. We provide provisional responses for some of the reviewer’s general comments below.

Public Reviews:

Reviewer #1 (Public review):

SMC5/6 is a highly conserved complex able to dynamically alter chromatin structure, playing in this way critical roles in genome stability and integrity that include homologous recombination and telomere maintenance. In the last years, a number of studies have revealed the importance of SMC5/6 in restricting viral expression, which is in part related to its ability to repress transcription from circular DNA. In this context, Oravcova and colleagues recently reported how SMC5/6 is recruited by two mutually exclusive complexes (orthologs of yeast Nse5/6) to SV40 LT-induced PML nuclear bodies (SIMC/SLF2) and DNA lesions (SLF1/2). In this current work, the authors extend this study, providing some new results. However, as a whole, the story lacks unity and does not delve into the molecular mechanisms responsible for the silencing process. One has the feeling that the story is somewhat incomplete, putting together not directly connected results.

Please see the introductory overview above.

(1) In the first part of the work, the authors confirm previous conclusions about the relevance of a conserved domain defined by the interaction of SIMC and SLF2 for their binding to SMC6, and extend the structural analysis to the modelling of the SIMC/SLF2/SMC complex by AlphaFold. Their data support a model where this conserved surface of SIMC/SLF2 interacts with SMC at the backside of SMC6's head domain, confirming the relevance of this interaction site with specific mutations. These results are interesting but confirmatory of a previous and more complete structural analysis in yeast (Li et al. NSMB 2024). In any case, they reveal the conservation of the interaction. My major concern is the lack of connection with the rest of the article. This structure does not help to understand the process of transcriptional silencing reported later beyond its relevance to recruit SMC5/6 to its targets, which was already demonstrated in the previous study.

Demonstrating the existence of a conserved interface between the Nse5/6-like complexes and SMC6 in both yeast and human is foundationally important and was not revealed in our previous study. It remains unclear how this interface regulates SMC5/6 function, but yeast studies suggest a potential role in inhibiting the SMC5/6 ATPase cycle. Nevertheless, the precise function of Nse5/6 and its human orthologs in SMC5/6 regulation remain undefined, largely due to technical limitations in available in vivo analyses. The SIMC1/SLF2/SMC6 complex structure likely extends to the SLF1/2/SMC6 complex, suggesting a unifying function of the Nse5/6-like complexes in SMC5/6 regulation, albeit in the distinct processes of ecDNA silencing and DNA repair. There have been no studies to date (including this one) showing that SIMC1-SLF2 is required for SMC5/6 recruitment to ecDNA. Our previous study showed that SIMC1 was needed for SMC5/6 to colocalize with SV40 LT antigen at PML NBs. Here we show that SIMC1 is required for ecDNA repression, in the absence of PML NBs, which was not anticipated.

(2) In the second part of the work, the authors focus on the functionality of the different complexes. The authors demonstrate that SMC5/6's role in transcription silencing is specific to its interaction with SIMC/SLF2, whereas SMC5/6's role in DNA repair depends on SLF1/2. These results are quite expected according to previous results. The authors already demonstrated that SLF1/2, but not SIMC/SLF2, are recruited to DNA lesions. Accordingly, they observe here that SMC5/6 recruitment to DNA lesions requires SLF1/2 but not SIMC/SLF2.

Our previous study only examined the localization of SLF1 and SIMC1 at DNA lesions. The localization of these subcomplexes alone should not be used to define their roles in SMC5/6 localization. Indeed, the field is split in terms of whether Nse5/6-like complexes are required for ecDNA binding/loading, or regulation of SMC5/6 once bound.

Likewise, the authors already demonstrated that SIMC/SLF2, but not SLF1/2, targets SMC5/6 to PML NBs. Taking into account the evidence that connects SMC5/6's viral resistance at PML NBs with transcription repression, the observed requirement of SIMC/SLF2 but not SLF1/2 in plasmid silencing is somehow expected. This does not mean the expectation has not to be experimentally confirmed. However, the study falls short in advancing the mechanistic process, despite some interesting results as the dispensability of the PML NBs or the antagonistic role of the SV40 large T antigen. It had been interesting to explore how LT overcomes SMC5/6-mediated repression: Does LT prevent SIMC/SLF2 from interacting with SMC5/6? Or does it prevent SMC5/6 from binding the plasmid? Is the transcription-dependent plasmid topology altered in cells lacking SIMC/SLF2? And in cells expressing LT? In its current form, the study is confirmatory and preliminary. In agreement with this, the cartoons modelling results here and in the previous work look basically the same.

We agree, determining the potential mechanism of action of LT in overcoming SMC5/6-based repression is an important next step. It will require the identification of any direct interactions with SMC5/6 subunits, and better methods for assessing SMC5/6 loading and activity on ecDNAs. Unlike HBx, Vpr, and BNRF1 it does not appear to induce degradation of SMC5/6, making it a more complex and interesting challenge. Also, the dispensability of PML NBs in plasmid silencing versus viral silencing raises multiple important questions about SMC5/6’s repression mechanism.

(3) There are some points about the presented data that need to be clarified.

Reviewer #2 (Public review):

Oracová et al. present data supporting a role for SIMC1/SLF2 in silencing plasmid DNA via the SMC5/6 complex. Their findings are of interest, and they provide further mechanistic detail of how the SMC5/6 complex is recruited to disparate DNA elements. In essence, the present report builds on the author's previous paper in eLife in 2022 (PMID: 36373674, "The Nse5/6-like SIMC1-SLF2 complex localizes SMC5/6 to viral replication centers") by showing the role of SIMC1/SLF2 in localisation of the SMC5/6 complex to plasmid DNA, and the distinct requirements as compared to recruitment to DNA damage foci. Although the findings of the manuscript are of interest, we are not yet convinced that the new data presented here represents a compelling new body of work and would better fit the format of a "research advance" article. In their previous paper, Oracová et al. show that the recruitment of SMC5/6 to SV40 replication centres is dependent on SIMC1, and specifically, that it is dependent on SIMC1 residues adjacent to neighbouring SLF2.

We agree, this manuscript fits the Research Advance model, which is the format that this manuscript was submitted in.

Reviewer #3 (Public review):

Summary:

This study by the Boddy and Otomo laboratories further characterizes the roles of SMC5/6 loader proteins and related factors in SMC5/6-mediated repression of extrachromosomal circular DNA. The work shows that mutations engineered at an AlphaFold-predicted protein-protein interface formed between the loader SLF2/SIMC1 and SMC6 (similar to the interface in the yeast counterparts observed by cryo-EM) prevent co-IP of the respective proteins. The mutations in SLF2 also hinder plasmid DNA silencing when expressed in SLF2-/- cell lines, suggesting that this interface is needed for silencing. SIMC1 is dispensable for recruitment of SMC5/6 to sites of DNA damage, while SLF1 is required, thus separating the functions of the two loader complexes. Preventing SUMOylation (with a chemical inhibitor) increases transcription from plasmids but does not in SLF2-deleted cell lines, indicating the SMC5/6 silences plasmids in a SUMOylation dependent manner. Expression of LT is sufficient for increased expression, and again, not additive or synergistic with SIMC1 or SLF2 deletion, indicating that LT prevents silencing by directly inhibiting 5/6. In contrast, PML bodies appear dispensable for plasmid silencing.

Strengths:

The manuscript defines the requirements for plasmid silencing by SMC5/6 (an interaction of Smc6 with the loader complex SLF2/SIMC1, SUMOylation activity) and shows that SLF1 and PML bodies are dispensable for silencing. Furthermore, the authors show that LT can overcome silencing, likely by directly binding to (but not degrading) SMC5/6.

Weaknesses:

(1) Many of the findings were expected based on recent publications.

Please see introductory paragraphs above.

Reviewer #3 (Public review):

This paper, with a slightly modified title from the initial version, presents the cognitive implications of claims made in two accompanying papers (Berger et al. 2023, 2024) about the creation of rock engravings, the intentional disposal of the dead, and fire use by Homo naledi. The importance of the paper, therefore, still relies on the validity of the claims for the presence of socio-culturally complex and cognitively demanding behaviors that are presented in the associated papers. Given the archaeological, hominin, and taphonomic analyses in the associated papers are not adequate to enable the exceptional claims for naledi-associated complex behaviors, the inferences made in this paper are currently incomplete.

The claimed behaviors are widely recognized as complex and even quintessential to Homo sapiens. The implications of their unequivocal association with such a small-brained Middle Pleistocene hominin are thus far reaching. Accordingly, the main thrust of the paper is to highlight that greater cognition and complex socio-cultural behaviors were not necessarily associated with a positively encephalized brain. This argument begs the obvious question of whether absolute brain size and/or encephalization quotient (i.e., the actual brain volume of a given species relative the expected brain size for a species of the same average body size) can measure cognitive capacity and the complexity of socio-cultural behaviors among late Middle Pleistocene hominins.

Claims for a positive correlation between absolute and/or relative brain size and cognitive ability are not common in discussions surrounding the evolution of Middle- and Late Pleistocene hominin behavior. Currently, the bulk of the evidence for early complex technological and social behaviors derives from multiple sites across South Africa and postdates the emergence of H. sapiens by more than 100,000 years. Such lag in the expression of complex technologies and behaviors within our species renders the brain size-implies-cognitive capacity argument moot. Instead, a rich body of research over the past several decades has focused on aspects related to socio-cultural, environmental, and even the wiring of the brain in order to understand factors underlying the expression of the capacity for greater behavioral variability. In this regard, even if the claimed evidence for complex behaviors among the small-brained naledi populations proves valid, the exploration of the specific/potential socio-cultural, neuro-structural, ecological and other factors will be more informative than the emphasis on absolute/relative brain size.

The paper presents as supporting evidence previous claims for the appearance of similar complex behaviors predating the emergence of our species, H. sapiens, although it does acknowledge their controversial nature. It then uses the current claims for the association of such behaviors with H. naledi as decisive. Given the inadequate analyses in the accompanying papers, and the lack of evidence for stone tools in the naledi sites, the present claims for the expression of culturally and symbolically mediated behaviors by this small-brained hominin must be adequately established. The importance of the paper thus rests on the validity of the claimed evidence-including contextual aspects-for rock engraving, mortuary practices, and the use of fire presented in the associated two papers.

eLife Assessment

This study provides an important understanding of the contribution of different striatal subregions, the anterior Dorsal Lateral Striatum (aDLS) and the posterior Ventrolateral Striatum (pVLS), to auditory discrimination learning. The authors have included robust behavior combined with multiple observational and perturbation techniques. The data provided are convincing of the relevance of task-related activity in these two subregions during learning.

Reviewer #1 (Public review):

In this study, Setogawa et al. employ an auditory discrimination task in freely moving rats, coupled with small animal imaging, electrophysiological recordings, and pharmacological inhibition/lesioning experiments to better understand the role of two striatal subregions: the anterior Dorsal Lateral Striatum (aDLS) and the posterior Ventrolateral Striatum (pVLS), during auditory discrimination learning. Attempting to better understand the contribution of different striatal subregions to sensory discrimination learning strikes me as a highly relevant and timely question, and the data presented in this study are certainly of major interest to the field. The authors have set up a robust behavioral task, systematically tackled the question about a striatal role in learning with multiple observational and manipulative techniques. Additionally, the structured approach the authors take by using neuroimaging to inform their pharmacological manipulation experiments and electrophysiological recordings is a strength.

Reviewer #2 (Public review):

The study by Setogawa et al. aims to understand the role that different striatal subregions belonging to parallel brain circuits have in associative learning and discrimination learning (S-O-R and S-R tasks). Strengths of the study are the use of multiple methodologies to measure and manipulate brain activity in rats, from microPET imaging to excitotoxic lesions and multielectrode recordings across anterior dorsolateral (aDLS), posterior ventral lateral (pVLS)and dorsomedial (DMS) striatum.

Author response:

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

Although we have no further revisions on the manuscript, we would like to respond to the remaining comments from the reviewers as follows.

Reviewer 1:

The authors have addressed some concerns raised in the initial review but some remain. In particular it is still unclear what conclusions can be drawn about taskrelated activity from scans that are performed 30 minutes after the behavioral task. I continue to think that a reorganization/analysis data according to event type would be useful and easier to interpret across the two brain areas, but the authors did not choose to do this. Finally, switching the cue-response association, I am convinced, would help to strengthen this study.

As for the task-related activity, the strategy for PET scan was explained in our response to the comment 2 from Reviewer 2. Briefly, rats receive intravenous administration of 18F-FDG solution before the start of the behavioral session. The 18FFDG uptake into the cells starts immediately and reaches the maximum level until 30 min, being kept at least for 1 h. A 30-min PET scan is executed 25 min after the session. Therefore, the brain activity reflects the metabolic state during task performance in rats.

Regarding data presentation of the electrophysiological experiments, we described the subpopulations of event-related neurons showing notable neuronal activity patterns in the order of aDLS and pVLS, according to the procedure of explanations for the behavioral study

For switching the cue-response association, we mentioned the difference in firing activity between HR and LL trials, suggesting that different combinations between the stimulus and response may affect the level of firing activity. As suggested by the reviewer, an examination of switching the cue-response association is useful to confirm our interpretation. We will address this issue in our future studies.

Reviewer 2:

The authors have made important revisions to the manuscript and it has improved in clarity. They also added several figures in the rebuttal letter to answer questions by the reviewers. I would ask that these figures are also made public as part of the authors' response or if not, included in the manuscript.

We will present the figures publicly available as part of our response.

eLife Assessment

This valuable paper used a longitudinal cohort of individuals initiating ART to suggest that CD8+ T cells may contribute to the clearance of intact HIV DNA during long-term antiretroviral therapy (ART) for HIV, which is relevant to our understanding of the mechanisms driving reservoir persistence in people living with HIV. The reviewers concluded that the evidence presented is incomplete to fully support these claims, as the cohort sampling is relatively infrequent, and the association direction could be bi-directional or due to other confounding variables.

Reviewer #1 (Public review):

Summary:

In this work, van Paassen et al. have studied how CD8 T cell functionality and levels predict HIV DNA decline. The article touches on interesting facets of HIV DNA decay, but ultimately comes across as somewhat hastily done and not convincing due to the major issues.

(1) The use of only 2 time points to make many claims about longitudinal dynamics is not convincing. For instance, the fact that raw data do not show decay in intact, but do for defective/total, suggests that the present data is underpowered. The authors speculate that rising intact levels could be due to patients who have reservoirs with many proviruses with survival advantages, but this is not the parsimonious explanation vs the data simply being noisy without sufficient longitudinal follow-up. n=12 is fine, or even reasonably good for HIV reservoir studies, but to mitigate these issues would likely require more time points measured per person.

1b) Relatedly, the timing of the first time point (6 months) could be causing a number of issues because this is in the ballpark for when the HIV DNA decay decelerates, as shown by many papers. This unfortunate study design means some of these participants may already have stabilized HIV DNA levels, so earlier measurements would help to observe early kinetics, but also later measurements would be critical to be confident about stability.

(2) Statistical analysis is frequently not sufficient for the claims being made, such that overinterpretation of the data is problematic in many places.

2a) First, though plausible that cd8s influence reservoir decay, much more rigorous statistical analysis would be needed to assert this directionality; this is an association, which could just as well be inverted (reservoir disappearance drives CD8 T cell disappearance).

2b) Words like "strong" for correlations must be justified by correlation coefficients, and these heat maps indicate many comparisons were made, such that p-values must be corrected appropriately.

(3) There is not enough introduction and references to put this work in the context of a large/mature field. The impacts of CD8s in HIV acute infection and HIV reservoirs are both deep fields with a lot of complexity.

Reviewer #2 (Public review):

Summary:

This study investigated the impact of early HIV specific CD8 T cell responses on the viral reservoir size after 24 weeks and 3 years of follow-up in individuals who started ART during acute infection. Viral reservoir quantification showed that total and defective HIV DNA, but not intact, declined significantly between 24 weeks and 3 years post-ART. The authors also showed that functional HIV-specific CD8⁺ T-cell responses persisted over three years and that early CD8⁺ T-cell proliferative capacity was linked to reservoir decline, supporting early immune intervention in the design of curative strategies.

Strengths:

The paper is well written, easy to read, and the findings are clearly presented. The study is novel as it demonstrates the effect of HIV specific CD8 T cell responses on different states of the HIV reservoir, that is HIV-DNA (intact and defective), the transcriptionally active and inducible reservoir. Although small, the study cohort was relevant and well-characterized as it included individuals who initiated ART during acute infection, 12 of whom were followed longitudinally for 3 years, providing unique insights into the beneficial effects of early treatment on both immune responses and the viral reservoir. The study uses advanced methodology. I enjoyed reading the paper.

Weaknesses:

All participants were male (acknowledged by the authors), potentially reducing the generalizability of the findings to broader populations. A control group receiving ART during chronic infection would have been an interesting comparison.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this work, van Paassen et al. have studied how CD8 T cell functionality and levels predict HIV DNA decline. The article touches on interesting facets of HIV DNA decay, but ultimately comes across as somewhat hastily done and not convincing due to the major issues.

(1) The use of only 2 time points to make many claims about longitudinal dynamics is not convincing. For instance, the fact that raw data do not show decay in intact, but do for defective/total, suggests that the present data is underpowered. The authors speculate that rising intact levels could be due to patients who have reservoirs with many proviruses with survival advantages, but this is not the parsimonious explanation vs the data simply being noisy without sufficient longitudinal follow-up. n=12 is fine, or even reasonably good for HIV reservoir studies, but to mitigate these issues would likely require more time points measured per person.

(1b) Relatedly, the timing of the first time point (6 months) could be causing a number of issues because this is in the ballpark for when the HIV DNA decay decelerates, as shown by many papers. This unfortunate study design means some of these participants may already have stabilized HIV DNA levels, so earlier measurements would help to observe early kinetics, but also later measurements would be critical to be confident about stability.

We agree that in order to thoroughly investigate reservoir decay in acutely treated individuals, more participants and/or more time points measured per participant would increase the power of the study and potentially, in line with literature, show a significant decay in intact HIV DNA as well. By its design (1) the NOVA study allows for a detailed longitudinal follow-up of reservoir and immunity from start ART onwards. In the present analysis in the NOVA cohort, we decided to focus on the 24- and 156-week time points. We plan to include more individuals in our analysis in the future, so that we can better model the longitudinal dynamics of the HIV reservoir.

The main goal of the present study, however, was not to investigate the decay or longitudinal dynamics of the viral reservoir, but to understand the relationship of the HIV-specific CD8 T-cell responses early on ART with the reservoir changes across the subsequent 2.5-year period on suppressive therapy. We will revise the manuscript in order to clarify this. Moreover, we agree with the reviewer that the early time point (24 weeks) is a time at which many virological and immunological processes are ongoing and the reservoir may not have stabilized yet for every participant. We will highlight this in the revised manuscript.

(2) Statistical analysis is frequently not sufficient for the claims being made, such that overinterpretation of the data is problematic in many places.

(2a) First, though plausible that cd8s influence reservoir decay, much more rigorous statistical analysis would be needed to assert this directionality; this is an association, which could just as well be inverted (reservoir disappearance drives CD8 T cell disappearance).

The second point that was raised by reviewer 1 is the statistical analysis, which is referred to as “not sufficient for the claims being made”. Moreover, a more “rigorous statistical analysis would be needed”. At this stage, it is unclear from the reviewer's comments what specific type of additional statistical analysis is being requested. Correlation analyses, such as the one used in this study, are a well-established approach to investigate the relationship between the immune response and reservoir size. However, as we aim to perform the most rigorous analysis possible, for the revised submission we will adjust our analysis for putative confounders (e.g. age and antiretroviral regimen).

We would also like to note that the association between the CD8 T-cell response at 24 weeks and the subsequent decline (the difference between 24 and 156 weeks) in the reservoir cannot be bi-directional (that can only be the case when both variables are measured at the same time point).

(2b) Words like "strong" for correlations must be justified by correlation coefficients, and these heat maps indicate many comparisons were made, such that p-values must be corrected appropriately.

For the revised submission, we will provide correlation coefficients to justify the wording, and will adjust the p-values for multiple comparisons.

(3) There is not enough introduction and references to put this work in the context of a large/mature field. The impacts of CD8s in HIV acute infection and HIV reservoirs are both deep fields with a lot of complexity.

Lastly, reviewer 1 referred to the introduction and asked for more references and a more focused viewpoint because the field is large and complex. We aim to revise the introduction/discussion based on the suggestions from the reviewer.

Reviewer #2 (Public review):

Summary:

This study investigated the impact of early HIV specific CD8 T cell responses on the viral reservoir size after 24 weeks and 3 years of follow-up in individuals who started ART during acute infection. Viral reservoir quantification showed that total and defective HIV DNA, but not intact, declined significantly between 24 weeks and 3 years post-ART. The authors also showed that functional HIV-specific CD8⁺ T-cell responses persisted over three years and that early CD8⁺ T-cell proliferative capacity was linked to reservoir decline, supporting early immune intervention in the design of curative strategies.

Strengths:

The paper is well written, easy to read, and the findings are clearly presented. The study is novel as it demonstrates the effect of HIV specific CD8 T cell responses on different states of the HIV reservoir, that is HIV-DNA (intact and defective), the transcriptionally active and inducible reservoir. Although small, the study cohort was relevant and well-characterized as it included individuals who initiated ART during acute infection, 12 of whom were followed longitudinally for 3 years, providing unique insights into the beneficial effects of early treatment on both immune responses and the viral reservoir. The study uses advanced methodology. I enjoyed reading the paper.

Weaknesses:

All participants were male (acknowledged by the authors), potentially reducing the generalizability of the findings to broader populations. A control group receiving ART during chronic infection would have been an interesting comparison.

We thank the reviewer for their appreciation of our study. The reviewer raises the point that it would be useful to compare our data to a control group. Unfortunately, these samples are not yet available, but our study protocol allows for a control group (chronic infection) to ensure we can include a control group in the future.

(1) Dijkstra M, Prins H, Prins JM, Reiss P, Boucher C, Verbon A, et al. Cohort profile: the Netherlands Cohort Study on Acute HIV infection (NOVA), a prospective cohort study of people with acute or early HIV infection who immediately initiate HIV treatment. BMJ Open. 2021;11(11):e048582.

eLife Assessment

This paper examines selection on induced epigenetic variation ("Lamarckian evolution") in response to herbivory in Arabidopsis thaliana. The authors find weak evidence for such adaptation, which contrasts with a recently published study that reported extensive heritable variation induced by the environment. The authors convincingly demonstrate that the findings of the previous study were confounded by mix-ups of genetically distinct material, so that standing genetic variation was mistaken for acquired (epigenetic) variation. Given the controversy surrounding the influence of heritable epigenetic variation on phenotypic variation and adaptation, this study is an important, clarifying contribution; it serves as a timely reminder that sequence-based verification of genetic material should be prioritized when either genetic identity or divergence is of importance to the conclusions.

Reviewer #1 (Public review):

Summary:

The authors extended a previous study of selective response to herbivory in Arabidopsis, in order to look specifically for selection on induced epigenetic variation ("Lamarckian evolution"). They found no evidence. In addition, the re-examined result from a previously published study arguing that environmentally induced epigenetic variation was common, and found that these findings were almost certainly artifactual.

Strengths:

The paper is very clearly written, there is no hype, and the methods used are state-of-the-art.

Weaknesses:

The result is negative, so the best you can do is put an upper bound on any effects.

Significance:

Claims about epigenetic inheritance and Lamarckian evolution continue to be made based on very shaky evidence. Convincing negative results are therefore important. In addition, the study presents results that, to this reviewer, suggest that the 2024 paper by Lin et al. [26] should probably be retracted.

Reviewer #2 (Public review):

In this paper, the authors examine the extent to which epigenetic variation acquired during a selection treatment (as opposed to standing epigenetic variation) can contribute to adaptation in Arabidopsis. They find weak evidence for such adaptation and few differences in DNA methylation between experimental groups, which contrasts with another recent study (reference 26) that reported extensive heritable variation in response to the environment. The authors convincingly demonstrate that the conclusions of the previous study were caused by experimental error, so that standing genetic variation was mistaken for acquired (epigenetic) variation. Given the controversy surrounding the possible role of epigenetic variation in mediating phenotypic variation and adaptation, this is an important, clarifying contribution.

I have a few specific comments about the analysis of DNA methylation:

(1) The authors group their methylation analysis by sequence context (CG, CHG, CHH). I feel this is insufficient, because CG methylation can appear in two distinct forms: gene body methylation (gbM), which is CG-only methylation within genes, and transposable element (TE) and TE-like methylation (teM), which typically involves all sequence contexts and generally affects TEs, but can also be found within genes. GbM and teM have distinct epigenetic dynamics, and it is hard to know how methylation patterns are changing during the experiment if gbM and teM are mixed. This can also have downstream consequences (see point below).

(2) For GO analysis, the authors use all annotated genes as a control. However, most of the methylation differences they observe are likely gbM, and gbM genes are not representative of all genes. The authors' results might therefore be explained purely as a consequence of analyzing gbM genes, and not an enrichment of methylation changes in any particular GO group.

Author response:

We thank you and the reviewers very much for the insightful comments on our manuscript. We plan to revise the manuscript as follows:

(A) As suggested by Reviewer 1, we will carefully read through the entire manuscript and try to improve its clarity. Regarding the comments and recommendations from Reviewer 2, we plan to address the first recommendation and the specific comments about the analysis of DNA methylation. We can currently not address the second recommendation because the person responsible for gathering the data works at a different university now. However, we keep this in mind for future projects.

(B) Regarding the two main comments of Reviewer 2, we plan the following:

(1) The authors group their methylation analysis by sequence context (CG, CHG, CHH). I feel this is insufficient, because CG methylation can appear in two distinct forms: gene body methylation (gbM), which is CG-only methylation within genes, and transposable element (TE) and TE-like methylation (teM), which typically involves all sequence contexts and generally affects TEs, but can also be found within genes. GbM and teM have distinct epigenetic dynamics, and it is hard to know how methylation patterns are changing during the experiment if gbM and teM are mixed. This can also have downstream consequences (see point below).

We thank Reviewer 2 for this suggestion. We usually separate the three contexts because they are set by different enzymes and not because of the entire process or function. It would indeed be informative to group DMCs into gbM and teM but as there are many regions with overlaps between genes and transposons, this also adds some complexity. Given that there were very few DMCs, we wanted to keep it short and simple. Therefore, we wrote that 87.3% of the DMCs were close to or within genes and that 98.1% were close to and within genes or transposons. Together with the clear overrepresentation of the CG context, this indicates that most of the DMCs were related to gbM. We will update the paragraph and specifically refer to gbM to make this clear.

(2) For GO analysis, the authors use all annotated genes as a control. However, most of the methylation differences they observe are likely gbM, and gbM genes are not representative of all genes. The authors' results might therefore be explained purely as a consequence of analyzing gbM genes, and not an enrichment of methylation changes in any particular GO group.

This indeed a point worth considering. We will update the GO analysis and define the background as genes with cytosines that we tested for differences in methylation and which also exhibited overall at least 10% methylation (i.e., one cytosine per gene was sufficient). This will reduce the background gene set from 34'615 to 18'315 genes. A first analysis shows that results will change with respect to the post-translational protein modifications but will remain similar for epigenetic regulation and terms related to transport and growth processes. We will update the paragraph accordingly.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Syed et al. investigate the circuit underpinnings for leg grooming in the fruit fly. They identify two populations of local interneurons in the right front leg neuromere of ventral nerve cord, i.e. 62 13A neurons and 64 13B neurons. Hierarchical clustering analysis identifies 10 morphological classes for both populations. Connectome analysis reveals their circuit interactions: these GABAergic interneurons provide synaptic inhibition either between the two subpopulations, i.e., 13B onto 13A, or among each other, i.e., 13As onto other 13As, and/or onto leg motoneurons, i.e., 13As and 13Bs onto leg motoneurons. Interestingly, 13A interneurons fall into two categories, with one providing inhibition onto a broad group of motoneurons, being called "generalists", while others project to a few motoneurons only, being called "specialists". Optogenetic activation and silencing of both subsets strongly affect leg grooming. As well as activating or silencing subpopulations, i.e., 3 to 6 elements of the 13A and 13B groups, has marked effects on leg grooming, including frequency and joint positions, and even interrupting leg grooming. The authors present a computational model with the four circuit motifs found, i.e., feed-forward inhibition, disinhibition, reciprocal inhibition, and redundant inhibition. This model can reproduce relevant aspects of the grooming behavior.

Strengths:

The authors succeeded in providing evidence for neural circuits interacting by means of synaptic inhibition to play an important role in the generation of a fast rhythmic insect motor behavior, i.e., grooming. Two populations of local interneurons in the fruit fly VNC comprise four inhibitory circuit motifs of neural action and interaction: feed-forward inhibition, disinhibition, reciprocal inhibition, and redundant inhibition. Connectome analysis identifies the similarities and differences between individual members of the two interneuron populations. Modulating the activity of small subsets of these interneuron populations markedly affects the generation of the motor behavior, thereby exemplifying their important role in generating grooming.

We thank the reviewer for their thoughtful and constructive evaluation of our work. We are encouraged by their recognition of the major contributions of our study, including the identification of multiple inhibitory circuit motifs and their contribution to organizing rhythmic leg grooming behavior. We also appreciate the reviewer’s comments highlighting our use of connectomics, targeted manipulations, and modeling to reveal how distinct subsets of inhibitory interneurons contribute to motor behavior.

Weaknesses:

Effects of modulating activity in the interneuron populations by means of optogenetics were conducted in the so-called closed-loop condition. This does not allow for differentiation between direct and secondary effects of the experimental modification in neural activity, as feedforward and feedback effects cannot be disentangled. To do so, open loop experiments, e.g., in deafferented conditions, would be important. Given that many members of the two populations of interneurons do not show one, but two or more circuit motifs, it remains to be disentangled which role the individual circuit motif plays in the generation of the motor behavior in intact animals.

We appreciate the reviewer’s point regarding the role of sensory feedback in our experimental design. We agree that reafferent (sensory) input from ongoing movements could contribute to the behavioral outcomes of our optogenetic manipulations. However, our aim was not to isolate central versus peripheral contributions, but rather to assess the role of 13A/B neurons within the intact, operational sensorimotor system during natural grooming behavior.

These inhibitory neurons form recurrent loops, synapse onto motor neurons, and receive proprioceptive input—placing them in a position to both shape central motor output and process sensory feedback. As such, manipulating their activity engages both central control and sensory consequences.

The finding that silencing 13A neurons in dusted flies disrupts rhythmic leg coordination highlights their role in organizing grooming movements. Prior studies (e.g., Ravbar et al., 2021) show that grooming rhythms persist when sensory input is reduced, indicating a central origin, while sensory feedback refines timing, coordination, and long-timescale stability. We concluded that rhythmicity arises centrally but is shaped and stabilized by mechanosensory or proprioceptive feedback. Our current results are consistent with this view and support a model in which inhibitory premotor neurons participate in a closed-loop control architecture that generates and tunes rhythmic output.

While we agree that fully removing sensory feedback and parsing distinct roles for neurons that participate in multiple circuit motifs would be desirable, we do not see a plausible experimental path to accomplish this - we would welcome suggestions!

We considered the method used by Mendes and Mann (eLife 2023) to assess sensory feedback to walking, 5-40-GAL4, DacRE-flp, UAS->stop>TNT + 13A/B-spGAL4 X UAS-csChrimson. This would require converting one targeting system to LexA and presents significant technical challenges. More importantly, we believe the core interpretation issue would remain: broadly silencing proprioceptors would produce pleiotropic effects and impair baseline coordination, making it difficult to distinguish whether observed changes reflect disrupted rhythm generation or secondary consequences of impaired sensory input.

We will clarify in the revised manuscript that our behavioral experiments were performed in freely moving flies under closed-loop conditions. We thank the reviewer for highlighting these important considerations and will revise the manuscript to better communicate the scope and interpretation of our findings.

Reviewer #2 (Public review):

Summary:

This manuscript by Syed et al. presents a detailed investigation of inhibitory interneurons, specifically from the 13A and 13B hemilineages, which contribute to the generation of rhythmic leg movements underlying grooming behavior in Drosophila. After performing a detailed connectomic analysis, which offers novel insights into the organization of premotor inhibitory circuits, the authors build on this anatomical framework by performing optogenetic perturbation experiments to functionally test predictions derived from the connectome. Finally, they integrate these findings into a computational model that links anatomical connectivity with behavior, offering a systems-level view of how inhibitory circuits may contribute to grooming pattern generation.

Strengths:

(1) Performing an extensive and detailed connectomic analysis, which offers novel insights into the organization of premotor inhibitory circuits.

(2) Making sense of the largely uncharacterized 13A/13B nerve cord circuitry by combining connectomics and optogenetics is very impressive and will lay the foundation for future experiments in this field.

(3) Testing the predictions from experiments using a simplified and elegant model.

We thank the reviewer for their thoughtful and encouraging evaluation of our work. We are especially grateful for their recognition of our detailed connectome analysis and its contribution to understanding the organization of premotor inhibitory circuits. We appreciate the reviewer’s comments highlighting the integration of connectomics with optogenetic perturbations to functionally interrogate the 13A and 13B circuits, as well as their recognition of our modeling approach as a valuable framework for linking circuit architecture to behavior.

Weaknesses:

(1) In Figure 4, while the authors report statistically significant shifts in both proximal inter-leg distance and movement frequency across conditions, the distributions largely overlap, and only in Panel K (13B silencing) is there a noticeable deviation from the expected 7-8 Hz grooming frequency. Could the authors clarify whether these changes truly reflect disruption of the grooming rhythm?

We are re-analyzing the whole dataset in the light of the reviews (specifically, we are now applying LMM to these statistics). For the panels in question (H-J), there is indeed a large overlap between the frequency distributions, but the box plots show median and quartiles, which partially overlap. (In the current analysis, as it stands, differences in means were small yet significant.) However, there is a noticeable (not yet quantified) difference in variability between the frequencies (the experimental group being the more variable one). If the activations/deactivations of 13A/B circuits disrupt the rhythm, we would indeed expect the frequencies to become more variable. So, in the revised version we will quantify the differences in both the means and the variabilities, and establish whether either shows significance after applying the LMM.

More importantly, all this data would make the most sense if it were performed in undusted flies (with controls) as is done in the next figure.

In our assay conditions, undusted flies groom infrequently. We used undusted flies for some optogenetic activation experiments, where the neuron activation triggers behavior initiation, but we chose to analyze the effect of silencing inhibitory neurons in dusted flies because dust reliably activates mechanosensory neurons and elicits robust grooming behavior, enabling us to assess how manipulation of 13A/B neurons alters grooming rhythmicity and leg coordination.

(2) In Figure 4-Figure Supplement 1, the inclusion of walking assays in dusted flies is problematic, as these flies are already strongly biased toward grooming behavior and rarely walk. To assess how 13A neuron activation influences walking, such experiments should be conducted in undusted flies under baseline locomotor conditions.

We agree that there are better ways to assay potential contributions of 13A/13B neurons to walking. We intended to focus on how normal activity in these inhibitory neurons affects coordination during grooming, and we included walking because we observed it in our optogenetic experiments and because it also involves rhythmic leg movements. The walking data is reported in a supplementary figure because we think this merits further study with assays designed to quantify walking specifically. We will make these goals clearer in the revised manuscript and we are happy to share our reagents with other research groups more equipped to analyze walking differences.

(3) For broader lines targeting six or more 13A neurons, the authors provide specific predictions about expected behavioral effects-e.g., that activation should bias the limb toward flexion and silencing should bias toward extension based on connectivity to motor neurons. Yet, when using the more restricted line labeling only two 13A neurons (Figure 4 - Figure Supplement 2), no such prediction is made. The authors report disrupted grooming but do not specify whether the disruption is expected to bias the movement toward flexion or extension, nor do they discuss the muscle target. This is a missed opportunity to apply the same level of mechanistic reasoning that was used for broader manipulations.

While we know which two neurons are labeled based on confocal expression, assigning their exact identity in the EM datasets has been challenging. One of these neurons appears absent from our 13A reconstructions of the right T1 neuropil in FANC, although we did locate it in MANC. However, its annotation in MANC has undergone multiple revisions, making confident assignment difficult at this time. Since we can’t be sure which motor neurons and muscles are most directly connected, we did not want to predict this line’s effect on leg movements.

(4) Regarding Figure 5: The 70ms on/off stimulation with a slow opsin seems problematic. CsChrimson off kinetics are slow and unlikely to cause actual activity changes in the desired neurons with the temporal precision the authors are suggesting they get. Regardless, it is amazing that the authors get the behavior! It would still be important for the authors to mention the optogenetics caveat, and potentially supplement the data with stimulation at different frequencies, or using faster opsins like ChrimsonR.

We were also surprised - and intrigued - by the behavioral consequences of activating these inhibitory neurons with CsChrimson. We tried several different activation paradigms: pulsed from 8Hz to 500Hz and with various on/off intervals. Because several of these different stimulation protocols resulted in grooming, and with different rhythmic frequencies, we think the phenotypes are a specific property of the neural circuits we have activated, rather than the kinetics of CsChrimson itself.

We will include the data from other frequencies in a new Supplementary Figure, we will discuss the caveats CsChrimson’s slow off-kinetics present to precise temporal control of neural activity, and we will try ChrimsonR in future experiments.

Overall, I think the strengths outweigh the weaknesses, and I consider this a timely and comprehensive addition to the field.

Thank you!

Reviewer #3 (Public review):

Summary:

The authors set out to determine how GABAergic inhibitory premotor circuits contribute to the rhythmic alternation of leg flexion and extension during Drosophila grooming. To do this, they first mapped the ~120 13A and 13B hemilineage inhibitory neurons in the prothoracic segment of the VNC and clustered them by morphology and synaptic partners. They then tested the contribution of these cells to flexion and extension using optogenetic activation and inhibition and kinematic analyses of limb joints. Finally, they produced a computational model representing an abstract version of the circuit to determine how the connectivity identified in EM might relate to functional output. The study, in its current form, makes an important but overclaimed contribution to the literature due to a mismatch between the claims in the paper and the data presented.

Strengths:

The authors have identified an interesting question and use a strong set of complementary tools to address it:

(1) They analysed serial‐section TEM data to obtain reconstructions of every 13A and 13B neuron in the prothoracic segment. They manually proofread over 60 13A neurons and 64 13B neurons, then used automated synapse detection to build detailed connectivity maps and cluster neurons into functional motifs.

(2) They used optogenetic tools with a range of genetic driver lines in freely behaving flies to test the contribution of subsets of 13A and 13B neurons.

(3) They used a connectome-constrained computational model to determine how the mapped connectivity relates to the rhythmic output of the behavior.

We appreciate the reviewer’s thorough and constructive feedback on our work. We are encouraged by their recognition of the complementary approaches used in our study.

Weaknesses:

The manuscript aims to reveal an instructive, rhythm-generating role for premotor inhibition in coordinating the multi-joint leg synergies underlying grooming. It makes a valuable contribution, but currently, the main claims in the paper are not well-supported by the presented evidence.

Major points

(1) Starting with the title of this manuscript, "Inhibitory circuits generate rhythms for leg movements during Drosophila grooming", the authors raise the expectation that they will show that the 13A and 13B hemilineages produce rhythmic output that underlies grooming. This manuscript does not show that. For instance, to test how they drive the rhythmic leg movements that underlie grooming requires the authors to test whether these neurons produce the rhythmic output underlying behavior in the absence of rhythmic input. Because the optogenetic pulses used for stimulation were rhythmic, the authors cannot make this point, and the modelling uses a "black box" excitatory network, the output of which might be rhythmic (this is not shown). Therefore, the evidence (behavioral entrainment; perturbation effects; computational model) is all indirect, meaning that the paper's claim that "inhibitory circuits generate rhythms" rests on inferred sufficiency. A direct recording (e.g., calcium imaging or patch-clamp) from 13A/13B during grooming - outside the scope of the study - would be needed to show intrinsic rhythmogenesis. The conclusions drawn from the data should therefore be tempered. Moreover, the "black box" needs to be opened. What output does it produce? How exactly is it connected to the 13A-13B circuit?

We will modify the title to better reflect our strongest conclusions: “Inhibitory circuits coordinate rhythmic leg movements during Drosophila grooming”

Our optogenetic activation was delivered in a patterned (70 ms on/off) fashion that entrains rhythmic movements but does not rule out the possibility that the rhythm is imposed externally. In the manuscript, we state that we used pulsed light to mimic a flexion-extension cycle and note that this approach tests whether inhibition is sufficient to drive rhythmic leg movements when temporally patterned. While this does not prove that 13A/13B neurons are intrinsic rhythm generators, it does demonstrate that activating subsets of inhibitory neurons is sufficient to elicit alternating leg movements resembling natural grooming and walking.

Our goal with the model was to demonstrate that it is possible to produce rhythmic outputs with this 13A/B circuit, based on the connectome. The “black box” is a small recurrent neural network (RNN) consisting of 40 neurons in its hidden layer. The inputs are the “dust” levels from the environment (the green pixels in Figure 6I), the “proprioceptive” inputs (“efference copy” from motor neurons), and the amount of dust accumulated on both legs. The outputs (all positive) connect to the 13A neurons, the 13B neurons, and to the motor neurons. We refer to it as the “black box” because we make no claims about the actual excitatory inputs to these circuits. Its function is to provide input, needed to run the network, that reflects the distribution of “dust” in the environment as well as the information about the position of the legs.

The output of the “black box” component of the model might be rhythmic. In fact, in most instances of the model implementation this is indeed the case. However, as mentioned in the current version of the manuscript: “But the 13A circuitry can still produce rhythmic behavior even without those external sensory inputs (or when set to a constant value), although the legs become less coordinated.” Indeed, when we refine the model (with the evolutionary training) without the “black box” (using a constant input of 0.1) the behavior is still rhythmic and sustained. Therefore, the rhythmic activity and behavior can emerge from the premotor circuitry itself without a rhythmic input.

The context in which the 13A and 13B hemilineages sit also needs to be explained. What do we know about the other inputs to the motorneurons studied? What excitatory circuits are there?

We agree that there are many more excitatory and inhibitory, direct and indirect, connections to motor neurons that will also affect leg movements for grooming and walking. Our goal was to demonstrate what is possible from a constrained circuit of inhibitory neurons that we mapped in detail, and we hope to add additional components to better replicate the biological circuit as behavioral and biomechanical data is obtained by us and others. We will add this clarification of the limits of the scope to the Discussion.

Furthermore, the introduction ignores many decades of work in other species on the role of inhibitory cell types in motor systems. There is some mention of this in the discussion, but even previous work in Drosophila larvae is not mentioned, nor crustacean STG, nor any other cell types previously studied. This manuscript makes a valuable contribution, but it is not the first to study inhibition in motor systems, and this should be made clear to the reader.

We thank the reviewer for this important reminder and we will expand our discussion of the relevant history and context in our revision. Previous work on the contribution of inhibitory neurons to invertebrate motor control certainly influenced our research and we should acknowledge this better.

(2) The experimental evidence is not always presented convincingly, at times lacking data, quantification, explanation, appropriate rationales, or sufficient interpretation.

We are committed to improving the clarity, rationale, and completeness of our experimental descriptions. We will revisit the statistical tests applied throughout the manuscript and expand the Methods.

(3) The statistics used are unlike any I remember having seen, essentially one big t-test followed by correction for multiple comparisons. I wonder whether this approach is optimal for these nested, high‐dimensional behavioral data. For instance, the authors do not report any formal test of normality. This might be an issue given the often skewed distributions of kinematic variables that are reported. Moreover, each fly contributes many video segments, and each segment results in multiple measurements. By treating every segment as an independent observation, the non‐independence of measurements within the same animal is ignored. I think a linear mixed‐effects model (LMM) or generalized linear mixed model (GLMM) might be more appropriate.

We thank the reviewer for raising this important point regarding the statistical treatment of our segmented behavioral data. Our initial analysis used independent t-tests with Bonferroni correction across behavioral classes and features, which allowed us to identify broad effects. However, we acknowledge that this approach does not account for the nested structure of the data. To address this, we will re-analyze key comparisons using linear mixed-effects models (LMMs) as suggested by the reviewer. This approach will allow us to more appropriately model within-fly variability and test the robustness of our conclusions. We will update the manuscript based on the outcomes of these analyses.

(4) The manuscript mentions that legs are used for walking as well as grooming. While this is welcome, the authors then do not discuss the implications of this in sufficient detail. For instance, how should we interpret that pulsed stimulation of a subset of 13A neurons produces grooming and walking behaviours? How does neural control of grooming interact with that of walking?

We do not know how the inhibitory neurons we investigated will affect walking or how circuits for control of grooming and walking might compete. We speculate that overlapping pre-motor circuits may participate in walking and grooming because both behaviors have extension flexion cycles at similar frequencies, but we do not have hard experimental data to support. This would be an interesting area for future research. Here, we focused on the consequences of activating specific 13A/B neurons during grooming because they were identified through a behavioral screen for grooming disruptions, and we had developed high-resolution assays and familiarity with the normal movements in this behavior. We will clarify this rationale in the revised discussion.

(5) The manuscript needs to be proofread and edited as there are inconsistencies in labelling in figures, phrasing errors, missing citations of figures in the text, or citations that are not in the correct order, and referencing errors (examples: 81 and 83 are identical; 94 is missing in text).

We will carefully proofread the manuscript to fix all figure labeling, citation order, and referencing errors.

eLife Assessment

Using a combination of connectomics, optogenetics, behavioral analysis, and modeling, this study provides important findings on the role of two populations of inhibitory neurons in the generation of leg grooming movements in Drosophila. The data as presented provide incomplete evidence that the identified neuronal populations contribute to the alternation of flexion and extension by inhibiting specific sets of motor neurons while disinhibiting their counterparts. While the manuscript provides comprehensive details about the 13A/B neuronal populations involved in grooming control, updates on statistics, and explicit mentioning of experimental/modeling caveats would strengthen the study. The work will interest neuroscientists, and particularly those working on motor control.

Reviewer #1 (Public review):

Summary:

Syed et al. investigate the circuit underpinnings for leg grooming in the fruit fly. They identify two populations of local interneurons in the right front leg neuromere of ventral nerve cord, i.e. 62 13A neurons and 64 13B neurons. Hierarchical clustering analysis identifies 10 morphological classes for both populations. Connectome analysis reveals their circuit interactions: these GABAergic interneurons provide synaptic inhibition either between the two subpopulations, i.e., 13B onto 13A, or among each other, i.e., 13As onto other 13As, and/or onto leg motoneurons, i.e., 13As and 13Bs onto leg motoneurons. Interestingly, 13A interneurons fall into two categories, with one providing inhibition onto a broad group of motoneurons, being called "generalists", while others project to a few motoneurons only, being called "specialists". Optogenetic activation and silencing of both subsets strongly affect leg grooming. As well aas ctivating or silencing subpopulations, i.e., 3 to 6 elements of the 13A and 13B groups, has marked effects on leg grooming, including frequency and joint positions, and even interrupting leg grooming. The authors present a computational model with the four circuit motifs found, i.e., feed-forward inhibition, disinhibition, reciprocal inhibition, and redundant inhibition. This model can reproduce relevant aspects of the grooming behavior.

Strengths:

The authors succeeded in providing evidence for neural circuits interacting by means of synaptic inhibition to play an important role in the generation of a fast rhythmic insect motor behavior, i.e., grooming. Two populations of local interneurons in the fruit fly VNC comprise four inhibitory circuit motifs of neural action and interaction: feed-forward inhibition, disinhibition, reciprocal inhibition, and redundant inhibition. Connectome analysis identifies the similarities and differences between individual members of the two interneuron populations. Modulating the activity of small subsets of these interneuron populations markedly affects the generation of the motor behavior, thereby exemplifying their important role in generating grooming.

Weaknesses:

Effects of modulating activity in the interneuron populations by means of optogenetics were conducted in the so-called closed-loop condition. This does not allow for differentiation between direct and secondary effects of the experimental modification in neural activity, as feedforward and feedback effects cannot be disentangled. To do so, open loop experiments, e.g., in deafferented conditions, would be important. Given that many members of the two populations of interneurons do not show one, but two or more circuit motifs, it remains to be disentangled which role the individual circuit motif plays in the generation of the motor behavior in intact animals.

Reviewer #2 (Public review):

Summary:

This manuscript by Syed et al. presents a detailed investigation of inhibitory interneurons, specifically from the 13A and 13B hemilineages, which contribute to the generation of rhythmic leg movements underlying grooming behavior in Drosophila. After performing a detailed connectomic analysis, which offers novel insights into the organization of premotor inhibitory circuits, the authors build on this anatomical framework by performing optogenetic perturbation experiments to functionally test predictions derived from the connectome. Finally, they integrate these findings into a computational model that links anatomical connectivity with behavior, offering a systems-level view of how inhibitory circuits may contribute to grooming pattern generation.

Strengths:

(1) Performing an extensive and detailed connectomic analysis, which offers novel insights into the organization of premotor inhibitory circuits.

(2) Making sense of the largely uncharacterized 13A/13B nerve cord circuitry by combining connectomics and optogenetics is very impressive and will lay the foundation for future experiments in this field.

(3) Testing the predictions from experiments using a simplified and elegant model.

Weaknesses:

(1) In Figure 4, while the authors report statistically significant shifts in both proximal inter-leg distance and movement frequency across conditions, the distributions largely overlap, and only in Panel K (13B silencing) is there a noticeable deviation from the expected 7-8 Hz grooming frequency. Could the authors clarify whether these changes truly reflect disruption of the grooming rhythm? More importantly, all this data would make the most sense if it were performed in undusted flies (with controls) as is done in the next figure.

(2) In Figure 4-Figure Supplement 1, the inclusion of walking assays in dusted flies is problematic, as these flies are already strongly biased toward grooming behavior and rarely walk. To assess how 13A neuron activation influences walking, such experiments should be conducted in undusted flies under baseline locomotor conditions.

(3) For broader lines targeting six or more 13A neurons, the authors provide specific predictions about expected behavioral effects-e.g., that activation should bias the limb toward flexion and silencing should bias toward extension based on connectivity to motor neurons. Yet, when using the more restricted line labeling only two 13A neurons (Figure 4 - Figure Supplement 2), no such prediction is made. The authors report disrupted grooming but do not specify whether the disruption is expected to bias the movement toward flexion or extension, nor do they discuss the muscle target. This is a missed opportunity to apply the same level of mechanistic reasoning that was used for broader manipulations.

(4) Regarding Figure 5: The 70ms on/off stimulation with a slow opsin seems problematic. CsChrimson off kinetics are slow and unlikely to cause actual activity changes in the desired neurons with the temporal precision the authors are suggesting they get. Regardless, it is amazing that the authors get the behavior! It would still be important for the authors to mention the optogenetics caveat, and potentially supplement the data with stimulation at different frequencies, or using faster opsins like ChrimsonR.

Overall, I think the strengths outweigh the weaknesses, and I consider this a timely and comprehensive addition to the field.

Reviewer #3 (Public review):

Summary:

The authors set out to determine how GABAergic inhibitory premotor circuits contribute to the rhythmic alternation of leg flexion and extension during Drosophila grooming. To do this, they first mapped the ~120 13A and 13B hemilineage inhibitory neurons in the prothoracic segment of the VNC and clustered them by morphology and synaptic partners. They then tested the contribution of these cells to flexion and extension using optogenetic activation and inhibition and kinematic analyses of limb joints. Finally, they produced a computational model representing an abstract version of the circuit to determine how the connectivity identified in EM might relate to functional output. The study, in its current form, makes an important but overclaimed contribution to the literature due to a mismatch between the claims in the paper and the data presented.

Strengths:

The authors have identified an interesting question and use a strong set of complementary tools to address it:

(1) They analysed serial‐section TEM data to obtain reconstructions of every 13A and 13B neuron in the prothoracic segment. They manually proofread over 60 13A neurons and 64 13B neurons, then used automated synapse detection to build detailed connectivity maps and cluster neurons into functional motifs.

(2) They used optogenetic tools with a range of genetic driver lines in freely behaving flies to test the contribution of subsets of 13A and 13B neurons.

(3) They used a connectome-constrained computational model to determine how the mapped connectivity relates to the rhythmic output of the behavior.

Weaknesses:

The manuscript aims to reveal an instructive, rhythm-generating role for premotor inhibition in coordinating the multi-joint leg synergies underlying grooming. It makes a valuable contribution, but currently, the main claims in the paper are not well-supported by the presented evidence.

Major points

(1) Starting with the title of this manuscript, "Inhibitory circuits generate rhythms for leg movements during Drosophila grooming", the authors raise the expectation that they will show that the 13A and 13B hemilineages produce rhythmic output that underlies grooming. This manuscript does not show that. For instance, to test how they drive the rhythmic leg movements that underlie grooming requires the authors to test whether these neurons produce the rhythmic output underlying behavior in the absence of rhythmic input. Because the optogenetic pulses used for stimulation were rhythmic, the authors cannot make this point, and the modelling uses a "black box" excitatory network, the output of which might be rhythmic (this is not shown). Therefore, the evidence (behavioral entrainment; perturbation effects; computational model) is all indirect, meaning that the paper's claim that "inhibitory circuits generate rhythms" rests on inferred sufficiency. A direct recording (e.g., calcium imaging or patch-clamp) from 13A/13B during grooming - outside the scope of the study - would be needed to show intrinsic rhythmogenesis. The conclusions drawn from the data should therefore be tempered. Moreover, the "black box" needs to be opened. What output does it produce? How exactly is it connected to the 13A-13B circuit? The context in which the 13A and 13B hemilineages sit also needs to be explained. What do we know about the other inputs to the motorneurons studied? What excitatory circuits are there? Furthermore, the introduction ignores many decades of work in other species on the role of inhibitory cell types in motor systems. There is some mention of this in the discussion, but even previous work in Drosophila larvae is not mentioned, nor crustacean STG, nor any other cell types previously studied. This manuscript makes a valuable contribution, but it is not the first to study inhibition in motor systems, and this should be made clear to the reader.

(2) The experimental evidence is not always presented convincingly, at times lacking data, quantification, explanation, appropriate rationales, or sufficient interpretation.

(3) The statistics used are unlike any I remember having seen, essentially one big t-test followed by correction for multiple comparisons. I wonder whether this approach is optimal for these nested, high‐dimensional behavioral data. For instance, the authors do not report any formal test of normality. This might be an issue given the often skewed distributions of kinematic variables that are reported. Moreover, each fly contributes many video segments, and each segment results in multiple measurements. By treating every segment as an independent observation, the non‐independence of measurements within the same animal is ignored. I think a linear mixed‐effects model (LMM) or generalized linear mixed model (GLMM) might be more appropriate.

(4) The manuscript mentions that legs are used for walking as well as grooming. While this is welcome, the authors then do not discuss the implications of this in sufficient detail. For instance, how should we interpret that pulsed stimulation of a subset of 13A neurons produces grooming and walking behaviours? How does neural control of grooming interact with that of walking?

(5) The manuscript needs to be proofread and edited as there are inconsistencies in labelling in figures, phrasing errors, missing citations of figures in the text, or citations that are not in the correct order, and referencing errors (examples: 81 and 83 are identical; 94 is missing in text).

eLife Assessment

This study presents a valuable finding on the perturbed pyruvate metabolism in models of repetitive traumatic brain injury. The evidence supporting the main claims of the authors is solid, but much of the accompanying analysis and interpretation relies on incomplete evidence. The work will be of interest to those working on the imaging of traumatic brain injury.

Joint Public Review:

Summary:

The authors present a metabolic imaging study of pyruvate metabolism in a mouse model of repetitive traumatic brain injury in the chronic recovery stage. They measure pyruvate metabolism with hyperpolarised 13C magnetic resonance spectroscopic imaging. This is acquired alongside semi-quantitative MR imaging metrics, a behavioural measure, and postmortem measures of relevant enzyme activity and expression of metabolic transporter proteins. They find that the MRSI-measured cortical lactate/pyruvate ratio (and signal from pyruvate and lactate independently) can differentiate the rTBI group from the sham group. They additionally find that postmortem, cortical pyruvate dehydrogenase activity is a statistically significant discriminator. All other metrics (MRI and enzyme/transporter measures) are not significantly different between groups. Finally, using a machine learning approach, the authors investigate the predictive power of combinations of all measures.

Strengths:

The primary strength of this work is the likely robustness of the primary finding - that hyperpolarised 13C lactate/pyruvate metabolite ratios are perturbed in this chronic rTBI model compared to the sham control.

Weaknesses:

Focal alterations in blood-brain-barrier permeability may affect the primary lactate/pyruvate measures. Whilst 13C urea measures perfusion, urea remains purely extracellular; whilst in the metabolism of the healthy brain, pyruvate must be transported through two levels of monocarboxylate transporters (MCTs) - in the endothelium surrounding the capillary bed and then into the parenchyma. By mechanically disrupting the brain, tight junctions in the BBB may be disrupted, therefore increasing the flux of pyruvate across the BBB and increasing pyruvate availability. In this case, lac/pyr would be a poor measure of metabolism as "delivery" has changed. While the authors assess perfusion using HP urea, it is unclear whether or how this metric would change in the presence of BBB disruption in relatively large and well-vascularised voxels.

The finding that "HP [1-13C]pyruvate levels were 1.05 fold higher" indicates that delivery of pyruvate might be increased. It is unclear if normalisation to the combined amplitude of lactate and pyruvate is fair in the case that the volume fraction in the voxel might have increased. Ideally, the authors would estimate polarisation separately as a normalisation.

No estimate of uncertainty is provided for the primary metabolic measures. Note that the lactate-pyruvate ratio is not normally distributed (see doi: 10.1002/mrm.26615), and this should be accounted for when carrying out statistical tests.

All metabolic maps are shown masked to the brain and interpolated to the structural MRI resolution (around 20 times). Nor is there any characterisation of the spectroscopic imaging's voxel volume, including the effect of the point spread function. It is, therefore, hard to have confidence in any spatial effects or potential partial volume effects from the tissue surrounding the brain.

The t2-weighted and SWI MRI measures used in this work are not quantitative. Normalisation in each case is carried out without regard to any spatially variable transmit and receive coil sensitivities (B1{plus minus}), which vary per subject. This adds intersubject variance, which could mask any effect between groups. No quality metrics (SNR or uncertainty estimates) are given for the MRI metrics.

Spectroscopic imaging was conducted 16 s after injection. Given the high heart rate of a mouse, measures of perfusion (using urea) could , therefore, be considered in a steady state, lowering sensitivity to any changes in perfusion or metabolite delivery. Furthermore, it is unclear how any changes in BBB permeability would manifest with the relatively low spatial resolution of MRSI. Would signal always be dominated by vascular compartments?

There is no apparent attempt to understand if an immune response occurs at this chronic time point. Macrophages are glycolytic and could affect the pyruvate measurement. Furthermore, is there any evidence for cellular changes in this model, namely density, cell type fraction, or microstructure? Are there any expected changes in glucose uptake?

There is no information or references provided for the accuracy or precision of the postmortem assays or their correlation with in vivo processes. What is the effect of cell density changes after injury on the assay kits?

The proposed interpretation of T1 as a measure of oxidative stress would seem to ignore the many confounding interpretations of T1.

Aims and impact:

In summary, the authors broadly achieve one aim, which is to find that HP 13C measured lac/pyruvate is a biomarker for the chronic effects of rTBI in a mouse model. As the authors themselves highlight in the discussion, the interpretation of this finding is tricky alongside their post-mortem assay results. The MR imaging in this work seems inconclusive, given the potential for inter-subject variance in the normalisation method.

The work, therefore, continues to highlight that HP 13C MRSI is a highly promising avenue of investigation to identify, characterise, and understand traumatic brain injury. It suggests that HP 13C MRSI is more promising in this sense than some standard MRI contrasts. The work currently fails to convincingly interpret the HP 13C MR results in conjunction with the other metrics.

eLife Assessment

This is a valuable and rigorous study that addresses the question of what determines the spatial organization of endocytic zones at synapses. The authors use compelling approaches, in both Drosophila and rodent model systems, to define the role of activity and active zone structure on the organization of the peri-active zone. While the findings are primarily negative, they are carefully executed and contribute to the field by refining existing models of presynaptic organization.

Reviewer #1 (Public review):

Summary:

In this manuscript, Emperador-Melero et al. seek to determine whether recruitment of endocytic machinery to the periactive zone is activity-dependent or tethered to delivery of active zone machinery. They use genetic knockouts and pharmacological block in two model synapses - cultured mouse hippocampal neurons and Drosophila neuromuscular junctions - to determine how well endocytic machinery localizes after chronic inhibition or acute depolarization by super-resolution imaging. They find that acute depolarization in both models has minimal to no effect on the localization of endocytic machinery at the periactive zone, suggesting that these proteins are constitutively maintained rather than upregulated in response to transient activity. Interestingly, chronic inhibition slightly increases endocytic machinery levels, implying a potential homeostatic upregulation in preparation for rebound depolarization. Using genetic knockouts, the authors show that localization of endocytic machinery to periactive zones occurs independently of proper active zone assembly, even in the absence of upstream organizers like Liprin-α.

Overall, they propose that the constitutive deployment of endocytic machinery reflects its critical role in facilitating rapid and reliable membrane internalization during synaptic functions beyond classical endocytosis, such as regulation of the exocytic fusion pore and dense-core vesicle fusion. Although many experiments reveal limited changes in the localization or abundance of endocytic machinery, the findings are thorough, and data substantially support a model in which endocytic components are organized through a pathway distinct from that of the active zone. This work advances our understanding of synaptic dynamics by supporting a model in which endocytic machinery is constitutively recruited and regulated by distinct upstream organizers compared to active zone proteins. It also highlights the utility of super-resolution imaging across diverse synapse types to uncover functionally conserved elements of synaptic biology.

Strengths:

The study's technical strengths, particularly the use of super-resolution microscopy and rigorous image analyses developed by the group, bolster their findings.

Weaknesses:

One notable limitation, however, is the absence of interrogation of endocytic proteins previously suggested to be recruited in an activity-dependent manner, in particular, endophilin.

Reviewer #2 (Public review):

Summary:

This study examines whether the localization of endocytic proteins to presynaptic periactive zones depends on synaptic activity or active zone scaffolds. Using a combination of genetic and pharmacological perturbations in Drosophila and mouse neurons, the authors show that proteins such as Dynamin, Amphiphysin, AP-180, and others are still recruited to periactive zones even when evoked release or active zone architecture is disrupted. While the results are mostly negative, the study is methodologically solid and contributes to a more nuanced understanding of synaptic vesicle recycling machinery.

Strengths:

(1) The experimental design is careful and systematic, covering both fly and mammalian systems.

(2) The use of advanced genetic models (e.g., Liprin-α quadruple knockout mice) is a notable strength.

(3) High-resolution imaging (STED, Airyscan) is well used to assess spatial localization.

(4) The findings clarify that certain core assumptions - such as strict activity dependence of endocytic recruitment - may not hold universally.

Weaknesses:

(1) The study would benefit from a clearer positive control to demonstrate activity-dependent recruitment (e.g., Endophilin).

(2) The reliance on Tetanus toxin in the Drosophila NMJ experiments in my eyes is a limitation, as it does not block all presynaptic fusion events; this should be discussed more directly.

(3) The potential role of Dynamin in organizing other periactive zone proteins is not addressed and could be an important next step.

(4) Some small changes in protein levels upon silencing are reported; their biological meaning (e.g., compensation vs. variability) is not fully clarified.

(5) While alternative organizing mechanisms (actin, lipids, adhesion molecules) are mentioned, a more forward-looking discussion of how to test these models would be helpful.

(6) The authors should consider including, or at least discussing, a well-established activity-dependent endocytic protein (e.g., Endophilin) as a positive control to help contextualize the negative findings.

Reviewer #3 (Public review):

Summary:

This study examines how synaptic endocytic zones are positioned using a combination of cultured neurons and the Drosophila neuromuscular junction. The authors test whether neuronal activity, active zone assembly, or liprin-α function is required to localize endocytic zone markers, including Dynamin, Amphiphysin, Nervous Wreck, PIPK1γ, and AP-180. None of the manipulations tested caused a coordinated disruption in the localization or abundance of these markers, leading to the conclusion that endocytic zones form independently of synaptic activity and active zone scaffolds.

Strengths:

The work is systematic and carefully executed, using multiple manipulations and two complementary model systems. The authors consistently examine multiple molecular markers, strengthening the interpretation that endocytic zone positioning is robust to changes in activity and structural assembly.

Weaknesses:

The main limitation is that the study does not test whether the methods used are sensitive enough to detect subtle functional disruption, and no condition tested produces clear disorganization of the endocytic zone. As a result, the conclusion that these zones assemble independently is supported by negative data, without a strong positive control for disassembly or mislocalization.

This paper addresses a longstanding question in synaptic biology and provides a well-supported boundary on the types of mechanisms that are likely to govern endocytic zone localization. The conclusions are well justified by the data, though additional evidence would be needed to define the assembly mechanism itself.

eLife Assessment

The authors conducted a valuable study that investigates a molecular pathway mediating the transformation of a cell aggregate into a sheet known as the nucleus laminaris, a crucial site for auditory processing. While the study offers a comprehensive view of the sequence of developmental events and suggests possible roles for FGF signaling, the transcription factor Mafb, and the cell surface adhesive molecule Cadherin-23 in this process, the current data were considered incomplete.

Reviewer #1 (Public review):

Summary:

In this study, the authors sought to define a molecular pathway that mediates the transformation of an aggregate of cells into a sheet known as the nucleus laminaris, a key site for auditory processing. The data offer a comprehensive view of the sequence of developmental events and suggest possible roles for FGF signaling, the transcription factor Mafb, and the cell surface adhesive molecule Cadherin-23 in this process.

Strengths:

The description of nL development is thorough and well-done, with extensive quantification of the overall structure of the nucleus and also of neuron number. Additionally, the study implicates several molecules in nL development, starting with a clear description of when and where FGF8, Mafb, and several cadherins are expressed, including antibody stains suggesting that one cadherin, cdh2, is localized to the neuronal dendrites. A series of perturbation experiments supports the idea that these three molecules play a role in nL formation. The computational model is an interesting addition that helps to conceptualize how cadherin-mediated adhesion might influence nL morphogenesis.

Weaknesses:

A number of weaknesses limit the impact of this work.

One problem is how the data is interpreted. The logic is often circular in that the same molecules are used both as markers of nL and also as players in its development. An independent measure of nL formation is needed. Along the same lines, while the experiments implicate each molecule, the data do not actually demonstrate that FGF directly modulates Mafb, which in turn modulates cadherin expression, especially as overexpression of cdh2 has no effect on FGF8 expression or lamina organization, and no manipulations of cdh22 are presented.

The other type of problem relates to how the experiments were performed and analyzed. Important details about the experiments, as well as key controls, are missing throughout. Sample sizes are rarely presented, and there is no evidence that either dominant negative construct actually acts as proposed. Some results are not well quantified, which further undermines the strength of the conclusions. For instance, the changes in mafb and cdh22 expression (Figure 7) are subtle and were not quantified for any of the conditions. Likewise, the claim that FGF8 has a dose-dependent effect on lamina size and neuron number needs to be supported by statistics.

There are also some questions about the quality of the data. Much of the histology is of poor quality and does not always show the same piece of brain in the same orientation from experiment to experiment, which makes it challenging to interpret the results. In particular, the quality of the in situ hybridization varies, with much more background in some cases than others, which makes it hard to know what signal is real.

Finally, there are some misstatements and problems with citations that weaken the scholarly nature of the paper. FGF signaling has been studied extensively in the hindbrain and even in auditory nucleus development (Abraira et al., 2007), but this literature is not discussed at all.

Due to these weaknesses, the authors have achieved their aims only in part. The data are suggestive, but the results do not yet fully support their conclusions.

Few labs study how populations of neurons assemble into spatially organized structures. This work has the potential to be very interesting to other developmental neuroscientists studying brain morphogenesis.

Reviewer #2 (Public review):

Summary:

The overall goal of this study by Smith et al. was to understand the mechanisms through which groups of cells form specific nuclei during development. These cell groupings may have importance for the development of nervous system connections. Smith et al. have taken advantage of the ordered structure of the nucleus laminaris of the chick, which plays an important role in sound source localization. They used a candidate gene approach to both mark cells in nL and to test for signaling pathways that regulate nucleogenesis. They found that MafB, FGF8, and cadherins were expressed in the auditory hindbrain at the critical ages. They used in ovo electroporation to test gene function effects on nL lamina formation. They found that both increasing and decreasing FGF signaling (through introduction of mouse FGF8 and expression of a dominant negative FGF receptor, respectively) reduced lamina formation in the nL. An optimal concentration of FGF needed for this process was obtained using cultured hindbrain slices. Misexpression of cadherins also perturbed the normal lamina formation. The authors showed that FGF regulates MafB expression, which in turn regulates cadherin expression, suggesting a pathway that shapes lamina development. They constructed computational models of adhesion on the development of nL cells and found that laminar formation is favored by nL cells modeled as bipolar adhesive units. Overall, the study has demonstrated the importance of these adhesion pathways for the formation of the nucleus laminaris, and the findings likely have significance for the development of other nuclei as well.

Strengths:

The experiments have used in situ hybridization, immunofluorescence, electroporation, and brainstem slice cultures to test their hypotheses, which were based on well-selected candidate molecules. The modeling adds to the rigor of the studies, particularly in light of the observation that cadherin expression is localized to nL dendrites.

Weaknesses:

(1) Some references should be considered more carefully for accuracy, and additional references may be needed (introduction and results).

(2) Information on animal numbers and statistical tests should be added.

eLife Assessment

This manuscript provides valuable information on the neurodynamics of emotional processing while participants were watching movie clips. The methods and results were solid in deciphering the temporal-spatial dynamics of emotional processing. This work will be of interest to affective neuroscientists.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors endeavor to capture the dynamics of emotion-related brain networks. They employ slice-based fMRI combined with ICA on fMRI time series recorded while participants viewed a short movie clip. This approach allowed them to track the time course of four non-noise independent components at an effective 2s temporal resolution at the BOLD level. Notably, the authors report a temporal sequence from input to meaning, followed by response, and finally default mode networks, with significant overlap between stages. The use of ICA offers a data-driven method to identify large-scale networks involved in dynamic emotion processing. Overall, this paradigm and analytical strategy mark an important step forward in shifting affective neuroscience toward investigating temporal dynamics rather than relying solely on static network assessments

Strengths:

(1) One of the main advantages highlighted is the improved temporal resolution offered by slice-based fMRI. However, the manuscript does not clearly explain how this method achieves a higher effective resolution, especially since the results still show a 2s temporal resolution, comparable to conventional methods. Clarification on this point would help readers understand the true benefit of the approach.

(2) While combining ICA with task fMRI is an innovative approach to study the spatiotemporal dynamics of emotion processing, task fMRI typically relies on modeling the hemodynamic response (e.g., using FIR or IR models) to mitigate noise and collinearity across adjacent trials. The current analysis uses unmodeled BOLD time series, which might risk suffering from these issues.

(3) The study's claims about emotion dynamics are derived from fMRI data, which are inherently affected by the hemodynamic delay. This delay means that the observed time courses may differ substantially from those obtained through electrophysiology or MEG studies. A discussion on how these fMRI-derived dynamics relate to - or complement - is critical for the field to understand the emotion dynamics.

(4) Although using ICA to differentiate emotion elements is a convenient approach to tell a story, it may also be misleading. For instance, the observed delayed onset and peak latency of the 'response network' might imply that emotional responses occur much later than other stages, which contradicts many established emotion theories. Given the involvement of large-scale brain regions in this network, the underlying reasons for this delay could be very complex.

Concerns and suggestions:

However, I have several concerns regarding the specific presentation of temporal dynamics in the current manuscript and offer the following suggestions.

(1) One selling point of this work regarding the advantages of testing temporal dynamics is the application of slice-based fMRI, which, in theory, should improve the temporal resolution of the fMRI time course. Improving fMRI temporal resolution is critical for a research project on this topic. The authors present a detailed schematic figure (Figure 2) to help readers understand it. However, I have difficulty understanding the benefits of this method in terms of temporal resolution.

a) In Figure 2A, if we examine a specific voxel in slice 2, the slice acquisitions occur at 0.7s, 2.7s, and 4.7s, which implies a temporal resolution of 2s rather than 0.7s. I am unclear on how the temporal resolution could be 0.7s for this specific voxel. I would prefer that the authors clarify this point further, as it would benefit readers who are not familiar with this technology.

b) Even with the claim of an increased temporal resolution (0.7s), the actual data (Figure 3) still appears to have a 2s resolution. I wonder what specific benefit slice-based fMRI brings in terms of testing temporal dynamics, aside from correcting the temporal distortions that conventional fMRI exhibits.

(2) In task-fMRI, the hemodynamic response is usually estimated using a specific model (e.g., FIR, IR model; see Lindquist et al., 2009). These models are effective at reducing noise and collinearity across adjacent trials. The current method appears to be conducted on unmodeled BOLD time series.

a) I am wondering how the authors avoid the issues that are typically addressed by these HRF modeling approaches. For example, if we examine the baseline period (say, -4 to 0s relative to stimulus onset), the activation of most networks does not remain around zero, which could be due to delayed influences from the previous trial. This suggests that the current time course may not be completely accurate.

b) A related question: if the authors take the spatial map of a certain network and apply a modeling approach to estimate a time series within that network, would the results be similar to the current ICA time series?

(3) Human emotion should be inherently fast to ensure survival, as shown in many electrophysiology and MEG studies. For example, the dynamics of a fearful face can occur within 100ms in subcortical regions (Méndez-Bértolo et al., 2016), and general valence and arousal effects can occur as early as 200ms (e.g., Grootswagers et al., 2020; Bo et al., 2022). In contrast, the time-to-peak or onset timing in the BOLD time series spans a much larger time range due to the hemodynamic delay. fMRI findings indeed add spatial precision to our understanding of the temporal dynamics of emotion, but could the authors comment on how the current temporal dynamics supplement those electrophysiology studies that operate on much finer temporal scales?

(4) The response network shows activation as late as 15 to 20s, which is surprising. Could the authors discuss further why it takes so long for participants to generate an emotional response in the brain?

(5) Related to 4. In many theories, the emotion processing stages-including perception, valuation, and response-are usually considered iterative processes (e.g., Gross, 2015), especially in real-world scenarios. The advantage of the current paradigm is that it incorporates more dynamic elements of emotional stimuli and is closer to reality. Therefore, one might expect some degree of dynamic fluctuation within the tested brain networks to reflect those potential iterative processes (input, meaning, response). However, we still do not observe much brain dynamics in the data. In Figure 5, after the initial onset, most network activations remain sustained for an extended period of time. Does this suggest that emotion processing is less dynamic in the brain than we thought, or could it be related to limitations in temporal resolution? It could also be that the dynamics of each individual trial differ, and averaging them eliminates these variations. I would like to hear the authors' comments on this topic.

(6) The activation of the default mode network (DMN), although relatively late, is very interesting. Generally, one would expect a deactivation of this network during ongoing external stimulation. Could this suggest that participants are mind-wandering during the later portion of the task?

Reviewer #2 (Public review):

Summary:

This manuscript examined the neural correlates of the temporal-spatial dynamics of emotional processing while participants were watching short movie clips (each 12.5 s long) from the movie "Forrest Gump". Participants not only watched each film clip, but also gave emotional responses, followed by a brief resting period. Employing fMRI to track the BOLD responses during these stages of emotional processing, the authors found four large-scale brain networks (labeled as IC0,1,2,4) were differentially involved in emotional processing. Overall, this work provides valuable information on the neurodynamics of emotional processing.

Strengths:

This work employs a naturalistic movie watching paradigm to elicit emotional experiences. The authors used a slice-based fMRI method to examine the temporal dynamics of BOLD responses. Compared to previous emotional research that uses static images, this work provides some new data and insights into how the brain supports emotional processing from a temporal dynamics view.

Weaknesses:

Some major conclusions are unwarranted and do not have relevant evidence. For example, the authors seemed to interpret some neuroimaging results to be related to emotion regulation. However, there were no explicit instructions about emotional regulation, and there was no evidence suggesting participants regulated their emotions. How to best interpret the corresponding results thus requires caution.

Relatedly, the authors argued that "In turn, our findings underscore the utility of examining temporal metrics to capture subtle nuances of emotional processing that may remain undetectable using standard static analyses." While this sentence makes sense and is reasonable, it remains unclear how the results here support this argument. In particular, there were only three emotional categories: sad, happy, and fear. These three emotional categories are highly different from each other. Thus, how exactly the temporal metrics captured the "subtle nuances of emotional processing" shall be further elaborated.

The writing also contained many claims about the study's clinical utility. However, the authors did not develop their reasoning nor elaborate on the clinical relevance. While examining emotional processing certainly could have clinical relevance, please unpack the argument and provide more information on how the results obtained here can be used in clinical settings.

Importantly, how are the temporal dynamics of BOLD responses and subjective feelings related? The authors showed that "the time-to-peak differences in IC2 ("response") align closely with response latency results, with sad trials showing faster response latencies and earlier peak times". Does this mean that people typically experience sad feelings faster than happy or fear? Yet this is inconsistent with ideas such that fear detection is often rapid, while sadness can be more sustained. Understandably, the study uses movie clips, which can be very different from previous work, mostly using static images (e.g., a fearful or a sad face). But the authors shall explicitly discuss what these temporal dynamics mean for subjective feelings.

eLife Assessment

This important study looks into the effect of exogenous CoA on the response of TLR4-activated macrophages. Specifically, CoA enhances the LPS response by examining metabolomics, 13C tracing, and assessments of transcription and acetylation. Together, these provide a compelling series of findings that show exogenous CoA is taken up by macrophages, and this facilitates histone acetylation and transcription associated with activation and antimicrobial activity.

Reviewer #1 (Public review):

Summary:

This paper describes how CoA can overcome suppression of OXPHOS in TLR3 signaling, acting as what the authors term a 'metabolic adjuvant'. Supplementing with CoA enhances TLR signaling, reverses tolerance, and promotes OXPHOS. It promotes histone acetylation, leading to epigenetic modulation of target genes. CoA is further shown to have adjuvant effects in vivo, in anti-tumor immunity, and also in host defense.

Strengths:

Something of a tour-de-force - impressive methodologies and the conclusions are well supported by the data.

Weaknesses:

I was unable to follow the basis for some experiments and have a question around the data on itaconate, since this metabolite should limit IL-1beta production. Also, this is a very wordy manuscript - editing should help the reader.

Reviewer #2 (Public review):

In this manuscript, Timblin et al provide a model where exogenous CoA is taken up by macrophages and utilized to support transcriptional events associated with activation. They provide a series of important findings, and for the most part, the data are clear and convincing. However, additional clarity on a few points would be helpful.

First of all, the contention that endogenous TLR ligands from the bone marrow cultures are driving the tonic signaling that makes exogenous CoA beneficial in unstimulated cells seems counter to the well-described anergic state of myeloid cells derived from TLR-null mice. This reviewer's understanding was that myeloid cells in MyD88 nulls or similar are developmentally anergic due to the lack of TLR stimulation in vivo. The data here (Figure 5G, etc) show these cells have much lower TLR responses, but the authors attribute it to loss of response to endogenous ligands during the cultures rather than in vivo. Testing some of the phenotypes ex vivo, etc, might make this argument more compelling and rule out that this is an effect in vivo.

Second, the data suggesting that CoA enhances anti-microbial activity via itaconate production needs additional context and/or clarification. Interactions between itaconate and CoA have been demonstrated. Itaconate exposure can deplete the CoA pool as it is converted into Itaconyl-CoA. The Irg-/- cells should not have reduced CoA due to the lack of the need to activate itaconate for metabolism. Has this been addressed by the authors? I believe that low levels of itaconate production have been shown in "resting" bone marrow cultures. The data show a full log of more bugs in the macs that lack Irg, confirming that endogenous itaconate is at work. In addition, itaconate, which is made very quickly and is likely there in considerable amounts in 4 hrs, is known to affect transcription via action on TET2. Perhaps this explains some of the connections to CoA?

Lastly, the idea that Acetyl-CoA phenocopies CoA suggests that CoA is the effector is interesting but could be supported more. Did the authors do the "unlabeling" experiment with Acetyl-CoA to confirm that it is readily converted to the CoA pool?

Do the ACLY inhibitors have the expected effects on the ChIP seq data?

eLife Assessment

Karimian et al. present a valuable new model to explain how gamma-band synchrony (30-80 Hz) can support human visual feature binding by selectively grouping image elements, countering recent criticisms that the stimulus dependence of gamma oscillations limits their functional role. Grounded in the theory of weakly coupled oscillators and informed by primate electrophysiology, the model captures behavioural patterns observed in human psychophysics, offering support for the potential role of synchrony-based mechanisms, but incomplete evidence for a specific role of gamma oscillations. This work could be strengthened by more direct evidence for the proposed mechanism, and expanding beyond figure-only model inputs with limited ecological validity.

Reviewer #1 (Public review):

Summary:

This paper by Karimian et al proposes an oscillator model tuned to implement binding by synchrony (BBS*) principles in a visual task. The authors set out to show how well these BBS principles explain human behavior in figure-ground segregation tasks. The model is inspired by electrophysiological findings in non-human primates, suggesting that gamma oscillations in early visual cortex implement feature-binding through a synchronization of feature-selective neurons. The psychophysics experiment involves the identification of a figure consisting of gabor annuli, presented on a background of gabor annuli. The participants' task is to identify the orientation of the figure. The task difficulty is varied based on the contrast and density of the gabor annuli that make up the figure. The same figures (without the background) are used as inputs to the oscillator model. The authors report that both the discrimination accuracy in the psychophysics experiment and the synchrony of the oscillators in the proposed model follow a similar "Arnold Tongue" relationship when depicted as a function of the texture-defining features of the figure. This finding is interpreted as evidence for BBS/gamma synchrony being the underlying mechanism of the figure-ground segregation.

• Note that I chose to use "BBS" over gamma synchrony (used by the authors) in this review, as I am not convinced that the authors show evidence for synchronization in the gamma-band.

Strengths:

The design of the proposed model is well-informed by electrophysiological findings, and the idea of using computational modeling to bridge between intracranial recordings in non-human primates and behavioral results in human participants is interesting. Previous work has criticized the BBS synchrony theory based on the observation that synchronization in the gamma-band is highly localized and the frequency of the oscillation depends on the visual features of the stimulus. I appreciate how the authors demonstrate that frequency-dependence and local synchronization can be features of BBS, and not contradictory to the theory. As such, I feel that this work has the potential to contribute meaningfully to the debate on whether BBS is a biophysically realistic model of feature-binding in visual cortex.

Weaknesses:

I have several concerns regarding the presented claims, assessment of meaning and size of the presented effects, particularly with regard to the absence of a priori defined effect sizes.

Firstly, the paper makes strong claims about the frequency-specificity (i.e., gamma synchrony) and anatomical correlates (early visual cortex) of the observed effects. These claims are informed by previous electrophysiological work in non-human primates but are not directly supported by the paper itself. For instance, the title contains the word "gamma synchrony", but the authors do not demonstrate any EEG/MEG or intracranial data in from their human subjects supporting such claims, nor do they demonstrate that the frequencies in the oscillator model are within the gamma band. I think that the paper should more clearly distinguish between statements that are directly supported by the paper (such as: "an oscillator model based on BBS principles accounts for variance in human behavior") and abstract inferences based on the literature (such as "these effects could be attributed to gamma oscillations in early visual cortex, as the model was designed based on those principles").

Secondly, unlike the human participants, the model strictly does not perform figure-ground segregation, as it only receives the figure as an input. Finally, it is unclear what effect sizes the authors would have expected a priori, making it difficult to assess whether their oscillator model represents the data well or poorly. I consider this a major concern, as the relationship between the synchrony of the oscillatory model and the performance of the human participants is confounded by the visual features of the figure. Specifically, the authors use the BBS literature to motivate the hypothesis that perception of the texture-defined figure is related to the density and contrast heterogeneity of the texture elements (gabor annuli) of the figure. This hypothesis has to be true regardless of synchrony, as the figure will be easier to spot if it consists of a higher number of high-contrast gabors than the background. As the frequency and phase of the oscillators and coupling strength between oscillators in the grid change as a function of these visual features, I wonder how much of the correlation between model synchrony and human performance is mediated by the features of the figure. To interpret to what extent the similarity between model and human behavior relies on the oscillatory nature of the model, the authors should find a way to estimate an empirical threshold that accounts for these confounding effects. Alternatively, it would be interesting to understand whether a model based on competing theories (e.g., Binding by Enhanced Firing, Roelfsema, 2023) would perform better or worse at explaining the data.

Reviewer #2 (Public review):

The authors aimed to investigate whether gamma synchrony serves a functional role in figure-ground perception. They specifically sought to test whether the stimulus-dependence of gamma synchrony, often considered a limitation, actually facilitates perceptual grouping. Using the theory of weakly coupled oscillators (TWCO), they developed a framework wherein synchronization depends on both frequency detuning (related to contrast heterogeneity) and coupling strength (related to proximity between visual elements). Through psychophysical experiments with texture discrimination tasks and computational modeling, they tested whether human performance follows patterns predicted by TWCO and whether perceptual learning enhances synchrony-based grouping.

Strengths:

(1) The theoretical framework connecting TWCO to visual perception is innovative and well-articulated, providing a potential mechanistic explanation for how gamma synchrony might contribute to both feature binding and separation.

(2) The methodology combines psychophysical measurements with computational modeling, with a solid quantitative agreement between model predictions and human performance.

(3) In particular, the demonstration that coupling strengths can be modified through experience is remarkable and suggests gamma synchrony could be an adaptable mechanism that improves with visual learning.

(4) The cross-validation approach, wherein model parameters derived from macaque neurophysiology successfully predict human performance, strengthens the biological plausibility of the framework.

Weaknesses:

(1) The highly controlled stimuli are far removed from natural scenes, raising questions about generalisability. But, of course, control (almost) excludes ecological validity. The study does not address the challenges of natural vision or leverage the rich statistical structure afforded by natural scenes.

(2) The experimental design appears primarily confirmatory rather than attempting to challenge the TWCO framework or test boundary conditions where it might fail.

(3) Alternative explanations for the observed behavioral effects are not thoroughly explored. While the model provides a good fit to the data, this does not conclusively prove that gamma synchrony is the actual mechanism underlying the observed effects.

(4) Direct neurophysiological evidence linking the observed behavioral effects to gamma synchrony in humans is absent, creating a gap between the model and the neural mechanism.

Achievement of Aims and Support for Conclusions:

The authors largely achieved their primary aim of demonstrating that human figure-ground perception follows patterns predicted by TWCO principles. Their psychophysical results reveal a behavioral "Arnold tongue" that matches the synchronization patterns predicted by their model, and their learning experiment shows that perceptual improvements correlate with predicted increases in synchrony.

The evidence supports their conclusion that gamma synchrony could serve as a viable neural grouping mechanism for figure-ground segregation. However, the conclusion that "stimulus-dependence of gamma synchrony is adaptable to the statistics of visual experiences" is only partially supported, as the study uses highly controlled artificial stimuli rather than naturalistic visual statistics, or shows a sensitivity to the structure of experience.

Likely Impact and Utility:

This work offers a fresh perspective on the functional role of gamma oscillations in visual perception. The integration of TWCO with perceptual learning provides a novel theoretical framework that could influence future research on neural synchrony.

The computational model, with parameters derived from neurophysiological data, offers a useful tool for predicting perceptual performance based on synchronization principles. This approach might be extended to study other perceptual phenomena and could inspire designs for artificial vision systems.

The learning component of the study may have a particular impact, as it suggests a mechanism by which perceptual expertise develops through modified coupling between neural assemblies. This could influence thinking about perceptual learning more broadly, but also raises questions about the underlying mechanism that the paper does not address.

Additional Context:

Historically, the functional significance of gamma oscillations has been debated, with early theories of temporal binding giving way to skepticism based on gamma's stimulus-dependence. This study reframes this debate by suggesting that stimulus-dependence is exactly what makes gamma useful for perceptual grouping.

The successful combination of computational neuroscience and psychophysics is a significant strength of this study.

The field would benefit from future work extending (if possible) these findings to more naturalistic stimuli and directly measuring neural activity during perceptual tasks. Additionally, studies comparing predictions from synchrony-based models against alternative mechanisms would help establish the specificity of the proposed framework.

Author response:

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

Reviewer #1 (Public Review):

Summary:

In this study, the authors provide a new computational platform called Vermouth to automate topology generation, a crucial step that any biomolecular simulation starts with. Given a wide arrange of chemical structures that need to be simulated, varying qualities of structural models as inputs obtained from various sources, and diverse force fields and molecular dynamics engines employed for simulations, automation of this fundamental step is challenging, especially for complex systems and in case that there is a need to conduct high-throughput simulations in the application of computer-aided drug design (CADD). To overcome this challenge, the authors develop a programing library composed of components that carry out various types of fundamental functionalities that are commonly encountered in topological generation. These components are intended to be general for any type of molecules and not to depend on any specific force field and MD engines. To demonstrate the applicability of this library, the authors employ those components to re-assemble a pipeline called Martinize2 used in topology generation for simulations with a widely used coarse-grained model (CG) MARTINI. This pipeline can fully recapitulate the functionality of its original version Martinize but exhibit greatly enhanced generality, as confirmed by the ability of the pipeline to faithfully generate topologies for two high-complexity benchmarking sets of proteins.

Strengths:

The main strength of this work is the use of concepts and algorithms associated with induced subgraph in graph theory to automate several key but non-trivial steps of topology generation such as the identification of monomer residue units (MRU), the repair of input structures with missing atoms, the mapping of topologies between different resolutions, and the generation of parameters needed for describing interactions between MRUs. In addition, the documentation website provided by the authors is very informative, allowing users to get quickly started with Vermouth.

Weaknesses:

Although the Vermouth library is designed as a general tool for topology generation for molecular simulations, only its applications with MARTINI have been demonstrated in the current study. Thus, the claimed generality of Vermouth remains to be exmained. The authors may consider to point out this in their manuscript.

In order to demonstrate generality of the here proposed concepts for generating topologies for molecular dynamics simulations, we have now implemented and tested a workflow that will produce topologies for the popular CHARMM36 all-atom force field. To facilitate generation of all-atom topologies with Martinize2 a .rtp reader was introduced, which allows users to provide .rtp files that are the native GROMACS topology files for proteins instead of .ff files. These .rtp files exist for all major atomic protein forcefields. In addition, for CHARMM36 we also included modification files, which describe non-standard pH amino acids, histidine tautomers, and end terminal modifications. Thus, the current implementation unlocks all features available at the CG Martini level also for CHARMM36. We note that users must add the modifications files for other all-atom force fields e.g. AMBER.

We have added a new item in the main manuscript (p28) briefly describing this proof-of-concept implementation. However, we like to point out that there are many specialized tools for the various force fields adopted by the respective communities. Thus, an exhaustive discussion on the capabilities of Martinize2 for all-atom force fields seemed out of place.

Reviewer #2 (Public Review):

This work introduces a Vermouth library framework to enhance software development within the Martini community. Specifically, it presents a Vermouth-powered program, Martinize2, for generating coarse-grained structures and topologies from atomistic structures. In addition to introducing the Vermouth library and the Martinize2 program, this paper illustrates how Martinize2 identifies atoms, maps them to the Martini model, generates topology files, and identifies protonation states or post-translational modifications. Compared with the prior version, the authors provide a new figure to show that Martinize2 can be applied to various molecules, such as proteins, cofactors, and lipids. To demonstrate the general application, Martinize2 was used for converting 73% of 87,084 protein structures from the template library, with failed cases primarily blamed on missing coordinates.

I was hoping to see some fundamental changes in the resubmitted version. To my disappointment, the manuscript remains largely unchanged (even the typo I pointed out previously was not fixed). I do not doubt that Martinize2 and Vermouth are useful to the Martini community, and this paper will have some impact. The manuscript is very technical and limited to the Martini community. The scientific insight for the general coarse-grained modeling community is unclear. The goal of the work is ambitious (such as high-throughput simulations and whole-cell modeling), but the results show just a validation of Martinize2. This version does not reverse my previous impression that it is incremental. As I pointed out in my previous review (and no response from the authors), all the issues associated with the Martini model are still there, e.g. the need for ENM. In this shape, I feel this manuscript is suitable for a specialized journal in computational biophysics or stays as part of the GitHub repository.

We apologize for not fixing the typo; it was fixed but unfortunately got reintroduced in the final resubmitted version. We politely disagree that the goal of the work itself is high-throughput simulations and whole-cell modeling, but the Martinize2 tool is certainly an important element in our ambitions to achieve this. Given the broad interest in these goals by the modeling community in general, we believe this work has a much wider impact beyond the (already large) group of Martini users. Addressing limitations of the Martini model itself, which are certainly there, is clearly not the scope of the current work.

Reviewer #3 (Public Review):

The manuscript Kroon et al. described two algorithms, which when combined achieve high throughput automation of "martinizing" protein structures with selected protonation states and post-translational modifications. After the revisions provided by the authors, I recommend minor revision.

The authors have addressed most of my concerns provided previously. Specifically, showcasing the capability of coarse-graining other types of molecules (Figure 7) is a useful addition, especially for the booming field of therapeutic macrocycles. My only additional concern is that to justify Martinize2 and Vermouth as a "high-throughput" method, the speed of these tools needs to be addressed in some form in the manuscript as a guideline to users.

We have added some benchmark timings in the manuscript SI and pointed to the data in the discussion part, which addresses the timing. Martinize2 is certainly slower than martinize version 1 as we already pointed out in the previous versions. However, even for larger proteins (> 2000 residues) we are able to generate topologies in about 60s. As Martinize2 runs on a single core, it can be massively parallelized. Keeping this in mind the topology file generation is likely to take up only a fraction in a high-throughput pipeline compared to the more costly simulations themselves.

Reviewer #3 (Public review):

Summary:

The manuscript Kroon et al. described two algorithms, which when combined achieve high throughput automation of "martinizing" protein structures with selected protonation states and post-translational modifications.

The authors have addressed all of my concerns as provided previously. Specifically, Figure S2 will be a very useful guideline for future improvement (e.g., parallelization) of the code.

Reviewer #2 (Public review):

This work introduces a Vermouth library framework to enhance software development within the Martini community. Specifically, it presents a Vermouth-powered program, Martinize2, for generating coarse-grained structures and topologies from atomistic structures. In addition to introducing the Vermouth library and the Martinize2 program, this paper illustrates how Martinize2 identifies atoms, maps them to the Martini model, generates topology files, and identifies protonation states or post-translational modifications. Compared with the prior version, the authors provide a new figure to show that Martinize2 can be applied to various molecules, such as proteins, cofactors, and lipids. To demonstrate the general application, Martinize2 was used for converting 73% of 87,084 protein structures from the template library, with failed cases primarily blamed on missing coordinates.

I appreciate the changes that the authors made to clarify the novelty. I have no doubt this paper will receive attention and citations.

Reviewer #1 (Public review):

Summary:

In this study, the authors provide a new computational platform called Vermouth to automate topology generation, a crucial step that any biomolecular simulation starts with. Given a wide arrange of chemical structures that need to be simulated, varying qualities of structural models as inputs obtained from various sources, and diverse force fields and molecular dynamics engines employed for simulations, automation of this fundamental step is challenging, especially for complex systems and in case that there is a need to conduct high-throughput simulations in the application of computer-aided drug design (CADD). To overcome this challenge, the authors develop a programing library composed of components that carry out various types of fundamental functionalities that are commonly encountered in topological generation. These components are intended to be general for any type of molecules and not to depend on any specific force field and MD engines. To demonstrate the applicability of this library, the authors employ those components to reassemble a pipeline called Martinize2 used in topology generation for simulations with a widely used coarse-grained model (CG) MARTINI. This pipeline can fully recapitulate the functionality of its original version Martinize but exhibit greatly enhanced generality, as confirmed by the ability of the pipeline to faithfully generate topologies for two high-complexity benchmarking sets of proteins.

Strengths:

The main strength of this work is the use of concepts and algorithms associated with induced subgraph in graph theory to automate several key but non-trivial steps of topology generation such as the identification of monomer residue units (MRU), the repair of input structures with missing atoms, the mapping of topologies between different resolutions, and the generation of parameters needed for describing interactions between MRUs. In addition, the documentation website provided by the authors is very informative, allowing users to get quickly started with Vermouth.

Weaknesses:

Although the Vermouth library can work for different force fields, exhibiting certain generality, its application has been demonstrated only with GROMACS. The extension of the library to other major MD engines could be future directions for improvement but may not be needed for this study.

eLife Assessment

The authors present an important multi-scale computational platform, which aims to automate the workflow for coarse-grained simulations of biomolecules in the framework of the popular MARTINI model. The capability of the platform has been convincingly demonstrated by the application to a large number of proteins as well as macrocycles and polymers. This work will be of interest to both computational biophysicists and chemists.

Author response:

Public Review

Joint Public Review:

This manuscript presents an algorithm for identifying network topologies that exhibit a desired qualitative behaviour, with a particular focus on oscillations. The approach is first demonstrated on 3-node networks, where results can be validated through exhaustive search, and then extended to 5-node networks, where the search space becomes intractable. Network topologies are represented as directed graphs, and their dynamical behaviour is classified using stochastic simulations based on the Gillespie algorithm. To efficiently explore the large design space, the authors employ reinforcement learning via Monte Carlo Tree Search (MCTS), framing circuit design as a sequential decision-making process.

This work meaningfully extends the range of systems that can be explored in silico to uncover non-linear dynamics and represents a valuable methodological advance for the fields of systems and synthetic biology.

Strengths

The evidence presented is strong and compelling. The authors validate their results for 3-node networks through exhaustive search, and the findings for 5-node networks are consistent with previously reported motifs, lending credibility to the approach. The use of reinforcement learning to navigate the vast space of possible topologies is both original and effective, and represents a novel contribution to the field. The algorithm demonstrates convincing efficiency, and the ability to identify robust oscillatory topologies is particularly valuable. Expanding the scale of systems that can be systematically explored in silico marks a significant advance for the study of complex gene regulatory networks.

Weaknesses

The principal weakness of the manuscript lies in the interpretation of biological robustness. The authors identify network topologies that sustain oscillatory behaviour despite perturbations to the system or parameters. However, in many cases, this persistence is due to the presence of partially redundant oscillatory motifs within the network. While this observation is interesting and of clear value for circuit design, framing it as evidence of evolutionary robustness may be misleading. The "mutant" systems frequently exhibit altered oscillatory properties, such as changes in frequency or amplitude. From a functional cellular perspective, mere oscillation is insufficient - preservation of specific oscillation characteristics is often essential. This is particularly true in systems like circadian clocks, where misalignment with environmental cycles can have deleterious effects. Robustness, from an evolutionary standpoint, should therefore be framed as the capacity to maintain the functional phenotype, not merely the qualitative behaviour.

A secondary limitation is that, despite the methodological advances, the scale of the systems explored remains modest. While moving from 3- to 5-node systems is non-trivial, five elements still represent a relatively small network. It is somewhat surprising that the algorithm does not scale further, particularly when considering the performance of MCTS in other domains - for instance, modern chess engines routinely explore far larger decision trees. A discussion on current performance bottlenecks and potential avenues for improving scalability would be valuable.

Finally, it is worth noting that the emergence of oscillations in a model often depends not only on the topology but also critically on parameter choices and the nature of the nonlinearities. The use of Hill functions and high Hill coefficients is a common strategy to induce oscillatory dynamics. Thus, the reported results should be interpreted within the context of the modelling assumptions and parameter regimes employed in the simulations.

We thank the reviewers for their careful consideration of our work and for the interesting feedback and scientific discussion. We are working on a revised text based on their recommendations, which will include some of the discussion below.

This work meaningfully extends the range of systems that can be explored in silico to uncover non-linear dynamics and represents a valuable methodological advance for the fields of systems and synthetic biology.

We thank the reviewers for their positive assessment of our work’s impact!

The use of reinforcement learning to navigate the vast space of possible topologies is both original and effective, and represents a novel contribution to the field. The algorithm demonstrates convincing efficiency, and the ability to identify robust oscillatory topologies is particularly valuable. Expanding the scale of systems that can be systematically explored in silico marks a significant advance for the study of complex gene regulatory networks.

We appreciate these kind comments about our work’s merits. We are excited to share our reinforcement learning (RL) based method with the fields of systems and synthetic biology, and we consider it a valuable tool for the systematic analysis and design of larger-scale regulatory networks!

The principal weakness of the manuscript lies in the interpretation of biological robustness. The authors identify network topologies that sustain oscillatory behaviour despite perturbations to the system or parameters… [However, these] "mutant" systems frequently exhibit altered oscillatory properties, such as changes in frequency or amplitude. From a functional cellular perspective, mere oscillation is insufficient - preservation of specific oscillation characteristics is often essential. This is particularly true in systems like circadian clocks, where misalignment with environmental cycles can have deleterious effects. Robustness, from an evolutionary standpoint, should therefore be framed as the capacity to maintain the functional phenotype, not merely the qualitative behaviour.

We thank the reviewers for their attention to this point. In the large-scale circuit search, summarized in Figures 4A and 4B, we ran a search for 5-component oscillators that can spontaneously oscillate even when subjected to the deletion of a random gene. Some of the best performing circuits under these conditions exhibited a design feature we call “motif multiplexing,” in which multiple smaller motifs are interleaved in a way that makes oscillation possible under many different mutational scenarios. Interestingly, despite not selecting for preservation of frequency, the 3Ai+3Rep circuit (a 5-gene circuit highlighted in Figure 5) anecdotally appears to have a natural frequency that is robust to partial gene knockdowns, although not to complete gene deletions. As shown in Figure 5C, this circuit has a natural frequency of 6 cycles/hr (with one particular parameterization), and it can sustain a knockdown of any of its 5 genes to 50% of the wild-type transcription rate without altering the natural frequency by more than 20%.

However, we agree that there are salient differences between this training scenario and natural evolution. The revised text will clarify that these differences limit what conclusions can be drawn about biological evolution by analogy. As the reviewers point out, we use the presence of spontaneous oscillations (with or without the deletion) as a measure of fitness, regardless of frequency, so as to screen for designs with promising behavior. Also, the deletion mutations introduced during training likely represent larger perturbations to the system than a typical mutation encountered during genome replication (for example, a point mutation in a response element leading to a moderate change in binding affinity). Finally, we do not introduce any entrainment. Real circadian oscillators are aligned to a 24-hour period (“entrained”) by environmental inputs such as light and temperature. For this reason, natural circadian clocks may have natural frequencies that are slightly shorter or longer than 24 hours, although a close proximity to the 24-hour period does seem to be an important selective factor [1].

...despite the methodological advances, the scale of the systems explored remains modest. While moving from 3- to 5-node systems is non-trivial, five elements still represent a relatively small network. It is somewhat surprising that the algorithm does not scale further, particularly when considering the performance of MCTS in other domains - for instance, modern chess engines routinely explore far larger decision trees. A discussion on current performance bottlenecks and potential avenues for improving scalability would be valuable.

We thank the reviewers for their attention to this point. The main limitation we encountered to exploring circuits with more than 5 nodes in this work was the poor computational scaling of the Gillespie stochastic simulation algorithm, rather than a limitation of MCTS itself. While the average runtime of a 3-node circuit simulation was roughly 7 seconds, this number increased to 18-20 seconds with 5-node circuits. For this reason, we limited the search to topologies with ≤15 interaction arrows (15 sec/simulation). In general, the simulation time was proportional to the square of the number of transcription factors (TFs). We will revise the text to include the reason for stopping at 5 nodes, which is significant for understanding CircuiTree’s scaling properties.

With regards to scaling, an important advantage of CircuiTree is its ability to generate useful candidate designs after exploring only a portion of the search space. Like exhaustive search, given enough time, MCTS will comprehensively explore the search space and find all possible solutions. However, for large search spaces, RL-based agents are generally given a finite number of simulations (or time) to learn as much as possible.

Across machine learning (ML) applications [2] and particularly with RL models [3], this training time tends to obey a power law with respect to the underlying complexity of the problem. Thus we can use the complexity of the 3-node and 5-node searches to infer the current scaling limits of CircuiTree. The first oscillator topology was discovered after 2,280 simulations for the 3-node search, and in the 5-node search, the first oscillator using 5 nodes appeared at ~8e5 simulations, resulting in a power law of Y ~ 84.4 X^0.333. Thus, useful candidate designs may be found for 6-node and 7-node searches after 4.5e7 and 5.26e9 simulations, respectively, even though these spaces contain 1.5e17 and 2.5e23 topologies, respectively. Thus, running a 7-node search with the current implementation of CircuiTree would require resources close to the current boundaries of computation, requiring roughly 1.8 million CPU-hours, or 2 weeks on 5,000 CPUs, assuming a 1-second simulation. These points will be incorporated into both the results and discussion sections in our revised text.

However, we are optimistic about CircuiTree’s potential to scale to much larger circuits with modifications to its algorithm. CircuiTree uses the original (so-called “vanilla”) implementation of MCTS, which has not been used in professional game-playing AIs in over a decade. Contemporary RL-based game-playing engines leverage deep neural networks to dramatically reduce the training time, using value networks to identify game-winning positions and policy networks to find game-winning moves. AlphaZero, developed by Google DeepMind to learn games by self-play and without domain knowledge, outperformed all other chess AIs after 44 million training games, much smaller than the 10^43 possible chess states [4]. Similarly, the game of go has 10¹⁷⁰ possible states, but AlphaZero outperformed other AIs after only 140 million games [4]. Large circuits live in similarly large search spaces; for example, 19-node and 20-node circuits represent spaces of 10¹⁷² and 10¹⁹⁰ possible topologies. The revised text will include this discussion and identify value and policy networks, as well as more scalable simulation paradigms such as ODEs and neural ODEs, as our future directions for improving CircuiTree’s scalability.

Finally, our revised discussion will note some important differences between game-playing and biological circuit design. Unlike deterministic games like chess, the final value of a circuit topology is determined stochastically, by running a simulation whose fitness depends on the parameter set and initial conditions. Thus, state-for-state, it is possible that training an agent for circuit design may inherently require more simulations to achieve the same level of certainty compared to classical games. Additionally, while we often possess a priori knowledge about a game such as its overall difficulty or certain known strategies, we lack this frame of reference when searching for circuit designs. Thus, it remains challenging to know if and when a large space of designs has been “satisfactorily” or “comprehensively” searched, since the answer depends on data that are unknown, namely the quantity, quality, and location of solutions residing in the search space.

Not accounting for redundancy due to structural symmetries

Finally, it is worth noting that the emergence of oscillations in a model often depends not only on the topology but also critically on parameter choices and the nature of the nonlinearities. The use of Hill functions and high Hill coefficients is a common strategy to induce oscillatory dynamics. Thus, the reported results should be interpreted within the context of the modelling assumptions and parameter regimes employed in the simulations.

In our dynamical modeling of transcription factor (TF) networks, we do not rely on continuum assumptions about promoter occupancy such as Hill functions. Rather, we model each reaction - transcription, translation, TF binding/unbinding, and degradation - explicitly, and individual molecules appear and disappear via stochastic birth and death events. Many natural TFs are homodimers that bind cooperatively to regulate transcription; similarly, we assume that pairs of TFs bind more stably to their response element than individual TFs. Thus, our model has similar cooperativity to a Hill function, and it can be shown that in the continuum limit, the effective Hill coefficient is always ≤2. Our revision will clarify this aspect of the modeling and include a derivation of this property. Currently, the parameter values used in the figures are shown in Table 2. In the revised text, these will be displayed in the body of the text as well for clarity.

Bibliography (1) Spoelstra, K., Wikelski, M., Daan, S., Loudon, A. S. I., & Hau, M. (2015). Natural selection against a circadian clock gene mutation in mice. PNAS, 113(3), 686–691. https://doi.org/https://doi.org/10.1073/pnas.1516442113<br /> (2) Neumann, O., & Gros, C. (2023). Scaling Laws for a Multi-Agent Reinforcement Learning Model. The Eleventh International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=ZrEbzL9eQ3W (3) Jones, A. L. (2021). Scaling Scaling Laws with Board Games. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/2104.03113 (4) Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., & Hassabis, D. (2018). A general reinforcement learning algorithm that Masters Chess, Shogi, and go through self-play. Science, 362(6419), 1140–1144. https://doi.org/10.1126/science.aar6404

Author response:

Reviewer #1 (Public Review):

Summary: 

BMP signaling is, arguably, best known for its role in the dorsoventral patterning, but not in nematodes, where it regulates body size. In their paper, Vora et al. analyze ChIP-Seq and RNA-Seq data to identify direct transcriptional targets of SMA-3 (Smad) and SMA-9 (Schnurri) and understand the respective roles of SMA-3 and SMA-9 in the nematode model Caenorhabditis elegans. The authors use publicly available SMA-3 and SMA-9 ChIP-Seq data, own RNA-Seq data from SMA-3 and SMA-9 mutants, and bioinformatic analyses to identify the genes directly controlled by these two transcription factors (TFs) and find approximately 350 such targets for each. They show that all SMA-3-controlled targets are positively controlled by SMA-3 binding, while SMA-9-controlled targets can be either up or downregulated by SMA-9. 129 direct targets were shared by SMA-3 and SMA-9, and, curiously, the expression of 15 of them was activated by SMA-3 but repressed by SMA-9. Since genes responsible for cuticle collagen production were eminent among the SMA-3 targets, the authors focused on trying to understand the body size defect known to be elicited by the modulation of BMP signaling. Vora et al. provide compelling evidence that this defect is likely to be due to problems with the BMP signaling-dependent collagen secretion necessary for cuticle formation. 

We thank the reviewer for this supportive summary. We would like to clarify the status of the publicly available ChIP-seq data. We generated the GFP tagged SMA-3 and SMA‑9 strains and submitted them to be entered into the queue for ChIP-seq processing by the modENCODE (later modERN) consortium. Due to the nature of the consortium’s funding, the data were required to be released publicly upon completion. Nevertheless, we have provided the first comprehensive analysis of these datasets.

Strengths: 

Vora et al. provide a valuable analysis of ChIP-Seq and RNA-Seq datasets, which will be very useful for the community. They also shed light on the mechanism of the BMP-dependent body size control by identifying SMA-3 target genes regulating cuticle collagen synthesis and by showing that downregulation of these genes affects body size in C. elegans. 

Weaknesses: 

(1) Although the analysis of the SMA-3 and SMA-9 ChIP-Seq and RNA-Seq data is extremely useful, the goal "to untangle the roles of Smad and Schnurri transcription factors in the developing C. elegans larva", has not been reached. While the role of SMA-3 as a transcriptional activator appears to be quite straightforward, the function of SMA-9 in the BMP signaling remains obscure. The authors write that in SMA-9 mutants, body size is affected, but they do not show any data on the mechanism of this effect. 

We thank the reviewer for directing our attention to the lack of clarity about SMA-9’s function. We will revise the text to highlight what this study and others demonstrate about SMA-9’s role in body size. We also plan to analyze additional target genes to deepen our model for how SMA-3 and SMA-9 interact functionally to produce a given transcriptional response.

(2) The authors clearly show that both TFs can bind independently of each other, however, by using distances between SMA-3 and SMA-9 ChIP peaks, they claim that when the peaks are close these two TFs act as complexes. In the absence of proof that SMA-3 and SMA-9 physically interact (e.g. that they co-immunoprecipitate - as they do in Drosophila), this is an unfounded claim, which should either be experimentally substantiated or toned down. 

A physical interaction between Smads and Schnurri has been amply demonstrated in other systems. The limitation in the previous work is that only a small number of target genes was analyzed. Our goal in this study was to determine how widespread this interaction is on a genomic scale.  Our analyses demonstrate for the first time that a Schnurri transcription factor has significant numbers of both Smad-dependent and Smad-independent target genes. We will revise the text to clarify this point.

(3) The second part of the paper (the collagen story) is very loosely connected to the first part. dpy-11 encodes an enzyme important for cuticle development, and it is a differentially expressed direct target of SMA-3. dpy-11 can be bound by SMA-9, but it is not affected by this binding according to RNA-Seq. Thus, technically, this part of the paper does not require any information about SMA-9. However, this can likely be improved by addressing the function of the 15 genes, with the opposing mode of regulation by SMA-3 and SMA-9. 

We appreciate this suggestion and will clarify how SMA-9 and its target genes contribute to collagen organization and body size regulation.

(4) The Discussion does not add much to the paper - it simply repeats the results in a more streamlined fashion. 

We thank the reviewer for this suggestion. We will add more context to the Discussion.

Reviewer #2 (Public Review): 

In the present study, Vora et al. elucidated the transcription factors downstream of the BMP pathway components Smad and Schnurri in C. elegans and their effects on body size. Using a combination of a broad range of techniques, they compiled a comprehensive list of genome-wide downstream targets of the Smads SMA-3 and SMA-9. They found that both proteins have an overlapping spectrum of transcriptional target sites they control, but also unique ones. Thereby, they also identified genes involved in one-carbon metabolism or the endoplasmic reticulum (ER) secretory pathway. In an elaborate effort, the authors set out to characterize the effects of numerous of these targets on the regulation of body size in vivo as the BMP pathway is involved in this process. Using the reporter ROL-6::wrmScarlet, they further revealed that not only collagen production, as previously shown, but also collagen secretion into the cuticle is controlled by SMA-3 and SMA-9. The data presented by Vora et al. provide in-depth insight into the means by which the BMP pathway regulates body size, thus offering a whole new set of downstream mechanisms that are potentially interesting to a broad field of researchers. 

The paper is mostly well-researched, and the conclusions are comprehensive and supported by the data presented. However, certain aspects need clarification and potentially extended data. 

(1) The BMP pathway is active during development and growth. Thus, it is logical that the data shown in the study by Vora et al. is based on L2 worms. However, it raises the question of if and how the pattern of transcriptional targets of SMA-3 and SMA-9 changes with age or in the male tail, where the BMP pathway also has been shown to play a role. Is there any data to shed light on this matter or are there any speculations or hypotheses? 

We agree that these are intriguing questions and we are interested in the roles of transcriptional targets at other developmental stages and in other physiological functions, but these analyses are beyond the scope of the current study.

(2) As it was shown that SMA-3 and SMA-9 potentially act in a complex to regulate the transcription of several genes, it would be interesting to know whether the two interact with each other or if the cooperation is more indirect. 

A physical interaction between Smads and Schnurri has been amply demonstrated in other systems. Our goal in this study was not to validate this physical interaction, but to analyze functional interactions on a genome-wide scale.

(3) It would help the understanding of the data even more if the authors could specifically state if there were collagens among the genes regulated by SMA-3 and SMA-9 and which. 

We thank the reviewer for this suggestion and will add the requested information in the text.

(4) The data on the role of SMA-3 and SMA-9 in the regulation of the secretion of collagens from the hypodermis is highly intriguing. The authors use ROL-6 as a reporter for the secretion of collagens. Is ROL-6 a target of SMA-9 or SMA-3? Even if this is not the case, the data would gain even more strength if a comparable quantification of the cuticular levels of ROL-6 were shown in Figure 6, and potentially a ratio of cuticular versus hypodermal levels. By that, the levels of secretion versus production can be better appreciated. 

rol-6 has been identified as a transcriptional target of this pathway. The level of ROL-6 protein, however, is not changed in sma-3 and sma-9 mutants, indicating that there is post-transcriptional compensation. We will include these data in the revised manuscript.

(5) It is known that the BMP pathway controls several processes besides body size. The discussion would benefit from a broader overview of how the identified genes could contribute to body size. The focus of the study is on collagen production and secretion, but it would be interesting to have some insights into whether and how other identified proteins could play a role or whether they are likely to not be involved here (such as the ones normally associated with lipid metabolism, etc.). 

We will add this information to the Discussion.

eLife Assessment

This study provides a fundamental analysis of the EmrE efflux pump, highlighting the role of the C-terminal domain in influencing uncoupled proton leak. The integration of biophysical techniques with molecular dynamics simulations offers solid support for the key findings and adds substantial evidence toward a definitive understanding of EmrE transport mechanism.

Reviewer #1 (Public review):

Summary:

Work by Brosseau et. al. combines NMR, biochemical assays, and MD simulations to characterize the influence of the C-terminal tail of EmrE, a model multi-drug efflux pump, on proton leak. The authors compare the WT pump to a C-terminal tail deletion, delta_107, finding that the mutant has increased proton leak in proteoliposome assays, shifted pH dependence with a new titratable residue, faster alternating access at high pH values, and reduced growth, consistent with proton leak of the PMF.

Strengths:

The work combines thorough experimental analysis of structural, dynamic, and electrochemical properties of the mutant relative to WT proteins. The computational work is well aligned in vision and analysis. Although all questions are not answered, the authors lay out a logical exploration of the possible explanations.

Weaknesses:

A few analyses that were missing in the first submission were included/corrected in the revision.

Reviewer #2 (Public review):

Summary:

This manuscript explores the role of the C-terminal tail of EmrE in controlling uncoupled proton flux. Leakage occurs in the wild-type transporter under certain conditions but is amplified in the C-terminal truncation mutant D107. The authors use an impressive combination of growth assays, transport assays, NMR on WT and mutants with and without key substrates, classical MD, and reactive MD to address this problem. Overall, I think that the claims are well supported by the data, but I am most concerned about the reproducibility of the MD data, initial structures used for simulations, and the stochasticity of the water wire formation. These can all be addressed in a revision with more simulations as I point out below. I want to point out that the discussion was very nicely written, and I enjoyed reading the summary of the data and the connection to other studies very much.

Strengths:

The Henzler-Wildman lab is at the forefront of using quantitative experiments to probe the peculiarities in transporter biophysics, and the MD work from the Voth lab complements the experiments quite well. The sheer number of different types of experimental and computational approaches performed here is impressive.

Weaknesses:

The primary weaknesses are related to the reproducibility of the MD results with regard to the formation of water wires in the WT and truncation mutant. This could be resolved with simulations starting from structures built using very different loops and C-terminal tails.

The water wire gates identified in the MD should be tested experimentally with site-directed mutagenesis to determine if those residues do impact leak.

Comments on revisions:

Having reviewed the latest version of the manuscript, I continue to believe that this is a solid paper with important results. I find the new data regarding the computational pKa estimate of E14 compelling.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Work by Brosseau et. al. combines NMR, biochemical assays, and MD simulations to characterize the influence of the C-terminal tail of EmrE, a model multi-drug efflux pump, on proton leak. The authors compare the WT pump to a C-terminal tail deletion, delta_107, finding that the mutant has increased proton leak in proteoliposome assays, shifted pH dependence with a new titratable residue, faster-alternating access at high pH values, and reduced growth, consistent with proton leak of the PMF.

Strengths:

The work combines thorough experimental analysis of structural, dynamic, and electrochemical properties of the mutant relative to WT proteins. The computational work is well aligned in vision and analysis. Although all questions are not answered, the authors lay out a logical exploration of the possible explanations.

Weaknesses:

There are a few analyses that are missing and important data left out. For example, the relative rate of drug efflux of the mutant should be reported to justify the focus on proton leak. Additionally, the correlation between structural interactions should be directly analyzed and the mutant PMF also analyzed to justify the claims based on hydration alone. Some aspects of the increased dynamics at high pH due to a potential salt bridge are not clear.

Reviewer #2 (Public review):

Summary:

This manuscript explores the role of the C-terminal tail of EmrE in controlling uncoupled proton flux. Leakage occurs in the wild-type transporter under certain conditions but is amplified in the C-terminal truncation mutant D107. The authors use an impressive combination of growth assays, transport assays, NMR on WT and mutants with and without key substrates, classical MD, and reactive MD to address this problem. Overall, I think that the claims are well supported by the data, but I am most concerned about the reproducibility of the MD data, initial structures used for simulations, and the stochasticity of the water wire formation. These can all be addressed in a revision with more simulations as I point out below. I want to point out that the discussion was very nicely written, and I enjoyed reading the summary of the data and the connection to other studies very much.

Strengths:

The Henzler-Wildman lab is at the forefront of using quantitative experiments to probe the peculiarities in transporter biophysics, and the MD work from the Voth lab complements the experiments quite well. The sheer number of different types of experimental and computational approaches performed here is impressive.

Weaknesses:

The primary weaknesses are related to the reproducibility of the MD results with regard to the formation of water wires in the WT and truncation mutant. This could be resolved with simulations starting from structures built using very different loops and C-terminal tails.

The water wire gates identified in the MD should be tested experimentally with site-directed mutagenesis to determine if those residues do impact leak.

We appreciate the reviewers thoughtful consideration of our manuscript, and their recognition of the variety of experimental and computational approaches we have brought to bear in probing the very challenging question of uncoupled proton leak through EmrE.

We did record SSME measurements with MeTPP+, a small molecule substrate at two different protein:lipid ratios. These experiments report the rate of net flux when both proton-coupled substrate antiport and substrate-gated proton leak are possible. We will add this data to the revision, including data acquired with different lipid:protein ratio that confirms we are detecting transport rather than binding. In brief, this data shows that the net flux is highly dependent on both proton concentration (pH) and drug-substrate concentration, as predicted by our mechanistic model. This demonstrates that both types of transport contribute to net flux when small molecule substrates are present.

In the absence of drug-substrate, proton leak is the only possible transport pathway. The pyranine assay directly assesses proton leak under these conditions and unambiguously shows faster proton entry into proteoliposomes through the ∆107-EmrE mutant than through WT EmrE, with the rate of proton entry into ∆107-EmrE proteoliposomes matching the rate of proton entry achieved by the protonophore CCCP. We have revised the text to more clearly emphasize how this directly measures proton leak independently of any other type of transport activity. The SSME experiments with a proton gradient only (no small molecule substrate present) provide additional data on shorter timescales that is consistent with the pyranine data. The consistency of the data across multiple LPRs and comparison of transport to proton leak in the SSME assays further strengthens the importance of the C-terminal tail in determining the rate of flux.

None of the current structural models have good resolution (crystallography, EM) or sufficient restraints (NMR) to define the loop and tail conformations sufficiently for comparison with this work. We are in the process of refining an experimental structure of EmrE with better resolution of the loop and tail regions implicated in proton-entry and leak. Direct assessment of structural interactions via mutagenesis is complicated because of the antiparallel homodimer structure of EmrE. Any point mutation necessarily affects both subunits of the dimer, and mutations designed to probe the hydrophobic gate on the more open face of the transporter also have the potential to disrupt closure on the opposite face, particularly in the absence of sufficient resolution in the available structures. Thus, mutagenesis to test specific predicted structural features is deferred until our structure is complete so that we can appropriately interpret the results.

In our simulation setup, the MD results can be considered representative and meaningful for two reasons. First, the C-terminal tail, not present in the prior structure and thus modeled by us, is only 4 residues long. We will show in the revision and detailed response that the system will lose memory of its previous conformation very quickly, such that velocity initialization alone is enough for a diverse starting point. Second, our simulation is more like simulated annealing, starting from a high free energy state to show that, given such random initialization, the tail conformation we get in the end is consistent with what we reported. It is also difficult to sample back-and-forth tail motion within a realistic MD timescale. Therefore, it can be unconclusive to causally infer the allosteric motions with unbiased MD of the wildtype alone. The best viable way is to look at the equilibrium statistics of the most stable states between WT- and ∆107-EmrE and compare the differences.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The work is well done and well presented. In my opinion, the authors must address the following questions.

(1) It is unclear to a non-SSME-expert, why the net charge translocated in delta_107 is larger than in WT. For such small pH gradients (0.5-1pH unit), it seems that only a few protons would leave the liposome before the internal pH is adjusted to be the same as the external. This number can be estimated given the size of the liposomes. What is it? Once the pH gradient is dissipated, no more net proton transport should be observed. So, why would more protons flow out of the mutant relative to WT?

We appreciate the complexity of both the system and assay and have made revisions to both the main text and SI to address these points more clearly. While we can estimate liposomes size, we cannot easily quantify the number of liposomes on the sensor surface so cannot calculate the amount of charge movement as suggested by the reviewer. We have revised Fig. 3.2 and added additional data at low and high pH with different lipid to protein ratios to distinguish pre-steady state (proton release from the protein) and steady state processes (transport). An extended Fig. 3.2 caption and revised discussion in the main text clarify these points.

We have also revised SI figure 3.2 to include an example of transport driven by an infinite drug gradient. Drug-proton antiport results in net charge build-up in the liposome since two protons will be driven out for every +1 drug transported in. This also creates a pH gradient is created (higher proton concentration outside). The negative inside potential inhibits further antiport of drug. However, both the negative-inside potential and proton gradient will drives protons back into the liposome if there is a leak pathway available. This is clearly visible with a reversal of current negative (antiport) to positive (proton backflow), and the magnitude of this back flow is larger for ∆107-EmrE which lacks the regulatory elements provided by the C-terminal tail. We have amended the main text and SI to include this discussion.

(2) Given the estimated rate of transport, size of liposomes, and pH gradient, how quickly would the SSME liposomes reach pH balance?

Since SSME measurements are due to capacitive coupling and will represent the net charge movement, including pre-steady state contributions, the current values will be incredibly sensitive to individual rates of alternating access, proton and drug on- and off-rates. Time to pH balance would, therefore, differ based on the construct, LPR, absolute pH or drug concentrations as well as the magnitude of the given gradients. For this reason, we necessarily use integrated currents (transported charge over time) when comparing mutants as it reflects kinetic differences inherent to the mutant without over-processing the data, for example, by normalizing to peak currents which would over emphasize certain properties that will differ across mutants. This process allows for qualitative comparisons by subjecting mutants to the same pH and substrate gradients when the same density of transporter construct is present, and care is given to not overstate the importance of the actual quantities of charges that are moving as they will be highly context dependent. This is clearly seen in Fig 3.2 where the current is not zero and the net transported charge is still changing at the end of 1 second. We have amended SI figure 3.2 and the main text to include this discussion.

(3) Given that H110 and E14 would deprotonate when the external pH is elevated above 7 and that these protons would be released to external bulk, the external bulk pH would decrease twice as much for WT compared to delta107. This would decrease the pH gradient for WT relative to the mutant. Can these effects be quantified and accounted for? Would this ostensibly decrease the amount of charge that transfers into the liposomes for WT? How would this impact the current interpretation that the two systems are driven by the same gradient?

The reviewer is correct that there will be differences in deprotonation of WT and ∆107 and the amount of proton release will also change with pH. We have amended Figure 3.2 to clarify this difference and its significance. For the proton gradient only conditions in Figure 3, each set of liposomes were equilibrated to the starting pH by repeated washings and incubation before measurement occurred. For example, for the pH 6.5 inside, pH 7 outside condition, both the inside and outside pH were equilibrated at 6.5, and both E14 residues will be predominantly protonated in WT and ∆107, and H110 will be predominantly protonated in WT-EmrE. Upon application of the external pH 7 solution, protons will be released from the E14 of either construct, with additional proton being released from H110 for WT-EmrE causing a large pre-steady state negative contribution to the signal (Fig. 3.2A). Under this pH condition, we the peak current correlates with the LPR, as this release of protons will depend on density of the transporter. However, we also see that the longer-time decay of the signal correlates with the construct (WT or ∆107) and is relatively independent of LPR, consistent with a transport process rather than a rapid pre-steady state release of protons. Therefore, when we look at the actual transported charge over time, despite the higher contribution of proton release to the WT-EmrE signal, the significant increase in uncoupled proton transport for the C-terminal deletion mutant dominates the signal.

As a contrast, we apply this same analysis to the pH 8 inside, pH 8.5 outside condition where both sets of transports will be deprotonated from the start (Fig. 3.2B). Now the peak currents, decay rates, and transported charge over time are all consistent for a given construct (WT or ∆107). The two LPRs for an individual construct match within error, as the differences in overall charge movement and transported charge over time are independent of pre-steady-state proton release from the transporter at high pH.

(4) A related question, how does the protonation of H110 influence the potential rate of proton transport between the two systems? Does the proton on H110 transfer to E14?

The protonation of H110 will only influence the rate of transport of WT-EmrE as its protonation is required for formation of the hydrogen bonding network that coordinates gating. However, protonation of both E14s will influence the rate of proton transport of both systems as protonation state affects the rate of alternating access which is necessary for proton turnover. This is another reason we use the transported charge over time metric to compare mutants as it allows for a common metric for mutants with altered rates which are present in the same density and under the same gradient conditions. We do not have any evidence to support transfer of proton from H110 to E14, but there is also no evidence to exclude this possibility. We do not discuss this in the manuscript because it would be entirely speculative.

(5) Is the pKa in the simulations (Figure 6B) consistent with the experiment?

We calculated the pKa from this WT PMF and got a pKa of 7.1, which is in close proximity of the experimental value of 6.8

(6) Why isn't the PMF for delta_107 compared to WT to corroborate the prediction that hydration sufficiently alters both the rate and pKa of E14?

We appreciate the reviewer’s suggestion and agree that a direct comparison would be valuable. However, several factors limit the interpretability of such an analysis in this context:

(a) Our data indicate that the primary difference in free energy barriers between WT and Δ107 lies in the hydration step rather than proton transport itself. To fully resolve this, a 2D PMF calculation via 2D umbrella sampling would be required which can be very expensive. Solely looking at the proton transport side of this PMF will not give much difference.

(b) Given this, the aim for us to calculate this PMF is to support our conjecture that the bottleneck for such transport is the hydrophobic gate.

(7) The authors suggest that A61 rotation 'controls the water wire formation' by measuring the distribution of water connectivity (water-water distances via logS) and average distances between A61 and I68/I67. Delta_107 has a larger inter-residue distance (Figure 6A) more probable small log S closer waters connecting E14 and two residues near the top of the protein (Figure 5A). However, it strikes me that looking at average distances and the distribution of log S is not the best way to do this. Why not quantify the correlation between log S and A61 orientation and/or A61-I68/I71 distances as well as their correlation to the proposed tail interactions (D84-R106 interactions) to directly verify the correlation (and suggest causation) of these interactions on the hydration in this region. Additionally, plotting the RMSD or probability of waters below I68 and I171 as a function of A61-I68 distances and/or numbers over time would support the log S analysis.

The reviewer requested that we provide direct correlation analyses between A61 orientation, residue distances (A61-I68/I71), and water connectivity (logS) to better support the claim about water wire formation, rather than relying solely on average distances and distributions.

We appreciate the reviewer’s suggestion to strengthen our analysis with direct correlations. However, due to the slow kinetics of hydration/dehydration events, unbiased simulation timescales do not permit sufficient sampling of multiple transitions to perform statistically robust dynamic correlation analyses. Instead, our approach focuses on equilibrium statistics, which reveal the dominant conformational states of WT- and Δ107-EmrE and provide meaningful insights into shifts in hydration patterns.

(8) It looks like the D84-R106 salt bridge controls this A61-I68 opening. Could this also be quantifiably correlated?

As discussed in response to the previous question, the unbiased simulation timescales do not permit sufficient sampling of multiple transitions to perform statistically robust dynamic correlation analyses.

(9) The NMR results show that alternating access increases in frequency from ~4/s for WT at low and high pH to ~17/s for delta_107 only at high pH. They then go on to analyze potential titration changes in the delta_107 mutant, finding two residues with approximate pKa values of 5.6 and 7.1. The former is assigned to E14, consistent with WT. But the latter is suggested to be either D84, which salt bridges to R106, or the C-terminal carboxylate. If it is D84, why would deprotonation, which would be essential to form the salt bridge, increase the rate of alternating access relative to WT?

We note that the faster alternating access rate was observed for TPP+-bound ∆107-EmrE, not the transporter in the absence of substrate. In the absence of substrate the relatively broad lines preclude quantitative determination of the alternating access rate by NMR making it difficult to judge the validity of the reviewers reasoning. Identification of which residue (D84 or H110) corresponds to the shifted pKa is ultimately of little consequence as this mutant does not reflect the native conditions of the transporter. It is far more important to acknowledge that both R106 and D84 are sensitive to this deprotonation as it indicates these residues are close in space and provides experimental support for the existence of the salt bridge identified in the MD simulations, as discussed in the manuscript.

(10) In a more general sense, can the authors speculate why an efflux pump would evolve this type of secondary gate that can be thrown off by tight binding in the allosteric site such as that demonstrated by Harmane? What potential advantage is there to having a tail-regulated gate?

This was likely a necessity to allow for better coupling as these transporters evolved to be more promiscuous. The C-terminal tail is absent in tightly coupled family members such as Gdx who are specific for a single substrate and have a better-defined transport stoichiometry. We have included this discussion in the main text and are currently investigating this phenomenon further. Those experiments are beyond the scope of the current manuscript.

(11) It is hard to visualize the PT reaction coordinate. Is the e_PT unit vector defined for each window separately based on the initial steered MD pathway? If so, how reliant is the PT pathway on this initial approximate path? Also, how does this position for each window change if/when E14 rotates? This could be checked by plotting the x,y,z distributions for each window and quantifying the overlap between windows in cartesian space. These clouds of distributions could also be plotted in the protein following alignment so the reader can visualize the reaction coordinate. Does the CEC localization ever stray to different, disconnected regions of cartesian phase space that are hidden by the reaction coordinate definition?

The unit vector e_PT is the same across all windows based on unbiased MD. Therefore, the reaction coordinate (a scalar) is the vector from the starting point to the CEC, projected on this unit vector. E14 rotation does not significantly change the window definition a lot unless the CEC is very close to E14, where we found this to be a better CV. For detailed discussions about this CV, especially a comparison between a curvilinear CV, please see J. Am. Chem. Soc. 2018, 140, 48, 16535–16543 “Simulations of the Proton Transport” and its SI Figure S1.In the Supplementary Information, we added figure 6.1 to show the average X, Y, Z coordinates of each umbrella window.

(12) Lastly, perhaps I missed it, but it's unclear if the rate of substrate efflux is also increased in the delta_107 mutant. If this is also increased, then the overall rate of exchange is faster, including proton leak. This would be important to distinguish since the focus now is entirely on proton leaks. I.e., is it only leak or is it overall efflux and leak?

We have amended SI figure 3.2 to include a gradient condition where an infinite drug gradient is created across the liposome. The infinite gradient allows for rapid transport of drug into the liposomes until charge build-up opposes further transport. This peak is at the same time for both LPRs of WT- and ∆107-EmrE suggesting the rate of substrate transport is similar. Differences in the peak heights across LPRs can be attributed to competition between drug and proton for the primary binding site such that more proton will be released for the higher density constructs as described above. This process does also create a proton gradient as drug moving in is coupled to two protons moving out so as charge build-up inhibits further drug movement, the building proton gradient will also begin to drive proton back in which is another example of uncoupled leak. Here, again we see that this back-flow of protons or leak is of greater magnitude for ∆107-EmrE proteoliposomes that for those with WT-EmrE. We have included this discussion in the SI and main text.

Minor

(1) Introduction - the authors describe EmrE as a model system for studying the molecular mechanism of proton-coupled transport. This is a rather broad categorization that could include a wide range of phenomena distal from drug transport across membranes or through efflux pumps. I suggest further specifying to not overgeneralize.

We revised to note the context of multidrug efflux.

Reviewer #2 (Recommendations for the authors):

Simulations. The initial water wire analysis is based on 4 different 1 ms simulations presented in Figure 5. The 3 WT replicates show similar results for the tail-blocking water wire formation, but the details of the system build and loop/C-terminal tail placement are not clear. It does appear that a single C-terminal tail model was created for all WT replicates. Was there also modeling for any parts of the truncation mutant? Regardless, since these initial placements and uncertainties in the structures may impact the results and subsequent water wire formation, I would like a discussion of how these starting structures impacted the formation or not of wires. I think that another WT replicate should be run starting from a completely new build that places the tail in a different (but hopefully reasonable location). This could be built with any number of tools to generate reasonable starting structures. It's critical to ensure that multiple independent simulations across different initial builds show the same water wire behavior so that we know the results are robust and insensitive to the starting structure and stochastic variation.

We thank Reviewer 2 for their suggestion regarding the discussion of the initial structure. In our simulations, the C-terminal tail was initially modeled in an extended conformation (solvent-exposed) to mimic its disordered state prior to folding. This approach resembles an annealing process, where the system evolves from a higher free-energy state toward equilibrium. Notably, across all three replicas, we observed consistent folding of the tail onto the protein surface, supporting the robustness of this conformational preference.

For the Δ107 truncation mutant, minimal modeling was required, as most experimental structures resolve residues up to S105 or R106. To rigorously assess the influence of the starting configuration, we analyzed the tail’s dynamics using backbone dihedral angle auto- and cross-correlation functions (new Supplementary Figures 10.1 and 10.2). These analyses reveal rapid decay of correlations—consistent with the tail’s short length (5 residues) and high flexibility—indicating that the system "forgets" its initial configuration well within the simulation timescale. Thus, we conclude that our sampling is sufficient to capture equilibrium behavior, independent of the starting structure.

What does the size of the barrier in the PMF (Figure 6B) imply about the rate of proton transfer/leak and can the pKa shift of the acidic residue be estimated with this energy value compared to bulk?

We noticed this point aligns with a related concern raised by Reviewer 1. For a detailed discussion please refer to Point 5 in our response to Reviewer 1.

Experimental validation. The hypotheses generated by this work would be better buttressed if there were some mutation work at the hydrophobic gate (61, 68, 71) to support it. I realize that this may be hard, but it would significantly improve the quality.

Due to the small size of the transporter, any mutagenesis of EmrE should necessarily be accompanied by functional characterization to fully assess the effects of the mutation on rate-limiting steps. We have revised the manuscript to add a discussion of the challenges with analyzing simple point mutants and citing what is known from prior scanning mutagenesis studies of EmrE.

eLife Assessment

This fundamental study investigates the role of polyunsaturated fatty acids (PUFAs) in physiology and membrane biology, using a unique model to perform a thorough genetic screen that demonstrates that PUFA synthesis defects cannot be compensated for by mutations in other pathways. These findings are supported by compelling evidence from a high quality genetic screen, functional validation of their hits, and lipid analyses. This study will appeal to researchers in membrane biology, lipid metabolism, and C. elegans genetics.

Reviewer #1 (Public review):

Summary:

This study addresses the roles of polyunsaturated fatty acids (PUFAs) in animal physiology and membrane function. A C. elegans strain carrying the fat-2(wa17) mutation possesses a very limited ability to synthesize PUFAs and there is no dietary input because the E. coli diet consumed by lab grown C. elegans does not contain any PUFAs. The fat-2 mutant strain was characterized to confirm that the worms grow slowly, have rigid membranes, and have a constitutive mitochondrial stress response. The authors showed that chemical treatments or mutations known to increase membrane fluidity did not rescue growth defects. A thorough genetic screen was performed to identify genetic changes to compensate for the lack of PUFAs. The newly isolated suppressor mutations that compensated for FAT-2 growth defects included intergenic suppressors in the fat-2 gene, as well as constitutive mutations in the hypoxia sensing pathway components EGL-9 and HIF-1, and loss of function mutations in ftn-2, a gene encoding the iron storage protein ferritin. Taken together, these mutations lead to the model that increased intracellular iron, an essential cofactor for fatty acid desaturases, allows the minimally functional FAT-2(wa17) enzyme to be more active, resulting in increased desaturation and increased PUFA synthesis.

Strengths:

(1) This study provides new information further characterizing fat-2 mutants. The authors measured increased rigidity of membranes compared to wild type worms, however this rigidity is not able to be rescued with other fluidity treatments such as detergent or mutants. Rescue was only achieved with polyunsaturated fatty acid supplementation.

(2) A very thorough genetic suppressor screen was performed. In addition to some internal fat-2 compensatory mutations, the only changes in pathways identified that are capable of compensating for deficient PUFA synthesis was the hypoxia pathway and the iron storage protein ferritin. Suppressor mutations included an egl-9 mutation that constitutively activates HIF-1, and Gain of function mutations in hif-1 that are dominant. This increased activity of HIF conferred by specific egl-9 and hif-1 mutations lead to decreased expression of ftn-2. Indeed, loss of ftn-2 leads to higher intracellular iron. The increased iron apparently makes the FAT-2 fatty acid desaturase enzyme more active, allowing for the production of more PUFAs.

(3) The mutations isolated in the suppressor screen show that the only mutations able to compensate for lack of PUFAs were ones that increased PUFA synthesis by the defective FAT-2 desaturase, thus demonstrating the essential need for PUFAs that cannot be overcome by changes in other pathways. This is a very novel study, taking advantage of genetic analysis of C. elegans, and it confirms the observations in humans that certain essential PUFAs are required for growth and development.

(4) Overall, the paper is well written, and the experiments were carried out carefully and thoroughly. The conclusions are well supported by the results.

Weaknesses:

Overall, there are not many weaknesses. The main one I noticed is that the lipidomic analysis shown in Figs 3C, 7C, S1 and S3. While these data are an essential part of the analysis and provide strong evidence for the conclusions of the study, it is unfortunate that the methods used did not enable the distinction between two 18:1 isomers. These two isomers of 18:1 are important in C. elegans biology, because one is a substrate for FAT-2 (18:1n-9, oleic acid) and the other is not (18:1n-7, cis vaccenic acid). Although rarer in mammals, cis-vaccenic acid is the most abundant fatty acid in C. elegans and is likely the most important structural MUFA. The measurement of these two isomers is not essential for the conclusions of the study.

Reviewer #2 (Public review):

Summary:

The authors use a genetic screen in C. elegans to investigate the physiological roles of polyunsaturated fatty acids (PUFAs). They screen for mutations that rescue fat-2 mutants, which have strong reductions in PUFAs. As a result, either mutations in fat-2 itself or mutations in genes involved in the HIF-1 pathway were found to rescue fat-2 mutants. Mutants in the HIF-1 pathway rescue fat-2 mutants by boosting their catalytic activity (via upregulated Fe2+). Thus, the authors show that in the context of fat-2 mutation, the sole genetic means to rescue PUFA insufficiency is to restore PUFA levels.

Strengths:

As C. elegans can produce PUFAs de novo as essential lipids, the genetic model is well-suited to study the fundamental roles of PUFAs. The genetic screen finds mutations in convergent pathways, suggesting that it has reached near-saturation. The authors extensively validate the results of the screening and provide sufficient mechanistic insights to show how PUFA levels are restored in HIF-1 pathway mutants. As many of the mutations found to rescue fat-2 mutants are of gain-of-function, it is unlikely that similar discoveries could have been made with other approaches like genome-wide CRISPR screenings, making the current study distinctive. Consequently, the study provides important messages. First, it shows that PUFAs are essential for life. The inability to genetically rescue PUFA deficiency, except for mutations that restore PUFA levels, suggests that they have pleiotropic essential functions. In addition, the results suggest that the most essential functions of PUFAs are not in fluidity regulation, which is consistent with recent reviews proposing that the importance of unsaturation goes beyond fluidity (doi: 10.1016/j.tibs.2023.08.004 and doi: 10.1101/cshperspect.a041409). Thus, the study provides fundamental insights about how membrane lipid composition can be linked to biological functions.

Weaknesses:

The authors put in a lot of effort to answer the questions that arose through peer review, and now all the claims seem to be supported by experimental data. Thus, I do not see obvious weaknesses. Of course, it remains unclear what PUFAs do beyond fluidity regulation, but this is something that cannot be answered from a single study.

Author response:

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

Reviewer #1:

The addition of the discussion about the two isomers of 18:1 didn't quite work in the place that the authors added. What the authors wrote on line 126 is true about 18:1 isomers in wild type worms. However, they are reporting their lipidomics results of the fat-2(wa17) mutant worms. In this case, a substantial amount of the 18:1 is the oleic acid (18:1n-9) isomer. The authors can check Table 2 in their reference [10] and see that wild type and other fat mutants indeed contain approximately 10 fold more cis vaccenic than oleic acid, the fat-2(wa17) mutants do accumulate oleic acid, because the wild type activity of FAT-2 is to convert oleic acid to linoleic acid, where it can be converted to downstream PUFAs. I suggest editing their sentence on line 126 to say that the high 18:1 they observed agrees with [10], and then comment about reference 10 showing the majority of 18:1 being the cis-vaccenic isomer in most strains, but the oleic acid isomer is more abundantly in the fat-2(wa17) mutant strain.

We thank the reviewer for spotting that and sparing us a bit of embarrassment. We have now modified the text and hope we got it right this time:

"Even though the lipid analysis methods used here are not able to distinguish between different 18:1 species, a previous study showed that the majority of the 18:1 fatty acids in the fat-2(wa17) mutant is actually 18:1n9 (OA) [10] and not 18:1n7 (vaccenic acid) as in most other strains [10,23]; this is because OA is the substrate of FAT-2 and thus accumulates in the mutant."

Reviewer #2:

I still do not agree with the answer to my previous comment 6 regarding Figure S2E. The authors claim that hif-1(et69) suppresses fat-2(wa17) in a ftn-2 null background (in Figure S2 legend for example). To claim so, they would need to compare the triple mutant with fat2(wa17);ftn-2(ok404) and show some rescue. However, we see in Figure 5H that ftn2(ok404) alone rescues fat-2(wa17). Thus, by comparing both figures, I see no additional effect of hif-1(et69) in an ftn-2(ok404) background. I actually think that this makes more sense, since the authors claim that hif-1(et69) is a gain-of-function mutation that acts through suppression of ftn-2 expression. Thus, I would expect that without ftn-2 from the beginning, hif-1(et69) does not have an additional effect, and this seems to be what we see from the data. Thus, I would suggest that the authors reformulate their claims regarding the effect of hif1(et69) in the ftn-2(ok404) background, which seems to be absent (consistently with what one would expect).

We completely agree with the reviewer and indeed this is the meaning that we tried to convey all along. The text has now been modified as follows:

"Lastly, ftn-2(et68) is still a potent fat-2(wa17) suppressor when hif-1 is knocked out (S2D Fig), suggesting that no other HIF-1-dependent functions are required as long as ftn-2 is downregulated; this conclusion is supported by the observation that the potency of the ftn2(ok404) null allele to act as a fat-2(wa17) suppressor is not increased by including the hif-1(et69) allele (compare Fig 5H and S2E Fig)."

eLife Assessment

The authors design and implement an elegant strategy to delete genomic sequences encoding the dopamine receptor dop1R2 from specific subsets of mushroom body neurons (ab, a'b' and gamma) and show that while none of these manipulations affect short term appetitive or aversive memory, loss of dop1R2 from ab or a'b' block the ability of flies to display measurable forms of longer forms of memory. These findings are important in confirming and extending prior observations, and well supported by convincing evidence that build on precise techniques for genetic perturbation.

Reviewer #1 (Public review):

Summary:

In this manuscript the authors present a novel CRISPR/Cas9-based genetic tool for the dopamine receptor dop1R2. Based on the known function of the receptor in learning and memory, they tested the efficacy of the genetic tool by knocking out the receptor specifically in mushroom body neurons. The data suggest that dop1R2 is necessary for longer lasting memories through its action on ⍺/ß and ⍺'/ß' neurons but is dispensable for short-term memory and thus in ɣ neurons. The experiments impressively demonstrate the value of such a genetic tool and illustrate the specific function of the receptor in subpopulations of KCs for longer-term memories.

Reviewer #2 (Public review):

Summary:

This manuscript examines the role of the dopamine receptor, Dop1R2, in memory formation. This receptor has complex roles in supporting different stages of memory, and the neural mechanisms for these functions is poorly understood. The authors are able to localize Dop1R2 function to the vertical lobes of the mushroom body, revealing a role in later (presumably middle-term) aversive and appetitive memory. In general the experimental design is rigorous, and statistics are appropriately applied. The manuscript provides a thorough assessment of how Dop1R2 functions within the mushroom bodies to regulate protein-synthesis dependent and independent memory, and provides a valuable new tool for the community.

Strengths:

(1) The FRT lines generated provide a novel tool for temporal and spatially precise manipulation of Dop1R2 function. This tool will be valuable to study the role of Dop1R2 in memory and other behaviors potentially regulated by this gene.

(2) Given the highly conserved role of Dop1R2 in memory and other processes, these findings have high potential to translate to vertebrate species.

Reviewer #3 (Public review):

Summary:

Kaldun et al. investigated the role of Dopamine Receptor Dop1R2 in different types and stages of olfactory associative memory in Drosophila melanogaster. Dop1R2 is a type 1 Dopamine receptor that can act both through Gs-cAMP and Gq-ERCa2+ pathways. The authors first developed a sophisticated tool where tissue-specific knock-out mutants can be generated using Crispr/Cas9 technology in combination with the Gal4/UAS gene-expression toolkit. They direct the K.O. mutation to intrinsic neurons of the main associative memory centre fly brain: the mushroom body (MB). There are three main types of MB-neurons, or Kenyon cells, according to their axonal projections: a/b; a'/b' and g neurons.

Kaldun et al. found that, while not required for short-term memory, dop1R2 is necessary in a/b and a'/b' but not in gamma neurons to display normal appetitive and aversive middle-term (2h) and long-term (24h) memory. These results showcase a compartmentalized role of Dop1R2 in specific neuronal subtypes of the main memory centre of the fly brain for the expression of middle and long-term memories.

The conclusions of this paper are very well supported by the data, and the authors systematically addressed the requirement of a very interesting type of dopamine receptor in both appetitive and aversive memories. These findings are important for the fields of learning and memory and dopaminergic neuromodulation, among others.

Importantly, the authors of this paper produced a tool to generate tissue-specific knock out mutants of dop1R2. Although reports on the requirement of this gene in different memory phases exist, the genetic tools used here represent the most sophisticated approach to induce a loss of function phenotypes in neurons of interest.

Overall, the authors generated a very useful tool to study dopamine neuromodulation in any given circuit when used in combination with the powerful genetic toolkit available in Drosophila. The reports on this paper confirmed a previously described role of Dop1R2 in the expression of aversive and appetitive LTM providing spatio-temporal resolution and additionally, they mapped these effects to two types of memory neurons in the fly brain, shedding light into the intricate modulation of dopamine in memory circuits.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors present a novel CRISPR/Cas9-based genetic tool for the dopamine receptor dop1R2. Based on the known function of the receptor in learning and memory, they tested the efficacy of the genetic tool by knocking out the receptor specifically in mushroom body neurons. The data suggest that dop1R2 is necessary for longer-lasting memories through its action on ⍺/ß and ⍺'/ß' neurons but is dispensable for short-term memory and thus in ɣ neurons. The experiments impressively demonstrate the value of such a genetic tool and illustrate the specific function of the receptor in subpopulations of KCs for longer-term memories. The data presented in this manuscript are significant.

Reviewer #2 (Public Review):

Summary:

This manuscript examines the role of the dopamine receptor, Dop1R2, in memory formation. This receptor has complex roles in supporting different stages of memory, and the neural mechanisms for these functions are poorly understood. The authors are able to localize Dop1R2 function to the vertical lobes of the mushroom body, revealing a role in later (presumably middle-term) aversive and appetitive memory. In general, the experimental design is rigorous, and statistics are appropriately applied. While the manuscript provides a useful tool, it would be strengthened further by additional mechanistic studies that build on the rich literature examining the roles of dopamine signaling in memory formation. The claim that Dop1R2 is involved in memory formation is strongly supported by the data presented, and this manuscript adds to a growing literature revealing that dopamine is a critical regulator of olfactory memory. However, the manuscript does not necessarily extend much beyond our understanding of Dop1R2 in memory formation, and future work will be needed to fully characterize this reagent and define the role of Dop1R2 in memory.

Strengths:

(1) The FRT lines generated provide a novel tool for temporal and spatially precise manipulation of Dop1R2 function. This tool will be valuable to study the role of Dop1R2 in memory and other behaviors potentially regulated by this gene.

(2) Given the highly conserved role of Dop1R2 in memory and other processes, these findings have a high potential to translate to vertebrate species.

Weaknesses:

(1) The authors state Dop1R2 associates with two different G-proteins. It would be useful to know which one is mediating the loss of aversive and appetitive memory in Dop1R2 knockout flies.

We thank you for the insightful comment. We agree that it would be very useful to know which G-proteins are transmitting Dop1R2 signaling. To that extent, we examined single-cell transcriptomics data to check the level of co-expression of Dop1R2 with G-proteins that are of interest to us. (Figure 1 S1)

Lines 312-325

“Some RNA binding proteins and Immediate early genes help maintain identities of Mushroom body cells and are regulators of local transcription and translation (de Queiroz et al., 2025; Raun et al., 2025). So, the availability of different G-proteins may change in different lobes and during different phases of memory. The G-protein via which GPCRs signal, may depend on the pool of available G-proteins in the cell/sub-cellular region (Hermans, 2003)., Therefore, Dop1R2 may signal via different G-proteins in different compartments of the Mushroom body and also different compartments of the neuron. We looked at Gαo and Gαq as they are known to have roles in learning and forgetting (Ferris et al., 2006; Himmelreich et al., 2017). We found that Dop1R2 co-expresses more frequently with Gαo than with Gαq (Figure 1 S1). While there is evidence for Dop1R2 to act via Gαq (Himmelreich et al., 2017). It is difficult to determine whether this interaction is exclusive, or if Dop1R2 can also be coupled to other G-proteins. It will be interesting to determine the breadth of G-proteins that are involved in Dop1R2 signaling.”

(2) It would be interesting to examine 24hr aversive memory, in addition to 24hr appetitive memory.

This is indeed an important point and we agree that it will complete the assessment of temporally distinct memory traces. We therefore performed the Aversive LTM experiments and include them in the results.

Lines 208-228

“24h memory is impaired by loss of Dop1R2

Next, we wanted to see if later memory forms are also affected. One cycle of reward training is sufficient to create LTM (Krashes & Waddell, 2008), while for aversive memory, 5-6 cycles of electroshock-trainings are required to obtain robust long-term memory scores (Tully et al., 1994). So, we looked at both, 24h aversive and appetitive memory. For aversive LTM, the flies were tested on the Y-Maze apparatus as described in (Mohandasan et al., (2022).

Flipping out Dop1R2 in the whole MB causes a reduced 24h memory performance (Figure 4A, E). No phenotype was observed when Ddop1R2 was flipped out in the γ-lobe (Figure 4B, F). However, similar to 2h memory, loss of Ddop1R2 in the α/β-lobes (Figure 4C, G) or the α’/β’-lobes (Figure 4D, H) causes a reduction in memory performance. Thus, Dop1R2 seems to be involved in aversive and appetitive LTM in the α/β-lobes and the α’/β’-lobes.

Previous studies have shown mutation in the Dop1R2 receptor leads to improvement in LTM when a single shock training paradigm is used (Berry et al., 2012). As we found that it disrupts LTM, we wanted to verify if the absence of Dop1R2 outside the MB is what leads to an improvement in memory. To that extent, we tested panneuronal flip-out of Dop1R2 flies for 6hr and 24hr memory upon single shock using the elav-Gal4 driver. We found that it did not improve memory at both time points (Figure 4 S1). Confirming that flipping out Dop1R2 panneuronally does not improve LTM (Figure 4 S1C) and highlighting its irrelevance in memory outside the MB.”

(3) The manuscript would be strengthened by added functional analysis. What are the DANs that signal through Dop1R. How do these knockouts impact MBONs?

We thank you for this question. We indeed agree that it is a highly relevand and open question, how distinct DANs signal via distinct Dopamine receptors. Our work here uniquely focusses on Dop1R2 within the MB. We aim to investigate other DopRs and the connection between DANs in the future using similar approaches.

(4) Also in Figure 2, the lobe-specific knockouts might be moved to supplemental since there is no effect. Instead, consider moving the control sensory tests into the main figure.

We thank you for this suggestion and understand that in Figure 2 no significant difference is seen. However, we have emphasized in the text that the results from the supplementary figures are just to confirm that the modifications made at the Dop1R2 locus did not alter its normal function.

Lines 156-162

“We wanted to see if flipping out Dop1R2 in the MB affects memory acquisition and STM by using classical olfactory conditioning. In short, a group of flies is presented with an odor coupled to an electric shock (aversive) or sugar (appetitive) followed by a second odor without stimulus. For assessing their memory, flies can freely choose between the odors either directly after training (STM) or at a later timepoint.

To ensure that the introduced genetic changes to the Dop1R2 locus do not interfere with behavior we first checked the sensory responses of that line”

(5) Can the single-cell atlas data be used to narrow down the cell types in the vertical lobes that express Dop1R2? Is it all or just a subset?

This is indeed an interesting question, and we thank you for mentioning it. To address this as best as we could, we analyzed the single cell transcriptomic data from (Davie et al., 2018) and presented it in Figure 1 S1.

Reviewer #3 (Public Review):

Summary:

Kaldun et al. investigated the role of Dopamine Receptor Dop1R2 in different types and stages of olfactory associative memory in Drosophila melanogaster. Dop1R2 is a type 1 Dopamine receptor that can act both through Gs-cAMP and Gq-ERCa2+ pathways. The authors first developed a very useful tool, where tissue-specific knock-out mutants can be generated, using Crispr/Cas9 technology in combination with the powerful Gal4/UAS gene-expression toolkit, very common in fruit flies.

They direct the K.O. mutation to intrinsic neurons of the main associative memory centre fly brain-the mushroom body (MB). There are three main types of MB-neurons, or Kenyon cells, according to their axonal projections: a/b; a'/b', and g neurons.

Kaldun et al. found that flies lacking dop1R2 all over the MB displayed impaired appetitive middle-term (2h) and long-term (24h) memory, whereas appetitive short-term memory remained intact. Knocking-out dop1R2 in the three MB neuron subtypes also impaired middle-term, but not short-term, aversive memory.

These memory defects were recapitulated when the loss of the dop1R2 gene was restricted to either a/b or a'/b', but not when the loss of the gene was restricted to g neurons, showcasing a compartmentalized role of Dop1R2 in specific neuronal subtypes of the main memory centre of the fly brain for the expression of middle and long-term memories.

Strengths:

(1) The conclusions of this paper are very well supported by the data, and the authors systematically addressed the requirement of a very interesting type of dopamine receptor in both appetitive and aversive memories. These findings are important for the fields of learning and memory and dopaminergic neuromodulation among others. The evidence in the literature so far was generated in different labs, each using different tools (mutants, RNAi knockdowns driven in different developmental stages...), different time points (short, middle, and long-term memory), different types of memories (Anesthesia resistant, which is a type of protein synthesis independent consolidated memory; anesthesia sensitive, which is a type of protein synthesis-dependent consolidated memory; aversive memory; appetitive memory...) and different behavioral paradigms. A study like this one allows for direct comparison of the results, and generalized observations.

(2) Additionally, Kaldun and collaborators addressed the requirement of different types of Kenyon cells, that have been classically involved in different memory stages: g KCs for memory acquisition and a/b or a'/b' for later memory phases. This systematical approach has not been performed before.

(3) Importantly, the authors of this paper produced a tool to generate tissue-specific knock-out mutants of dop1R2. Although this is not the first time that the requirement of this gene in different memory phases has been studied, the tools used here represent the most sophisticated genetic approach to induce a loss of function phenotypes exclusively in MB neurons.

Weaknesses:

(1) Although the paper does have important strengths, the main weakness of this work is that the advancement in the field could be considered incremental: the main findings of the manuscript had been reported before by several groups, using tissue-specific conditional knockdowns through interference RNAi. The requirement of Dop1R2 in MB for middle-term and long-term memories has been shown both for appetitive (Musso et al 2015, Sun et al 2020) and aversive associations (Plaçais et al 2017).

Thank you for this comment. We believe that the main takeaway from the paper is the elegant tool we developed, to study the role of Dop1R2 in fruit flies by effectively flipping it out spatio-temporally. Additionally, we studied its role in all types of olfactory associative memory to establish it as a robust tool that can be used for further research in place of RNAi knockouts which are shown to be less efficient in insects as mentioned in the texts in line 394-398.

“The genetic tool we generated here to study the role of the Dop1R2 dopamine receptor in cells of interest, is not only a good substitute for RNAi knockouts, which are known to be less efficient in insects (Joga et al., 2016), but also provides versatile possibilities as it can be used in combination with the powerful genetic tools of Drosophila.”

(2) The approach used here to genetically modify memory neurons is not temporally restricted. Considering the role of dopamine in the correct development of the nervous system, one must consider the possible effects that this manipulation can have in the establishment of memory circuits. However, previous studies addressing this question restricted the manipulation of Dop1R2 expression to adulthood, leading to the same findings than the ones reported in this paper for both aversive and appetitive memories, which solidifies the findings of this paper.

We thank you for this comment and we agree that it would be important to show a temporally restricted effect of Dop1R2 knockout. To assess this and rule out potential developmental defects we decided to restrict the knockout to the post-eclosion stage and to include these results.

Lines 230-250

“Developmental defects are ruled out in a temporally restricted Dop1R2 conditional knockout.

To exclude developmental defects in the MB caused by flip-out of Dop1R2, we stained fly brains with a FasII antibody. Compared to genetic controls, flies lacking Dop1R2 in the mushroom body had unaltered lobes (Figure 4 S2C).

Regardless, we wanted to control for developmental defects leading to memory loss in flip-out flies. So, we generated a Gal80ts-containing line, enabling the temporal control of Dop1R2 knockout in the entire mushroom body (MB). Given that the half-life of the receptor remains unknown, we assessed both aversive short-term memory (STM) and long-term memory (LTM) to determine whether post-eclosion ablation of Dop1R2 in the MB produced differences compared to our previously tested line, in which Dop1R2 was constitutively knocked out from fertilization. To achieve this, flies were maintained at 18°C until eclosion and subsequently shifted to 30°C for five to seven days. On the fifth day, training was conducted, followed by memory testing. Our results indicate that aversive STM was not significantly impaired in Dop1R2-deficient MBs compared to control flies (Figure 4 S3), consistent with our previous findings (Figure 2). However, aversive LTM was significantly impaired relative to control lines (Figure 4 S3), which also aligned with prior observations. These findings strongly indicate that memory loss caused by Dop1R2 flip-out is not due to developmental defects.”

(3) The authors state that they aim to resolve disparities of findings in the field regarding the specific role of Dop1R2 in memory, offering a potent tool to generate mutants and addressing systematically their effects on different types of memory. Their results support the role of this receptor in the expression of long-term memories, however in the experiments performed here do not address temporal resolution of the genetic manipulations that could bring light into the mechanisms of action of Dop1R2 in memory. Several hypotheses have been proposed, from stabilization of memory, effects on forgetting, or integration of sequences of events (sensory experiences and dopamine release).

We thank you for this comment. We agree that it would be interesting to dissect the memory stages by knocking out the receptor selectively in some of them (encoding, consolidation, retrieval). However, our tool irreversibly flips out Dop1R2 preventing us from investigating the receptor’s role in retrieval. Our results show that the receptor is dispensable for STM formation (Figure 2, Figure 4 Supplement 3), suggesting that it is not involved in encoding new information. On the other hand, it is instead involved in consolidation and/or retrieval of long-term and middle-term memories (Figure 3, Figure 4, Figure 5B).

Overall, the authors generated a very useful tool to study dopamine neuromodulation in any given circuit when used in combination with the powerful genetic toolkit available in Drosophila. The reports in this paper confirmed a previously described role of Dop1R2 in the expression of aversive and appetitive LTM and mapped these effects to two specific types of memory neurons in the fly brain, previously implicated in the expression and consolidation of long-term associative memories.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) On the first view, the results shown here are different from studies published earlier, while in the same line with others (e.g. Sun et al, for appetitive 24h memories). For example, Berry et al showed that the loss of dop1R2 impairs immediate memory, while memory scores are enhanced 3h, 6h, and 24h after training. Further, they showed data that shock avoidance, at least for higher shock intensities, is reduced in mutant (damb) flies. All in all, this favors how important it is to improve the genetic tools for tissue-specific manipulation. Despite the authors nicely discussing their data with respect to the previous studies, I wondered whether it would be suitable to use the new tool and knock out dop1R2 panneuronally to see whether the obtained data match the results published by Berry et al.. Further, as stated in line 105ff: "As these studies used different learning assays - aversive and appetitive respectively as well as different methods, it is unclear if Dop1R2 has different functions for the different reinforcement stimulus" I wondered why the authors tested aversive and appetitive learning for STM and 2h memory, but only appetitive memory for 24h.

Thank you for this comment. To that extent, as mentioned above in response to reviewer #2, we included in the results the aversive LTM experiment (Figure 4). Moreover, we performed experiments along the line of Berry et al. using our tool as shown in Figure 4 S1. Our results support that Dop1R2 is required for LTM, rather than to promote forgetting.

(2) Line 165ff: I can´t find any of the supplementary data mentioned here. Please add the corresponding figures.

Thank you for pointing this out. In that line we don’t refer to any supplementary data, but to the Figure 1F, showing the absence of the HA-tag in our MB knock-out line. We have clarified this in the text (lines 151-153)

(3) I can't imagine that the scale bar in Figure 1D-F is correct. I would also like to suggest to show a more detailed analysis of the expression pattern. For example, both anterior and posterior views would be appropriate, perhaps including the VNC. This would allow the expression pattern obtained with this novel tool to be better compared with previously published results. Also, in relation to my comment above (1), it may help to understand the functional differences with previous studies, especially as the authors themselves state that the receptor is "mainly" expressed in the mushroom body (line 99). It would be interesting to see where else it is expressed (if so). This would also be interesting for the panneuronal knockdown experiment suggested under (1). If the receptor is indeed expressed outside the mushroom body, this may explain the differences to Berry et al.

Thank you for noting this, there was indeed a mistake in the scale bar which we now fixed. Since with our HA-tag immunostaining we could not detect any noticeable signal outside of the MB, we decided to analyze previously existing single cell transcriptomics data that showed expression of the receptor in 7.99% of cells in the VNC and in 13.8% of cells outside the MB (lines 98-100) confirming its sparse expression in the nervous system. The lack of detection of these cells is likely due to the sparse and low expression of the protein. The HA-tag allows to detect the endogenous level of the locus (it is possible that a Gal4/UAS amplification of the signal might allow to detect these cells).

Regarding the panneuronal knockout, we decided to try to replicate the experiment shown in Berry et al. in Figure 4 S1 and found that Dop1R2 is required for LTM.

(4) Related to learning data shown in Figures 2-4, the authors should show statistical differences between all groups obtained in the ANOVA + PostHoc tests. Currently, only an asterisk is placed above the experimental group, which does not adequately reflect the statistical differences between the groups. In addition, I would like to suggest adding statistical tests to the chance level as it may be interesting to know whether, for example, scores of knockout flies in 3C and 3D are different from the chance level.

Many thanks for this correction, we agree with the fact that the way significance scores were shown was not informative enough. We fixed the point by now showing significance between all the control groups and the experimental ones. We also inserted the chance level results in the figure legends.

(5) Unfortunately, the manuscript has some typing errors, so I would like to ask the authors to check the manuscript again carefully.

Some Examples:

Line 31: the the

Line 56: G-Protein

Line 64: c-AMP

Line 68: Dopamine

Line 70: G-Protein (It alternates between G-protein and G-Protein)

Line 76: References are formatted incorrectly

Line 126: Ha-Tag (It alternates between Ha and HA)

Line 248: missing space before the bracket...is often found

Thank you for noticing these errors, we have now corrected the spelling throughout the manuscript.

(6) In the figures the axes are labelled Preference Index (Pref"I"). In the methods, however, the calculation formula is defined as "PREF".

We thank you for drawing attention to this. To avoid confusion, we changed the definition in the methods section so that it could be clear and coherent (“Memory tests” paragraph in the methods section).

“PREF = ((N_arm1 - N_arm2) 100) / N_total the two preference indices were calculated from the two reciprocal experiments. The average of these two PREFs gives a learning index (LI). LI = (PREF₁ + PREF₂) / 2.

In case of all Long-term Aversive memory experiments, Y-Maze protocol was adapted to test flies 24 hours post training. Testing using the Y-Maze was done following the protocol as described in (Mohandasan et al., 2022) where flies were loaded at the bottom of 20-minutes odorized 3D-printed Y-Mazes from where they would climb up to a choice point and choose between the two odors. The learning index was then calculated after counting the flies in each odorized vial as follows: LI = ((N_CS- - N_CS+) 100) / N_total. Where NCS- and NCS+ are the number of flies that were found trapped in the untrained and trained odor tube respectively.

Reviewer #2 (Recommendations For The Authors):

(1) In Figures 2 and 3, the legends running two different subfigures is confusing. Would be helpful to find a different way to present.

Thank you for your suggestion. We modified how we present legends, placing them vertically so that it is clearer.

(2) Use additional drivers to verify middle and long-term memory phenotypes.

We agree that it would be interesting to see the role of Dop1R2 in other neurons. To that extent, we looked at long term aversive memory in flies where the receptor was panneuronaly flipped out, and did not find evidence that suggested involvement of Dop1R2 in memory processes outside the MB. (Figure 4 S1)

(3) Additional discussion of genetic background for fly lines would be helpful.

Thank you for your advice. We have mentioned the genetic background of flies in the key resources table of the methods sections. Additionally, we also included further explanation on how the lines were created and their genetic background (see “Fly Husbandry” paragraph in the methods section).

“UAS-flp;;Dop1R2 cko flies and Gal4;Dop1R2^cko flies were crossed back with ;;Dop^cko flies to obtain appropriate genetic controls which were heterozygous for UAS and Gal4 but not Dop1R2^cko.”

Reviewer #3 (Recommendations For The Authors):

Line 109 states that to resolve the problem a tool is developed to knock down Dop1R2 in s spatial and temporal specific manner- while I agree that this is within the potential of the tool, there is no temporal control of the flipase action in this study; at least I cannot find references to the use of target/gene switch to control stages of development or different memory phases. However the version available for download is missing supplementary information, so I did not have access to supplementary figures and tables.

Thank you for the comment, as mentioned before it would be great to be able to dissect the memory phases. We show in lines 232 – 250 and Figure 4 S3 that the temporally restricted flip-out to the post-eclosion life stage gave us coherent results with the previous findings, ruling out potential developmental defects.

In relation to my comment on the possible developmental effects of the loss of the gene, Figure 1F could showcase an underdeveloped g lobe when looking at the lobe profiles. I understand this is not within the scope of the figure, but maybe a different z projection can be provided to confirm there are no obvious anatomical alterations due to the loss of the receptor.

We understand the doubt about the correct development of the MB and we thank you for your insightful comment. To that extent we decided to perform a FasII immunostaining that could show us the MB in the different lines (Figure 4 S2) and it appears that there are no notable differences in the lobes development in our knockout line.

It seems that the obvious missing piece of the puzzle would be to address the effects of knocking out Dop1R2 in aversive LTM. The idea of systematically addressing different types of memory at different time points and in different KCs is the most attractive aspect of this study beyond the technical sophistication, and it feels that the aim of the study is not delivered without that component.

We agree and we thank you for the clarification. As mentioned above in response to Reviewer #2, we decided to test aversive LTM as described in lines –208-228, Figure 4, Figure 4 S1.

Some statements of the discussion seem too vague, and I think could benefit from editing:

Line 284 "however other receptors could use Gq and mediate forgetting"- does this refer to other dopamine receptors? Other neuromodulators? Examples?

Thank you for pointing this out. We Agree and therefore decided to omit this line.

Line 289 "using a space training protocol and a Dop1R2 line" - this refers to RNAi lines, but it should be stated clearly.

That is correct, we thank you for bringing attention to this and clarified it in the manuscript.

–Lines 329-330

“Interestingly, using a spaced training protocol and a Dop1R2 RNAi knockout line another study showed impaired LTM (Placais et al., 2017).”

The paragraph starting in line 305 could be re-written to improve clarity and flow. Some statements seem disconnected and require specific citations. For example "In aversive memory formation, loss of Dop1R2 could lead to enhanced or impaired memory, depending on the activated signaling pathways and the internal state of the animal...". This is not accurate. Berry et al 2012 report enhanced LTM performance in dop1R2 mutants whereas Plaçais et al 2017 report LTM defects in Dop1R2 knock-downs, but these different findings do not seem to rely on different internal states or signaling pathways. Maybe further elaboration can help the reader understand this speculation.

We agree and we thank you for this advice. We decided to add additional details and citations to validate our speculation

Lines 350-353

“In aversive memory formation, loss of Dop1R2 could lead to enhanced or impaired memory, depending on the activated signaling pathways. The signaling pathway that is activated further depends on the available pool of secondary messengers in the cell (Hermans, 2003) which may be regulated by the internal state of the animal.”

"...for reward memory formation, loss of Dop1R2 seems to impair memory", this seems redundant at this point, as it has been discussed in detail, however, citations should be provided in any case (Musso 2015, Sun 2020)

Thank you for noting this. We recognize the redundancy and decided to exclude the line.

Finally, it would be useful to additionally refer to the anatomical terminology when introducing neuron names; for example MBON MVP2 (MBON-g1pedc>a/b), etc.

Thank you for this suggestion. We understand the importance of anatomical terminologies for the neurons. Therefore, we included them when we introduce neurons in the paper.

We thank you for your observations. We recognize their value, so we have made appropriate changes in the discussion to sound less vague and more comprehensive.

Reviewer #1 (Public review):

Summary:

The study by Deng et al reports single cell expression analysis of developing mouse hearts and examines the requirements for cardiac fibroblasts in heart maturation. The work includes extensive gene expression profiling and bioinformatic analysis. The prenatal fibroblast ablation studies show new information on the requirement of these cells on heart maturation before birth.

The strengths of the manuscript are the new single cell datasets and comprehensive approach to ablating cardiac fibroblasts in pre and postnatal development in mice. Extensive data are presented on mouse embryo fibroblast diversity and morphology in response to fibroblast ablation. Histological data support localization of major cardiac cell types and effects of fibroblast ablation on cardiac gene expression at different times of development.

A weakness of the study is that the major conclusions regarding collagen signaling and heart maturation are based on gene expression patterns and are not functionally validated.

Comments on Revised Version (from BRE):

Most of my comments have been adequately addressed. Additional comments on new data in the revised manuscript are below.

(1) In the new figure S11, it is not really possible to draw major conclusions on mitral valve morphology and maturation since the planes of sections to not seem comparable. Observations regarding attachment to the papillary muscle might be dependent on the particular section being evaluated. However, it is useful to see that the valves are not severely affected in the ablated animals.

(2) In the last supplemental figure S19, it is not possible to determine if results are or are not statistically significant for n=2 as shown for FS and EF for the ablated animals and controls. The text says that there is a trend of improved heart function, but evaluation of additional animals is needed to support this conclusion.

eLife Assessment

This study provides a comprehensive analysis of gene expression and bioinformatics data, offering important insights into the roles of fibroblasts in cardiac development. The large and well-analyzed single-cell RNA sequencing (scRNA-seq) dataset is compelling and a significant contribution to the field, and will be of broad interest to the scientific community.

Reviewer #2 (Public review):

This study aims to elucidate the role of fibroblasts in regulating myocardium and vascular development through signaling to cardiomyocytes and endothelial cells. This focus is significant, given that fibroblasts, cardiomyocytes, and vascular endothelial cells are the three primary cell types in the heart. The authors employed a Pdgfra-CreER-controlled diphtheria toxin A (DTA) system to ablate fibroblasts at various embryonic and postnatal stages, characterizing the resulting cardiac defects, particularly in myocardium and vasculature development. Single-cell RNA sequencing (scRNA-seq) analysis of the ablated hearts identified collagen as a crucial signaling molecule from fibroblasts that influences the development of cardiomyocytes and vascular endothelial cells.

This is an interesting manuscript; however, there are several major issues, including an over-reliance on the scRNA-seq data, which shows inconsistencies between replicates.

Some of the major issues are described below.

(1) The CD31 immunostaining data (Figure 3B-G) indicate a reduction in endothelial cell numbers following fibroblast deletion using PdgfraCreER+/-; RosaDTA+/- mice. However, the scRNA-seq data show no percentage change in the endothelial cell population (Figure 4D). Furthermore, while the percentage of Vas_ECs decreased in ablated samples at E16.5, the results at E18.5 were inconsistent, showing an increase in one replicate and a decrease in another, raising concerns about the reliability of the RNA-seq findings.

(2) Similarly, while the percentage of Ven_CMs increased at E18.5, it exhibited differing trends at E16.5 (Fig. 4E), further highlighting the inconsistency of the scRNA-seq analysis with the other data.

(3) Furthermore, the authors noted that the ablated samples had slightly higher percentages of cardiomyocytes in the G1 phase compared to controls (Fig. 4H, S11D), which aligns with the enrichment of pathways related to heart development, sarcomere organization, heart tube morphogenesis, and cell proliferation. However, it is unclear how this correlates with heart development, given that the hearts of ablated mice are significantly smaller than those of controls (Figure 3E). Additionally, the heart sections from ablated samples used for CD31/DAPI staining in Figure 3F appear much larger than those of the controls, raising further inconsistencies in the manuscript.

(4) The manuscript relies heavily on the scRNA-seq dataset, which shows inconsistencies between the two replicates. Furthermore, the morphological and histological analyses do not align with the scRNA-seq findings.

(5) There is a lack of mechanistic insight into how collagen, as a key signaling molecule from fibroblasts, affects the development of cardiomyocytes and vascular endothelial cells.

(6) In Figure 1B, Col1a1 expression is observed in the epicardial cells (Figure 1A, E11.5), but this is not represented in the accompanying cartoon.

(7) Do the PdgfraCreER+/-; RosaDTA+/- mice survive after birth when induced at E15.5, and do they exhibit any cardiac defects?

Comments on Revised Version (from BRE):

The manuscript has greatly improved following the revision, and I have no additional comments to offer.

Reviewer #3 (Public review):

Summary:

The authors investigated fibroblasts' communication with key cell types in developing and neonatal hearts, with focus on critical roles of fibroblast-cardiomyocyte and fibroblast-endothelial cells network in cardiac morphogenesis. They tried to map the spatial distribution of these cell types and reported the major pathways and signaling molecules driving the communication. They also used Cre-DTA system to ablate Pdgfra labeled cells and observed myocardial and endothelial cell defects at development. They screened the pathways and genes using sequencing data of ablated heart. Lastly they reported a compensatory collagen expression in long term ablated neonate heart. Overall, this study provides us with important insight on fibroblasts' roles in cardiac development and will be a powerful resource for collagens and ECM focused research.

Strengths:

The authors utilized good analyzing tools to investigate on multiple database of single cell sequencing and Multi-seq. They identified significant pathways, cellular and molecular interactions of fibroblasts. Additionally, they compared some of their analytic findings with human database, and identified several groups of ECM genes with varying roles in mice.

Weaknesses:

This study is majorly based on sequencing data analysis. At the bench, they used very strident technique to study fibroblast functions by ablating one of the major cell population of heart. Also, experimental validation of their analyzed downstream pathways will be required eventually.

Comments on Revised Version (from BRE):

The authors did a good job addressing the questions asked at first review. However, I have some minor concerns.

(1) The paper notes that collagen signaling is observed in FB-VasEC in humans, but not in FB-VenCM, unlike mice. Did the authors analyze predictive ligand receptor interaction as they did with control and ablated mice heart? This could add valuable new insights that how FB regulate ventricular CM in human heart.

(2) The authors provided data on Defect in CD31 expression in several models. Did they observe any other phenotypes associated with defective endothelial or vascular system? Such as, blood accumulation in pericardium, larger/smaller capillaries? Did they also examine percentage of Cdh5+ cells?

(3) Please mention the sample age of Figure 2A-C.

(4) Please follow the same style to describe X axis in graphs in Figure 3D (and all similar graphs in the manuscript) as followed in 3G.

(5) It is important to provide echocardiographic M mode images with a comparable number of cardiac cycles in control and ablated (Fig. 6H).

(6) In the long-term neonatal ablation experiments, collagen expressions return to normal. The manuscript attributes this to possible "compensatory expression," Do they have any thoughts how this is regulated? Are other cell types stepping in, or are surviving FBs proliferating?

(7) While collagen is shown to be a dominant signaling molecule, its centrality is inferred primarily from scRNA-seq and ligand-receptor predictions. Did authors try any functional rescue experiment (e.g., exogenous collagen supplementation or receptor blockade) to directly validate this pathway's role in vivo?

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study by Deng et al reports single cell expression analysis of developing mouse hearts and examines the requirements for cardiac fibroblasts in heart maturation. The work includes extensive gene expression profiling and bioinformatic analysis. The prenatal fibroblast ablation studies show new information on the requirement of these cells on heart maturation before birth.

The strengths of the manuscript are the new single cell datasets and comprehensive approach to ablating cardiac fibroblasts in pre and postnatal development in mice. Extensive data are presented on mouse embryo fibroblast diversity and morphology in response to fibroblast ablation. Histological data support localization of major cardiac cell types and effects of fibroblast ablation on cardiac gene expression at different times of development.

A weakness of the study is that the major conclusions regarding collagen signaling and heart maturation are based on gene expression patterns and are not functionally validated.

Reviewer #2 (Public review):

This study aims to elucidate the role of fibroblasts in regulating myocardium and vascular development through signaling to cardiomyocytes and endothelial cells. This focus is significant, given that fibroblasts, cardiomyocytes, and vascular endothelial cells are the three primary cell types in the heart. The authors employed a Pdgfra-CreER-controlled diphtheria toxin A (DTA) system to ablate fibroblasts at various embryonic and postnatal stages, characterizing the resulting cardiac defects, particularly in myocardium and vasculature development. Single-cell RNA sequencing (scRNA-seq) analysis of the ablated hearts identified collagen as a crucial signaling molecule from fibroblasts that influences the development of cardiomyocytes and vascular endothelial cells.

This is an interesting manuscript; however, there are several major issues, including an over-reliance on the scRNA-seq data, which shows inconsistencies between replicates.

We thank the reviewer for carefully reading our revised manuscript. All of the questions listed below were raised in the previous round and have been addressed in the current revision. As noted in the “Recommendations for the Authors” section, the reviewer has no additional comments at this time.

Some of the major issues are described below.

(1) The CD31 immunostaining data (Figure 3B-G) indicate a reduction in endothelial cell numbers following fibroblast deletion using PdgfraCreER+/-; RosaDTA+/- mice. However, the scRNA-seq data show no percentage change in the endothelial cell population (Figure 4D). Furthermore, while the percentage of Vas_ECs decreased in ablated samples at E16.5, the results at E18.5 were inconsistent, showing an increase in one replicate and a decrease in another, raising concerns about the reliability of the RNA-seq findings.

(2) Similarly, while the percentage of Ven_CMs increased at E18.5, it exhibited differing trends at E16.5 (Fig. 4E), further highlighting the inconsistency of the scRNA-seq analysis with the other data.

(3) Furthermore, the authors noted that the ablated samples had slightly higher percentages of cardiomyocytes in the G1 phase compared to controls (Fig. 4H, S11D), which aligns with the enrichment of pathways related to heart development, sarcomere organization, heart tube morphogenesis, and cell proliferation. However, it is unclear how this correlates with heart development, given that the hearts of ablated mice are significantly smaller than those of controls (Figure 3E). Additionally, the heart sections from ablated samples used for CD31/DAPI staining in Figure 3F appear much larger than those of the controls, raising further inconsistencies in the manuscript.

(4) The manuscript relies heavily on the scRNA-seq dataset, which shows inconsistencies between the two replicates. Furthermore, the morphological and histological analyses do not align with the scRNA-seq findings.

(5) There is a lack of mechanistic insight into how collagen, as a key signaling molecule from fibroblasts, affects the development of cardiomyocytes and vascular endothelial cells.

(6) In Figure 1B, Col1a1 expression is observed in the epicardial cells (Figure 1A, E11.5), but this is not represented in the accompanying cartoon.

(7) Do the PdgfraCreER+/-; RosaDTA+/- mice survive after birth when induced at E15.5, and do they exhibit any cardiac defects?

Reviewer #3 (Public review):

Summary:

The authors investigated fibroblasts' communication with key cell types in developing and neonatal hearts, with focus on critical roles of fibroblast-cardiomyocyte and fibroblast-endothelial cells network in cardiac morphogenesis. They tried to map the spatial distribution of these cell types and reported the major pathways and signaling molecules driving the communication. They also used Cre-DTA system to ablate Pdgfra labeled cells and observed myocardial and endothelial cell defects at development. They screened the pathways and genes using sequencing data of ablated heart. Lastly they reported a compensatory collagen expression in long term ablated neonate heart. Overall, this study provides us with important insight on fibroblasts' roles in cardiac development and will be a powerful resource for collagens and ECM focused research.

Strengths:

The authors utilized good analyzing tools to investigate on multiple database of single cell sequencing and Multi-seq. They identified significant pathways, cellular and molecular interactions of fibroblasts. Additionally, they compared some of their analytic findings with human database, and identified several groups of ECM genes with varying roles in mice.

Weaknesses:

This study is majorly based on sequencing data analysis. At the bench, they used very strident technique to study fibroblast functions by ablating one of the major cell population of heart. Also, experimental validation of their analyzed downstream pathways will be required eventually.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Most of my comments have been adequately addressed. Additional comments on new data in the revised manuscript are below.

(1) In the new figure S11, it is not really possible to draw major conclusions on mitral valve morphology and maturation since the planes of sections to not seem comparable. Observations regarding attachment to the papillary muscle might be dependent on the particular section being evaluated. However, it is useful to see that the valves are not severely affected in the ablated animals.

We appreciate the reviewer’s comment and agree with the reviewer’s observation. Accordingly, we have updated the manuscript by removing the original conclusion-related statement and instead highlighting that the valves were not severely affected in the ablated animals (page 6).

(2) In the last supplemental figure S19, it is not possible to determine if results are or are not statistically significant for n=2 as shown for FS and EF for the ablated animals and controls. The text says that there is a trend of improved heart function, but evaluation of additional animals is needed to support this conclusion.

We thank the reviewer for the comment and agree that a sample size of n = 2 is too small to draw meaningful conclusions. As previously suggested by the reviewer, we have removed this result from the manuscript (page 10).

Reviewer #2 (Recommendations for the authors):

The manuscript has greatly improved following the revision, and I have no additional comments to offer.

Thanks!

Reviewer #3 (Recommendations for the authors):

Authors did a good job addressing questions asked at first review. However, I have some minor concerns.

(1) The paper notes that collagen signaling is observed in FB-VasEC in humans, but not in FB-VenCM, unlike mice. Did authors analyze predictive ligand receptor interaction as they did with control and ablated mice heart? This could add valuable new insights that how FB regulate ventricular CM in human heart.

Thank you. We have analyzed the predicted ligand-receptor interactions between Fb and Ven_CM, as well as between Fb and Vas_EC, using human scRNA-seq data. The results are provided as a supplemental figure (Fig. S8C).

(2) The authors provided data on Defect in CD31 expression in several models. Did they observed any other phenotypes associated with defective endothelial or vascular system? Such as, blood accumulation in pericardium, larger/smaller capillaries? Did they also examined percentage of Cdh5+ cells?

We thank the reviewer for the questions. We did not observe clear evidence of blood accumulation in the pericardium of the ablated hearts, as shown in figure 3B, 3E, 6B, and 6F. Additionally, we did not perform Cdh5 staining in either the control or ablated hearts.

(3) Please mention the sample age of Figure 2A-C.

These are single-cell mRNA sequencing data from CD1 mice across 18 developmental stages, ranging from E9.5 to P9. We have added this information to the manuscript (page 4).

(4) Please follow the same style to describe X axis in graphs in Figure 3D (and all similar graphs in manuscript) as followed in 3G.

Thank you. We assume the reviewer was referring to the descriptions in the relevant figure legends. We have updated the legend for Figure 3D to ensure consistency with the description provided for Figure 3G (page 15).

(5) It is important to provide echocardiographic M mode images with a comparable number of cardiac cycles in control and ablated (Fig. 6H).

We thank the reviewer for the comment. As explained in our previous response, the echocardiographic data for both control and mutant mice were collected in conscious animals. The differences in their cardiac cycles reflect variations in heart rate, which represent a disease phenotype and cannot be altered. Therefore, we are unable to provide M-mode images with a similar number of cardiac cycles for control and ablated mice.

(6) In the long-term neonatal ablation experiments, collagen expressions return to normal. The manuscript attributes this to possible "compensatory expression," Do they have any thoughts how this is regulated? Are other cell types stepping in, or are surviving FBs proliferating?

We thank the reviewer for the question. As suggested, the compensatory collagen expression could be driven by surviving fibroblasts or other cell types. Since we currently lack evidence to exclude either possibility, we believe both could be contributing factors.

(7) While collagen is shown to be a dominant signaling molecule, its centrality is inferred primarily from scRNAseq and ligand-receptor predictions. Did authors try any functional rescue experiment (e.g., exogenous collagen supplementation or receptor blockade) to directly validate this pathway's role in vivo?

We thank the reviewer for the comment. As noted in our previous revision in response to similar questions from the other two reviewers, we agree that these rescue experiments are of interest but are beyond the scope of the current study. We plan to pursue these investigations in future work and share our findings when available.

eLife Assessment

This important study presents an alternative platform for nanobody discovery using phage-displayed synthetic libraries. The evidence supporting the platform, which is used to isolate and validate nanobodies targeting Drosophila secreted proteins, is compelling. By making the library openly accessible, this provides an excellent resource to the wider scientific community. The paper presents a detailed protocol for nanobody screening; as this protocol is refined and optimized over time, this will increase the success rate for discovering nanobodies with improved properties using this alternative platform.

Reviewer #2 (Public review):

Summary:

In this study, authors propose an alternative platform for nanobody discovery using a phage-displayed synthetic library. Authors relied on DNA templates originally created by McMahon et al. (2018) to build the yeast-displayed synthetic library. To validate their platform, authors screened for nanobodies against 8 Drosophila secreted proteins. Nanobody screening has been performed with phage-displayed nanobody libraries followed by an enzyme-linked immunosorbent assay (ELISA) to validate positive hits. Nanobodies with higher affinity have been then tested for immunostaining and immunoblotting applications using Drosophila adult guts and hemolymph, respectively.

Strengths:

The authors presented a detailed protocol with various and complementary approaches to select nanobodies and test their application for immunostaining and immunoblotting experiments. Data are convincing and the manuscript is well-written, clear and easy to read.

Weaknesses:

When using membrane-tethered forms of the antigens to test the affinity of nanobodies identified by ELISA, many nanobodies fail to recognize the antigens. While authors suggested a low affinity of these nanobodies for their antigens, this hypothesis has not been tested in the manuscript.

Improving the protocol at each step for nanobody selection would greatly increase a successful rate for nanobodies discovery with high affinity.

Author response:

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

Reviewer #1 (Public review):

Summary:

Using highly specific antibody reagents for biological research is of prime importance. In the past few years, novel approaches have been proposed to gain easier access to such reagents. This manuscript describes an important step forward toward the rapid and widespread isolation of antibody reagents. Via the refinement and improvement of previous approaches, the Perrimon lab describes a novel phage-displayed synthetic library for nanobody isolation. They used the library to isolate nanobodies targeting Drosophila secreted proteins. They used these nanobodies in immunostainings and immunoblottings, as well as in tissue immunostainings and live cell assays (by tethering the antigens on the cell surface).

Since the library is made freely available, it will contribute to gaining access to better research reagents for non-profit use, an important step towards the democratisation of science.

Strengths:

(1) New design for a phage-displayed library of high content.

(2) Isolation of valuble novel tools.

(3) Detailed description of the methods such that they can be used by many other labs.

We are grateful for these supportive comments.

Weaknesses:

My comments largely concentrate on the representation of the data in the different Figures.

We have made adjustments according to the reviewer’s recommendations.

Reviewer #2 (Public review):

Summary:

In this study, the authors propose an alternative platform for nanobody discovery using a phage-displayed synthetic library. The authors relied on DNA templates originally created by McMahon et al. (2018) to build the yeast-displayed synthetic library. To validate their platform, the authors screened for nanobodies against 8 Drosophila secreted proteins. Nanobody screening has been performed with phage-displayed nanobody libraries followed by an enzyme-linked immunosorbent assay (ELISA) to validate positive hits. Nanobodies with higher affinity have been tested for immunostaining and immunoblotting applications using Drosophila adult guts and hemolymph, respectively.

Strengths:

The authors presented a detailed protocol with various and complementary approaches to select nanobodies and test their application for immunostaining and immunoblotting experiments. Data are convincing and the manuscript is well-written, clear, and easy to read.

We thank the reviewer for these supportive comments.

Weaknesses:

On the eight Drosophila secreted proteins selected to screen for nanobodies, the authors failed to identify nanobodies for three of them. While the authors mentioned potential improvements of the protocol in the discussion, none of them have been tested in this manuscript.

We prepared all eight antigens by single-step IgG purification (see Materials and Methods) without additional biophysical quality control (e.g., size-exclusion chromatography). Consequently, we cannot definitively determine whether the three “no-binder” cases resulted from the aggregation or misfolding of the antigens, versus gaps in our naive library’s sequence space. While approaches such as additional purification steps or affinity maturation of weak binders would likely rescue these difficult targets, comprehensive pipeline optimization is beyond the scope of establishing and validating the phage-displayed nanobody platform. We have clarified this limitation and suggested these strategies in third paragraph of the Discussion.

The same comment applies to the experiments using membrane-tethered forms of the antigens to test the affinity of nanobodies identified by ELISA. Many nanobodies fail to recognize the antigens. While authors suggested a low affinity of these nanobodies for their antigens, this hypothesis has not been tested in the manuscript.

We observed that several nanobodies with strong ELISA signals showed reduced binding to membrane-displayed antigens. This discrepancy may result from low affinity of the nanobodies or differences in post-translational modifications (e.g., glycosylation) and antigen context between secreted IgG-fusion proteins (used for panning/ELISA) and GPI- or mCD8-anchored proteins. In an ongoing work, we have performed affinity maturation of the nanobodies and successfully increased the affinity toward the target antigen. These results will be reported separately.

Improving the protocol at each step for nanobody selection would greatly increase the success rate for the discovery of nanobodies with high affinity.

We fully agree that systematic optimization—from antigen preparation (e.g., additional purification steps) through screening conditions (e.g., buffer composition, additional affinity-maturation steps)—could substantially increase the success rate and nanobody affinity. These represent important directions for future work.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 3. The merge of two GFP channels does not make much sense. Can the authors not use artificial colours? And show the panels at higher resolution, such that a viewer can really see and judge what they are seeing? The same comments apply to all Supplementary Figures.

We appreciate the reviewer’s comment. In the revised Figure 3, we have replaced the cyan/green overlay with red/green overlay and used enlarged pictures so that GFP-positive cells and corresponding nanobody staining are clearly visible. We applied the same layout to all relevant Supplementary Figures.

(2) Figure 4. Also, in this Figure, it is not really possible to see what the authors say one should see. The resolution should be higher, and arrows or arrowheads should point to important structures.

We appreciate the reviewer’s comment. In the revised Figure 4A, we have added arrows to point to the immunostaining signal in cells with smaller nuclei and added inset panels to show a closer view of representative NbMip-4G staining.

Reviewer #2 (Recommendations for the authors):

(1) Images are sometimes quite small and difficult to interpret. For example, Figures S2C-D.

We thank the reviewer for this suggestion. In the revised figures, we have replaced the cyan/green overlay with red/green overlay and used enlarged pictures that clearly show GFP-positive cells alongside their corresponding nanobody staining.

(2) Supplemental figures are not always cited in the text.

Thank you for the comment. To eliminate this misunderstanding, we have updated the Nesfatin1 nanobody screen data as Supplementary Figure 1 and Mip nanobody screen data as Supplementary Figure 2. We have made the corresponding changes in the Results section.

eLife Assessment

This study reveals a neural signature of a common behavioural phenomenon: serial dependence, whereby estimates of a visual feature (here motion direction) are attracted towards the recent history of encoded and reported stimuli. The study provides solid evidence that this phenomenon arises primarily during working memory maintenance. The pervasiveness of serial dependencies across modalities and species makes these findings important for researchers interested in perceptual decision-making across subfields.

Reviewer #1 (Public review):

This study uses MEG to test for a neural signature of the trial history effect known as 'serial dependence.' This is a behavioral phenomenon whereby stimuli are judged to be more similar than they really are, in feature space, to stimuli that were relevant in the recent past (i.e., the preceding trials). This attractive bias is prevalent across stimulus classes and modalities, but a neural source has been elusive. This topic has generated great interest in recent years, and I believe this study makes a unique contribution to the field.

Specifically, while previous neuroimaging studies have found apparent reactivations of previous information, or repulsive biases that may indirectly relate to serial dependence, here Fischer at al. find an attractive bias in neural activity patterns that aligns with the direction of the behavioral effect. Moreover, the data show that the bias emerges later in a trial, after perceptual encoding, which speaks to an ongoing debate about whether such biases are perceptual or decisional.

The revised preprint thoroughly addresses many of the initial concerns, but the results are still open to interpretation. For instance, the model training/testing regime allows that some training data timepoints may be inherently noisier than others (e.g., delay period more so than encoding), and potentially more (or differently) susceptible to bias. The S1 and S2 epochs show no attractive bias, but they may also be based on more high fidelity training sets (i.e., encoding), and therefore less susceptible to the bias that is evident in the retrocue epoch. So, the results could reflect that serial dependence is indeed a post-perceptual process, or it may instead be that the WM representations, as detected with these MEG analyses, become noisier and more subject to reveal the attractive bias over time.

The results are intriguing, but the study was not powered to examine whether there is any feature-specificity to the neural bias (e.g., whether it matches the behavioral pattern that biases are amplified within a particular range of feature distances between stimuli). Nor do analyses get at temporally precise information about when attractive and repulsive biases appear, which would help to better reconcile the work with previous findings. As in, the reconstructions average across coarse trial epochs. The S1 and S2 reconstructions show no attractive bias, and appear to show subtle repulsion, but if the timing were examined more precisely, we might see repulsion magnified at earlier timepoints that shift toward attraction at later time points, thereby counteracting the effect. That is to say that the averaging approach, across feature values and timepoints, still leaves these important theoretical questions unresolved.

Nonetheless, the work marks an important step in identifying the neurophysiological bases of serial dependence. Ideally, all of the data, including the eye-tracking, would be made available so that others might try to address some of these follow-up questions.

Reviewer #2 (Public review):

Summary:

The study aims to probe the neural correlates of visual serial dependence - the phenomenon that estimates of a visual feature (here motion direction) are attracted towards the recent history of encoded and reported stimuli. The authors utilize an established retro-cue working memory task together with magnetoencephalography, which allows to probe neural representations of motion direction during encoding and retrieval (retro-cue) periods of each trial. The main finding is that neural representations of motion direction are not systematically biased during the encoding of motion stimuli, but are attracted towards the motion direction of the previous trial's target during the retrieval (retro-cue period), just prior to the behavioral response. By demonstrating a neural signature of attractive biases in working memory representations, which align with attractive behavioral biases, this study highlights the importance of post-encoding memory processes in visual serial dependence.

Strengths:

The main strength of the study is its elegant use of a retro-cue working memory task together with high temporal resolution MEG, enabling to probe neural representations related to stimulus encoding and working memory. The behavioral task elicits robust behavioral serial dependence and replicates previous behavioral findings by the same research group. The careful neural decoding analysis benefits from a large number of trials per participant, considering the slow-paced nature of the working memory paradigm. This is crucial in a paradigm with considerable trial-by-trial behavioral variability (serial dependence biases are typically small, relative to the overall variability in response errors). While the current study is broadly consistent with previous studies showing that attractive biases in neural responses are absent during stimulus encoding (prev. studies reported repulsive biases), to my knowledge, it is the first study showing attractive biases in current stimulus representations during working memory. The study also connects to previous literature showing reactivations of previous stimulus representations, although the link between reactivations and biases remains somewhat vague in the current manuscript. Together, the study reveals an interesting avenue for future studies investigating the neural basis of visual serial dependence.

Weaknesses:

The main weakness of the current manuscript is that the authors could have done more analyses to address the concern that their neural decoding results are driven by signals related to eye movements. The authors show that participants' gaze position systematically depended on the current stimuli's motion directions, which, together with previous studies on eye movement-related confounds in neural decoding, justifies such a concern. The authors seek to rule out this confound by showing that the consistency of stimulus-dependent gaze position does not correlate with (a) the neural reconstruction fidelity and (b) the attractive shift in reconstructed motion direction. However, the authors' approach of quantifying stimulus-dependent eye movements only considers gaze angle and not gaze amplitude, and thus potentially misses important features of eye movements that could manifest in the MEG data. Moreover, it is unclear whether the gaze consistency metric should correlate with attractive history biases in neural decoding, if there were a confound. These two concerns could be potentially addressed by (1) directly decoding stimulus motion direction from x-y gaze coordinates and relating this decoding performance to neural reconstruction fidelity, and (2) investigating whether gaze coordinates themselves are history-dependent and are attracted to the average gaze position associated with the previous trials' target stimulus. If the authors could show that (2) is not the case, I would be much more convinced that their main finding is not driven by eye movement confounds.

The sample size (n = 10) is definitely at the lower end of sample sizes in this field. The authors collected two sessions per participant, which partly alleviates the concern. However, given that serial dependencies can be very variable across participants, I believe that future studies should aim for larger sample sizes.

It would have been great to see an analysis in source space. As the authors mention in their introduction, different brain areas, such as PPC, mPFC and dlPFC have been implicated in serial biases. This begs the question which brain areas contribute to the serial dependencies observed in the current study? For instance, it would be interesting to see whether attractive shifts in current representations and pre-stimulus reactivations of previous stimuli are evident in the same or different brain areas.

Reviewer #2 (Public review):

Summary:

The study aims to probe the neural correlates of visual serial dependence - the phenomenon that estimates of a visual feature (here motion direction) are attracted towards the recent history of encoded and reported stimuli. The authors utilize an established retro-cue working memory task together with magnetoencephalography, which allows to probe neural representations of motion direction during encoding and retrieval (retro-cue) periods of each trial. The main finding is that neural representations of motion direction are not systematically biased during the encoding of motion stimuli, but are attracted towards the motion direction of the previous trial's target during the retrieval (retro-cue period), just prior to the behavioral response. By demonstrating a neural signature of attractive biases in working memory representations, which align with attractive behavioral biases, this study highlights the importance of post-encoding memory processes in visual serial dependence.

Strengths:

The main strength of the study is its elegant use of a retro-cue working memory task together with high temporal resolution MEG, enabling to probe neural representations related to stimulus encoding and working memory. The behavioral task elicits robust behavioral serial dependence and replicates previous behavioral findings by the same research group. The careful neural decoding analysis benefits from a large number of trials per participant, considering the slow-paced nature of the working memory paradigm. This is crucial in a paradigm with considerable trial-by-trial behavioral variability (serial dependence biases are typically small, relative to the overall variability in response errors). While the current study is broadly consistent with previous studies showing that attractive biases in neural responses are absent during stimulus encoding (prev. studies reported repulsive biases), to my knowledge, it is the first study showing attractive biases in current stimulus representations during working memory. The study also connects to previous literature showing reactivations of previous stimulus representations, although the link between reactivations and biases remains somewhat vague in the current manuscript. Together, the study reveals an interesting avenue for future studies investigating the neural basis of visual serial dependence.

Weaknesses:

The main weakness of the current manuscript is that the authors could have done more analyses to address the concern that their neural decoding results are driven by signals related to eye movements. The authors show that participants' gaze position systematically depended on the current stimuli's motion directions, which, together with previous studies on eye movement-related confounds in neural decoding, justifies such a concern. The authors seek to rule out this confound by showing that the consistency of stimulus-dependent gaze position does not correlate with (a) the neural reconstruction fidelity and (b) the attractive shift in reconstructed motion direction. However, the authors' approach of quantifying stimulus-dependent eye movements only considers gaze angle and not gaze amplitude, and thus potentially misses important features of eye movements that could manifest in the MEG data. Moreover, it is unclear whether the gaze consistency metric should correlate with attractive history biases in neural decoding, if there were a confound. These two concerns could be potentially addressed by (1) directly decoding stimulus motion direction from x-y gaze coordinates and relating this decoding performance to neural reconstruction fidelity, and (2) investigating whether gaze coordinates themselves are history-dependent and are attracted to the average gaze position associated with the previous trials' target stimulus. If the authors could show that (2) is not the case, I would be much more convinced that their main finding is not driven by eye movement confounds.

The sample size (n = 10) is definitely at the lower end of sample sizes in this field. The authors collected two sessions per participant, which partly alleviates the concern. However, given that serial dependencies can be very variable across participants, I believe that future studies should aim for larger sample sizes.

It would have been great to see an analysis in source space. As the authors mention in their introduction, different brain areas, such as PPC, mPFC and dlPFC have been implicated in serial biases. This begs the question which brain areas contribute to the serial dependencies observed in the current study? For instance, it would be interesting to see whether attractive shifts in current representations and pre-stimulus reactivations of previous stimuli are evident in the same or different brain areas.

eLife Assessment

In this valuable study, Nold et al. examined exercise-induced pain modulation in a pharmacological within-subject fMRI study using the opioid-antagonist naloxone and different levels of aerobic exercise intensity and pain. This investigation provides solid evidence to show that the intensity of exercise does not seem to impact the hypoalgesic effect. Moreover, exploratory analysis identified that fitness level and sex may potentially play a role in exercise-induced hypoalgesia, and that further confirmatory studies are required in order to verify these findings.

Reviewer #1 (Public review):

Summary:

Participants in this study completed three visits. In the first, participants received experimental thermal stimulations which were calibrated to elicit three specific pain responses (30, 50, 70) on a 0-100 visual analogue scale (VAS). Experimental pressure stimulations were also calibrated at an intensity to the same three pain intensity responses. In the subsequent two visits, participants completed another pre-calibration check (Visit 2 of 3 only). Then, prior to the exercise NALOXONE or a SALINE placebo-control was administered intravenously. Participants then completed 1 of 4 blocks of HIGH (100%) or LOW (55%) intensity cycling which was tailored according to a functional threshold power (FTP) test completed in Visit 1. After each block of cycling lasting 10 minutes, participants entered an MRI scanner and were stimulated with the same thermal and pressure stimulations that corresponded to 30, 50, and 70 pain intensity ratings from the calibration stage. Therefore, this study ultimately sought to investigate whether aerobic exercise does indeed incur a hypoalgesia effect. More specifically, researchers tested the validity of the proposed endogenous pain modulation mechanism. Further investigation into whether the intensity of exercise had an effect on pain and the neurological activation of pain-related brain centres were also explored. Results show that in the experimental visits (Visit 2 and 3), when participants exercised at two distinct intensities as intended. Power output, heart rate, and perceived effort ratings were higher during the HIGH versus LOW intensity cycling. In particular. HIGH intensity exercise was perceived as "hard" / ~15 on the Borg (1974, 1998) scale, whereas LOW intensity exercise was perceived as "very light" / ~9 on the same scale.

The fMRI data from Figure 1 indicates that the anterior insula, dorsal posterior insula and middle cingulate cortex show pronounced activation as stimulation intensity and subsequent pain responses increased, thus linking these brain regions with pain intensity and corroborating what many studies have shown before.

Results also showed that participants rated a higher pain intensity in the NALOXONE condition at all three stimulation intensities compared to the SALINE condition. Therefore, the expected effect of NALOXONE in this study seemed to occur whereby opioid receptors were "blocked" and thus resulted in higher pain ratings compared to a SALINE condition where opioid receptors were "not blocked". When accounting for participant sex, NALOXONE had negligible effects at lower experimental nociceptive stimulations for females compared to males who showed a hyperalgesia effect to NALOXONE at all stimulation intensities (peak effect at 50 VAS). Females did show a hyperalgesia effect at stimulation intensities corresponding to 50 and 70 VAS pain ratings. The fMRI data showed that the periaqueductal gray (PAG) showed increased activation in the NALOXONE versus SALINE condition at higher thermal stimulation intensities. The PAG is well-linked to endogenous pain modulation.

When assessing the effects of NALOXONE and SALINE after exercise, results showed no significant differences in subsequent pain intensity ratings.

When assessing the effect of aerobic exercise intensity on subsequent pain intensity ratings, authors suggested that aerobic exercise in the form of a continuous cycling exercise tailored to an individual's FTP is not effective at eliciting an exercise-induced hypoalgesia response -irrespective of exercise intensity. This is because results showed that pain responses did not differ significantly between HIGH and LOW intensity exercise with (NALOXONE) and without (SALINE) an opioid antagonist. Therefore, authors have also questioned the mechanisms (endogenous opioids) behind this effect.

Strengths:

Altogether, the paper is great piece of work that has provided some truly useful insight into the neurological and perceptual mechanisms associated with pain and exercise-induced modulation of pain. The authors have gone to great lengths to delve into their research question(s) and their methodological approach is relatively sound. The study has incorporated effective pseudo-randomisation and conducted a rigorous set of statistical analysis to account for as many confounds as possible. I will particularly credit the authors on their analysis which explores the impact of sex and female participants' stage of menses on the study outcomes. It would be particularly interesting for future work to pursue some of these lines of research which investigate the differences in the endogenous opioid mechanism between sexes and the added interaction of stage of menses or training status - all of which the authors point out in their discussion.

There are certainly many other areas that this article contributes to the literature due to the depth of methods the research team have used. For example, the authors provide much insight into: the impact of exercise intensity on the exercise-induced hypoalgesia effect; the impact of sex on the endogenous opioid modulation mechanism; and the impact of exercise intensity on the neurological indices associated with endogenous pain modulation and pain processing. All of which, the researchers should be credited for due to the time and effort they have spent completing this study. Indeed, their in-depth analysis of many of these areas provides ample support for the claims they make in relation to these specific questions. As such, I consider their evidence concerning the fMRI data to be very convincing (and interesting).

Weaknesses:

Although the authors have their own view of their results, I, however, do still maintain a slightly different take on what the post-exercise pain ratings seem to show and its implications for judging whether an exercise-induced hypoalgesia effect is present or not and whether this is related to the opioid system.

For example, my basic assumptions relate to data which appears to show that there is an exercise-induced hypoalgesia effect as average pain ratings are ~30% lower than pre-calibrated/resting pain ratings within the SALINE condition at the same temperature of stimulation. Then, it appears there is evidence for the endogenous opioid mechanism as the NALOXONE condition demonstrates a minimal hypoalgesia effect after exercise. I.e., NALOXONE indeed blocked the opioid receptors, and such inhibition prevented the endogenous opioid system from taking effect.

However, through a comprehensive revision of their work, the authors have addressed many areas that myself and my fellow reviewer have questioned and provided a comprehensive set of responses and edits about this. So while I may have some opposing views on the mechanisms at play, I believe that each reader can decide and interpret the data for themselves which has been presented well by the authors.

Reviewer #2 (Public review):

Summary:

This interesting study compared two different intensities of aerobic exercise (low-intensity, high-intensity) and their efficacy in inducing a hypoalgesic reaction (i.e. exercise-induced hypoalgesia; EIH). fMRI was used to identify signal changes in the brain, with infusion of naloxone used to identify hypoalgesia mechanisms. No differences were found in post exercise pain perception between the high-intensity and low-intensity conditions, with naloxone infusion causing increased pain perception across both conditions which was mirrored by activation in the medial frontal cortex (identified by fRMI).

Strengths:

• The use of fMRI and naloxone provides a strong approach by which to identify possible mechanisms of EIH.

• The infusion of naloxone to maintain a stable concentration helps to ensure a consistent effect and that the time-course of the protocol won't affect consistency of changes in pain perception

• The manipulation checks (differences in intensity of exercise, appropriate pain induction) are approached in a systematic way.

• The interactions for fitness level and sex provide some interesting findings which should be explored further.

Weaknesses:

• Given the absence of a baseline/control condition (for exercise), the efficacy of high/low intensity exercise on EIH cannot be assessed. Providing this would have extended and strengthened the findings/conclusions.

• Whilst the exercise test (functional threshold power) used to set the intensity of the low/high exercise bouts set participants to exercise at different intensities, this method does not ensure that they exercised above/below particular thresholds (i.e. within either heavy or severe domains). This could have created very different relative challenges between participants.

Author response:

Reviewer #1 (Public review):

Summary:

The manuscript "Rho-ROCK liberates sequestered claudin for rapid de novo tight junction formation" by Cho and colleagues investigates de novo tight junction formation during the differentiation of immortalized human HaCaT keratinocytes to granular-like cells, as well as during epithelial remodeling that occurs upon the apoptotic of individual cells in confluent monolayers of the representative epithelial cell line EpH4. The authors demonstrate the involvement of Rho-ROCK with well-conducted experiments and convincing images. Moreover, they unravel the underlying molecular mechanism, with Rho-ROCK activity activating the transmembrane serine protease Matriptase, which in turn leads to the cleavage of EpCAM and TROP2, respectively, releasing Claudins from EpCAM/TROP2/Claudin complexes at the cell membrane to become available for polymerization and de novo tight junction formation. These functional studies in the two different cell culture systems are complemented by localization studies of the according proteins in the stratified mouse epidermis in vivo.

In total, these are new and very intriguing and interesting findings that add important new insights into the molecular mechanisms of tight junction formation, identifying Matriptase as the "missing link" in the cascade of formerly described regulators. The involvement of TROP2/EpCAM/Claudin has been reported recently (Szabo et al., Biol. Open 2022; Bugge lab), and Matriptase had been formerly described to be required for in tight junction formation as well, again from the Bugge lab. Yet, the functional correlation/epistasis between them, and their relation to Rho signaling, had not been known thus far.

However, experiments addressing the role of Matriptase require a little more work.

Strengths:

Convincing functional studies in two different cell culture systems, complemented by supporting protein localization studies in vivo. The manuscript is clearly written and most data are convincingly demonstrated, with beautiful images and movies.

Weaknesses:

The central finding that Rho signaling leads to increased Matriptase activity needs to be more rigorously demonstrated (e.g. western blot specifically detecting the activated version or distinguishing between the full-length/inactive and processed/active version).

We plan to provide more direct evidence that matriptase activation is regulated by the Rho-ROCK pathway, utilizing antibodies that specifically recognize the activated form of matriptase.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate how epithelia maintain intercellular barrier function despite and during cellular rearrangements upon e.g. apoptotic extrusion in simple epithelia or regenerative turnover in stratified epithelia like this epidermis. A fundamental question in epithelial biology. Previous literature has shown that Rho-mediated local regulation of actomyosin is essential not only for cellular rearrangement itself but also for directly controlling tight junction barrier function. The molecular mechanics however remained unclear. Here the authors use extensive fluorescent imaging of fixed and live cells together with genetic and drug-mediated interference to show that Rho activation is required and sufficient to form novo tight junctional strands at intercellular contacts in epidermal keratinocytes (HaCat) and mammary epithelial cells. After having confirmed previous literature they then show that Rho activation activates the transmembrane protease Matriptase which cleaves EpCAM and TROP2, two claudin-binding transmembrane proteins, to release claudins and enable claudin strand formation and therefore tight junction barrier function.

Strengths:

The presented mechanism is shown to be relevant for epithelial barriers being conserved in simple and stratifying epithelial cells and mainly differs due to tissue-specific expression of EpCAM and TROP2. The authors present careful state-of-the-art imaging and logical experiments that convincingly support the statements and conclusion. The manuscript is well-written and easy to follow.

Weaknesses:

Whereas the in vitro evidence of the presented mechanism is strongly supported by the data, the in vivo confirmation is mostly based on the predicted distribution of TROP2. Whereas the causality of Rho-mediated Matriptase activation has been nicely demonstrated it remains unclear how Rho activates Matriptase.

As noted, while we have demonstrated that Rho activation is both necessary and sufficient to induce matriptase activation, the precise mechanism by which Rho mediates this activation remains unclear. As discussed in the manuscript, several potential molecular mechanisms could underlie the contribution of Rho to matriptase activation. As part of our future work, we intend to systematically investigate each of these mechanisms.

eLife Assessment

The study is a timely and important contribution to our knowledge of the circuit mechanisms of fear analgesia. The novel cue-induced analgesia paradigm allowed a compelling identification of a brainstem circuit element, i.e., somatostatin-expressing neurons within the ventrolateral periaqueductal grey that project to the rostroventral medulla, in mediating fear analgesia. The vlPAG is a known region of pain modulation, and this study adds key insight to the circuit involved in fear-associated analgesia. This work will be of interest to systems and behavioral neuroscientists, especially those interested in emotional behavior, pain, and/or brainstem function.

Reviewer #1 (Public review):

Summary:

In the manuscript by Winke et al, the authors present evidence that fear-induced analgesia is mediated by somatostatin projection cells from the vlPAG to the RVM. This study uses a mouse model of fear-induced analgesia, and incorporates optogenetic circuit manipulation with behaviour and electrophysiology to gain a meaningful insight into a novel circuit involved in fear-induced analgesia.

Strengths:

(1) This is a well-constructed study with appropriate controls and analyses.

(2) Alternative interpretations of the data are systematically considered and eliminated via rational experiments. The authors are commended for a nice piece of experimental work.

(3) The vlPAG is a known region of pain modulation, and this study adds valuable insight to the circuit involved in fear-associated analgesia.

Weaknesses:

(1) Only male mice are included in this study.

(2) Animals are excluded from analyses based on clearly defined criteria, but it is not clear how many mice were excluded from each group.

(3) The authors implement a pain sensitivity assay that involves a hot plate with progressively increasing temperature. The time to nociceptive responses is reported. Without reporting the actual temperature at which the mice respond, it makes it difficult to compare nociceptive responses to previously published work (which typically use a defined and static hotplate temperature).

(4) The authors present evidence that inhibition of SST vlPAG cells enhances spinal nociceptive electrophysiological responses, but the corresponding pain sensitivity is not altered (Figure 2, CS- condition). The reason for the discrepancy between electrophysiological and behavioural responses is not clear.

Reviewer #2 (Public review):

Summary:

Wenke et al. investigated the role of vlPAG somatostatin-expressing neurons in the mediation of analgesia during defensive states. A newly developed paradigm of cued fear-conditioned analgesia, which consists of a combination of an auditory fear retrieval session and a pain test, was used to evaluate this cell population's contribution to fear-mediated analgesia. Optogenetic manipulation of vlPAG SST+ neurons modulated the responses to a nociceptive cue (Hot Plate) presented concomitantly with an aversively conditioned tone. At the same time, alterations in the freezing levels could be observed during optogenetic activation of vlPAG SST+ neurons. In order to disentangle the impact of these cells on analgesia from their impact on the expression of defensive behaviors, the authors performed electrophysiological recordings from the dorsal horn in the spinal cord of anesthetized mice. A vlPAG-RVM-DH pathway was identified to trigger nociceptive C-fibers upon optic activation of the RVM. Finally, pathway-specific activation of SST+ vlPAG-RVM neurons could abolish CS-induced analgesia.

Strengths:

The study addresses a relevant topic, that is, brainstem circuits for pain-modulatory mechanisms as part of defensive states evoked by threat. This is important because the circuit mechanisms underlying pain are still not fully understood, and defining molecular markers of cellular circuit substrates may support the identification of potential pharmaceutical targets in treating pain. The authors confirm a previous study in that a somatostatin-positive cellular population presents a crucial vlPAG circuit element mediating anti-nociceptive effects. Key novelty aspects of the present study are the demonstration that these neurons seem to play a role specifically in threat-induced analgesia. This was possible by the elegant design and application of a novel fear analgesia paradigm, combined with cell- and pathway-specific optogenetics.

Weaknesses:

Despite the convincing and rigorous experimental approach, the study leaves some interpretational room when it comes to the proposed circuit mechanism. This could either be addressed by additional experiments or by more discussion of alternative circuit layouts.

Major Comments:

(1) The paper by Zhang et al. (https://pubmed.ncbi.nlm.nih.gov/36641028/), which identified a role for vlPAG SOM+ neurons in mediating anti-nociception in neuropathic pain, needs to be referenced and its results discussed, if not reconciled. While functionally, both studies find an analgetic role of vlPAG SOM+ neurons projecting to the RVM, Zhang et al., using slice physiology, characterize those neurons as glutamatergic. In Figure 4E of Zhang et al. they find general (fear-independent) analgetic effects with PAG-RVM specificity by performing chemogenetic experiments.

It can be argued that in addition to the two functionally distinct inhibitory SOM subtypes hypothesized by Winke et al., there is another, excitatory subpopulation. Also, the different experimental conditions (chronic vs. acute pain, non-threat vs. fearful cues/contexts may recruit different vlPAG SOM+ populations. All of this is conceivable, yet I wonder whether the contrasting findings could more parsimoniously be reconciled. The author's own results presented here in Supplementary Figure 3 suggests that SOM+ vlPAG cells are co-localizing with glutamate and thus could also be excitatory. In addition to this rather complementary piece of evidence, a more extensive characterization of vlPAG neurons using IHC and slice physiology would be needed to justify the unambiguous identification of their inhibitory nature.

In the absence of a direct identification of these cells exclusively releasing GABA, an alternative explanation should be considered. What about looking at vlPAG SOM+ neurons as a putatively mixed bag of local, inhibitory interneurons and long-range, RVM-projecting excitatory cells? This model would then open up interesting questions as to the actual function of somatostatin as a modulator of vlPAG circuit activity and associated function, and from my perspective, would nicely fit into the view of PAG circuits as integrators of complex survival responses.

(2) "Our data indicate that the optogenetic inhibition of SST+ vlPAG cells promotes analgesia irrespective of the animal's defensive state. In contrast, the optogenetic activation of long-range SST+ vlPAG cells that project to the rostral ventromedial medulla (RVM) abolishes the analgesia mediated by fear behavior." (lines 32-35). Consider toning down these conclusions, as contrasting activation with inhibition of two different (though overlapping) populations cannot be fully conclusive. Alternatively, a pathway-specific (vlPAG-RVM) inhibitory experiment could help to fully understand the circuit mechanism and verify the necessity of these neurons.

(3) Despite an overall very thorough reporting style, some information is missing from the manuscript:

a) In Figures 2d and f, what are the freezing levels during optogenetic manipulation? From Figure 3d, one can expect that freezing is inhibited during the hot plate test, which could bias the NC response towards shorter latencies. b) In Figure 5, the histological experiment showing the vlPAG-to-RVM pathway is presented by a qualitative image only. Here, some quantification would strengthen the finding. c) In Figures 6 c and d "Consistently, activation of the SST+ vlPAG-RVM pathway during CFCA had no impact on CS-presentation, whereas the same manipulation performed during CS+ blocked the increase in NC response latency compared to GFP controls." (line 194-196). Is it possible that the NC response cannot be any lower than the one during CS-, thus constituting a floor effect? d) Connected to major point 1- this experiment is important for defining the circuit mode and therefore should be as convincing as possible. However, for the colocalization experiment in Supplementary Figure 3, the methodological description is missing and thus makes it hard to comprehend how this data set was generated (how many data points, etc.). The visual depiction of the results is non-standard and not easily graspable. Consider e.g., a Venn diagram.

Reviewer #3 (Public review):

Summary:

Conditioned analgesia refers to the ability of a learned fear cue to suppress pain-related behavior and neural activity. Understudied, the authors developed a novel conditioned analgesia procedure in which a cue that had been paired or unpaired with shock was played while a hot plate increased temperature. Compared to several control conditions, the authors found increased latency to a nociceptive response (paw licking). The authors identified somatostatin neurons in the periaqueductal gray as a likely mediator of the behavior. They then showed that: (1) stimulating vlPAG-SST neurons blocked nociceptive response latency increases to the CS+, (2) stimulating vlPAG-SST neurons suppressed fear retrieval freezing, (3) stimulating vs. inhibiting vlPAG-SST neurons drove opposing modulation of c-fibers and Aδ-fibers, (4) direct-projecting vlPAG SST neurons modulate freezing while RVM-projecting vlPAG SST neurons modulate conditioned analgesia.

Strengths:

These experiments have many strengths. The behavioral assay is chief among them. The assay is robust and controls for confounding factors to reveal a repeatable effect of a shock-paired cue to delay nociceptive responding. The optogenetic experiments provide the correct level of temporal precision, given the authors' time-specific interest in cued responding. Combining neuronal manipulations with spinal recordings is particularly innovative, especially in the context of more behavioral neuroscience-based assays. All-in-all, I found this to be an exceptionally strong set of experiments.

Weaknesses:

No obvious weaknesses were identified by this Reviewer.

eLife Assessment

This valuable study addresses the structural basis of voltage-activation of BK channels using atomistic simulations of several microseconds, to assess conformational changes that underlie both voltage-sensing and gating of the pore. The findings, including movement of specific charged residues, combined with the degree to which these movements are coupled to pore movements, provide a solid basis for understanding voltage-gating mechanisms in this class of channels. This paper will likely be of interest to ion channel biologists and biophysicists focused on voltage-dependent channel gating mechanisms.

Reviewer #1 (Public review):

Summary:

This study provides new insight into the non-canonicial voltage-gating mechanism of BK channels through prolonged (10 us) MD simulations of the Slo1 transmembrane domain conformation and K+ conduction in response to high imposed voltages (300, 750 mV). The results support previous conclusions based on functional and structural data and MD simulations that the voltage-sensor domain (VSD) of Slo1 undergoes limited conformational changes compared to Kv channels, and predicts gating charge movement comparable in magnitude to experimental results. The gating charge calculations further indicate that R213 and R210 in S4 are the main contributors owing to their large side chain movements and the presence of a locally focused electric field, consistent with recent experimental and MD simulation results by Carrasquel-Ursulaez et al.,2022. Most interestingly, changes in pore conformation and K+ conduction driven by VSD activation are resolved, providing information regarding changes in VSD/pore interaction through S4/S5/S6 segments proposed to underly electromechanical coupling.

Strengths:

Include that the prolonged timescale and high voltage of the simulation allow apparent equilibration in the voltage-sensor domain (VSD) conformational changes and at least partial opening of the pore. The study extends the results of previous MD simulations of VSD activation by providing quantitative estimates of gating charge movement, showing how the electric field distribution across the VSD is altered in resting and activated states, and testing the hypothesis that R213 and R210 are the primary gating charges by steered MD simulations. The ability to estimate gating charge contributions of individual residues in the WT channel is useful as a comparison to experimental studies based on mutagenesis which have yielded conflicting results that could reflect perturbations in structure. Use of dynamic community analysis to identify coupling pathways and information flow for VSD-pore (electromechanical) coupling as well as analysis of state-dependent S4/S5/S6 interactions that could mediate coupling provide useful predictions extending beyond what has been experimentally tested.

Weaknesses:

Weaknesses include that a truncated channel (lacking the C-terminal gating ring) was used for simulations, which is known to have reduced single channel conductance and electromechanical coupling compared to the full-length channel. In addition, as VSD activation in BK channels is much faster than opening, the timescale of simulations was likely insufficient to achieve a fully open state as supported by differences in the degree of pore expansion in replicate simulations, which are also smaller than observed in Ca-bound open structures of the full-length channel. Taken together, these limitations suggest that inferences regarding coupling pathways and interactions in the fully open voltage-activated channel may be only partially supported and therefore incomplete. That said, adequate discussion regarding these limitations are provided together with dynamic community analysis based on the Ca-bound open structure. The latter supports the main conclusions based on simulations, while providing an indication of potential interaction differences between simulated and fully open conformations. Another limitation is that while the simulations convincingly demonstrate voltage-dependent channel opening as evidenced by pore expansion and conduction of K+ and water through the pore, single channel conductance is underestimated by at least an order of magnitude, as in previous studies of other K+ channels. These quantitative discrepancies suggest that MD simulations may not yet be sufficiently advanced to provide insight into mechanisms underlying the extraordinarily large conductance of BK channels.

Comments on revisions:

My previous questions and concerns have been adequately addressed.

My only new comment is that the numbering of residues in Fig. S8 does not match the standard convention for hSlo and needs to be doublechecked. For the residues I checked, the numbers appear to be shifted 3 compared hSlo (e.g. Y315, P317, E318, G324 should be Y318, P320, E321, G327).

Reviewer #2 (Public review):

Summary:

The manuscript by Jia and Chen addresses the structural basis of voltage-activation of BK channels using computational approaches. Although a number of experimental studies using gating current and patch-clamp recording have analyzed voltage-activation in terms of observed charge movements and the apparent energetic coupling between voltage-sensor movement and channel opening, the structural changes that underlie this phenomenon have been unclear. The present studies use a reduced molecular system comprising the transmembrane portion of the BK channel (i.e. the cytosolic domain was deleted), embedded in a POPC membrane, with either 0 or 750 mV applied across the membrane. This system enabled acquisition of long simulations of 10 microseconds, to permit tracking of conformational changes of the channel. The authors principal findings were that the side chains of R210 and R213 rapidly moved toward the extracellular side of the membrane (by 8 - 10 Å), with greater displacements than any of the other charged transmembrane residues. These movements appeared tightly coupled to movement of the pore-lining helix, pore hydration, and ion permeation. The authors estimate that R210 and R213 contribute 0.25 and 0.19 elementary charges per residue to the gating current, which is roughly consistent with estimates based on electrophysiological measurements that used the full-length channel.

Strengths:

The methodologies used in this work are sound, and these studies certainly contribute to our understanding of voltage-gating of BK channels. An intriguing observation is the strongly coupled movement of the S4, S5, and S6 helices that appear to underlie voltage-dependent opening. Based on Fig 2a-d, the substantial movements of the R210 and R213 side chains occur nearly simultaneously to the S6 movement (between 4 - 5 usec of simulation time). This seems to provide support for a "helix-packing" mechanism of voltage gating in the so-called "non-domain-swapped" voltage-gated K channels.

Weaknesses:

The main limitation is that these studies used a truncated version of the BK channel, and there are likely to be differences in VSD-pore coupling in the context of the full-length channels that will not be resolved in the present work. Nonetheless, the authors provide a strong rationale for their use of the truncated channel, and the results presented will provide a good starting point for future computational studies of this channel.

Author response:

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

Recommendations for the authors:

Reviewing Editor Comments:

The resubmitted version of the manuscript adequately addressed several initial comments made by reviewing editors, including a more detailed analysis of the results (such as those of bilayer thickness). This version was seen by 2 reviewers. Both reviewers recognize this work as being an important contribution to the field of BK and voltage-dependent ion channels in general. The long trajectories and the rigorous/novel analyses have revealed important insights into the mechanisms of voltage-sensing and electromechanical coupling in the context of a truncated variant of the BK channel. Many of these observations are consistent with structural and functional measurements of the channel, available thus far. The authors also identify a novel partially expanded state of the channel pore that is accessed after gating-charge displacement, which informs the sequence of structural events accompanying voltage-dependent opening of BK.

However, there are key concerns regarding the use of the truncated channel in the simulations. While many gating features of BK are preserved in the truncated variant, studies have suggested that opening of the channel pore to voltage-sensing domain rearrangement is impaired upon gating-ring deletion. So the inferences made here might only represent a partial view of the mechanism of electromechanical coupling.

It is also not entirely clear whether the partially expanded pore represents a functionally open, sub-conductance, or another closed state. Although the authors provide evidence that the inner pore is hydrated in this partially open state, in the absence of additional structural/functional restraints, a confident assignment of a functional state to this structure state is difficult. Functional measurements of the truncated channel seem to suggest that not only is their single channel conductance lower than full-length channels, but they also appear to have a voltage-independent step that causes the gates to open. It is unclear whether it is this voltage-independent step that remains to be captured in these MD trajectories. A clean cut resolution of this conundrum might not be feasible at this time, but it could help present the various possibilities to the readers.

We appreciate the positive comments and agree that there will likely be important differences between the mechanistic details of voltage activation between the Core-MT and full-length constructs of BK channels. We also agree that the dilated pore observed in the simulation may not be the fully open state of Core-MT.

Nonetheless, the notion that the simulation may not have captured the full pore opening transition or the contribution of the CTD should not render the current work “incomplete”, because a complete understanding of BK activation would be an unrealistic goal beyond the scope of this work. We respectfully emphasize that the main insights of the current simulations are the mechanisms of voltage sensing (e.g., the nature of VSD movements, contributions of various charged residues, how small charge movements allow voltage sensing, etc.) as well as the role of the S4-S5-S6 interface in VSD-pore coupling. As noted by the Editor and reviewers, these insights represent important steps towards establishing a more complete understanding of BK activation.

Below are the specific comments of the two experts who have assessed the work and made specific suggestions to improve the manuscript.

Reviewer #1 (Recommendations for the authors):

(1) Although the successful simulation of V-dependent K+ conduction through the BK channel pore and analysis of associated state dependent VSD/pore interactions and coupling analysis is significant, there are two related questions that are relevant to the conclusions and of interest to the BK channel community which I think should be addressed or discussed.

One key feature of BK channels is their extraordinarily large conductance compared to other K+ selective channels. Do the simulations of K+ conductance provide any insight into this difference? Is the predicted conductance of BK larger than that of other K+ channels studied by similar methods? Is there any difference in the conductance mechanism (e.g., the hard and soft knock-on effects mentioned for BK)?

The molecular basis of the large conductance of BK channels is indeed an interesting and fundamental question. Unfortunately, this is beyond the scope of this work and the current simulation does not appear to provide any insight into the basis of large conductance. It is interesting to note, though, the conductance is apparently related to the level of pore dilation and the pore hydration level, as increasing hydration level from ~30 to ~40 waters in the pore increases the simulated conductance from ~1.5 to 6 pS (page 8). This is consistent with previous atomistic simulations (Gu and de Groot, Nature Communications 2023; ref. 33) showing that the pore hydration level is strongly correlated with observed conductance. As noted in the manuscript, the conductance mechanism through the filter appears highly similar to previous simulations of other K+ channels (Page 8). Given the limit conductance events observed in the current simulations, we will refrain from discussing possible basis of the large conductance in BK channels except commenting on the role of pore hydration (page 8; also see below in response to #5).

The pore in the MD simulations does not open as wide as the Ca-bound open structure, which (as the authors note) may mean that full opening requires longer than 10 us. I think that is highly likely given that the two 750 mV simulations yielded different degrees of opening and that in BK channels opening is generally much slower than charge movement. Therefore, a question is - do any of the conclusions illustrated in Figures 6, S5, S6 differ if the Ca-bound structure is used as the open state? For example, I expect the interactions between S5 and S6 might at least change to some extent as S6 moves to its final position. In this case, would conclusions about which residues interact, and get stronger or weaker, be the same as in Figures S6 b,c? Providing a comparison may help indicate to what extent the conclusions are dependent on achieving a fully open conformation.

We appreciate the reviewer’s suggestion and have further analyzed the information flow and coupling pathways using the simulation trajectory initiated from the Ca2+-bound cryo-EM structure (sim 7, Table S1). The new results are shown in two new SI Figures S7 and S8, and new discussion has been added to pages 14-15. Comparing Figures 5 and S7, we find that dynamic community, coupling pathways, and information flow are highly similar between simulation of the open and closed states, even though there are significant differences in S5 contacts in the simulated open state vs Ca2+-bound open state (Figure S8). Interestingly, there are significant differences in S4-S5 packing in the simulated and Ca2+-bound open states (Figure S8 top panel), which likely reflect important difference in VSD/pore interactions during voltage vs Ca2+ activation.

(2) P4 Significance -"first, successful direct simulation of voltage-activation"

This statement may need rewording. As noted above Carrasquel-Ursulaez et al.,2022 (reference 39) simulated voltage sensor activation under comparable conditions to the current manuscript (3.9 us simulation at +400 mV), and made some similar conclusions regarding R210, R213 movement, and electric field focusing within the VSD. However, they did not report what happens to the pore or simulate K+ movement. So do the authors here mean something like "first, successful direct simulation of voltage-dependent channel opening"?

We agree with the reviewer and have revised the statement to “ … the first successful direct simulation of voltage-dependent activation of the big potassium (BK) channel, ..”

(3) P5 "We compare the membrane thickness at 300 and 750 mV and the results reveal no significant difference in the membrane thickness (Figure S2)" The figure also shows membrane thickness at 0 mV and indicates it is 1.4 Angstroms less than that at 300 or 750 mV. Whether or not this difference is significant should be stated, as the question being addressed is whether the structure is perturbed owing to the use of non-physiological voltages (which would include both 300 and 750 mV).

We have revised the Figure S2 caption to clarify that one-way ANOVA suggest the difference is not significant.

(4) P7 "It should be noted that the full-length BK channel in the Ca2+ bound state has an even larger intracellular opening (Figure 2f, green trace), suggesting that additional dilation of the pore may occur at longer timescales."

As noted above, I agree it is likely that additional pore dilation may occur at longer timescales. However, for completeness, I suppose an alternative hypothesis should be noted, e.g. "...suggesting that additional dilation of the pore may occur at longer timescales, or in response to Ca-binding to the full length channel."

This is a great suggestion. Revised as suggested.

(5) Since the authors raise the possibility that they are simulating a subconductance state, some more discussion on this point would be helpful, especially in relation to the hydrophobic gate concept. Although the Magleby group concluded that the cytoplasmic mouth of the (fully open) pore has little impact on single channel conductance, that doesn't rule out that it becomes limiting in a partially open conformation. The simulation in Figure 3A shows an initial hydration of the pore with ~15 waters with little conductance events, suggesting that hydration per se may not suffice to define a fully open state. Indeed, the authors indicate that the simulated open state (w/ ~30-40 waters) has 1/4th the simulated conductance of the open structure (w/ ~60 waters). So is it the degree of hydration that limits conductance? Or is there a threshold of hydration that permits conductance and then other factors that limit conductance until the pore widens further? Addressing these issues might also be relevant to understanding the extraordinarily large conductance of fully open BK compared to other K channels.

We agree with the reviewer’s proposal that pore hydration seems to be a major factor that can affect conductance. This is also well in-line with the previous computational study by Gu and de Groot (2023). We have now added a brief discussion on page 8, stating “Besides the limitation of the current fixed charge force fields in quantitively predicting channel conductance, we note that the molecular basis for the large conductance of BK channels is actually poorly understood (78). It is noteworthy that the pore hydration level appears to be an important factor in determining the apparent conductance in the simulation, which has also been proposed in a previous atomistic simulation study of the Aplysia BK channel (33).”

Minor points

(1) P5 "the fully relaxed pore profile (red trace in Figure S1d, top row) shows substantial differences compared to that of the Ca2+-free Cryo-EM structure of the full-length channel." For clarity, I suggest indicating which is the Ca-free profile - "... Ca2+-free Cryo-EM structure of the full-length channel (black trace)."

We greatly appreciate the thoughtful suggestion. Revised as suggested.

(2) P8 "Consistent with previous simulations (78-80), the conductance follows a multi-ion mechanism, where there are at least two K+ ions inside the filter" For clarity, I suggest indicating these are not previous simulations of BK channels (e.g., "previous simulations of other K+ channels ...").

Revised as suggested. Thank you.

(3) Figure 2, S1 - grey traces representing individual subunits are very difficult to see (especially if printed). I wonder if they should be made slightly darker. Similar traces in Figure 3 are easier to see.

The traces in Figure S1 are actually the same thickness in Figure 3 and they appear lighter due to the size of the figure. Figure 2 panels a-c have been updated to improve the resolution.

(4) Figure 2 - suggest labeling S6 as "S6 313-324" (similar to S4 notation) to indicate it is not the entire segment.

Figure 2 panel d) has been updated as suggested.

(5) Figure 2 legend - "Voltage activation of Core-MT BK channels. a-d)..."

It would be easier to find details corresponding to individual panels if they were referenced individually. For example:

"a-d) results from a 10-μs simulation under 750 mV (sim2b in Table S1). Each data point represents the average of four subunits for a given snapshot (thin grey lines), and the colored thick lines plot the running average. a) z-displacement of key side chain charged groups from initial positions. The locations of charged groups were taken as those of guanidinium CZ atoms (for Arg) and sidechain carboxyl carbons (for Asp/Glu) b) z-displacement of centers-of-mass of VSD helices from initial positions, c) backbone RMSD of the pore-lining S6 (F307-L325) to the open state, and d) tilt angles of all TM helices. Only residues 313-324 of S6 were included inthe tilt angle calculation, and the values in the open and closed Cryo-EM structures are marked using purple dashed lines. "

We appreciate the thoughtful suggestion and have revised the caption as suggested.

(6) Figure S1 - column labels a,b,c, and d should be referenced in the legend.

The references to column labels have been added to Figure S1 caption.

(7) References need to be double-checked for duplicates and formatting.

a) I noticed several duplicate references, but did not do a complete search: Budelli et al 2013 (#68, 100), Horrigan Aldrich 2002 (#22,97), Sun Horrigan 2022 (#40, 86), Jensen et al 2012 (#56,81).

b) Reference #38 is incorrectly cited with the first name spelled out and the last name abbreviated.

We appreciate the careful proofreading of the reviewer. The duplicated references were introduced by mistake due to the use of multiple reference libraries. We have gone through the manuscript and removed a total of 5 duplicated references.

Reviewer #2 (Recommendations for the authors):

This manuscript has been through a previous level of review. The authors have provided their responses to the previous reviewers, which appear to be satisfactory, and I have no additional comments, beyond the caveats concerning interpretations based on the truncated channel, which are noted above.

We greatly appreciate the constructive comments and insightful advice. Please see above response to the Reviewing Editor’s comments for response and changes regarding the caveats concerning interpretations of the current simulations.

eLife Assessment

Sanchez-Vasquez et al establish an innovative approach to induce aneuploidy in preimplantation embryos. This important study extends the author's previous publications evaluating the consequences of aneuploidy in the mammalian embryo. In this work, the authors investigate the developmental potential of aneuploid embryos and characterize changes in gene expression profiles under normoxic and hypoxic culture conditions. Using a solid methodology they identify sensitivity to Hif1alpha loss in aneuploid embryos, and in further convincing experiments they assess how levels of DNA damage and DNA repair are altered under hypoxic and normoxic conditions.

Reviewer #1 (Public review):

Summary:

This paper developed a model of chromosome mosaicism by using a new aneuploidy-inducing drug (AZ3146), and compared this to their previous work where they used reversine, to demonstrate the fate of aneuploid cells during murine preimplantation embryo development. They found that AZ3146 acts similarly to reversine in inducing aneuploidy in embryos, but interestingly showed that the developmental potential of embryos is higher in AZ3146-treated vs. reversine-treated embryos. This difference was associated with changes in HIF1A, p53 gene regulation, DNA damage, and fate of euploid and aneuploid cells when embryos were cultured in a hypoxic environment.

Strengths:

In the current study, the authors investigate the fate of aneuploid cells in the preimplantation murine embryo using a specific aneuploidy-inducing compound to generate embryos that were chimeras of euploid and aneuploid cells. The strength of the work is that they investigate the developmental potential and changes in gene expression profiles under normoxic and hypoxic culture conditions. Further, they also assessed how levels of DNA damage and DNA repair are altered in these culture conditions. They also assessed the allocation of aneuploid cells to the divergent cell lineages of the blastocyst stage embryo.

Weaknesses:

The authors have still not addressed the inconsistent/missing description for sample size, the appropriate number of * for each figure panel, and the statistical tests used.

The authors assign 5% oxygen as hypoxia. This is not the case as the in vivo environment is close to this value. 5% is normoxia. Clinical IVF/embryo culture occurs at 5% O2. Please adjust your narrative around this.

Reviewer #2 (Public review):

Summary:

This study by Sanchez-Vasquez is a very innovative approach to induce aneuploidy and then study the contribution of treated cells to different lineages, including post implantation. It connects well to the authors previous work to induce mosaic aneuploidies. The authors identify sensitivity to HIF1a loss in treated embryos with likely aneuploidy. This work is part of an important line of work with evaluates the consequences of aneuploidy in mammalian embryo.

Weaknesses:

Given that this is a study on the induction of aneuploidy, it would be meaningful to assess aneuploidy immediately after induction, and then again before implantation. This is also applicable to the competition experiments on page 7/8. What is shown is the competitiveness of treated cells. Because the publication centers around aneuploidy, inclusion of such data in the main figure at all relevant points would strengthen it. There is some evaluation of karyotypes only in the supplemental - why? Would be good not to rely on a single assay that the authors appear to not give much importance.

eLife Assessment

This paper identifies a crucial step in the regulation of tight junction formation by identifying Rho-ROCK activity-dependent activation of the serine protease Matriptase, making Claudins available for tight junction formation. The reviewers were satisfied with the revisions and found the work important and the approach convincing.

Reviewer #1 (Public review):

Summary:

The manuscript by Cho and colleagues investigates de novo tight junction formation during the differentiation of immortalized human HaCaT keratinocytes to granular-like cells, as well as during epithelial remodeling that occurs upon the apoptotic of individual cells in confluent monolayers of the representative epithelial cell line EpH4. The authors demonstrate the involvement of Rho-ROCK with well-conducted experiments and convincing images. Moreover, they unravel the underlying molecular mechanism, with Rho-ROCK activity activating the transmembrane serine protease Matriptase, which in turn leads to the cleavage of EpCAM and TROP2, respectively, releasing Claudins from EpCAM/TROP2/Claudin complexes at the cell membrane to become available for polymerization and de novo tight junction formation. These functional studies in two different cell culture systems are complemented by localization studies of the according proteins in the stratified mouse epidermis in vivo.

In total, these are new and very intriguing and interesting findings that add important new insights into the molecular mechanisms of tight junction formation, identifying Matriptase as the "missing link" in the cascade of formerly described regulators. The involvement of TROP2/EpCAM/Claudin has been reported recently (Szabo et al., Biol. Open 2022; Bugge lab), and Matriptase had been formerly described to be required for tight junction formation as well, again from the Bugge lab. Yet, the functional correlation / epistasis between them, and their relation to Rho signaling, had not been known thus far.

Strengths:

Convincing functional studies in two different cell culture systems, complemented by supporting protein localization studies in vivo. The manuscript is clearly written and most data are convincingly demonstrated, with beautiful images and movies.

Weaknesses:

The previously described weaknesses have been fully wiped out during the revisions.

Reviewer #2 (Public review):

Summary:

In this manuscript the authors investigate how epithelia maintain intercellular barrier function despite and during cellular rearrangements upon e.g. apoptotic extrusion in simple epithelia or regenerative turnover in stratified epithelia like this epidermis. A fundamental question in epithelial biology. Previous literature has shown that Rho mediated local regulation of actomyosin is essential not only for cellular rearrangement itself but also directly controls tight junction barrier function. The molecular mechanics however remained unclear. Here the authors use extensive fluorescence imaging of fixed and live cells together with genetic and drug mediated interference to show that Rho activation is required and sufficient to form de novo tight junctional strands at intercellular contacts in epidermal keratinocytes (HaCat) and mammary epithelial cells. After having confirmed previous literature they then show that Rho activation activates the transmembrane protease matriptase which cleaves EpCAM and TROP2, two claudin binding transmembrane proteins, to release claudins and enable claudin strand formation and therefore tight junction barrier function.

Strengths:

The presented mechanism is shown to be relevant for epithelial barriers being conserved in simple and stratifying epithelial cells and mainly differs due to tissue specific expression of EpCAM and TROP2. The authors present carefull state of the art imaging and logical experiments that convincingly support the statements and conclusion. The manuscript is well written and easy to follow.

Weaknesses:

Whereas the in vitro evidence of the presented mechanism is strongly supported by the data, the in vivo confirmation is mostly based on the predicted distribution of TROP2. Whereas the causality of Rho mediated matriptase activation has been nicely demonstrated it remains unclear how Rho activates matriptase.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript "Rho-ROCK liberates sequestered claudin for rapid de novo tight junction formation" by Cho and colleagues investigates de novo tight junction formation during the differentiation of immortalized human HaCaT keratinocytes to granular-like cells, as well as during epithelial remodeling that occurs upon the apoptotic of individual cells in confluent monolayers of the representative epithelial cell line EpH4. The authors demonstrate the involvement of Rho-ROCK with well-conducted experiments and convincing images. Moreover, they unravel the underlying molecular mechanism, with Rho-ROCK activity activating the transmembrane serine protease Matriptase, which in turn leads to the cleavage of EpCAM and TROP2, respectively, releasing Claudins from EpCAM/TROP2/Claudin complexes at the cell membrane to become available for polymerization and de novo tight junction formation. These functional studies in the two different cell culture systems are complemented by localization studies of the according proteins in the stratified mouse epidermis in vivo.

In total, these are new and very intriguing and interesting findings that add important new insights into the molecular mechanisms of tight junction formation, identifying Matriptase as the "missing link" in the cascade of formerly described regulators. The involvement of TROP2/EpCAM/Claudin has been reported recently (Szabo et al., Biol. Open 2022; Bugge lab), and Matriptase had been formerly described to be required for in tight junction formation as well, again from the Bugge lab. Yet, the functional correlation/epistasis between them, and their relation to Rho signaling, had not been known thus far.

However, experiments addressing the role of Matriptase require a little more work.

Strengths:

Convincing functional studies in two different cell culture systems, complemented by supporting protein localization studies in vivo. The manuscript is clearly written and most data are convincingly demonstrated, with beautiful images and movies.

Weaknesses:

The central finding that Rho signaling leads to increased Matriptase activity needs to be more rigorously demonstrated (e.g. western blot specifically detecting the activated version or distinguishing between the full-length/inactive and processed/active version).

First, we thank the reviewer for their fair evaluation of our manuscript and for providing constructive feedback. Regarding the detection of matriptase activation—which Reviewer 1 identified as a weakness—we fully agree that direct validation is crucial. Therefore, in this revision we have carried out additional experiments using the M69 antibody, which specifically recognizes the activated form of matriptase. Details of these new experiments are provided in our point-by-point responses below.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate how epithelia maintain intercellular barrier function despite and during cellular rearrangements upon e.g. apoptotic extrusion in simple epithelia or regenerative turnover in stratified epithelia like this epidermis. A fundamental question in epithelial biology. Previous literature has shown that Rho-mediated local regulation of actomyosin is essential not only for cellular rearrangement itself but also for directly controlling tight junction barrier function. The molecular mechanics however remained unclear. Here the authors use extensive fluorescent imaging of fixed and live cells together with genetic and drug-mediated interference to show that Rho activation is required and sufficient to form novo tight junctional strands at intercellular contacts in epidermal keratinocytes (HaCat) and mammary epithelial cells. After having confirmed previous literature they then show that Rho activation activates the transmembrane protease Matriptase which cleaves EpCAM and TROP2, two claudin-binding transmembrane proteins, to release claudins and enable claudin strand formation and therefore tight junction barrier function.

Strengths:

The presented mechanism is shown to be relevant for epithelial barriers being conserved in simple and stratifying epithelial cells and mainly differs due to tissue-specific expression of EpCAM and TROP2. The authors present careful state-of-the-art imaging and logical experiments that convincingly support the statements and conclusion. The manuscript is well-written and easy to follow.

Weaknesses:

Whereas the in vitro evidence of the presented mechanism is strongly supported by the data, the in vivo confirmation is mostly based on the predicted distribution of TROP2. Whereas the causality of Rho-mediated Matriptase activation has been nicely demonstrated it remains unclear how Rho activates Matriptase.

Thank you for your valuable feedback on our manuscript. As Reviewer 2 points out, the precise mechanism by which the Rho/ROCK pathway activates matriptase remains unclear. We have discussed the possible molecular mechanisms in the Discussion section. Elucidating the detailed mechanism of matriptase activation will be the focus of our future work.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Comment 1-1 - Matriptase activation by Rho: The authors show activation of Matriptase in western blots by the simple reduction of (full-length?) protein level in Figures 5 and 7. Most publications however show activated Matriptase either by antibodies detecting specifically the active form (including the publication referenced in this manuscript), or the appearance of the activated form next to the inactive form (based on different molecular weights). Therefore, it is not completely clear whether the treatment with Rho activators (Figure 5) results in an overall decrease of Matriptase, or really in an increase in the activated form. Therefore, the authors should show the actual increase of the active form. As a control, the impact of camostat treatment and overexpression of Hai1 on the active form of Matriptase could be included. It also should be indicated in the figure legend how long cells had been treated with the drugs before being subjected to lysis. Moreover, the western blots need to be quantified.

We performed a more rigorous analysis using the M69 antibody, which specifically recognizes the activated form of matriptase and has been widely used in previous studies（e.g. Benaud et al., 2001; Hung et al., 2004; Wang et al., 2009）. We likewise confirmed a significant increase in M69 signals by both western blotting and immunostaining from samples in which matriptase was activated by acid medium treatment (Figure 5A). Crucially, we also observed matriptase activation with the M69 antibody both in Rho/ROCK activator-treated cells (Figure 5A) and in differentiated granular-layer-like cells (Figures 7A and 7D). These findings strongly support the conclusion that matriptase is activated downstream of the Rho/ROCK pathway.

Comment 1-2 - Based on their results, the authors conclude that Matriptase cleaves TROP2 in the SG2 layer of the epidermis, which is a little contradictory to former studies, which have shown Matriptase to be most prominently expressed and active in the basal layer and only little in the spinous layer (e.g Chen et al., Matriptase regulates proliferation and early, but not terminal, differentiation of human keratinocytes. J Invest Dermatol.2013). In this light, one could also argue that inhibiting Matriptase "simply" reduces epidermal differentiation. Can other differentiation markers be tested to rule that the effects on tight junctions are secondary consequences of interferences with earlier / more global steps of keratinocyte differentiation?

As the reviewer noted, previous studies have demonstrated that matriptase is essential for keratinocyte differentiation, and that it cleaves substrates beyond EpCAM and TROP2—any of which could potentially influence the differentiation process. To test this possibility, we chose to monitor maturation of adherens junction (AJ) as an indicator of keratinocyte differentiation into granular-layer cells. Prior work has shown that during differentiation into granular-layer cells, AJs develop and experience increased intercellular mechanical tension, and that this rise in mechanical tension at AJs is critical for subsequent TJ formation (Rübsam et al., 2017). To assess AJ tension, we stained with the α-18 monoclonal antibody, which specifically recognizes the tension-dependent conformational change of α-catenin, a core AJ component. In control cells, differentiation into granular-layer like cells led to a marked increase in α-18 signal at cell–cell adhesion sites. Importantly, when HaCaT cells were treated with Camostat to inhibit matriptase and then induced to differentiate, we observed an equivalent increase in α-18 signal at AJs (Figure 7F). However, we did not detect claudin enrichment at cell-cell contacts under these conditions (Figures 7F and 7H). These results suggest that matriptase inhibition does not impair AJ maturation during granular-layer differentiation, but does profoundly disrupt TJ formation. While we cannot rule out the possibility that matriptase acts more broadly from these results, we judged that a comprehensive substrate survey lies outside the scope of the present manuscript.

Comment 1-3 - In addition, as in Figure 5, full-length levels of Matriptase in Figure 7A need to be complemented by the active version to demonstrate more convincingly that TROP2 processing coincides with (and is most likely caused by) increased Matriptase activation. In the quantification in 7B, levels actually go up again after 2 and 4 hours. How is that explained, and what would this mean with respect to tight junction formation seen at 24 h of differentiation? The TROP2 cleavage shown in Figure 7A should be quantified.

This comment is related to Comment 1-1. Using the M69 antibody, which specifically recognizes the activated matriptase, we directly demonstrated that matriptase activation occurs during the differentiation of granular layer-like cells (Figures 7A and 7D). Furthermore, we performed quantitative analysis of TROP2 cleavage and found that, compared with undifferentiated cells, differentiation into granular-layer like cells was accompanied by an increase in the cleaved TROP2 fragments (Figures 7A and 7B).

Minor points:

Comment 1-4 - Figure 1B and C: Including orthogonal views would be a nice add-on to appreciate the findings.

In the revised version, we have added the corresponding orthogonal views to Figure 1B and Figure 1C.

Comment 1-5 - Figure 2D: last row: indication of orthogonal view.

We stated that the bottom panels are orthogonal views in the figure legend of Figure 2D.

Comment 1-6 - Figure 3A: quantification is missing. GST-Rhotekin assay is missing in methods.

In the revised manuscript, we have added quantitative analysis for Figure 3A. We have also supplemented the Materials and Methods section with detailed information on the GST–Rhotekin assay used to quantify levels of active RhoA.

Comment 1-7 - Figure 4H: quantification of the Western blot is missing.

In the revised manuscript, we have added quantitative analysis for Figure 4H as Figure 4I.

Comment 1-8 - Figure 5 and 6: Quantifications of Western blots are missing.

In the revised manuscript, we have added quantitative analyses for Figure 5D as Figure 5F and for Figure 6A as Figure 6B.

Comment 1-9 - Figure 7C: quantification of the Western blot is missing.

Figure 7C does not present western blotting data. For the other western blotting results, we have added quantitative analyses as suggested by Reviewer 1.

Comment 1-10 - Figure 8I: Including Hai1 overexpression would be good for a complete picture.

Following Reviewer 1’s suggestion, we have added staining data for Hai1-overexpressing cells to Figure 8J.

Comment 1-11 - Line 377: The authors say they found Matriptase always present in lateral membranes. I did not find evidence for this in the manuscript.

Previous studies have demonstrated that in polarized epithelial cells, matriptase is localized to the basolateral membrane below TJs (Buzza et al., 2010; Wang et al., 2009). We also found that matriptase consistently localizes to the basolateral membrane but more crucially that it becomes activated there during differentiation into granular layer cells. We added these new data as Figures 7C-7E in the revised manuscript. These findings suggest that matriptase activation occurs without a change in its subcellular localization.

Comment 1-12 - Line 381: should most likely say: and ADAM17 but it is not known whether...

We corrected the sentence in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

The authors have added a significant number of quantifications verifying their observations, which was a major comment in a previous version of the manuscript and thus I have only a few minor comments which should be addressed.

Comment 2-1 - It is not required to have scale bars in every image of a panel if the same scale is used.

Unnecessary scale bars were removed. Specifically, scale bars were removed from Figure 1B, 1C, 1F, 8F, 8G, and 8H.

Comment 2-2 - Throughout all figures: Please state for non-quantified images whether this is a representative example and for how many technical or biological repeats this is representative. Also for "N" number, state what the N stands for and if this is what the dots in the graph represent. Are these the number of junctions or technical, experimental or biological repeats?

In the revised manuscript, we have added the number of independent experiments and corresponding “N” values to the Quantification and Statistical Analysis subsection of the Materials and Methods.

Comment 2-3 - Some Zooms have a scale bar (6d), and some do not (e.g. 5b).

The scale bar was removed from the magnified image in Figure 6D.

eLife Assessment

This valuable study by Wu et al presents data on bacterial cell organization, demonstrating that the two structures that account for bacterial motility - the chemotaxis complex and the flagella - colocalize to the same pole in Pseudomonas aeruginosa cells. The work provides convincing results for the regulation underlying this spatial organization and its functioning.

Reviewer #1 (Public review):

Summary:

The study by Wu et al presents interesting data on bacterial cell organization, a field that is progressing now, mainly due to the advances in microscopy. Based mainly on fluorescence microscopy images, the authors aim to demonstrate that the two structures that account for bacterial motility, the chemotaxis complex and the flagella, colocalize to the same pole in Pseudomonas aeruginosa cells and to expose the regulation underlying their spatial organization and functioning.

Comments on revisions:

The authors have addressed all major and minor points that I raised in a satisfying way during the revision process. The work can now be regarded as complete, the assumptions were clarified, the results are convincing, the conclusions are justified, and the novelty has been made clear.

This manuscript will be of interest to cell biologists, mainly those studying bacteria, but not only

Reviewer #2 (Public review):

Summary:

Here, the authors studied the molecular mechanisms by which the chemoreceptor cluster and flagella motor of Pseudomonas aeruginosa (PA) are spatially organized in the cell. They argue that FlhF is involved in localizing the receptors-motor to the cell pole, and even without FlhF, the two are colocalized. Finally, the authors argue that the functional reason for this colocalization is to insulate chemotactic signaling from other signaling pathways, such as cyclic-di-GMP signaling.

Strength:

The experiments and data are high quality. It is clear that the motor and receptors co-localize, and that elevated CheY levels lead to elevated c-di-GMP.

Weakness:

The explanation for the functional importance of receptor-motor colocalization is plausible but is still not conclusively demonstrated. Colocalization might reduce CheY levels throughout the cell in order to reduce cross-talk with c-di-GMP. This would mean that if physiologically-relevant levels of CheYp near the pole were present throughout the cell, c-di-GMP levels would be elevated to a point that is problematic for the cell. Clearly demonstrating this seems challenging.

Reviewer #3 (Public review):

Summary:

The authors investigated the assembly and polar localization of the chemosensory cluster in P. aeruginosa. They discovered that a certain protein (FlhF) is required for the polar localization of the chemosensory cluster while a fully-assembled motor is necessary for the assembly of the cluster. They found that flagella and chemosensory clusters always co-localize in the cell; either at the cell pole in wild type cells or randomly-located in the cell in FlhF mutant cells. They hypothesize that this co-localization is required to keep the level of another protein (CheY-P), which controls motor switching, at low levels as the presence of high-levels of this protein (if the flagella and chemosensory clusters were not co-localized) is associated with high-levels of c-di-GMP and cell aggregations.

Strengths:

The manuscript is clearly written and straightforward. The authors applied multiple techniques to study the bacterial motility system including fluorescence light microscopy and gene editing. In general, the work enhances our understanding of the subtlety of interaction between the chemosensory cluster and the flagellar motor to regulate cell motility.

Weaknesses:

The major weakness for me in this paper is that the authors never discussed how the flagellar genes expression is controlled in P. aeruginosa. For example, in E. coli there is a transcriptional hierarchy for the flagellar genes (early, middle, and late genes, see Chilcott and Hughes, 2000). Similarly, Campylobacter and Helicobacter have a different regulatory cascade for their flagellar genes (See Lertsethtakarn, Ottemann, and Hendrixson, 2011). How does the expression of flagellar genes in P. aeruginosa compare to other species? how many classes are there for these genes? is there a hierarchy in their expression and how does this affect the results of the FliF and FliG mutants? In other words, if FliF and FliG are in class I (as in E. coli) then their absence might affect the expression of other later flagellar genes in subsequent classes (i.e., chemosensory genes). Also, in both FliF and FliG mutants no assembly intermediates of the flagellar motor are present in the cell as FliG is required for the assembly of FliF (see Hiroyuki Terashima et al. 2020, Kaplan et al. 2019, Kaplan et al. 2022). It could be argued that when the motor is not assembled then this will affect the expression of the other genes (e.g., those of the chemosensory cluster) which might play a role in the decreased level of chemosensory clusters the authors find in these mutants.

Comments on revisions:

I believe the authors have performed additional experiments that improved their manuscript and they have answered many of my comments and those of the other reviewers. I am supportive of publishing this manuscript, but I still find the following points that are not clear to me (probably I am misunderstanding some points; the authors can clarify).

(1) In response to reviewer 1, the authors say that they "analyzed and categorized the distribution of the chemotaxis complex in both wild-type and flhF mutant strains into three patterns: precise-polar, near-polar, and mid-cell localization." I can see what they mean by polar and mid-cell, but near-polar sounds a bit elusive? Can they provide examples of this stage and mention how accurately they can identify it? Also, do the pie charts they show in Figure S4 really show "significant alterations"? There is a difference between 98% and 85% as they mention in their response to reviewer 1, but I am not sure that this is significant? Probably they can explain/change the language in the text? Also, the number of cells they counted for FlhF mutant is more than the double of other strains (WT and FlhF FliF mutant)?

(2) One thing that also confused me is the following: One point that the authors stress is that FlhF localizes both the flagellum and the chemoreceptors to the pole. However, if I look at Figure 2B, the flagellum and the chemoreceptors still co-localize together (although not at the pole). If FlhF was responsible for co-localizing both of them to the pole, then wouldn't one expect them to be randomly localized in this mutant and by that I mean that they do not co-localize but that each of them (the flagellum and the chemoreceptors) are located in a different random location of the cell (not co-localized). The fact that they are still co-localized together in this mutant could also be interpreted by, for example, that FlhF localizes the flagellum to the pole and another mechanism localizes the chemoreceptors to the flagellum, hence, they still co-localize in this mutant because the chemoreceptors follow the flagellum by another mechanism to wherever it goes?

(3) In the response to reviewers, the authors mention "suggesting that the assembly of the receptor complex is likely influenced mainly by the C-ring and MS-ring structures rather than by the P ring" . However, in the article, they still write "The complete assembly of the motor serves as a partial prerequisite for the assembly of the chemotaxis complex, and its assembly site is also regulated by the polar anchor protein FlhF" despite their FlgI results which is not in accordance with this statement? Also, As I mentioned in my previous report, in FliG and FliF mutant the motor does not assemble (see Hiroyuki Terashima et al. 2020., and Kaplan et al., 2022).

(4) The authors have said in their response to my point "and currently, there is no evidence that FliA activity is influenced by proteins like FliG". I just want to clarify what I meant in my previous report: In E. coli, FliA binds to FlgM, and when the hook is assembled FlgM is secreted outside the cell allowing FliA to trigger the transcription of class III genes, which include the chemosensory genes (see Figure 5 in Beeby et al, 2020 in FEMS Microbiology, and Chilcott and Hughes, 2000). This implies that if the hook is not built, then late genes (including the chemoreceptors) should not be present. However, in Kaplan et al., 2019, the authors imaged a FliF mutant in Shewanella oneidensis (Figure S3) and still saw that chemoreceptors are present (I believe the authors must highlight this). This suggests that species such as Shewanella and Pseudomonas have a different assembly process than that E. coli, and although the authors say that in the text, I believe they still can refine this part more in the spirit of what I wrote here.

I do not like to ask for additional experiments in the second round of review, so for me if the authors modify the text to tackle these points and allow for probable alternative explanations/ highlight gaps/ modify language used for some claims, then that is fine with me.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study by Wu et al presents interesting data on bacterial cell organization, a field that is progressing now, mainly due to the advances in microscopy. Based mainly on fluorescence microscopy images, the authors aim to demonstrate that the two structures that account for bacterial motility, the chemotaxis complex and the flagella, colocalize to the same pole in Pseudomonas aeruginosa cells and to expose the regulation underlying their spatial organization and functioning.

Strengths:

The subject is of importance.

Weaknesses:

The conclusions are too strong for the presented data. The lack of statistical analysis makes this paper incomplete. The novelty of the findings is not clear.

We have strengthened the data analysis by including appropriate statistical tests to support our conclusions more convincingly. Additionally, we have refined the description of the research background to better emphasize the novelty and significance of our findings. Please see the detailed responses below for further information.

Major issues:

(1) The novelty is in question since in the Abstract the authors highlight their main finding, which is that both the chemotaxis complex and the flagella localize to the same pole, as surprising. However, in the Introduction they state that "pathway-related receptors that mediate chemotaxis, as well as the flagellum are localized at the same cell pole17,18". I am not a pseudomonas researcher and from my short glance at these references, I could not tell whether they report colocalization of the two structures to the same pole. However, I trust the authors that they know the literature on the localization of the chemotaxis complex and flagella in their organism. See also major issue number 5 on the novelty regarding the involvement of c-di-GMP.

We thank the reviewer for this valuable comment and appreciate the opportunity to clarify our statements.

Kazunobu et al. (ref. 18) used scanning electron microscopy to preliminarily characterize the flagellation pattern of Pseudomonas aeruginosa during cell division, showing that existing flagella are located at the old pole. Zehra et al. (ref. 17), through fluorescence microscopy, observed that CheA and CheY proteins in dividing cells are typically also present at the old pole. Based on these observations, we inferred in the Introduction that the chemotaxis complex and flagellum may localize to the same cell pole.

However, this inference is indirect and lacks direct live-cell evidence of colocalization, leaving its validity to be confirmed. This uncertainty was indeed the starting point and motivation for our study.

In our work, we simultaneously visualized flagellar filaments and core chemoreceptor proteins at the single-cell level in P. aeruginosa. We characterized the assembly and spatial coordination of the chemotaxis network and flagellar motor throughout the cell cycle, providing direct evidence of their colocalization and coordinated assembly. This represents a significant advance beyond prior indirect observations and supports the novelty of our study.

Accordingly, we have revised the relevant statements in lines 71-75 of the manuscript to better reflect the current state of the literature and emphasize the novelty of our direct observations.

(2) Statistics for the microscopy images, on which most conclusions in this manuscript are based, are completely missing. Given that most micrographs present one or very few cells, together with the fact that almost all conclusions depend on whether certain macromolecules are at one or two poles and whether different complexes are in the same pole, proper statistics, based on hundreds of cells in several fields, are absolutely required. Without this information, the results are anecdotal and do not support the conclusions. Due to the importance of statistics for this manuscript, strict statistical tests should be used and reported. Moreover, representative large fields with many cells should be added as supportive information.

We thank the reviewer for this important comment, which significantly improves the rigor and persuasiveness of our manuscript.

For the colocalization analyses presented in Fig. 1D and Fig. 2B, we quantified 145 and 101 cells with fluorescently labeled flagella, respectively, and observed consistent colocalization of the chemoreceptor complexes and flagella in all examined cells (now added in the figure legends). Regarding the distribution patterns of chemoreceptors shown in Fig. 3A, we have now included comprehensive statistical analyses for both wild-type and mutant strains. For each strain, more than 300 cells were analyzed across at least three independent microscopic fields, providing robust statistical power (detailed data are presented in Fig. 3C).

To further strengthen the evidence, statistical tests were applied to confirm the significance and reproducibility of our findings (Fig. 3C). In addition, representative large-field fluorescence images containing numerous cells have been added to the supplementary materials (Fig. S1 and Fig. S3).

The problem is more pronounced when the authors make strong statements, as in lines 157-158: "The results revealed that the chemoreceptor arrays no longer grow robustly at the cell pole (Figure 2A)". Looking at the seven cells shown in Figure 2A, five of them show polar localization of the chemoreceptors. The question is then: what is the percentage of cells that show precise polar, near-polar, or mid cell localization (the three patterns shown here) in the mutant and in the wild type? Since I know that these three patterns can also be observed in WT cells, what counts is the difference, and whether it is statistically significant.

We thank the reviewer for raising this important point. Following the reviewer's suggestion, we have now analyzed and categorized the distribution of the chemotaxis complex in both wild-type and flhF mutant strains into three patterns: precise-polar, near-polar, and mid-cell localization. For each strain, more than 200 cells across three independent fields of view were quantified.

Our statistical analysis shows that in the wild-type strain, approximately 98% of cells exhibit precise polar localization of the chemotaxis complex. In contrast, the ΔflhF mutant displays a clear shift in distribution, with about 5% of cells showing mid-cell localization and 9.5% showing near-polar localization. These differences demonstrate a significant alteration in the spatial pattern upon flhF deletion.

We have revised the relevant text in lines 166-170 accordingly and included the detailed statistical data in the newly added Fig. S4.

Even for the graphs shown in Figures 3C and 3D, where the proportion of cells with obvious chemoreceptor arrays and absolute fluorescence brightness of the chemosensory array are shown, respectively, the questions that arise are: for how many individual cells these values hold and what is the significance of the difference between each two strains?

The number of cells analyzed for each strain is indicated in the original manuscript: 372 wild-type cells (line 123), 221 ΔflhF cells (line 172), 234 ΔfliG cells (line 197), 323 ΔfliF cells (line 200), 672 ΔflhFΔfliF cells (line 202), and 242 ΔmotAΔmotCD cells (line 207). For each strain, data were collected from three independent fields of view. We have now also provided the number of cells in Fig. 3 legend.

We have now performed statistical comparisons using t-tests between strains. Notably, the measured values in Fig. 3C exhibit a clear, monotonic decrease with successive gene knockouts, supporting the robustness of the observed trend.

Regarding the absolute fluorescence intensity shown in the original Fig. 3D, the mutants did not display consistent directional changes compared to the wild type. Reliable comparison of absolute fluorescence intensity requires consistent fluorescent protein maturation levels across strains. Given the likely variability in maturation levels between strains, we concluded that this data may not accurately reflect true differences in protein concentrations. Therefore, we have removed the fluorescence intensity graph from the revised manuscript to avoid potential misinterpretation.

(3) The authors conclude that "Motor structural integrity is a prerequisite for chemoreceptor self-assembly" based on the reduction in cells with chemoreceptor clusters in mutants deleted for flagellar genes, despite the proper polar localization of the chemotaxis protein CheY. They show that the level of CheY in the WT and the mutant strains is similar, based on Western blot, which in my opinion is over-exposed. "To ascertain whether it is motor integrity rather than functionality that influences the efficiency of chemosensory array assembly", they constructed a mutant deleted for the flagella stator and found that the motor is stalled while CheY behaves like in WT cells. The authors further "quantified the proportion of cells with receptor clusters and the absolute fluorescence intensity of individual clusters (Figures 3C-D)". While Figure 3DC suggests that, indeed, the flagella mutants show fewer cells with a chemotaxis complex, Figure 3D suggests that the differences in fluorescence intensity are not statistically significant. Since it is obvious that the regulation of both structures' production and localization is codependent, I think that it takes more than a Western blot to make such a decision.

We thank the reviewer for the suggestions. To further clarify that the assembly of flagellar motors and chemoreceptor clusters occurs in an orderly manner rather than being merely codependent, we performed additional experiments. Specifically, we constructed a ΔcheA mutant strain, in which chemoreceptor clusters fail to assemble. Using in vivo fluorescent labeling of flagellar filaments, we observed that the proportion of cells with flagellar filaments in the ΔcheA strain was comparable to that of the wild type (Fig. S5).

In contrast, mutants lacking complete motor structures, such as ΔfliF and ΔfliG, showed a significant reduction in the proportion of cells with obvious receptor clusters (Fig. 3C). Based on these results, we conclude that the structural integrity of the flagellar motor is, to a certain extent, a prerequisite for the self-assembly of chemoreceptor clusters.

Accordingly, we have revised the relevant statement in lines 213-217 of the manuscript to reflect this clarification.

(4) I wonder why the authors chose to label CheY, which is the only component of the chemotaxis complex that shuttles back and forth to the base of the flagella. In any case, I think that they should strengthen their results by repeating some key experiments with labeled CheW or CheA.

We thank the reviewer for this valuable suggestion. In our study, we initially focused on the positional relationship between chemoreceptor clusters and flagella, then investigated factors influencing cluster distribution and assembly efficiency. The physiological significance of motor and cluster co-localization was ultimately proposed with CheY as the starting point.

Previous work by Harwood's group demonstrated that both CheY-YFP and CheA-GFP localize to the old poles of dividing Pseudomonas aeruginosa cells. Since our physiological hypothesis centers on CheY, we chose to label CheY-EYFP in our experiments.

To further strengthen our conclusions, we constructed a plasmid expressing CheA-CFP and introduced it into the cheY-eyfp strain via electroporation. Fluorescence imaging revealed a high degree of spatial overlap between CheA-CFP and CheY-EYFP (Fig. S2), confirming that CheY-EYFP accurately marks the location of the chemoreceptor complex.

We have revised the manuscript accordingly (lines 119-123) and added these data as Fig. S2.

(5) The last section of the results is very problematic, regarding the rationale, the conclusions, and the novelty. As far as the rationale is concerned, I do not understand why the authors assume that "a spatial separation between the chemoreceptors and flagellar motors should not significantly impact the temporal comparison in bacterial chemotaxis". Is there any proof for that?

We apologize for the lack of clarity in our original explanation. The rationale behind the statement was initially supported by comparing the timescales of CheY-P diffusion and temporal comparison in chemotaxis. Specifically, the diffusion time for CheY-P to traverse the entire length of a bacterial cell is approximately 100 ms (refs 39&40), whereas the timescale for bacterial chemotaxis temporal comparison is on the order of seconds (ref 41).

To clarify and strengthen this argument, we have expanded the discussion as follows:

The diffusion coefficient of CheY in bacterial cells is about 10 µm2/s, which corresponds to an estimated end-to-end diffusion time on the order of 100 ms (refs 40&41). If the chemotaxis complexes were randomly distributed rather than localized, diffusion times would be even shorter. In contrast, the timescale for the chemotaxis temporal comparison is on the order of seconds (ref. 42). Additionally, a study by Fukuoka and colleagues reported that intracellular chemotaxis signal transduction requires approximately 240 ms beyond CheY or CheY-P diffusion time (ref. 41). Moreover, the intervals of counterclockwise (CCW) and clockwise (CW) rotation of the P. aeruginosa flagellar motor under normal conditions are 1-2 seconds, as determined by tethered cell or bead assays (refs. 30&43).

Taken together, these indicate that for P. aeruginosa, which moves via a run-reverse mode, the potential 100 ms reduction in response time due to co-localization of the chemotaxis complex and motor has a limited effect on overall chemotaxis timing.

We have revised the corresponding text accordingly (lines 238-245) to better explain this rationale.

More surprising for me was to read that "The signal transduction pathways in E. coli are relatively simple, and the chemotaxis response regulator CheY-P affects only the regulation of motor switching". There are degrees of complexity among signal transduction pathways in E. coli, but the chemotaxis seems to be ranked at the top. CheY is part of the adaptation. Perfect adaptation, as many other issues related to the chemotaxis pathway, which include the wide dynamic range, the robustness, the sensitivity, and the signal amplification (gain), are still largely unexplained. Hence, such assumptions are not justified.

We apologize for the confusion and imprecision in our original statements. Our intention was to convey that the chemotaxis pathway in E. coli is relatively simple compared to the more complex chemosensory systems in P. aeruginosa. We did not mean to generalize this simplicity to all signal transduction pathways in E. coli.

We acknowledge that E. coli chemotaxis is a highly sophisticated system, involving processes such as perfect adaptation, wide dynamic range, robustness, sensitivity, and signal amplification, many aspects of which remain incompletely understood. CheY indeed plays a crucial role in adaptation and motor switching regulation.

Accordingly, we have revised the original text (lines 249-255) to avoid any misunderstanding.

More perplexing is the novelty of the authors' documentation of the effect of the chemotaxis proteins on the c-di-GMP level. In 2013, Kulasekara et al. published a paper in eLife entitled "c-di-GMP heterogeneity is generated by the chemotaxis machinery to regulate flagellar motility". In the same year, Kulasekara published a paper entitled "Insight into a Mechanism Generating Cyclic di-GMP Heterogeneity in Pseudomonas aeruginosa". The authors did not cite these works and I wonder why.

We apologize for having been unaware of these important references and thank the reviewer for bringing them to our attention. We have now cited the eLife paper and the PhD thesis titled "Insight into a Mechanism Generating Cyclic di-GMP Heterogeneity in Pseudomonas aeruginosa" by Kulasekara et al.

Regarding novelty, there are key differences between our findings and those reported by Kulasekara et al. While they proposed that CheA influences c-di-GMP heterogeneity through interaction with a specific phosphodiesterase (PDE), our results demonstrate that overexpression of CheY leads to an increase in intracellular c-di-GMP levels.

We have revised the original text accordingly (lines 358-362) to clarify these distinctions.

(6) Throughout the manuscript, the authors refer to foci of fluorescent CheY as "chemoreceptor arrays". If anything, these foci signify the chemotaxis complex, not the membrane-traversing chemoreceptors.

We thank the reviewer for this clarification. We have revised the manuscript accordingly to refer to the fluorescent CheY foci as representing the chemotaxis complex rather than the chemoreceptor arrays.

Conclusions:

The manuscript addresses an interesting subject and contains interesting, but incomplete, data.

Reviewer #2 (Public Review):

Summary:

Here, the authors studied the molecular mechanisms by which the chemoreceptor cluster and flagella motor of Pseudomonas aeruginosa (PA) are spatially organized in the cell. They argue that FlhF is involved in localizing the receptors-motor to the cell pole, and even without FlhF, the two are colocalized. FlhF is known to cause the motor to localize to the pole in a different bacterial species, Vibrio cholera, but it is not involved in receptor localization in that bacterium. Finally, the authors argue that the functional reason for this colocalization is to insulate chemotactic signaling from other signaling pathways, such as cyclic-di-GMP signaling.

Strengths:

The experiments and data look to be high-quality.

Weaknesses:

However, the interpretations and conclusions drawn from the experimental observations are not fully justified in my opinion.

I see two main issues with the evidence provided for the authors' claims.

(1) Assumptions about receptor localization:

The authors rely on YFP-tagged CheY to identify the location of the receptor cluster, but CheY is a diffusible cytoplasmic protein. In E. coli, CheY has been shown to localize at the receptor cluster, but the evidence for this in PA is less strong. The authors refer to a paper by Guvener et al 2006, which showed that CheY localizes to a cell pole, and CheA (a receptor cluster protein) also localizes to a pole, but my understanding is that colocalization of CheY and CheA was not shown. My concern is that CheY could instead localize to the motor in PA, say by binding FliM. This "null model" would explain the authors' observations, without colocalization of the receptors and motor. Verifying that CheY and CheA are colocalized in PA would be a very helpful experiment to address this weakness.

We thank the reviewer for this valuable suggestion. We agree that verifying the colocalization of CheY and CheA would strengthen our conclusions. To address this, we constructed a plasmid expressing CheA-CFP and introduced it into the CheY-EYFP strain by electroporation. Fluorescence imaging revealed a high degree of spatial overlap between CheA-CFP and CheY-EYFP signals, indicating that CheY-EYFP indeed marks the location of the chemoreceptor complex rather than the flagellar motor.

We have revised the manuscript accordingly (lines 118-123) and included these results in the new Fig. S2.

(2) Argument for the functional importance of receptor-motor colocalization at the pole:

The authors argue that colocalization of the receptors and motors at the pole is important because it could keep phosphorylated CheY, CheY-p, restricted to a small region of the cell, preventing crosstalk with other signaling pathways. Their evidence for this is that overexpressing CheY leads to higher intracellular cdG levels and cell aggregation. Say that the receptors and motors are colocalized at the pole. In E. coli, CheY-p rapidly diffuses through the cell. What would prevent this from occurring in PA, even with colocalization?

We appreciate the reviewer's insightful question. The colocalization of both the signaling source (the kinase) and sink (the phosphatase) at the chemoreceptor complex at the cell pole results in a rapid decay of CheY-P concentration within approximately 0.2 µm from the cell pole, leading to a nearly uniform distribution elsewhere in the cell, as demonstrated by Vaknin and Berg (ref. 46). This spatial arrangement effectively confines high CheY-P levels to the pole region. When the motor is also localized at the cell pole, this reduces the need for elevated CheY-P concentrations throughout the cytoplasm, thereby minimizing potential crosstalk with other signaling pathways.

We have revised the manuscript accordingly (lines 280-286) to clarify this point.

Elevating CheY concentration may increase the concentration of CheY-p in the cell, but might also stress the cells in other unexpected ways. It is not so clear from this experiment that elevated CheY-p throughout the cell is the reason that they aggregate, or that this outcome is avoided by colocalizing the receptors and motor at the same pole. If localization of the receptor array and motor at one pole were important for keeping CheY-p levels low at the opposite pole, then we should expect cells in which the receptors and motor are not at the pole to have higher CheY-p at the opposite pole. According to the authors' argument, it seems like this should cause elevated cdG levels and aggregation in the delta flhF mutants with wild-type levels of CheY. But it does not look like this happened. Instead of varying CheY expression, the authors could test their hypothesis that receptor-motor colocalization at the pole is important for preventing crosstalk by measuring cdG levels in the flhF mutant, in which the motor (and maybe the receptor cluster) are no longer localized in the cell pole.

We thank the reviewer for raising the important point regarding potential cellular stress caused by elevated CheY concentrations, as well as for the suggestion to test the hypothesis using ΔflhF mutants.

First, as noted above, CheY-P concentration rapidly decreases away from the receptor complex. While deletion of flhF alters the position of the receptor complex, thereby shifting the region of high CheY-P concentration, it does not increase CheY-P levels elsewhere in the cell. Importantly, in the ΔflhF strain, the receptor complex and the motor still colocalize, so this mutant may not effectively test the role of receptor-motor colocalization in preventing crosstalk as suggested.

Regarding the possibility that elevated CheY levels stress the cells independently of CheY-P signaling, prior work in <i.E. coli by Cluzel et al. (ref. 11) showed that overexpressing CheY several-fold did not cause phenotypic changes, indicating that simple CheY overexpression alone may not be generally stressful. Furthermore, our data indicate that the increase in c-di-GMP levels and subsequent cell aggregation upon CheY overexpression is not an all-or-none switch but occurs progressively as CheY concentration rises.

To further confirm that CheY overexpression promotes aggregation through increased c-di-GMP levels, we performed additional experiments co-overexpressing CheY and a phosphodiesterase (PDE) from E. coli to reduce intracellular c-di-GMP. These experiments showed that PDE expression mitigates cell aggregation caused by CheY overexpression (Fig. S8).

We have revised the manuscript accordingly (lines 290-294) and added these new results in Fig. S8.

Reviewer #3 (Public Review):

Summary:

The authors investigated the assembly and polar localization of the chemosensory cluster in P. aeruginosa. They discovered that a certain protein (FlhF) is required for the polar localization of the chemosensory cluster while a fully-assembled motor is necessary for the assembly of the cluster. They found that flagella and chemosensory clusters always co-localize in the cell; either at the cell pole in wild-type cells or randomly-located in the cell in FlhF mutant cells. They hypothesize that this co-localization is required to keep the level of another protein (CheY-P), which controls motor switching, at low levels as the presence of high levels of this protein (if the flagella and chemosensory clusters were not co-localized) is associated with high-levels of c-di-GMP and cell aggregations.

Strengths:

The manuscript is clearly written and straightforward. The authors applied multiple techniques to study the bacterial motility system including fluorescence light microscopy and gene editing. In general, the work enhances our understanding of the subtlety of interaction between the chemosensory cluster and the flagellar motor to regulate cell motility.

Weaknesses:

The major weakness in this paper is that the authors never discussed how the flagellar gene expression is controlled in P. aeruginosa. For example, in E. coli there is a transcriptional hierarchy for the flagellar genes (early, middle, and late genes, see Chilcott and Hughes, 2000). Similarly, Campylobacter and Helicobacter have a different regulatory cascade for their flagellar genes (See Lertsethtakarn, Ottemann, and Hendrixson, 2011). How does the expression of flagellar genes in P. aeruginosa compare to other species? How many classes are there for these genes? Is there a hierarchy in their expression and how does this affect the results of the FliF and FliG mutants? In other words, if FliF and FliG are in class I (as in E. coli) then their absence might affect the expression of other later flagellar genes in subsequent classes (i.e., chemosensory genes). Also, in both FliF and FliG mutants no assembly intermediates of the flagellar motor are present in the cell as FliG is required for the assembly of FliF (see Hiroyuki Terashima et al. 2020, Kaplan et al. 2019, Kaplan et al. 2022). It could be argued that when the motor is not assembled then this will affect the expression of the other genes (e.g., those of the chemosensory cluster) which might play a role in the decreased level of chemosensory clusters the authors find in these mutants.

We thank the reviewer for the insightful comments. P. aeruginosa possesses a four-tiered transcriptional regulatory hierarchy controlling flagellar biogenesis. Within this system, fliF and fliG belong to class II genes and are regulated by the master regulator FleQ. In contrast, chemotaxis-related genes such as cheA and cheW are regulated by intracellular free FliA, and currently, there is no evidence that FliA activity is influenced by proteins like FliG.

To verify that the expression of core chemotaxis proteins was not affected by deletion of fliG, we performed Western blot analyses to compare CheY levels in wild-type, ΔfliF, and ΔfliG strains. We observed no significant differences, indicating that the reduced presence of receptor clusters in these mutants is not due to altered expression of chemotaxis proteins.

Accordingly, we have revised the manuscript (lines 341-348) and updated Fig. 3B to reflect these findings.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

The reviewers comment on several important aspects that should be addressed, namely: the lack of statistical analysis; the need for clarifications regarding assumptions made regarding receptor localization; the functional importance of receptor-motor colocalization; and the need for an elaborate discussion of flagellar gene expression. Also, two reviewers pointed out the need to prove the co-localization of CheY and CheA; This is important since CheY is dynamic, shuttling back and forth from the chemotaxis complex to the base of the flagella, whereas CheA (or cheW or, even better, the receptors) is considered less dynamic and an integral part of the chemotaxis complex.

Reviewer #1 (Recommendations For The Authors):

Minor points:

Line 43: "ubiquitous" - I would choose another word.

We changed "ubiquitous" to "widespread".

Line 49: "order" - change to organize.

We changed "order" to "organize".

Line 52: "To grow and colonize within the host, bacteria have evolved a mechanism for migrating...". Motility "towards more favorable environments" is an important survival strategy of bacteria in various ecological niches, not only within the host.

We revised it to "grow and colonize in various ecological niches".

Line 72: Define F6 in "F6 pathway-related receptors".

The proteins encoded by chemotaxis-related genes collectively constitute the F6 pathway, which we have now explained in the manuscript text.

Line 72-73: Do references 17 &18 really report colocalization of the chemotaxis receptor and flagella to the same pole? If these or other reports document such colocalization, then the sentence in the Abstract "Surprisingly, we found that both are located at the same cell pole..." is not correct.

Kazunobu et al. (ref. 18) used scanning electron microscopy to preliminarily characterize the flagellation pattern of Pseudomonas aeruginosa during cell division, showing that existing flagella are located at the old pole. Zehra et al. (ref. 17), through fluorescence microscopy, observed that CheA and CheY proteins in dividing cells are typically also present at the old pole. Based on these observations, we inferred in the Introduction that the chemotaxis complex and flagellum may localize to the same cell pole.

However, this inference is indirect and lacks direct live-cell evidence of colocalization, leaving its validity to be confirmed. This uncertainty was indeed the starting point and motivation for our study.

In our work, we simultaneously visualized flagellar filaments and core chemoreceptor proteins at the single-cell level in P. aeruginosa. We characterized the assembly and spatial coordination of the chemotaxis network and flagellar motor throughout the cell cycle, providing direct evidence of their colocalization and coordinated assembly. This represents a significant advance beyond prior indirect observations and supports the novelty of our study.

Accordingly, we have revised the relevant statements in lines 71-75 of the manuscript to better reflect the current state of the literature and emphasize the novelty of our direct observations.

Line 108: "CheY has been shown to colocalize with chemoreceptors". The authors rely here (reference 29) and in other places on findings in E. coli. However, in the Introduction, they describe the many differences between the motility systems of P. aeruginosa and E. coli, e.g., the number of chemosensory systems and their spatial distribution (E. coli is a peritrichous bacterium, as opposed to the monotrichous bacterium P. aeruginosa). There seem to be proofs for colocalization of the Che and MCP proteins in P. aeruginosa, which should be cited here.

Thank you for pointing this out. Harwood's group reported that a cheY-YFP fusion strain exhibited bright fluorescent spots at the cell pole, which disappeared upon knockout of cheA or cheW-genes encoding structural proteins of the chemotaxis complex. This strongly suggests colocalization of CheY with MCP proteins in P. aeruginosa. We have now cited this study as reference 17 in the manuscript.

Figure 1B: Please replace the order of the schematic presentations, so that the cheY-egfp fusion, which is described first in the text, is at the top.

We have modified the order of related images in Fig. 1B.

Line 127: "by introducing cysteine mutations". Replace either by "by introducing cysteines" or by "by substituting several residues with cysteines".

We changed the relevant statement to "by introducing cysteines".

Line 144-145: "Given that the physiological and physical environments of both cell poles are nearly identical.". I think that also the physical, but certainly the physiological environment of the two poles is not identical. First, one is an old pole, and the other a new pole. Second, many proteins and RNAs were detected mainly or only in one of the poles of rod-shaped Gram-negative bacteria that are regarded as symmetrically dividing. Although my intuition is that the authors are correct in assuming that "it is unlikely that the unipolar distribution of the chemoreceptor array can be attributed to passive regulatory factors", relating it to the (false) identity between the poles is incorrect.

We thank the reviewer for this important correction. We agree that the physiological environments of the two poles are not identical, given that one is the old pole and the other the new pole, and that many proteins and RNAs show polar localization in rod-shaped Gram-negative bacteria. Accordingly, we have revised the original text (lines 150-152) to read:

“Despite potential differences in the physical and especially physiological environments at the two cell poles, it is unlikely that the unipolar distribution of the chemotaxis complex can be attributed to passive regulatory factors.”

Lines 151-154: "Considering the consistent colocalization pattern between chemosensory arrays and flagellar motors in P. aeruginosa". Does the word consistent relate to different reports on such colocalization or to the results in Figure 1D? In case it is the latter, then what is the word consistent based on? All together only 7 cells are presented in the 5 micrographs that compose Figure 1D (back to statistics...).

We thank the reviewer for raising this point. To clarify, the word "consistent" refers to the observation of colocalization shown in Figure 1D & Figure S3. As noted in the revised figure legend for Figure 1D, a total of 145 cells with labeled flagella were analyzed, all exhibiting consistent colocalization between flagella and chemosensory arrays. Additionally, we have included a new image showing a large field of co-localization in the wild-type strain as Figure S3 to better illustrate this consistency.

Figure 2A: Omit "Subcellular localization of" from the beginning of the caption.

We removed the relevant expression from the caption.

Reviewer #2 (Recommendations For The Authors):

I strongly recommend checking that CheY localizes to the receptor cluster in PA. This could be done by tagging cheA with a different fluorophore and demonstrating their colocalization. It would also be helpful to check that they are colocalized in the delta flhF mutant.

We thank the reviewer for this valuable suggestion. We constructed a plasmid expressing CheA-CFP and introduced it into the CheY-EYFP strain by electroporation. Fluorescence imaging revealed a high degree of spatial overlap between CheA-CFP and CheY-EYFP signals, indicating that CheY-EYFP indeed marks the location of the chemoreceptor complex.

We have revised the manuscript accordingly (lines 118-123) and included these results in the new Fig. S2.

The experiments under- and over-expressing CheY part seemed too unrelated to receptor-motor colocalization. I think the authors should think about a more direct way of testing whether colocalization of the motor and receptors is important for preventing signaling crosstalk. One way would be to measure cdG levels in WT and in delta flhF mutants and see if there is a significant difference.

We thank the reviewer for raising the important point regarding potential cellular stress caused by elevated CheY concentrations, as well as for the suggestion to test the hypothesis using flhF mutants.

First, as noted in the response to your 2nd comment in Public Review, CheY-P concentration rapidly decreases away from the receptor complex. While deletion of flhF alters the position of the receptor complex, thereby shifting the region of high CheY-P concentration, it does not increase CheY-P levels elsewhere in the cell. Importantly, in the ΔflhF strain, the receptor complex and the motor still colocalize, so this mutant may not effectively test the role of receptor-motor colocalization in preventing crosstalk as suggested.

Regarding the possibility that elevated CheY levels stress the cells independently of CheY-P signaling, prior work in E. coli by Cluzel et al. (ref. 11) showed that overexpressing CheY several-fold did not cause phenotypic changes, indicating that simple CheY overexpression alone may not be generally stressful. Furthermore, our data indicate that the increase in c-di-GMP levels and subsequent cell aggregation upon CheY overexpression is not an all-or-none switch but occurs progressively as CheY concentration rises.

To further confirm that CheY overexpression promotes aggregation through increased c-di-GMP levels, we performed additional experiments co-overexpressing CheY and a phosphodiesterase (PDE) from E. coli to reduce intracellular c-di-GMP. These experiments showed that PDE expression mitigates cell aggregation caused by CheY overexpression (Fig. S8).

We have revised the manuscript accordingly (lines 290-294) and added these new results in Fig. S8.

Reviewer #3 (Recommendations For The Authors):

(1) Can the authors elaborate more on the hierarchy of flagellar gene expression in P. aeruginosa and how this relates to their work?

We thank the reviewer for the suggestion. We have now described the hierarchy of flagellar gene expression in P. aeruginosa in lines 341-348.

(2) I would suggest that the authors check other flagellar mutants (than FliF and FliG) where the motor is partially assembled (e.g., any of the rod proteins or the P-ring protein), together with FlhF mutant, to see how a partially assembled motor affects the assembly of the chemosensory cluster.

We thank the reviewer for this valuable suggestion. The P ring, primarily composed of FlgI, acts as a bushing for the peptidoglycan layer, and its absence leads to partial motor assembly. We constructed a ΔflgI mutant and observed that the proportion of cells exhibiting distinct chemotactic complexes was similar to that of the wild-type strain, suggesting that the assembly of the receptor complex is likely influenced mainly by the C-ring and MS-ring structures rather than by the P ring. We have revised the original text accordingly (lines 217-220) and added the corresponding data as Figure S6.

(3) I would suggest that the authors check the levels of CheY in cells induced with different concentrations of arabinose (i.e., using western blotting just like they did in Figure 3B).

We have assessed the levels of CheY in cells induced with different concentrations of arabinose using western blotting, as suggested. The results have been incorporated into the manuscript (lines 274-275) and are presented in Figure S7.

(4) To my eyes, most of the foci in FliF-FlhF mutant in Figure 3A are located at the pole (which is unlike the FlhF mutant in Figure 2). Is this correct? I would suggest that the authors also investigate this to see where the chemosensory cluster is located.

We thank the reviewer for pointing this out. The distribution of the chemotaxis complex in the ΔflhFΔfliF strain was investigated and showed in Fig. S4. Indeed, most of the chemoreceptor foci in this mutant are located at the pole. This probably suggests that, in the absence of both FlhF and an assembled motor, the position of the receptor complex may be largely influenced by passive factors such as membrane curvature. This interesting possibility warrants further investigation in future studies.

eLife Assessment

This important study demonstrates that slow fluctuations in serotonin release during wakefulness and non-REM sleep correspond to periods of heightened arousal or enhanced offline information processing. The evidence supporting this claim is convincing, and the methodology is robust and broadly applicable, likely to benefit many researchers in the field. This work will be of significant interest to neuroscientists studying sleep, memory, and neuromodulation.

Reviewer #1 (Public review):

Summary:

In this work, authors recorded the dynamics of the 5-HT with fiber photometry from CA1 in one hemisphere and LFP from CA1 in the other hemisphere. They have observed an ultra-slow oscillation in the 5-HT signal both during wakefulness and NREM sleep. The authors have studied different phases of the ultra-slow oscillation to examine the potential difference in the occurrence of some behavioral state-related physiological phenomena (hippocampal ripples, EMG, and inter-area coherence).

Strengths:

The relation between the falling/rising phase of the ultra-slow oscillation and the ripples is sufficiently shown. There are some minor concerns about the observed relations that should be addressed with some further analysis.

Systematic observations have started to establish a strong relation between the dynamics of neural activity across the brain and measures of behavioral arousal. Such relations span a wide range of temporal scales that are heavily inter-related. Ultra-slow time scales are specifically understudied due to technical limitations and neuromodulatory systems are the strongest mechanistic candidates for controlling/modulating the neural dynamics at these time scales. The hypothesis of the relation between a specific time scale and one certain neuromodulator (5-HT in this manuscript) could have a significant impact on the understanding of the hierarchy in the temporal scales of neural activity.

Weaknesses:

weaknesses appropriately addressed by reviewers in the current version

Reviewer #2 (Public review):

Summary:

In their study, Cooper et al. investigated the spontaneous fluctuations in extracellular 5-HT release in the CA1 region of the hippocampus using GRAB5-HT3.0. Their findings revealed the presence of ultra-low frequency (less than 0.05 Hz) oscillations in 5-HT levels during both NREM sleep and wakefulness. The phase of these 5-HT oscillations was found to be related to the timing of hippocampal ripples, microarousals, electromyogram (EMG) activity, and hippocampal-cortical coherence. In particular, ripples were observed to occur with greater frequency during the descending phase of 5-HT oscillations, and stronger ripples were noted to occur in proximity to the 5-HT peak during NREM. Microarousal and EMG peaks occurred with greater frequency during the ascending phase of 5-HT oscillations. Additionally, the strongest coherence between the hippocampus and cortex was observed during the ascending phase of 5-HT oscillations. These patterns were observed in both NREM sleep and the awake state, with a greater prevalence in NREM. The authors posit that 5-HT oscillations may temporally segregate internal processing (e.g., memory consolidation) and responsiveness to external stimuli in the brain.

Strengths:

The findings of this research are novel and intriguing. Slow brain oscillations lasting tens of seconds have been suggested to exist, but to my knowledge they have never been analyzed in such a clear way. Furthermore, although it is likely that ultra-slow neuromodulator oscillations exist, this is the first report of such oscillations, and the greatest strength of this study is that it has clarified this phenomenon both statistically and phenomenologically.

Weaknesses:

As with any paper, this one has some limitations. While there is no particular need to pursue them, I will describe ten of them below, including future directions:

Contralateral recordings: 5-HT levels and electrophysiological recordings were obtained from opposite hemispheres due to technical limitations. Ipsilateral simultaneous recordings may show more direct relationships.

Sample size: The number of mice used in the experiments is relatively small (n=6). Validation with a larger sample size would be desirable.

Lack of causality: The observed associations show correlations, not direct causal relationships, between 5-HT oscillations and neural activity patterns.

Limited behavioral states: The study focuses primarily on sleep and quiet wakefulness. Investigation of 5-HT oscillations during a wider range of behavioral states (e.g., exploratory behavior, learning tasks) may provide a more complete understanding.

Generalizability to other brain regions: The study focuses on the CA1 region of the hippocampus. It's unclear whether similar 5-HT oscillation patterns exist in other brain regions.

Long-term effects not assessed: Long-term effects of ultra-low 5-HT oscillations (e.g., on memory consolidation or learning) were not assessed.

Possible species differences: It's uncertain whether the findings in mice apply to other mammals, including humans.

Technical limitations: The temporal resolution and sensitivity of the GRAB5-HT3.0 sensor may not capture faster 5-HT dynamics.

Interactions with other neuromodulators: The study does not explore interactions with other neuromodulators (e.g., norepinephrine, acetylcholine) or their potential ultraslow oscillations.

Limited exploration of functional significance: While the study suggests a potential role for 5-HT oscillations in memory consolidation and arousal, direct tests of these functional implications are not included.

Reviewer #3 (Public review):

Summary:

Activity of serotonin (5-HT) releasing neurons as well as 5-HT levels in brain structures targeted by serotoninergic axons are known to fluctuate substantially across the animal's sleep/wake cycle, with high 5-HT during wakefulness (WAKE), intermediate 5-HT levels during non-REM sleep (NREM) and very low 5-HT levels during REM sleep. Recent studies have shown that during NREM, activity of 5-HT neurons in raphe nuclei oscillates at very low frequencies (0.01 - 0.05 Hz) and this ultraslow oscillation is negatively coupled to broadband EEG power. However, how exactly this 5-HT oscillation affects neural activity in downstream structures is unclear.

The present study addresses this gap by replicating the observation of the ultraslow oscillation in the 5-HT system, and further observing that hippocampal sharp wave-ripples (SWRs), biomarkers of offline memory processing, occur preferentially in barrages on the falling phase of the 5-HT oscillation during both wakefulness and NREM sleep. In contrast, the study found that the raising phase of the 5-HT oscillation is associated with microarousals during NREM and increased muscular activity during WAKE. Finally, the raising 5-HT phase was also found to be associated with increased synchrony between the hippocampus and neocortex.

In vivo findings are further supported by an ex vivo demonstration of dose-dependent serotonergic SWR modulation, lends support to the potential causal relationship between 5-HT slow oscillation and hippocampal dynamics.

Overall, the study constitutes a valuable contribution to the field by reporting a close association between, on one hand, raising 5-HT and arousal and, on the other hand, falling 5-HT and offline memory processes.

Strengths:

The study makes a compelling use of the state-of-the art methodology to address its aims: the genetically encoded 5-HT sensor used in the study is ideal for capturing the ultraslow 5-HT dynamics and the novel detection method for SWRs outperforms current state-of-the-art algorithms and will be useful to many scientists in the field. Explicit validation of both of these methods is a particular strength of this study.

The analytical methods used in the article are appropriate and are convincingly applied, the use of a general linear mixed model for statistical analysis is a particularly welcome choice as it guards against pseudoreplication while preserving statistical power.

Pharmacological demonstration of serotonergic SWR modulation in brain slices adds further weight to the possible direct role of 5-HT in hippocampal dynamics in vivo.

Overall, the manuscript makes a strong case for distinct sub-states across WAKE and NREM, associated with different phases of the 5-HT oscillation.

Weaknesses:

All in vivo evidence presented in the study is correlational, although the ex vivo results do suggest a possibility of a causal relationship between 5HT levels and hippocampal dynamics in the intact brain.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary

In this work, the authors recorded the dynamics of the 5-HT with fiber photometry from CA1 in one hemisphere and LFP from CA1 in the other hemisphere. They observed an ultra-slow oscillation in the 5-HT signal during both wake fulness and NREM sleep. The authors have studied different phases of the ultra-slow oscillation to examine the potential difference in the occurrence of some behavioral state-related physiological phenomena hippocampal ripples, EMG, and inter-area coherence).

Strengths

The relation between the falling/rising phase of the ultra-slow oscillation and the ripples is sufficiently shown. There are some minor concerns about the observed relations that should be addressed with some further analysis.

Systematic observations have started to establish a strong relation between the dynamics of neural activity across the brain and measures of behavioral arousal. Such relations span a wide range of temporal scales that are heavily inter-related. Ultra-slow time-scales are specifically under-studied due to technical limitations and neuromodulatory systems are the strongest mechanistic candidates for controlling/modulating the neural dynamics at these time-scales. The hypothesis of the relation between a specific time-scale and one certain neuromodulator (5-HT in this manuscript) could have a significant impact on the understanding of the hierarchy in the temporal scales of neural activity.

Weaknesses:

One major caveat of the study is that different neuromodulators are strongly correlated across all time scales and related to this, the authors need to discuss this point further and provide more evidence from the literature (if any) that suggests similar ultra-slow oscillations are weaker or lack from similar signals recorded for other neuromodulators such as Ach and NA.

The reviewer is correct to point out that the levels of different neuromodulators are often correlated. For example, most monoaminergic neurons, including serotonergic neurons of the raphe nuclei, show similar firing rates across behavioral states, firing most during wake behavior, less during NREM, and ceasing firing during ‘paradoxical sleep’ or REM (Eban-Rothschild et al 2018). Notably, other neuromodulators, such as acetylcholine (ACh), show the opposite pattern across states, with highest levels observed during REM, an intermediate level during wake behavior, and the lowest level during NREM (Vazquez et al. 2001). Despite these differences, ultraslow oscillations of both monoaminergic and non-monoaminergic neuromodulators, have been described, albeit only during NREM sleep (Zhang et al. 2021, Zhang et al. 2024, Osorio-Ferero et al. 2021, Kjaerby et al. 2022). How ultraslow oscillations of different neuromodulators are related has been only recently explored (Zhang et al. 2024). In this study, dual recording of oxytocin (Oxt) and ACh with GRAB sensors showed that the levels of the two neuromodulators were indeed correlated at ultraslow frequencies with a 2 s temporal shift. Furthermore, this shift could be explained by a hippocampal-to-lateral septum intermediate pathway, in which the level of ACh causally impacts hippocampal activity, which then in turn controls Oxt levels. Given the known temporal relationship between ripples, ACh and Oxt, and now with our work, between ripples and 5-HT, one could infer the relative timing of ultraslow oscillations of ACh, Oxt and 5-HT. While dual recordings of norepinephrine (NE) and 5-HT have not been performed, a similar correlation with temporal shift could be hypothesized given the parallel relationships between NE and spindles (OsorioFerero et al. 2021), and 5-HT and ripples, with the known temporal delay between ripples and spindles (Staresina et al. 2023). The fact that the locus coerulus receives particularly dense projections from the dorsal raphe nucleus (Kim et al. 2004) further suggests that 5-HT ultraslow oscillations could drive NE oscillations. How exactly ultraslow oscillations of serotonin are related to ultraslow oscillations of different neuromodulators in different brain regions remains to be studied.

We have further addressed this question and how it relates to the issue of causality in the Discussion section of the manuscript (p. 13):

“In addition to the difficulties involved with typical causal interventions already mentioned, the fact that the levels of different neuromodulators are interrelated and affected by ongoing brain activity makes it very hard to pinpoint ultraslow oscillations of one specific neuromodulator as controlling specific activity patterns, such as ripple timing. While a recent paper purported to show a causative effect of norepinephrine levels on ultraslow oscillations of sigma band power, the fact that optogenetic inhibition of locus coerulus (LC) cells, but also excitation, only caused a minor reduction of the ultraslow sigma power oscillation suggests that other factors also contribute (Osorio-Forero et al., 2021). Generally, it is thought that many neuromodulators together determine brain states in a combinatorial manner, and it is probable that the 5-HT oscillations we measure, like the similar oscillations in NE, are one factor among many.

Nevertheless, given the known effects of 5-HT on neurons, it is not unlikely that the 5-HT fluctuations we describe have some impact on the timing of ripples, MAs, hippocampal-cortical coherence, or EMG signals that correlate with either the rising or descending phase. In fact, causal effects of 5-HT on ripple incidence (Wang et al. 2015, ul Haq et al. 2016 and Shiozaki et al. 2023), MA frequency (Thomas et al. 2022), sensory gating (Lee et al. 2020), which is subserved by inter-areal coherence (Fisher et al. 2020), and movement (Takahashi et al. 2000, Alvarez et al. 2022, Jacobs et al. 1991 and Luchetti et al. 2020) have all been shown. Our added findings that serotonin affects ripple incidence in hippocampal slices in a dose-dependent manner (Figure S1) further suggests that the relationship between ultraslow 5-HT oscillations and ripples we report may indeed result, at least in part, from a direct effect of serotonin on the hippocampal network.

Whether these ‘causal’ relationships between 5-HT and the different activity measures we describe can be used to support a causal link between ultraslow 5-HT oscillations and the correlated activity we report remains an open question. To that point, some studies have described changes in ultraslow oscillations due to manipulation of serotonin signaling. Specifically, reduction of 5-HT1a receptors in the dentate gyrus was recently shown to reduce the power of ultraslow oscillations of calcium activity in the same region (Turi et al. 2024). Furthermore, psilocin, which largely acts on the 5-HT2a receptor, decreased NREM episode length from around 100 s to around 60 s, and increased the frequency of brief awakenings (Thomas et al. 2022). While ultraslow oscillations were not explicitly measured in this study, the change in the rhythmic pattern of NREM sleep episodes and brief awakenings, or microarousals, suggests an effect of psilocin on ultraslow oscillations during NREM. Although these studies do not necessarily point to an exclusive role for 5-HT in controlling ultraslow oscillations of different brain activity patterns, they show that changes in 5-HT can contribute to changes in brain activity at ultraslow frequencies.”

A major question that has been left out from the study and discussion is how the same level of serotonin before and after the peak could be differentially related to the opposite observed phenomenon. What are the possible parallel mechanisms for distinguishing between the rising and falling phases? Any neurophysiological evidence for sensing the direction of change in serotonin concentration (or any other neuromodulator), and is there any physiological functionality for such mechanisms?

We have added a paragraph in the discussion to address how this differentiation of the 5-HT signal may be carried out (Discussion, paragraph #3, p. 10):

“In order for the ultraslow oscillation phase to segregate brain activity, as we have observed, the hippocampal network must somehow be able to sense the direction of change of serotonin levels. While single-cell mechanisms related to membrane potential dynamics are typically too fast to explain this calculation, a theoretical work has suggested that feedback circuits can enable such temporal differentiation, also on the slower timescales we observe (Tripp and Eliasmith, 2010). Beyond the direction of change in serotonin levels, temporal differentiation could also enable the hippocampal network to discern the steeper rising slope versus the flatter descending slope that we observe in the ultraslow 5-HT oscillations (Figure S2), which may also be functionally relevant (Cole and Voytek, 2017). The distinction between the rising and falling phase of ultraslow oscillations is furthermore clearly discernible at the level of unit responses, with many units showing preferences for either half of the ultraslow period (Figure S6). Another factor that could help distinguish the rising from the falling phase is the level of other neuromodulators, as it is likely the combination of many neuromodulators at any given time that defines a behavioral substate. Given the finding that ACh and Oxt exhibit ultraslow oscillations with a temporal shift (Zhang et al. 2024), one could posit that distinct combinations of different levels of neuromodulators could segregate the rising from the falling phase via differential effects of the combination of neuromodulators on the hippocampal network.”

Functionally, the ability to distinguish between the rising and falling phases of an oscillatory cycle is a form of phase coding. A well-known example of this can be seen in hippocampal place cells, which fire relative to the ongoing theta oscillations. The key advantage of phase coding is that it introduces an additional dimension, i.e. phase of firing, beyond the simple rate of neural firing. This allows for the multiplexing of information (Panzeri et al., 2010), enabling the brain to encode more complex patterns of activity. Moreover, phase coding is metabolically more efficient than traditional spike-rate coding (Fries et al., 2007).

Reviewer #2 (Public review):

Summary:

In their study, Cooper et al. investigated the spontaneous fluctuations in extracellular 5-HT release in the CA1 region of the hippocampus using GRAB5-HT3.0. Their findings revealed the presence of ultralow frequency (less than 0.05 Hz) oscillations in 5-HT levels during both NREM sleep and wakefulness. The phase of these 5-HT oscillations was found to be related to the timing of hippocampal ripples, microarousals, electromyogram (EMG) activity, and hippocampal-cortical coherence. In particular, ripples were observed to occur with greater frequency during the descending phase of 5-HT oscillations, and stronger ripples were noted to occur in proximity to the 5-HT peak during NREM. Microarousal and EMG peaks occurred with greater frequency during the ascending phase of 5-HT oscillations. Additionally, the strongest coherence between the hippocampus and cortex was observed during the ascending phase of 5-HT oscillations. These patterns were observed in both NREM sleep and the awake state, with a greater prevalence in NREM. The authors posit that 5-HT oscillations may temporally segregate internal processing (e.g., memory consolidation) and responsiveness to external stimuli in the brain.

Strengths:

The findings of this research are novel and intriguing. Slow brain oscillations lasting tens of seconds have been suggested to exist, but to my knowledge they have never been analyzed in such a clear way. Furthermore, although it is likely that ultra-slow neuromodulator oscillations exist, this is the first report of such oscillations, and the greatest strength of this study is that it has clarified this phenomenon both statistically and phenomenologically.

Weaknesses:

As with any paper, this one has some limitations. While there is no particular need to pursue them, I will describe ten of them below, including future directions:

(1) Contralateral recordings: 5-HT levels and electrophysiological recordings were obtained from opposite hemispheres due to technical limitations. Ipsilateral simultaneous recordings may show more direct relationships.

Although we argue that bilateral symmetry defines both the serotonin system and many hippocampal activity patterns (Methods: Dual fiber photometry and silicon probe recordings), we agree that ipsilateral recordings would be superior to describe the link between serotonin and electrophysiology in the hippocampus. In addition to noting that a recent study has adopted the same contralateral design (Zhang et al. 2024), we add a reference further supporting bilateral hippocampal synchrony, specifically of dentate spikes (Farrell et al. 2024). However, as functional lateralization has been recently proposed to underlie certain hippocampal functions in the rodent (Jordan 2020), future studies should ideally include both imaging and electrophysiology in a single hemisphere to guarantee local correlations rather than assuming inter-hemispheric synchrony. This could be accomplished using an integrated probe with attached optical fibers, as described in Markowitz et al. 2018, which is however technically more challenging and has, to our knowledge, not yet been implemented with fiber photometry recordings with GRAB sensors. Given the required separation of a few hundred micrometers between the probe shanks and the optical fiber cannula, it is important to consider whether the recordings are capturing the same neuronal populations. For example, there is a risk of recording electrical activity from dorsal hippocampal neurons while simultaneously measuring light signals from neurons in the intermediate hippocampus, which are functionally distinct populations (Fanselow and Dong 2009).

(2) Sample size: The number of mice used in the experiments is relatively small (n=6). Validation with a larger sample size would be desirable.

While larger sample sizes generally reduce the influence of random variability and minimize the impact of outliers on conclusions, our use of mixed-effects models mitigates these concerns by accounting for both inter-session and inter-mouse variability. With this approach, we explicitly model random effects, such as the variability between individual mice and sessions, alongside fixed effects (such as treatment), which ensures that our results are not driven by random fluctuations in a few individual mice or sessions. Furthermore, the inclusion of random intercepts and slopes in the models allows for the possibility that different animals and/or sessions have different baseline characteristics and respond to different degrees of magnitude to the treatment. In summary, while validating these findings with a larger sample size would certainly help detect more subtle effects, we are confident in the robustness of the conclusions presented.

(3) Lack of causality: The observed associations show correlations, not direct causal relationships, between 5-HT oscillations and neural activity patterns.

We agree that the data we present in this study is largely correlational and generally avoid claims of causality in the manuscript. In the Discussion section, we discuss barriers to interpreting typical causal interventions in vivo, such as optogenetic activation of raphe nuclei: “The two previously mentioned in vivo studies showing reduced ripple incidence…”(paragraph #10, pg. 12), as well as an added section on further causality considerations in the Discussion section of the manuscript (paragraph #12, pg. 13): “In addition to the difficulties involved with…”

Due to these barriers, as a first step, we wanted to describe how physiological changes in serotonin levels are correlated to changes in the hippocampal activity. Equipped with a deeper understanding of physiological serotonin dynamics, future studies could explore interventions that modulate serotonin in keeping with the natural range of serotonin fluctuations for a given state. On that point, another challenge which we have not mentioned in the manuscript is that modulating serotonin, or any neuromodulator’s levels, has the potential, depending on the degree of modulation, to transition the brain to an entirely different behavioral state. This then complicates interpretation, as one is not sure whether effects observed are due to the changes in the neuromodulator itself, or secondary to changes in state. At the same time, 5-HT activity drives networks which in return can change the release of other neurotransmitters, leading to indirect effects.

The results of our in vitro experiments suggest that a causal relationship between serotonin and ripples is possible (Figure S1). Though the hippocampal slice preparation is clearly an artificial model, it provides a controlled environment to isolate the effects of serotonin manipulation on the hippocampal formation, without the confounding influence of systemic 5-HT fluctuations in other brain regions. Notably, the dose-dependent effects of serotonin (5-HT) wash-in on ripple incidence observed in vitro closely mirror the inverted-U dose-response curve seen in our in vivo experiments across states, where small increases in serotonin lead to the highest ripple incidence, and both lower and higher levels correspond to reduced ripple activity. This parallel suggests that the gradual washing of serotonin in our in vitro system may mimic the tonic firing changes in serotonergic neurons that occur during state transitions in vivo. These findings underscore the importance of studying how different dynamics of serotonin modulation can differentially affect hippocampal network activity.

(4) Limited behavioral states: The study focuses primarily on sleep and quiet wakefulness. Investigation of 5-HT oscillations during a wider range of behavioral states (e.g., exploratory behavior, learning tasks) may provide a more complete understanding.

We agree that future studies should investigate a broader range of behavioral states. For this study, as we were focused on general sleep and wake patterns, our recordings were done in the home cage, and we limited ourselves to the basic behavioral states described in the paper. Future studies should be designed to investigate ultraslow 5-HT oscillations during different behaviors, such as continuous treadmill running. Specifically, a finer segregation of extended wake behaviors by level of arousal could greatly add to our understanding of the role of ultraslow serotonin oscillations.

(5) Generalizability to other brain regions: The study focuses on the CA1 region of the hippocampus. It's unclear whether similar 5-HT oscillation patterns exist in other brain regions.

Given the reported ultraslow oscillations of population activity in serotonergic neurons of the dorsal raphe nucleus (Kato et al. 2022) as well as the widespread projections of the serotonergic nuclei, we would expect a broad expression of ultraslow 5-HT oscillations throughout the brain. So far, ultraslow 5-HT oscillations have been described in the basal forebrain, as well as in the dentate gyrus, in addition to what we have shown in CA1 (Deng et al. 2024 and Turi et al. 2024). Furthermore, our results showing that hippocampal-cortical coherence changes according to the phase of hippocampal ultraslow 5-HT oscillations suggests that 5-HT can affect oscillatory activity either indirectly by modulating hippocampal cells projecting to the cortical network or directly by modulating the cortical postsynaptic targets. Given the heterogeneity in projection strength, as well as in pre- and postsynaptic serotonin receptor densities across brain regions (de Filippo & Schmitz, 2024), it would be interesting to see whether local ultraslow 5-HT oscillations are differentially modulated, e.g. in terms of oscillation power. Future studies investigating different brain regions via implantation of multiple optic fibers in different brain areas or using the mesoscopic imaging approach adopted in Deng et al. 2024, will be needed to examine the extent of spatial heterogeneity in this ultraslow oscillation.

(6) Long-term effects not assessed: Long-term effects of ultra-low 5-HT oscillations (e.g., on memory consolidation or learning) were not assessed.

While beyond the scope of our current study, we agree that an important next step would involve modulating the ultraslow serotonin oscillation after learning, and then examining potential effects on memory consolidation, presumably via changes in ripple dynamics, though many possibilities could explain potential effects. There, our results suggest it would be important to isolate effects due to the change in ultraslow oscillation features, rather than simply overall levels of 5-HT. To that end, it would be important to test different modulation dynamics, specifically modulating the oscillation strength, around a constant mean 5-HT level by carefully timed optogenetic stimulation/inhibition. Afterwards, showing a clear correlation between the strength of the 5-HT modulation and memory performance would be important to establishing the relationship, as done in Lecci et al 2017, where more prominent ultraslow oscillations of sigma power in the cortex during sleep, alongside a higher density of spindles, were correlated with better memory consolidation. Given the tight coupling of spindles and ripples during sleep, it is possible that a similar effect on memory consolidation would be observed following changes in ultraslow 5-HT oscillation power.

(7) Possible species differences: It's uncertain whether the findings in mice apply to other mammals, including humans.

We agree that the experiments should ultimately be replicated in humans. In the 2017 study by Lecci et al., the authors highlighted the shared functional requirements for sleep across species, despite apparent differences, such as variations in sleep volume. To explore these commonalities, the researchers conducted parallel experiments in both mice and humans, aiming to identify a universal organizing structure. They discovered that the ultraslow oscillation of sigma power serves this role, enabling both species to balance the competing demands of arousability and sleep imperviousness. Based on this finding, it is plausible that ultraslow oscillations of serotonin, which similarly modulate activity according to arousal levels, would serve a comparable function in humans.

(8) Technical limitations: The temporal resolution and sensitivity of the GRAB5-HT3.0 sensor may not capture faster 5-HT dynamics.

The kinetics of the GRAB5-HT3.0 sensor used in this study limit the range of serotonin dynamics we can observe. However, the ultraslow oscillations we measure reflect temporal changes on the scale of 20 s and greater, whereas the GRAB sensor we use has sub-second on kinetics and below 2 s off kinetics (Deng et al. 2024). Therefore, the sensor is capable of reporting much faster activity than the ultraslow oscillations we observe, indicating that the ultraslow 5-HT signal accurately reflects the dynamics on this time scale. Furthermore, the presence of ultraslow oscillations in spiking activity—observed in the hippocampal formation (Gonzalo Cogno et al., 2024; Aghajan et al., 2023; Penttonen et al., 1999) and in the dorsal raphe (Mlinar et al., 2016), which are not affected by the same temporal smoothing, suggests that the oscillations we record are not likely due to signal aliasing, but instead reflect genuine oscillatory activity. Of course, this does not preclude that other, faster serotonin dynamics are also present in our signal, some of which may be too fast to be observed. For instance, rapid serotonin signaling via the ionotropic 5-HT3a receptors could be missed in our recordings. Additionally, with the fiber photometry approach we adopted, we are limited to capturing spatially broad trends in serotonin levels, potentially overlooking more localized dynamics.

(9) Interactions with other neuromodulators: The study does not explore interactions with other neuromodulators (e.g., norepinephrine, acetylcholine) or their potential ultraslow oscillations.

We agree that the interaction between neuromodulators in the context of ultraslow oscillations is an important issue, which we have addressed in our response to reviewer #1 under ‘Weaknesses.’

(10) Limited exploration of functional significance: While the study suggests a potential role for 5-HT oscillations in memory consolidation and arousal, direct tests of these functional implications are not included.

We agree and reference our answer to (6) regarding memory consolidation. Regarding arousal, direct tests of arousability to different sensory stimuli during different phases of the ultraslow 5-HT oscillation during sleep would be beneficial, in addition to the indirect measures of arousal we examine in the current study, e.g. degree of movement (icEMG) and long range coherence. In line with what we have shown, Cazettes et al. (2021) has demonstrated a direct relationship between 5-HT levels and pupil size, an indicator of arousal level, which like our findings, is consistent across behavioral states.

Reviewer #3 (Public review):

Summary:

The activity of serotonin (5-HT) releasing neurons as well as 5-HT levels in brain structures targeted by serotonergic axons are known to fluctuate substantially across the animal's sleep/wake cycle, with high 5-HT levels during wakefulness (WAKE), intermediate levels during non-REM sleep (NREM) and very low levels during REM sleep. Recent studies have shown that during NREM, the activity of 5HT neurons in raphe nuclei oscillates at very low frequencies (0.01 - 0.05 Hz) and this ultraslow oscillation is negatively coupled to broadband EEG power. However, how exactly this 5-HT oscillation affects neural activity in downstream structures is unclear.

The present study addresses this gap by replicating the observation of the ultraslow oscillation in the 5-HT system, and further observing that hippocampal sharp wave-ripples (SWRs), biomarkers of offline memory processing, occur preferentially in barrages on the falling phase of the 5-HT oscillation during both wakefulness and NREM sleep. In contrast, the raising phase of the 5-HT oscillation is associated with microarousals during NREM and increased muscular activity during WAKE. Finally, the raising 5-HT phase was also found to be associated with increased synchrony between the hippocampus and neocortex. Overall, the study constitutes a valuable contribution to the field by reporting a close association between raising 5-HT and arousal, as well as between falling 5-HT and offline memory processes.

Strengths:

The study makes compelling use of the state-of-the-art methodology to address its aims: the genetically encoded 5-HT sensor used in the study is ideal for capturing the ultraslow 5-HT dynamics and the novel detection method for SWRs outperforms current state-of-the-art algorithms and will be useful to many scientists in the field. Explicit validation of both of these methods is a particular strength of this study.

The analytical methods used in the article are appropriate and are convincingly applied, the use of a general linear mixed model for statistical analysis is a particularly welcome choice as it guards against pseudoreplication while preserving statistical power.

Overall, the manuscript makes a strong case for distinct sub-states across WAKE and NREM, associated with different phases of the 5-HT oscillation.

Weaknesses:

All of the evidence presented in the study is correlational. While the study mostly avoids claims of causality, it would still benefit from establishing whether the 5-HT oscillation has a direct role in the modulation of SWR rate via e.g. optogenetic activation/inactivation of 5-HT axons. As it stands, the possibility that 5-HT levels and SWRs are modulated by the same upstream mechanism cannot be excluded.

We agree that causality claims cannot be made with our data, and acknowledge the interest in exploring causal interactions between ultraslow serotonin oscillations and the correlated activity we measure. We address this point in depth in our answer to Reviewer #2, Weaknesses #3.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

One major question in the presented data is the nature of the asymmetrical shape of the targeted slow events. How much does it reflect the 5-HT concentration and how much is this shape affected by the dynamics of the designed 5-HT sensor? This needs to be addressed in more detail referencing the original paper for the used sensor.

We have added a paragraph in the Results section of the manuscript to address the asymmetric waveform of the ultraslow 5-HT oscillations and whether it could be affected by the asymmetric kinetics of the GRAB sensor we use: “The waveform of these ultraslow 5-HT oscillations…” (Results, paragraph #4, pg. 5). We include an extended answer to the question here:

Indeed, the GRAB5-HT3.0 sensor we use in the study shows activation response kinetics which are faster than their deactivation time, with time constants at 0.25 s and 1.39 s, respectively (Deng et al. 2024). Likewise, the slope of the rising phase of the ultraslow serotonin oscillation we measure is faster than the slope of the falling phase, and the ratio of time spent in the rising phase versus the falling phase is less than 1, indicating longer falling phases (Figure S2). Although we cannot completely rule out that the asymmetric shape of the ultraslow serotonin oscillations we record is affected by this asymmetry in the 5-HT sensor kinetics, we believe this is unlikely, as the 5-HT signal clearly contains reductions in 5-HT levels that are much faster than the descending phase of the ultraslow oscillation. Although it is difficult to directly compare the different-sized signals, the reported timescales of off kinetics, on the order of a few seconds (Deng et al. 2024), are far below the tens of seconds timescale of the ultraslow oscillation. Furthermore, the finding that some dorsal raphe neurons modulate their firing rate at ultraslow frequencies, and moreover that all examples of such ultraslow oscillations shown display clear asymmetry in rising time versus decay, suggests that the asymmetry we observe in our data could be due to neural activity rather than temporal smoothing by the sensor (Mlinar et al. 2016). In this same direction, another study found similar asymmetry in extracellular 5-HT levels measured with fast scan cyclic voltammetry (FSCV), a technique with greater temporal resolution (sampling rate of 10 Hz) than GRAB sensors, after single pulse stimulation (Bunin and Wightman 1998). In this study, 5-HT was shown to be released extrasynaptically, making the longer clearing time compared to the release time intuitive. Finally, the observation that the onsets and offsets of ripple clusters, recorded with a sampling rate of 20 kHz, are precisely aligned with the peaks and troughs of ultraslow serotonin oscillations (Figure 1, H1-2, columns 2-3) suggests that the duration of the falling phase is not artificially distorted by the temporal smoothing of the sensor dynamics.

Regardless of the dynamics of the serotonin concentration, it should be noted that the elicited neuronal effect might have different dynamics compared to the 5-HT concentration that need to be more studied: to address this one can either examine the average of the broadband LFP (not high passfiltered by the amplifier) or the distribution of simultaneously recorded spiking activity around the peak of ultra-slow oscillations.

We have added Figure S6, showing unit activity relative to the phase of ultraslow serotonin oscillations.

From this analysis, we uncover three groups of units which are largely preserved across states (Figure S6, E vs. F), albeit with a slight temporal shift rightward from NREM to WAKE (Figure S6, C vs. D). Namely, some units spike preferentially during the rising phase, some during the falling phase, and a third group have no clear phase preference. Unit activity during the falling phase is unsurprising, as it is where ripples largely occur, which themselves are associated with spike bursts. During the rising phase, the unit activity we observe could correspond to firing of the hippocampal subpopulation known to be active during NREM interruption states (Jarosiewicz et al. 2002, Miyawaki et al. 2017). While the units’ phase preference was tested based on the category of rising vs. falling phase, as this division described most variation in the data, a few units in the ‘No preference’ group showed heightened activity near the oscillation peak. However, given the very small number of units with this preference, more unit data is needed to describe this group, ideally with high-density recordings. Overall, most units showed a falling vs. rising phase preference, indicating a phase coding of hippocampal activity by 5-HT ultraslow oscillations.

Related to the previous point, it would be helpful to show the average cycle shape of these oscillations (relative to the phase 0 extracted in Figure 3) and do the shape comparison across sessions and also wake/NREM

We agree, and to this end we have added Figure S2. From this waveform analysis, we show that the ultraslow serotonin oscillation is asymmetric, with the rising phase having a greater slope, but shorter length, than the falling phase. While this asymmetry is observed both in NREM and WAKE, the slope difference and length ratio difference in rising vs. falling phase is greater in NREM (Figure S2. B).

In Figure 3D, there seem to be oscillatory rhythms with faster cycles on top of the targeted oscillations. That would make the phase estimation less accurate, e.g. in the left panel, in the second cycle, it is not clear if there are two faster cycles or it is one slow cycle as targeted, and if noted in the rising phase of the second fast cycle there are no ripples. This might suggest that regardless of specific oscillation frequency whenever 5-HT is started to get released, the ripples are suppressed and once the 5-HT is not synaptically effective anymore the ripples start to get generated while the photometry signal starts to wane with the serotonin being cleared. Still, if there is any rhythmicity between bouts of no ripple, it would suggest an ultra-slow regularity in the 5-HT release.

The reviewer is correct to point out that some faster increases in serotonin, which occur on top of the ultraslow oscillations we measure, seem to be associated with decreased ripple incidence, as in the example referenced. The dominance of ultraslow frequencies in the power spectrum of the 5-HT signal suggests, however, that oscillations faster than the ultraslow oscillations we describe are far less prevalent in the data. While there may be some coupling of ripples and other measures to serotonin oscillations of different frequencies, this may be hard or impossible to detect with phase analysis based on their infrequent occurrence and nonstationary nature. In fact, we show in Figure S3 that the strongest phase modulation of ripples by ultraslow serotonin oscillations is observed in the frequencies we use (0.01-0.06 Hz). Methodologically, phase analysis indeed assumes stationary signals, which are rare if not absent in physiological data (Lo et al. 2009), however generally the narrower the frequency band, the better the phase estimation. The narrow frequency band we use provides phase estimates that are largely robust and unaffected by the presence of faster oscillations, as can be seen in the example phase traces shown in Figure 4.

The hypothesis that the rising phase burst of synaptic serotonin is what silences ripples, and that with the clearing of serotonin from the synapses, ripples recover, is a possible explanation of our findings. However, if this were the case, one could expect the ripple rate to increase over the course of the falling phase of ultraslow 5-HT oscillations, as 5-HT decreases, and peak at the trough. This is at odds with what we observe, namely a fairly uniform distribution of ripples along the falling phase (Figure 3F2,F4). Furthermore, the Mlinar et al. 2016 study describes a subpopulation of raphe neurons whose firing rates themselves oscillate at ultraslow frequencies, rather than on-off bursting at ultraslow frequencies, which would argue against this hypothesis. However, as this study looks at a small number of neurons in slices, further in vivo experiments examining firing rates of median raphe neurons are required to understand how the ultraslow oscillation of extracellular serotonin that we measure is generated as well as how it is related to ripple rates.

In Figure 3B, it is not clear why IRI is z-scored. It would be informative to have the actual value of IRI. What is the z relative to? Is it the mean value of IRI in each recording session? Is this to reduce the variability across sessions?

We have now included in Figure 3D a box plot displaying the IRI distributions across different states and sessions. To minimize inter-session variability, data were z-scored within each session for visualization purposes. However, all general linear models were based on raw data, and as a result, the raw differences in IRI are shown in Figure 3C.

Figure 3E, panel labels don't match with the caption

We are grateful to the reviewer for pointing out this mistake, which we have corrected in the updated version of the manuscript.

In the text related to Figure 3E, the related analysis can be more clearly described. "phase preference of individual ripples" does not immediately suggest that the occurring phase of each ripple relative to the targeted oscillation is extracted. I suggest performing this analysis individually for each session and summarizing the results across the sessions.

We have reworded the sentence in Results: 5-HT and ripples to better reflect the analysis performed: “Next, we calculated the ultraslow 5-HT phases at which individual ripples occurred during both NREM and WAKE (3E-F) ...”. Regarding session-level data, we have added Figure S3, which shows session level mean phase vectors, as well as the grand mean across sessions for both NREM and WAKE. Included in this figure are session level means for frequency bands outside of the ultraslow band we used in our study, intended to show that ripples are most strongly timed by the ultraslow band (0.01-0.06 Hz), reflected by the greater amplitude of the mean phase vector for this band.

Figure 3E2, based on the result of ripple-triggered 5-HT in left panels of 2H1-2, one would expect to see a preferred phase closer to 180 (toward the end of the falling phase), it would be helpful to compare and discuss the results of these two analyses.

The reviewer is correct to point out the apparent discrepancy in where the mean ripple falls with respect to the ongoing serotonin oscillation between the two figures mentioned. We have addressed this point in Results: 5-HT and ripples, paragraph #4: “This result appear to be at odds with…”.

Regarding the analysis in 3F, please also compare the power distribution of ripples between NREM and wake. This will help to better understand the potential difference behind the observed difference: how much the strong ripples are comparable between wake and NREM. It is also necessary to report the ripple detection failure rate across ripples with different strengths.

We have added a figure showing analysis done on a subset of the data in which ripples were manually curated in order to evaluate the performance of the ripple detection model (Figure S7) and explanatory text in Methods: Model performance: ‘To ensure that our model …’. In summary, while missed ripples did tend to have lower power than correctly detected ripples, including them did not change the distribution of ripples by the phase of the ultraslow serotonin oscillation (Figure S7C). We would also note that while the phase preference is noisier than what is presented in Figure 3F because this analysis was done with a small subset of all recorded ripples, the fact that ripples occur more clearly on the falling phase is visible for both detected ripples and detected + false negative ripples.

The mixed-effects model examining the influence of 5-HT ultraslow oscillation phase on ripple power revealed no significant effect of state (p = 0.088). This indicates that whether the data were collected during NREM or wake periods did not significantly impact ripple power and that the lack of a significant effect (in Figure 3G,H) in WAKE is probably not due to a difference in the distribution of ripple power between states.

4D, y label is z?

We are grateful for the reviewer to point that out, yes, the y label should be ‘z-score’, as the two traces represent z-scored 5-HT (blue) and z-scored shuffled data (orange). Figure 4D2 and Figure 2H1-2, which show similar data, have been corrected to address this oversight.

Relating to Figure 4, EMG comparison across phases of the oscillations is insightful. Two related and complementary analyses are to compare the theta and gamma power between the falling and rising phases.

We have addressed this suggestion in Figure S5 A-C. While low gamma, high gamma and theta power are modulated identically in NREM, with higher power observed during the falling phase than the rising phase, during WAKE, different patterns can be seen. Specifically, low gamma power shows no phase preference, while high gamma shows a peak near the center of the ultraslow 5-HT oscillation. Theta power, as in NREM, is higher during the falling phase of ultraslow 5-HT oscillations. Increased power across many frequency bands was shown to coincide with decreases in DRN population activity during NREM, which matches with what we report here (Kato et al. 2022). In summary, while NREM patterns are consistent in all frequency bands tested, aligning with the pattern of ripple incidence, in WAKE low and high gamma power show different relationships to ultraslow 5-HT phase.

In the manuscript, we have used the data in both Figure S5 and S6 (unit activity relative to ultraslow 5-HT oscillations), to argue against the idea that our coherence findings result from a lack of activity in the rising phase (see next question), which would have the effect of ‘artificially’ reducing coherence in the falling phase relative the rising phase. The text can be found in Results: 5-HT and hippocampal cortical coherence, paragraph #2.

The results presented in Figure 5 could be puzzling and need to be further discussed: if the ripple band activity is weak during the rising phase, in what circumstances the coherence between cortex and CA1 is specifically very strong in this band?

As mentioned in the previous answer, we have addressed this concern in Results: 5-HT and hippocampal-cortical coherence, paragraph #2. In summary, it is true that the higher coherence in rising phase than in the falling phase for the highest frequency band (termed ‘high frequency oscillation’ (HFO), 100-150 Hz) could be unexpected, given that ripples occur largely during the falling phase. A few points could help explain this finding. Firstly, it should be noted that power in the 100-150 Hz band can arise from physiological activity outside of ripples, such as filtered non-rhythmic spike bursts (Liu et al. 2022), whose coherent occurrence in the rising phase could explain the coherence findings. Secondly, coherence is a compound measure which is affected by both phase consistency and amplitude covariation (Srinath and Ray 2014), thus from only amplitude one cannot predict coherence. Furthermore, HFO power in the cortex is highest near the peak of ultraslow 5-HT oscillations (Figure S5D), as opposed to the falling phase peak in the hippocampus. This shows a lack of covariation in amplitude by phase between the hippocampus and cortex at this frequency band. An alternative explanation of our findings regarding coherence could be that in the rising phase, there is simply little to no activity, which is easier to ‘synchronize’ than bouts of high activity. Hippocampal unit activity in the rising phase (Figure S6) suggests however, that it is not likely to be the absence of activity supporting higher coherence in the rising phase across frequencies. Additional experiments using high density recordings should be conducted to examine 5-HT ultraslow oscillations and their role in gating activity across brain regions, though these results strongly suggest some role exists.

Reviewer #2 (Recommendations for the authors):

I would like to offer two comments. I believe that these are not unusual requests, and thus I would like the authors to respond.

(1) It would be prudent to investigate the possibility that the observed correlation between ultraslow and hippocampal ripples/microarousals is merely superficial and that there are unidentified confounding factors at play. For example, it would be beneficial to provide evidence that administering a serotonin receptor inhibitor result in the disappearance of the slow oscillation of ripples and microarousals, or that the correlation with ultraslow is no longer present. Please note that the former experiments do not require GRAB5-HT3.0 imaging.

We agree that causality claims cannot be made with our data and acknowledge the interest in exploring causal interactions between ultraslow serotonin oscillations and the correlated activity we measure. We address this point in depth in our answer to Reviewer #2, Weaknesses #3. We would further like to note that given the large number of serotonin receptors and the lack of selectivity of many serotonin receptor antagonists, a pharmacological approach would be difficult, though the results certainly useful. Finally, we highlight the psilocin study, which reported changes in the rhythmic occurrence of microarousals, and therefore likely ultraslow oscillations, after administering a 5-HT2a receptor agonist, suggesting a potential causal effect of 5-HT (via 5-HT2a receptor) on MA occurrence (Thomas et al. 2022).

(2) The slow frequency appears to be associated with the default mode network as observed in fMRI signals. The neural basis of the default mode network remains unclear; therefore, a more detailed examination of this possibility would be beneficial.

We agree that it would be interesting to investigate the role of 5-HT in the neural basis of the DMN.

The DMN as described in humans (Raichle et al. 2001) and rodents (Lu et al. 2012) may indeed include some parts of the hippocampus and perhaps some of our neocortical recordings could also be considered part of the DMN. The fact that the activity across the inter-connected brain structures of the DMN is correlated at ultraslow time scales (Gutierrez-Barragan et al. 2019, Mantini et al. 2007), as well as serotonin’s ability to modulate the DMN is intriguing (Helmbold et al. 2016). Further studies simultaneously recording DMN activity via fMRI and electrical activity via silicon probes, as done in Logothetis et al. 2001, could elucidate further a potential link between ultraslow oscillations and the DMN, with serotonergic modulation as a means to understand any potential contribution of serotonin.

Reviewer #3 (Recommendations for the authors):

(1) The impact of the study would benefit from an experiment causally testing the effect of hippocampal 5-HT levels on hippocampal physiology, e.g. using optogenetic manipulations.

We agree that causality claims cannot be made with our data and acknowledge the interest in exploring causal interactions between ultraslow serotonin oscillations and the correlated activity we measure. We address this point in depth in our answer to Reviewer #2, Weaknesses #3.

(2) Data presentation: the figures are of poor resolution, making some diagram details and, more importantly, some example traces (e.g. Figure 1A, right) impossible to see. This should be corrected by either increasing figure resolution or making important figure elements large enough to be readable.

We apologize for the poor resolution and have corrected it in the updated version of the manuscript.

(3) Differences in some figure panels are not statistically assessed: Figure 1H (differences in spectrum peak power), Figure 3E1 & Figure 3E3 (directional bias of the circular distributions), Figure 4C (difference from 0 mean).

We acknowledge this oversight and have added statistical tests for all three figures, as well as further information regarding the models used in Methods: Statistics.

(4) Lines 279-280: the claim that the study shows "organization of activity by ultraslow oscillations of 5-HT" implies a causal role of 5-HT in organizing hippocampal activity. I suggest that this statement be toned down to reflect the correlational nature of the presented evidence.

We have rephrased the sentence in question to the following: “In our study, including both NREM and WAKE periods allowed us to additionally show that the temporal organization of activity relative to ultraslow 5-HT oscillations operates according to the same principles in both states...”, which we believe better reflects the temporal correlation we describe.

(5) While the study claims to use the EMG (i.e. electromyograph) signal, it does not describe any electrodes placed inside the muscle in the methods section. The SleepScoreMaster toolbox used in the study estimates the EMG using high-frequency activity correlated across recording channels, so I assume this is how this signal was obtained. While such activity may well reflect muscular noise to some degree, it is an indirect measure as the electrodes are not in the muscle. Since the EMG signal is central to the message of the manuscript, the method for calculating it should be described in the methods section and it should be explicitly labelled as an indirect measure in the main text, e.g. by referring to this signal as pseudo-EMG.

We agree and have added explanatory text to the State Scoring subsection in Methods. Given that the EMG we refer to is derived from intracranial data, and not from traditional EMG probes, we now refer to the EMG as intracranial EMG, or icEMG for short, throughout the main text.

(6) Is ripple frequency or ripple duration different across the rising and falling phases of the ultraslow oscillation?

We have now investigated this suggestion in Figure S4, where we show that ripple frequency is higher in the falling phase than rising phase, while ripple duration appears to show no phase preference.

(7) Lines 315-317: I am not sure why the manuscript refers to the coupling between EMG and 5-HT levels as 'puzzling' given that, as stated, the locomotion-inducing effects of 5-HT are well documented. While the fact that even non-locomotory motor activity may be associated with 5-HT rise is certainly interesting (although not sure if 'puzzling'), the manuscript does not directly compare the association of 5-HT levels with locomotory and non-locomotory EMG spikes. Thus, I think this discussion point is not fully warranted.

We agree and have rephrased the discussion point in question to reflect that the EMG link to serotonin oscillations is not necessarily surprising, given both the literature linking 5-HT and spontaneous movement in the hippocampus, as well as the involvement of 5-HT in repetitive movements, where the role for a regularly-occurring oscillation is perhaps more intuitive.

(8) Line 441: Reference #67 does not describe the use of fiber photometry.

The reviewer is to correct to point out this typo, which has been now corrected. The reference in question should be 64, where fiber photometry experiments are described. For further clarity, we have changed our referencing scheme to include authors and years in in-text references.

(9) In Figures 3E1-3, the phase has different bounds than in the other Figures in the manuscript (0:360 vs -180:180), this should be corrected for consistency.

We agree and have made changes so that all figures have a phase range of -180 to 180°.

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eLife Assessment

This important study by Lee et al. explores the heterogeneous response of non-growing bacteria to the antimicrobial peptide (AMP) tachyplesin. The authors identify a subpopulation of cells that evade lethal damage by limiting the intracellular accumulation of a fluorescently labeled tachyplesin analog. The study provides compelling evidence that reduced drug accumulation underlies the decreased susceptibility of this subpopulation to the AMP. The molecular basis of this phenotype is well supported by the data.

Reviewer #1 (Public review):

Summary:

This work contributes several important and interesting observations regarding the heterotolerance of non-growing Escherichia coli and Pseudomonas aeruginosa to the antimicrobial peptide tachyplesin. The primary mechanism of action of tachyplesin is thought to be disruption of the bacterial cell envelope, leading to leakage of cellular contents after a threshold level of accumulation. Although the MIC for tachyplesin in exponentially growing E. coli is just 1 ug/ml, the authors observe that a substantial fraction of a stationary phase population of bacteria survives much higher concentrations, up to 64 ug/ml. By using a fluorescently labelled analogue of tachyplesin, the authors show that the amount of per-cell intracellular accumulation of tachyplesin displays a bimodal distribution, and that the fraction of "low accumulators" correlates with the fraction of survivors. Using a microfluidic device, they show that low accumulators exclude propidium iodide, suggesting that their cell envelopes remain largely intact, while high accumulators of tachyplesin also stain with propidium iodide. They show that this phenomenon holds for several clinical isolates of E. coli with different genetic determinants of antibiotic resistance, and for a strain of Pseudomonas aeruginosa. However, the bimodal distribution does not occur in these organisms for several other antimicrobial peptides, or for tachyplesin in Klebsiella pneumoniae or Staphylococcus aureus, indicating some degree of specificity in the interaction between AMP and bacterial cell envelope. They next explore the dynamics of the fluorescent tachyplesin accumulation and show interestingly that a high degree of accumulation is initially seen in all cells, but that the "low accumulator" subpopulation manages to decrease the amount of intracellular fluorescence over time, while the "high accumulator"subpopulation continues to increase its intracellular fluorescence. Focusing on increased efflux as a hypothesised mechanism for the "low accumulator" phenotype, based on transcriptomic analysis of the two subpopulations, the authors screen putative efflux inhibitors to see if they can block the formation of the low accumulator subpopulation. They find that both the protonophore CCCP and the SSRI sertraline can block the formation of this subpopulation and that a combination of sertraline plus tachyplesin kills a greater fraction of the stationary phase cells than either agent alone, similar to the killing observed when growing cells are treated with tachyplesin.

Strengths:

This study provides new insight into the heterogeneous behaviours of non-growing bacteria when exposed to an antimicrobial peptide, and into the dynamics of their response. The single-cell analysis by FACS and microscopy is compelling. The results provide a much-needed single cell perspective on the phenomenon of tolerance to AMPs and a good starting point for further exploration.

Weaknesses:

The authors have substantially improved the clarity of the manuscript and have added additional experiments to probe further the location of the AMP relative to low and high accumulators, and the physiological states of these sub-populations. These experiments strengthen the assertion that low accumulators keep the AMP at the cell surface while high accumulators permit intracellular access to the AMP.

The phenomenon of the emergence of low accumulators, which are phenotypically tolerant to the antimicrobial peptide tachyplesin, is interesting and important even if there is still work to be done to understand the mechanism by which it occurs.

Reviewer #2 (Public review):

Summary:

This study reports on the existence of subpopulations of isogenic E. coli and P. aeruginosa cells that are tolerant to the antimicrobial peptide tachyplesin and are characterized by accumulation of low levels of a fluorescent tachyplesin-NBD conjugate. The authors then set out to address the molecular mechanisms, providing interesting insights even though the mechanism remains incompletely defined: The work demonstrates that increased efflux may cause this phenotype, putatively together with other changes in membrane lipid composition. The authors further demonstrate that pharmacological manipulation can prevent generation of tolerance. The authors are cautious in their interpretation and the claims made are largely justified by the data.

Strengths:

Going beyond the commonly used bulk techniques for studying susceptibility to AMPs, Lee et al. used of fluorescent antibiotic conjugates in combination with flow cytometry analysis to study variability in drug accumulation at the single cell level. This powerful approach enabled the authors to expose bimodal drug accumulation pattern that were condition dependent, but conserved across a variety of E. coli clinical isolates. Using cell sorting in combination with colony-forming unit assays as well as quantitative fluorescence microscopic analysis in a microfludics-setup the authors compellingly demonstrate that low accumulators (where fluorescence signal is mostly restricted to the membrane), can survive antibiotic treatment, whereas high accumulators (with high intracellular fluorescence) were killed.

The relevance of efflux for the ´low accumulator´ phenotype and its survival is convincingly demonstrated by the following lines of evidence: i) A time-course experiment on tachyplesin-NBD pre-loaded cells revealed that all cells initially were high accumulators, before a subpopulation of cells subsequently managed to reduce signal intensity, demonstrating that the ´low accumulator´ phenotype is an induced response and not a pre-existing property. Ii) Double-mutants deficient in the delta acrA delta tolC double-KO, which showed reduced levels of low accumulators´. Interestingly, ´low accumulator´populations were nearly abrogated in bacteria deficient in the qse quorum sensing system, suggesting its centrality for the tachyplesin response. Even though this system may control acrA, the strength of the phenotype may suggest that it may control additional as-of-yet unidenitified factors relevant in the response to tachyplesin. Iii) treatment with efflux pump inhibitor sertraline and verapamil (even though some caution needs to be taken since it is not perfectly selective, see weakness) prevents generation of low accumulators. The observation that sertraline enhances tachyplesin-based killing is an important basis for developing combination therapies.

The study convincingly illustrates how susceptibility to tachyplesin adaptively changes in a heterogeneous way dependent on the growth phases and nutrient availability. This is highly relevant also beyond the presented example of tachyplesin and similar subpopulation-based adaptive changes to the susceptibility towards antimicrobial peptides or other drugs may occur during infections in vivo and they would likely be missed by standardized in vitro susceptibility testing.

Weaknesses:

Some mechanistic questions regarding tachyplesin-accumulation and survival remain. One general shortcoming of the setup of the transcriptomics experiment is that the tachyplesin-NBD probe itself has antibiotic efficacy and induces phenotypes (and eventually cell death) in the ´high accumulator´ cells. As the authors state themselves, this makes it challenging to interpret whether any differences seen between the two groups are causative for the observed accumulation pattern of if they are a consequence of differential accumulation and downstream phenotypic effects.

Reviewer #3 (Public review):

Summary:

This important study shows that stationary phase bacteria survive antimicrobial peptide treatment by switching on efflux pumps, generating low accumulating subpopulations that evade killing-a finding with clear implications for the design of peptide based antibiotics and for researchers studying antimicrobial resistance. The evidence is solid and frequently convincing, as diverse single cell assays, genetics and chemical inhibition coherently link reduced intracellular peptide to survival, even though a few mechanistic details warrant further exploration.

Strengths:

The authors investigate how Escherichia coli (and, to a lesser extent, Pseudomonas aeruginosa) survive exposure to the antimicrobial peptide (AMP) tachyplesin. Because resistance to AMPs is thought to rely heavily on non genetic adaptations rather than on classical mutation based mechanisms, the study focuses on phenotypic heterogeneity and seeks to pinpoint the cellular processes that protect a subset of cells. Using fluorescently labelled tachyplesin, single cell imaging, flow cytometry, transcriptomics, targeted genetics, and chemical perturbations, the authors report that stationary phase cultures harbor two phenotypic states: high accumulating cells that die and low accumulating cells that survive. They further propose and show that inducible efflux activity is the primary driver of survival and show that either efflux inhibition (sertraline, verapamil) or nutrient supplementation prevents the emergence of low accumulators and boosts killing.

The experiments unambiguously reveal that the cells respond to stress heterogeneously, with two distinct subpopulations - one with better survival than the other. This primary phenotype is convincingly shown across various E. coli strains, including clinical isolates. The authors probed the underlying mechanism from several angles, with important additional experiments in the revised version that strengthens the original conclusions in several ways. Newly added efflux assays with ethidium bromide, together with proteinase treatment experiments and ΔacrAΔtolC and ΔqseB/qseC mutant data, illustrate that the low accumulating subpopulation can actively export intracellular compounds. The authors took great care to temper their language to acknowledge other potential alternatives that could explain some of the data such as altered influx, vesicle release or proteolysis, metabolic activity of the cells, indirect effects of sertraline treatment, etc. Additional metabolic dye measurements confirm that low accumulators are less metabolically active, and a new data on nutrient supplementation shows that forcing growth increases peptide uptake and lethality. The authors clarify the crucial point of where antimicrobial peptides actually bind on the cell within the broader survival mechanism and present their conclusions, along with potential caveats, with commendable clarity.

Weaknesses:

Despite these advances, the contribution of efflux may require more direct evidence to further dissect whether efflux is necessary, sufficient, or contributory. The facts that the key low-efflux mutant still retains a small fraction of survivors and that the inhibitors used may cause other physiological changes leading to higher efflux are still unaccounted for. The lipidomic and vesicle findings, while intriguing, remain descriptive, and direct tests of their functional relevance would further solidify the mechanistic models.

Conclusion:

Even with these limitations, the study provides valuable insight into non genetic resistance mechanisms to AMPs and highlights inducible heterogeneity as a critical obstacle to peptide therapeutics. In a much broader context, this study also underscores the importance of efflux physiology even for those antimicrobials that seemingly would not have intracellular targets.

Author response:

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

Reviewer 1:

We would like to thank Reviewer 1 for recognising the importance of our findings on the heterogeneity in bacterial responses to tachyplesin.

(1) A double deletion of acrA and tolC (two out of the three components of the major constitutive RND efflux pump) reduces the appearance of the low accumulator phenotype, but interestingly, the single deletions have no effect, and a well-characterised inhibitor of RND efflux pumps also has no effect. The authors identify a two-component system, qseCB, that appears necessary for the appearance of low accumulators, but this system has pleiotropic effects on many cellular systems, with only tenuous connections to efflux. The selected pharmacological agents that could prevent the appearance of low accumulators do not offer clear insight into the mechanism by which low accumulators arise, because they have diverse modes of action.

We have added that “QseBC, was previously inferred to mediate resistance to a tachyplesin analogue by upregulating efflux genes based on transcriptomic analysis and hyper susceptibility of ΔqseBΔqseC mutants[113]”. However, we have also acknowledged that “it is conceivable that the deletion of QseBC has pleiotropic effects on other cellular mechanisms involved in tachyplesin accumulation.” and that “it is also conceivable that sertraline prevented the formation of the low accumulator phenotype via efflux independent mechanisms”

These amendments are reported on lines 525-527, 532-534 and 539-541 of our revised manuscript.

(2) The transcriptomics data collected for low and high accumulator sub-populations are interesting, but in my opinion, the conclusions that can be drawn from these data remain overstated. It is not possible to make any claims about the total amount of "protein synthesis, energy production, and gene expression" on the basis of RNA-Seq data. The reads from each sample are normalised, so there is no information about the total amount of transcript. Many elements of total cellular activity are post-transcriptionally regulated, so it is impossible to assess from transcriptomics alone. Finally, the transcriptomic data are analysed in aggregated clusters of genes that are enriched for biological processes, for example: "Cluster 2 included processes involved in protein synthesis, energy production, and gene expression that were downregulated to a greater extent in low accumulators than high accumulators". However, this obscures the fact that these clusters include genes that are generally inhibitory of the process named, as well as genes that facilitate the process.

We have now acknowledged that “that our data do not take into account post-transcriptional modifications that represent a second control point to survive external stressors.”

These amendments are reported on lines 534-535 of our revised manuscript.

The raw transcript counts can be found in Figure 3 – Source Data, we had added these data in our previous manuscript as requested by this reviewer.

We would also like to clarify that we have analysed our transcriptomic data via both clustering (i.e. Figure 3) and direct comparison of genes of interest (Table S1) and transcription factors (i.e. genes that are generally inhibitory of the process named, as well as genes that facilitate the process, Figure S12).

Finally, we would like to point out that in our revised manuscript (both this and its previous version) we are stating “Cluster 2 included processes involved in protein synthesis, energy production, and gene expression that were downregulated to a greater extent in low accumulators than high accumulators”. We do not think this is an overstatement, we do not use these data to make conclusions on the total amount of "protein synthesis, energy production, and gene expression".

(3) The authors have added an experiment to attempt to assess overall metabolic activity in the low accumulator and high accumulator populations, which is a welcome addition. They apply the redox dye resazurin and observe lower resorufin (reduced form) fluorescence in the low accumulator population, which they take to indicate a lower respiration rate. This seems possible, however, an important caveat is that they have shown the low accumulator population to retain substantially lower amounts of multiple different fluorescent molecules (tachyplesin-NBD, propidium iodide, ethidium bromide) intracellularly compared to the high accumulator population. It seems possible that the low accumulator population is also capable of removing resazurin or resorufin from the intracellular space, regardless of metabolic rate. Indeed, it has previously been shown that efflux by RND efflux pumps influences resazurin reduction to resorufin in both P. aeruginosa and E. coli. By measuring only the retained redox dye using flow cytometry, the results may be confounded by the demonstrated ability of the low accumulator population to remove various fluorescent dyes. More work is needed to strongly support broad conclusions about the physiological states of the low and high accumulator populations. The phenomenon of the emergence of low accumulators, which are phenotypically tolerant to the antimicrobial peptide tachyplesin, is interesting and important even if there is still work to be done to understand the mechanism by which it occurs.

We have now clarified that these assays were performed in the presence of 50 μM CCCP and that “CCCP was included to minimise differences in efflux activity and preserve resorufin retention between low and high accumulators, though some variability in efflux may still persist.” We have now added this information on lines 401-406. This information was only present in the caption of Figure S16 of our previous version of this manuscript.

We agree with the reviewers that more work needs to be done to fully understand this new phenomenon and we had already acknowledged in our previous version of this manuscript that other mechanisms could play a role in this new phenomenon, see lines 489-517 of the current manuscript.

Reviewer 2:

We would like to thank the reviewer for recognising that all their previous comments have now been satisfactorily addressed.

(1) Some mechanistic questions regarding tachyplesin-accumulation and survival remain. One general shortcoming of the setup of the transcriptomics experiment is that the tachyplesin-NBD probe itself has antibiotic efficacy and induces phenotypes (and eventually cell death) in the ´high accumulator´ cells. As the authors state themselves, this makes it challenging to interpret whether any differences seen between the two groups are causative for the observed accumulation pattern of if they are a consequence of differential accumulation and downstream phenotypic effects.

We agree with the reviewer and we had explicitly acknowledged this possibility on lines 281-285 (of the previous and current version of this manuscript).

(2) The statement ´ Moreover, we found that the fluorescence of low accumulators decreased over time when bacteria were treated with 20 μg mL´ is, in my opinion, not supported by the data shown in Figure S4C. That figure shows that the abundance of ´low accumulator´ cells decreases over time. Following the rationale that protease K treatment may cleave surface associated/ extracellular tachyplesin-NDB, this should lead to a shift of ´low accumulator´ population to the left, indicating reduced fluorescence intensity per cell. This is not so case, but the population just disappears. However, after 120 min of treatment more cells appear in the ´high accumulator´ state. This result is somewhat puzzling.

We agree with the reviewer that our previous discussion of this data could have been misleading. We have now reworded this part of the text as following: “We found that the fluorescence of high accumulators did not decrease over time when tachyplesin-NBD was removed from the extracellular environment and bacteria were treated with 20 μg mL^-1 (0.7 μM) proteinase K, a widely-occurring serine protease that can cleave the peptide bonds of AMPs [43–45] (Figure S4B and C). These data suggest that tachyplesin-NBD primarily accumulates intracellularly in high accumulators.”

It is conceivable that extended exposure to proteinase K (i.e. we see a decrease in the abundance of low accumulators after 90 min treatment with proteinase K) increased the permeability to tachyplesin-NBD of low accumulators allowing tachyplesin-NBD to move from either the extracellular space or the membrane to the cell interior. However, we do not have data to prove this point.

Therefore, we have now removed our claim that the data obtained using proteinase K suggest that tachyplesin-NBD accumulates primarily in the membranes of low accumulators. We believe that our two separate microscopy analyses provide more direct, stronger and less ambiguous evidence that tachyplesin-NBD accumulates primarily in the membranes of low accumulators.

(3) The authors used the metabolic dye resazurin to measure the metabolic activity of low vs. high accumulators. I am not entirely convinced that the lower fluorescence resorufin fluorescence in tachyplesin-NBD accumulators really indicates lower metabolic activity, since a cell's fluorescence levels would also be affected by the cellular uptake and efflux. It appears plausible that the lower resorufin-fluorescence may result from reduced accumulation/increased efflux in the ‘low-tachyplesin NBD´ population.

We have now clarified that these assays were performed in the presence of 50 μM CCCP and that “CCCP was included to minimise differences in efflux activity and preserve resorufin retention between low and high accumulators, though some variability in efflux may still persist.” We have now added this information on lines 401-406. This information was only present in the caption of Figure S16 of our previous version of this manuscript.

(4) P8 line 343. The text should refer to Figure. 13B, instead of 14B

We have now changed the text accordingly on line 337.

Reviewer 3:

We would like to thank the reviewer for recognising that we have done a very impressive job in taking care of their comments.

(1) Despite these advances, the contribution of efflux may require more direct evidence to further dissect whether efflux is necessary, sufficient, or contributory. The facts that the key low efflux mutant still retains a small fraction of survivors and that the inhibitors used may cause other physiological changes leading to higher efflux are still unaccounted for. The lipidomic and vesicle findings, while intriguing, remain descriptive, and direct tests of their functional relevance would further solidify the mechanistic models.

We agree with the reviewers that more work needs to be done to fully understand this new phenomenon and we had already acknowledged in our previous version of this manuscript that other mechanisms could play a role in this new phenomenon, see lines 489-517 of the current manuscript.

eLife Assessment

This valuable study reports the development of a novel organoid system for studying the emergence of autorhythmic gut peristaltic contractions through the interaction between interstitial cells of Cajal and smooth muscle cells. The authors further utilized the system to provide convincing evidence for a previously unappreciated potential role for smooth muscle cells in regulating the firing rate of interstitial cells of Cajal. The work will be of interest to those studying development and physiology of the gut.

Reviewer #1 (Public review):

Summary:

In this study, the authors developed an organoid system containing smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs; pacemaker cells), but few enteric neurons. This system generates rhythmic contractions similar to those observed in the developing gut. The stereotypical arrangement of SMCs and ICCs within the organoid allowed the authors to identify these cell types without the need for antibody staining. Leveraging this feature, they used calcium imaging and pharmacological approaches to investigate how calcium transients develop through interactions between the two cell types.

The authors first show that calcium transients are synchronized among ICC-ICC, SMC-SMC, and SMC-ICC pairs. They then used gap junction inhibitors to suggest that gap junctions are specifically involved in ICC-to-SMC signaling. Finally, they applied inhibitors of myosin II and L-type Ca²⁺ channels to demonstrate that SMC contraction is crucial for the generation of rhythmic activity in ICCs, suggesting the presence of SMC-to-ICC signaling. Additionally, they show that two organoids become synchronized upon fusion, with SMCs mediating this synchronization.

Strengths:

The organoid system provides a useful model for studying the specific roles of SMCs and ICCs in live samples.

Weaknesses:

Since all functional analyses were conducted pharmacologically in vitro, the findings need to be further validated through genetic approaches in vivo in future studies.

Reviewer #2 (Public review):

Summary:

In this study, Yagasaki et al. describe an organoid system to study the interactions between smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). While these interactions are essential for the control of rhythmic intestinal contractility (i.e., peristalsis), they are poorly understood, largely due to the complexity of and access to the in vivo environment and the inability to co-culture these cell types in vitro for long term under physiological conditions. The "gut contractile organoids" organoids described herein are reconstituted from stromal cells of the fetal chicken hindgut that rapidly reorganize into multilayered spheroids containing an outer layer of smooth muscle cells and an inner core of interstitial cells. The authors demonstrate that they contract cyclically and additionally use calcium imagining to show that these contractions occur concomitantly with calcium transients that initiate in the interstitial cell core and are synchronized within the organoid and between ICCs and SMCs. Furthermore, they use several pharmacological inhibitors to show that these contractions are dependent upon non-muscle myosin activity and, surprisingly, independent of gap junction activity. Finally, they develop a 3D hydrogel for the culturing of multiple organoids and found that they synchronize their contractile activities through interconnecting smooth muscle cells, suggesting that this model can be used to study the emergence of pacemaking activities. Overall, this study provides a relatively easy-to-establish organoid system that will be of use in studies examining the emergence of rhythmic peristaltic smooth muscle contractions and how these are regulated by interstitial cell interactions. However, further validation and quantification will be necessary to conclusively determine show the cellular composition of the organoids and how reproducible their behaviors are.

Strengths:

This work establishes a new self-organizing organoid system that can easily be generated from the muscle layers of the chick fetal hindgut to study the emergence of spontaneous smooth muscle cell contractility. A key strength of this approach is that the organoids seem to contain few cell types (though more validation is needed), namely smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). These organoids are amenable to live imaging of calcium dynamics as well as pharmacological perturbations for functional assays, and since they are derived from developing tissues, the emergence of the interactions between cell types can be functionally studied. Thus, the gut contractile organoids represent a reductionist system to study the interactions between SMCs and ICCs in comparison to the more complex in vivo environment, which has made studying these interactions challenging.

Weaknesses:

The study lacks complementary in vivo experiments, but these will be exciting to follow up in future studies.

Reviewer #3 (Public review):

Summary:

The paper presents a novel contractile gut organoid system that allows for in vitro studying of rudimentary peristaltic motions in embryonic tissues by facilitating GCaMP-live imaging of Ca2+ dynamics, while highlighting the importance and sufficiency of ICC and SMC interactions in generating consistent contractions reminiscent of peristalsis. It also argues that ENS at later embryonic stages might not be necessary for coordination of peristalsis.

Strengths:

The manuscript by Yagasaki, Takahashi, and colleagues represents an exciting new addition to the toolkit available for studying fundamental questions in the development and physiology of the hindgut. The authors carefully lay out the protocol for generating contractile gut organoids from chick embryonic hindgut and perform a series of experiments that illustrate the broader utility of these organoids for studying the gut. This reviewer is highly supportive of the manuscript following highly responsive revisions in response to prior reviewer feedback.

Author response:

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

eLife Assessment

This valuable study reports the development of a novel organoid system for studying the emergence of autorhythmic gut peristaltic contractions through the interaction between interstitial cells of Cajal and smooth muscle cells. While the utility of the organoids for studying hindgut development is well illustrated by showing, for example, a previously unappreciated potential role for smooth muscle cells in regulating the firing rate of interstitial cells of Cajal, some of the functional analyses are incomplete. There are some concerns about the specificity and penetrance of perturbations and the reproducibility of the phenotypes. With these concerns properly addressed, this paper will be of interest to those studying the development and physiology of the gut.

We greatly appreciate constructive comments raised by the Editors and all the Reviewers. We have newly conducted pharmacological experiments using Nifedipine, a L-type Ca²⁺ blocker known to operate in smooth muscles (new Fig 7). The treatment abrogated not only the oscillation of SMCs but also that in ICCs, further corroborating our model that not only ICC-to-SMC interactions but also the reverse direction, namely SMC-to-ICC feedback signals, are operating to achieve coordinated/stable rhythm of gut contractile organoids.

Concerning the issues of the specificity and penetrance in pharmacological experiments with gap junction inhibitors, we have carefully re-examined effects by multiple blockers (CBX and 18b-GA) at different concentrations (new Fig 5D and Fig. S3B).We have newly found that: (1) the effects observed by CBX (100 µM) that the latency of Ca²⁺ peaks between ICCs (preceding) and SMCs (following) was abolished are not seen by 18b-GA at any concentrations including 100 µM, implying that the latency of Ca²⁺ peaks between these cells is governed by connexin(s) that are not inhibited by18bGA. Such difference in inhibiting effects by these two drugs were previously reported in multiple model systems including guts (Daniel et al., 2007; Parsons & Huizinga, 2015; Schultz et al., 2003).

Regarding the penetrance of the drugs, we have carried out earlier administration (Day 3) of the gap junction inhibitor, either CBX (100 µM) or 18b-GA (100 µM), in the course of organoidal formation in culture when cells are still at 2D to exclude a possible penetrance problem (new Fig. S3C). There treatments render no or little effects to the patterns of organoidal contractions in a way similar to the drug administration at Day 7. As already shown in the first version, CBX (100 µM) eliminates the latency of Ca²⁺ peaks, we believe that this drug successfully penetrates into the organoid and exerts its specific effects.

Unfortunately, due to very unstable condition in climate including extreme heat and sporadically occurring bird flu epidemic since the last summer in Japan, the poultry farm must have faced problems. In the course of revision experiments, we got in a serious trouble at multiple times with unhealthy eggs/embryos lasting from last summer until present. These unfortunate incidents did not allow us to engage in the revision experiments as fully as we originally planned. Nevertheless, we did our very best within a limited time fame, and we believe that the revised version is suitable as a final version of an eLife article.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, the authors developed an organoid system that contains smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs; pacemaker) but few enteric neurons, and generates rhythmic contractions as seen in the developing gut. The stereotypical arrangements of SMCs and ICCs in the organoid allowed the authors to identify these cell types in the organoid without antibody staining. The authors took advantage of this and used calcium imaging and pharmacology to study how calcium transients develop in this system through the interaction between the two types of cells. The authors first show that calcium transients are synchronized between ICC-ICC, SMC-SMC, and SMC-ICC. They then used gap junction inhibitors to suggest that gap junctions are specifically involved in ICC-to-SMC signaling. Finally, the authors used an inhibitor of myosin II to suggest that feedback from SMC contraction is crucial for the generation of rhythmic activities in ICCs. The authors also show that two organoids become synchronized as they fuse and SMCs mediate this synchronization.

Strengths:

The organoid system offers a useful model in which one can study the specific roles of SMCs and ICCs in live samples.

Thank you very much for the constructive comments.

Weaknesses:

Since only one blocker each for gap junction and myosin II was used, the specificities of the effects were unclear.

We appreciate these comments. We have addressed those of “weaknesses” as described in “Responses to the eLife assessment” (please see above).

Reviewer #2 (Public Review):

Summary:

In this study, Yagasaki et al. describe an organoid system to study the interactions between smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). While these interactions are essential for the control of rhythmic intestinal contractility (i.e., peristalsis), they are poorly understood, largely due to the complexity of and access to the in vivo environment and the inability to co-culture these cell types in vitro for long term under physiological conditions. The "gut contractile organoids" organoids described herein are reconstituted from stromal cells of the fetal chicken hindgut that rapidly reorganize into multilayered spheroids containing an outer layer of smooth muscle cells and an inner core of interstitial cells. The authors demonstrate that they contract cyclically and additionally use calcium imagining to show that these contractions occur concomitantly with calcium transients that initiate in the interstitial cell core and are synchronized within the organoid and between ICCs and SMCs. Furthermore, they use several pharmacological inhibitors to show that these contractions are dependent upon non-muscle myosin activity and, surprisingly, independent of gap junction activity. Finally, they develop a 3D hydrogel for the culturing of multiple organoids and found that they synchronize their contractile activities through interconnecting smooth muscle cells, suggesting that this model can be used to study the emergence of pacemaking activities. Overall, this study provides a relatively easy-to-establish organoid system that will be of use in studies examining the emergence of rhythmic peristaltic smooth muscle contractions and how these are regulated by interstitial cell interactions. However, further validation and quantification will be necessary to conclusively determine show the cellular composition of the organoids and how reproducible their behaviors are.

Strengths:

This work establishes a new self-organizing organoid system that can easily be generated from the muscle layers of the chick fetal hindgut to study the emergence of spontaneous smooth muscle cell contractility. A key strength of this approach is that the organoids seem to contain few cell types (though more validation is needed), namely smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). These organoids are amenable to live imaging of calcium dynamics as well as pharmacological perturbations for functional assays, and since they are derived from developing tissues, the emergence of the interactions between cell types can be functionally studied. Thus, the gut contractile organoids represent a reductionist system to study the interactions between SMCs and ICCs in comparison to the more complex in vivo environment, which has made studying these interactions challenging.

Thank you very much for the constructive comments.

Weaknesses:

The study falls short in the sense that it does not provide a rigorous amount of evidence to validate that the gut organoids are made of bona fide smooth muscle cells and ICCs. For example, only two "marker" proteins are used to support the claims of cell identity of SMCs and ICCs. At the same time, certain aspects of the data are not quantified sufficiently to appreciate the variance of organoid rhythmic contractility. For example, most contractility plots show the trace for a single organoid. This leads to a concern for how reproducible certain aspects of the organoid system (e.g. wavelength between contractions/rhythm) might be, or how these evolve uniquely over time in culture. Furthermore, while this study might be able to capture the emergence of ICC-SMC interactions as they related to muscle contraction and pacemaking, it is unclear how these interactions relate to adult gastrointestinal physiology given that the organoids are derived from fetal cells that might not be fully differentiated or might have distinct functions from the adult. Finally, despite the strength of this system, discoveries made in it will need to be validated in vivo. Thank you very much for the comments, which are helpful to improve our MS. In the revised version, we have additionally used antibody against desmin, known to be a maker for mature SMCs (new Fig 3B). The signal is seen only in the peripheral cells overlapping with the αSMA staining (line 169-170).

Concerning the reproducibility, while contractility changes were shown for a representative organoid in the original version, experiments had been carried out multiple times, and consistent data were reproduced as already mentioned in the text of the first version of MS. However, we agree with this reviewer that it must be more convincing if we assess quantitatively. We have therefore conducted quantitative assessments of organoidal contractions and Ca²⁺ transients (new Fig. 2B, new Fig. 4D, new Fig 5D, E, new Fig. 6B, new Fig. 7B, new Fig. 8C, new Fig. S2, S3). Details such as repeats of experiments and size of specimens are carefully described in the revised version (Figure legends)

In particular, in place of contraction numbers/time, we have plotted “contraction intervals” between two successive peaks (Fig. 2B and others). Actually, with your suggestion, we have tried to perform a periodicity analysis of organoid contractions. Unfortunately, no clear value has been obtained, probably because the contractions/Ca²⁺ transitions are not as “regularly periodical” as seen in conventional physics. This led us to perform the peak-interval analysis. Methods to quantify the contraction intervals are carefully explained in the revised version.

As already mentioned in the “Our provisional responses” following the receipt of Reviewers’ comments, we agree that our organoids derived from embryonic hind gut (E15) might not necessarily recapitulate the full function of cells in adult. However, it has well been accepted in the field of developmental biology that studies with embryonic tissue/cells make a huge contribution to unveil complicated physiological cell functions. Nevertheless, we have carefully considered in the revised version so that the MS would not send misleading messages. We agree that in vivo validation of our gut contractile organoid must be wonderful, and this is a next step to go.

Reviewer #3 (Public Review):

Summary:

The paper presents a novel contractile gut organoid system that allows for in vitro studying of rudimentary peristaltic motions in embryonic tissues by facilitating GCaMPlive imaging of Ca²⁺ dynamics, while highlighting the importance and sufficiency of ICC and SMC interactions in generating consistent contractions reminiscent of peristalsis. It also argues that ENS at later embryonic stages might not be necessary for coordination of peristalsis.

Strengths:

The manuscript by Yagasaki, Takahashi, and colleagues represents an exciting new addition to the toolkit available for studying fundamental questions in the development and physiology of the hindgut. The authors carefully lay out the protocol for generating contractile gut organoids from chick embryonic hindgut, and perform a series of experiments that illustrate the broader utility of these organoids for studying the gut. This reviewer is highly supportive of the manuscript, with only minor requests to improve confidence in the findings and broader impact of the work. These are detailed below.

Thank you very much for the constructive comments.

Weaknesses:

(1) Given that the literature is conflicting on the role GAP junctions in potentiating communication between intestinal cells of Cajal (ICCs) and smooth muscle cells (SMCs), the experiments involving CBX and 18Beta-GA are well-justified. However, because neither treatment altered contractile frequency or synchronization of Ca++ transients, it would be important to demonstrate that the treatments did indeed inhibit GAP junction function as administered. This would strengthen the conclusion that GAP junctions are not required, and eliminate the alternative explanation that the treatments themselves failed to block GAP junction activity.

Thank you for these comments, and we agree. In the revised version, we have verified the drugs, CBX and 18b-GA, using dissociated embryonic heart cells in culture, a well-established model for the gap junction study (new Fig. S3D, line 237-239). Expectedly, both inhibitors abrogate the rhythmic beats of heart cells, and importantly, cells’ beats resume after wash-out of the drug.

(2) Given that 5uM blebbistatin increases the frequency of contractions but 10uM completely abolishes contractions, confirming that cell viability is not compromised at the higher concentration would build confidence that the phenotype results from inhibition of myosin activity. One could either assay for cell death, or perform washout experiments to test for recovery of cyclic contractions upon removal of blebbistatin. The latter may provide access to other interesting questions as well. For example, do organoids retain memory of their prior setpoint or arrive at a new firing frequency after washout?

We greatly appreciate these suggestions and also interesting ideas to explore! In the revised version, we have newly conducted washout experiments (new Fig. 6B) (10 µM drug is washed-out from culture medium), and found that contractions resume, showing that cell viability is not compromised at 10 µM concentration (line 257-259). Intriguingly, the resumed rhythm appears more regular than that before drug administration. Thus, the contraction rhythm of the organoid might be determined by cellcell interactions at any given time rather than by memory of their prior setpoint. This is an interesting issue we would like to further explore in the future. These issues, although potentially interesting, are not mentioned in the text of the revised version, since it is too early to interpret there observations.

(3) Regulation of contractile activity was attributed to ICCs, with authors reasoning that Tuj1+ enteric neurons were only present in organoids in very small numbers (~1%).

However, neuronal function is not strictly dependent on abundance, and some experimental support for the relative importance of ICCs over Tuj1+ cells would strengthen a central assumption of the work that ICCs the predominant cell type regulating organoid contraction. For example, one could envision forming organoids from embryos in which neural crest cells have been ablated via microdissection or targeted electroporation. Another approach would be ablation of Tuj1+ cells from the formed organoids via tetrodotoxin treatment. The ability of organoids to maintain rhythmic contractile activity in the total absence of Tuj1+ cells would add confidence that the ICCs are indeed the driver of contractility in these organoids.

We agree. In the revised version, we have conducted TTX administration (new Fig. S2C). Changes in contractility by this treatment is not detected, supporting the argument that neural cells/activities are not essential for rhythmic contractions of the organoid (line 178-181).

(4) Given the implications of a time lag between Ca++ peaks in ICCs and SMCs, it would be important to quantify this, including standard deviations, rather than showing representative plots from a single sample.

In the revised version, we have elaborated a series of quantitative assessments as mentioned above (please see our responses to the “eLife assessments” at the beginning of these correspondences). The latency between Ca²⁺ peaks in ICCs and SMCs is shown in new Fig. 4D, in which measured value is 700 msec-terraced since the time-lapse imaging was performed with 700 msec intervals (as already described in the first version).

117 peaks for 14 organoids have been assessed (line 218).

(5) To validate the organoid as a faithful recreation of in vivo conditions, it would be helpful for authors to test some of the more exciting findings on explanted hindgut tissue. One could explant hindguts and test whether blebbistatin treatment silences peristaltic contractions as it does in organoids, or following RCAS-GCAMP infection at earlier stages, one could test the effects of GAP junction inhibitors on Ca++ transients in explanted hindguts. These would potentially serve as useful validation for the gut contractile organoid, and further emphasize the utility of studying these simplified systems for understanding more complex phenomena in vivo.

Thank you very much for insightful comments. We would love to explore these issues in near future. Just a note is that it was previously reported that Nifedipine silences peristaltic contractions in ex-vivo cultured gut (Chevalier et al., 2024; Der et al., 2000).

(6) Organoid fusion experiments are very interesting. It appears that immediately after fusion, the contraction frequency is markedly reduced. Authors should comment on this, and how it changes over time following fusion. Further, is there a relationship between aggregate size and contractile frequency? There are many interesting points that could be discussed here, even if experimental investigation of these points is left to future work.

It would indeed be interesting to explore how cell communications affect/determine the contraction rhythm, and our novel organoids must serve as an excellent model to address these fundamental questions. We have observed multiple times that when two organoids fuse, they undergo “pause”, and resume coordinated contractions as a whole, and we have mentioned such notice briefly in the revised version (line 282). To know what is going on during this pause time should be tempting. In addition, we have an impression that the larger in size organoids grow, the slower rhythm they count. We would love to explore this in near future.

(7) Minor: As seen in Movie 6 and Figure 6A, 5uM blebbistatin causes a remarkable increase in the frequency of contractions. Given the regular periodicity of these contractions, it is a surprising and potentially interesting finding, but authors do not comment on it. It would be helpful to note this disparity between 5 and 10 uM treatments, if not to speculate on what it means, even if it is beyond the scope of the present study to understand this further.

We assume that the increase in the frequency of contractions at 5 µM might be due to a shorter refractory period caused by a decreasing magnitude (amplitude) of contraction. We have made a short description in the revised text (line 256-257).

(8) Minor: While ENS cells are limited in the organoid, it would be helpful to quantify the number of SMCs for comparison in Supplemental Figure S2. In several images, the number of SMCs appears quite limited as well, and the comparison would lend context and a point of reference for the data presented in Figure S2B.

In the revised version, the number of SMCs has been counted and added in Fig. S2B. Contrary to that SMCs are more abundant than ICCs in an intact gut, the proportion is reversed in our organoid (line 181-183). It might due to treatments during cell dissociation/plating.

(9) Minor: additional details in the Figure 8 legend would improve interpretation of these results. For example, what is indicated in orange signal present in panels C, G and H? Is this GCAMP?

We apologize for this confusion. In the revised version, we have added labeling directly in the photos of new Fig. 9 (old Fig. 8). For C, G and H, the left photo is mRuby3+GCaMP6s, and the right one is GCaMP6s only.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I have a few comments for the authors to consider:

(1) Figure 4C: The authors propose that calcium signals propagate from ICC to SMC based on the results presented in this figure. While it is observed that the peak of the calcium signal in ICC precedes that in SMC, it's worth noting that the onset of the rise in calcium signals occurs simultaneously in ICC and SMC. Doesn't this suggest that they are activated simultaneously? The latency observed for the peaks of calcium signals could reflect different kinetics of the rise in calcium concentration in the two types of cells rather than the order of calcium signal propagation.

We greatly appreciate these comments. We have re-examined kinetics of GCaMP signals in ICC and SMC, but we did not succeed in validating rise points precisely. We agree that the possibility that the rise in calcium signals could be occurring simultaneously. To clarify these issues, analyses with higher resolution is required, such as using GCaMP6f or GCaMP7/8. Nevertheless, the disappearance of the latency of Ca²⁺ peak by CBX implies a role of gap junction in ICC to SMC signaling. In the revised version, we replaced the wording “rise” by “peak” when the latency is discussed.

(2) Figure 5C: The specific elimination of the latency in the calcium signal peaks between ICC and SMC is interesting. However, I am curious about how gap junction inhibitors specifically eliminate the latency between ICC and SMC without affecting other aspects of calcium transients in these cells, such as amplitude and synchronization among ICCs and/or SMCs. Readers of the manuscript would expect some discussion on possible mechanisms underlying this specificity. Additionally, I wonder if the elimination of the latency was observed consistently across all samples examined. The authors should provide information on the frequency and number of samples examined, and whether the elimination occurs when 18-beta-GA is used.

In the revised version, we have elaborated quantitative demonstration. For the effects by CBX on latency or Ca²⁺ peaks, a new graph has been added to new Fig 5, in which 100 µM eliminated the latency. Intriguingly, the latency appears to be attributed to a gap junction that is not inhibited by18-beta-GA (please see new Fig. S3E). As already mentioned above, inhibiting activity of both CBX and 18-beta-GA has been verified using dissociated cells of embryonic heart, a popular model for gap junction studies.

At present, we do not know how gap junction(s) contribute to the latency of Ca²⁺ peaks without affecting synchronization among ICCs and/or SMCs (we have not addressed amplitude of the oscillation in this study). Actually, it was surprising to us to find that GJ’s contribution is very limited. We do not exclude the importance of GJs, and currently speculate that GJs might be important for the initiation of contraction/oscillation signals, whereas the requirement of GJs diminishes once the ICC-SMC interacting rhythm is established. What we observed in this study might be the synchronization signals AFTER these interactions are established (Day 7 of organoidal culture). Upon the establishment, it is possible that mechanical signaling elicited by smooth muscles’ contraction might become prominent as a mediator for the (stable) synchronization, as implicated by experiments with blebbistatin and Nifedipin, the latter being newly added to the revised version (new Fig. 7). We have added such speculation, although briefly in Discussion (line 374-377)

(3) Figure 6: The significant effects of blebbistatin on calcium dynamics in both ICC and SMC are intriguing. However, since only one blocker is utilized, the specificity of the effects is unclear. If other blockers for muscle contraction are available, they should be employed. Considering that a rise in calcium concentration precedes contraction, calcium transients should persist even if muscle contraction is inhibited. One concern is whether blebbistatin inadvertently rendered the cells unhealthy. The authors should demonstrate at least that contraction and calcium transients recover after removal of the drug. The frequency and number of samples examined should be shown, as requested for Figure 5C above.

Thank you for these critical comments. A possible harmfulness of the drugs was also raised by other reviewers, and we have therefore conducted wash-out experiments in the revised version (new Fig. 6B). Contractions resume after wash-out showing that cell viability is not compromised at 10 µM concentration. The number of samples examined has been described more explicitly in the revised version. Regarding the blocker of SMC, we have newly carried out pharmacological assays using nifedipine, a blocker of a L-type Ca²⁺ channel known to operate in smooth muscle cells (new Fig 7) (Chevalier et al., 2024; Der et al., 2000). As already explained in the “Responses to eLife assessment”, the treatment abrogated ICCs’ rhythm and synchronous Ca²⁺ transients between ICCs and SMCs, further corroborating our model that not only ICC-to-SMC interactions but also SMC-to-ICC feedback signals are operating to achieve coordinated/stable rhythm of gut contractile organoids of Day 7 culture (please also see our responses shown above for Comment (2)).

Reviewer #2 (Recommendations For The Authors):

Major:

(1) The claim that organoids contain functional SMCs and ICCs is insufficient as it currently relies on only c-Kit and aSMA antibodies. This conclusion could be additionally supported by staining with other markers of contractile smooth muscle (e.g. TAGLN and MYH14) and an additional accepted marker of ICCs (e.g. ANO1/TMEM16). Moreover, it should be demonstrated whether these cells are PDGFRA+, as PDGFRA is a known marker of other mesenchymal fibroblast cell types. These experiments would additionally rule out whether these cells were simply less differentiated myofibroblasts. Given that there might not be available antibodies that react with chicken protein versions, the authors could support their conclusions using alternative approaches, such as fluorescent in situ hybridization. A more thorough approach, such as single-cell RNA sequencing to compare the cell composition of the in vitro organoids to the in vivo colon, would fully justify the use of these organoids as a system for studying in vivo cell physiology.

With these suggestions provided, we have newly stained contractile organoids with anti-desmin antibody, known to be a marker for differentiated SMCs. As shown in new Fig. 3B, desmin-positive cells perfectly overlapped with aSMA-staining, indicating that the peripherally enclosing cells are SMCs. Regarding the interior cells, as this Reviewer concerned, there are no antibodies against ANO1/TMEM16 which are available for avian specimens. The anti- c-Kit antibody used in this study is what we raised in our hands by spending years (Yagasaki et al., 2021)), in which the antibody was carefully validated in intact guts of chicken embryos by multiple methods including Western Blot analyses, immunostaining, and in situ hybridization. We have attempted several times to perform organoidal whole-mount in situ hybridization for expression of PDGFRα, but we have not succeeded so far. In addition, as explained to the Editor, the very unhealthy condition of purchased eggs these past 7 months did not allow us to continue any further. We are planning to interrogate cell types residing in the central area of the organoid, results of which will be reported in a separate paper in near future.

(2) The key ICC-SMC relationship and physiological interaction seems to arise developmentally, but the mechanisms of this transition are not well defined (Chevalier 2020). To further support the claim that ICC-SMC interactions can be interrogated in this system, this study would benefit from establishing organoids at distinct developmental stages to (a) show that they have unique contractile profiles, and (b) demonstrate that they evolve over time in vitro toward an ICC-driven mechanism.

We agree with these comments. We tried to prepare gut contractile organoids derived from different stages of development, and we had an impression that slightly younger hindguts are available for the organoid preparations. In addition, not only the hindgut, but also midgut and caecum also yield organoids. However, since formed organoids derived from these “non-E15 hindgut” vary substantially in shapes, contraction frequencies/amplitudes etc., we are currently not ready to report these preliminary observations. Instead, we decided to optimize and elaborate in vitro culture conditions by focusing on the E15 hindgut, which turned out to be most stable in our hands. Nevertheless, it is tempting to see how organoid evolves over time during gut development.

(3) This manuscript would be greatly enhanced by a functional examination of the prospective organoid ICCs. For example, the authors could test whether the c-Kit inhibitor Imatinib, which has previously been used to impair ICC differentiation and function in the developing chick gut (Chevalier 2020), has an effect on contractility at different stages.

Following the paper of (Chevalier 2020), we had already conducted similar experiments with Imatinib in the culture with our organoids, but we did not see detectable effects. In that paper, the midgut of younger embryos was used, whereas we used E15 hindgut to prepare organoids. It would be interesting to see if we add Imanitib earlier during organoidal formation, and this is a next step to go.

(4) It is claimed that there is a 690s msec delay in SMC spike relative to ICC spike, however, it is unclear where this average is derived from and whether the organoid calcium trace shown in Figure 4C is representative of the data. The latency quantification should be shown across multiple organoids, and again in the case of carbenoxolone treatment, to better understand the variations in treatment.

We apologize that the first version failed to clearly demonstrate quantitative assessments. In the revised version, we have elaborated quantitative assessments (117 peaks for 14 organoids) (line 216-218). In new Fig. 4D, measured value is 700 msecterraced since as already mentioned in the first version, the time-lapse imaging was performed with 700 msec intervals.

(5) As above, a larger issue is that only single traces are shown for each organoid. This makes it challenging to understand the variance in contractile properties across multiple organoids. While contraction frequencies are shown several times, the manuscript would benefit from additional quantifications, such as rhythm (average wavelength between events) in control and perturbed conditions.

We have substantially elaborated quantitative assessments (please also see our responses to the “Public Review”). In particular, in place of contraction numbers/time, we have plotted “contraction intervals” between two successive peaks (Fig. 2B and others). Actually, we have tried to perform a periodicity analysis of organoid contractions. Unfortunately, no clear value has been obtained, probably because the contractions/Ca²⁺ transitions are not as “regularly periodical” as seen in conventional physics. This led us to perform the peak-interval analysis. Methods to quantify the contraction intervals are carefully explained in the revised version.

(6) The synchronicity observed between ICCs and SMCs within the organoid is interesting, and should be emphasized by making analyses more quantitative so as to understand how consistent and reproducible this phenomenon is across organoids. Moreover, one of the most exciting parts of the study is the synchronicity established between organoids in the hydrogel system, but it is insufficiently quantified. For example, how rapidly is pacemaking synchronization achieved?

As we replied above to (5), and described in the responses to the “Public Review”, we have substantially elaborated quantitative assessments in the revised version. Concerning the synchronicity between ICCs and SMCs, our data explicitly show that as long as the organoid undergoes healthy contraction, they perfectly match their rhythm (Fig. 4) making it difficult to display quantitatively. Instead, to demonstrate such synchronicity more convincingly, we have carefully described the number of peaks and the number of independent organoids we analyzed in each of Figure legends. In the experiments with hydrogels, the time required for two organoids to start/resume synchronous contraction varies greatly. For example, for the experiment shown in new Fig 9F, it takes 1 day to 2 days for cells crawling out of organoids and cover the surface of the hydrogel. In the experiments shown in new Fig. 8, two organoids undergo “pause” before resuming contractions. In the revised version, we have briefly mentioned our notice and speculation that active cell communications take place during this pausing time, (line 282-283 in Result and line 437-439 in Discussion). We agree with this reviewer saying that the pausing time is potentially very interesting. However, it is currently difficult to quantify these phenomena. More elaborate experimental design might be needed.

(7) Smooth muscle layers in vivo are well organized into circular and longitudinal layers. To establish physiological relevance, the authors should demonstrate if these organoids have multiple layers (though it looks like just a single outer layer) and if they show supracellular organization across the organoid.

The immunostaining data suggest that peripherally lining cells are of a single layer, and we assume that they might be aligned in register with contracting direction. However, to clarify these issues, observation with higher resolution would be required.

(8) To further examine whether the organoids contain true functional ICCs, the authors should test whether their calcium transients are impacted by inhibitors of L-type calcium channels, such as nifedipine and nicardipine. These channels have been demonstrated to be important for SMCs but not ICCs, so one might expect to see continued transients in the core ICCs but a loss of them in SMCs (Lee et al., 1999; PMID: 10444456)

We appreciate these comments. We have accordingly conducted new experiments with Nifedipine. Contrary to the expectation, Nifedipine ceases not only organoidal contractions, but also ICC activities (and its resulting synchronization) (new Fig. 7). These findings actually corroborate our model already mentioned in the first version that ICCs receive mechanical feedback from SMC’s contraction to stably maintain their oscillatory rhythm. We believe that the additional findings with Nifedipine have improved the quality of our paper. Concerning the central cells in the organoid, we have additionally used anti-desmin antibody known to mark differentiated SMCs. Desmin signals perfectly overlap with those of aSMA in the peripheral single layer, supporting that the peripheral cells are SMCs and central cells are ICCs. The anti c-Kit antibody used in this study is what we raised in our hands by spending years (Yagasaki et al., 2021)), in which the antibody was carefully validated in intact guts of chicken embryos by multiple methods including Western Blot analyses, immunostaining, and in situ hybridization.

ANO1/TMEM16 are known to stain ICCs in mice. Antibodies against ANO1/TMEM16 available for avian specimens are awaited.

(9) Despite Tuj1+ enteric neurons only making up a small fraction of the organoids, the authors should still functionally test whether they regulate any aspect of contractility by treating organoids with an inhibitor such as tetrodotoxin to rule out a role for them.

Thank you for these advices, which are also raised by other reviewers. We have conducted TTX administration (new Fig. S2C). Changes in contractility by this treatment is not detected, supporting the argument that neural cells/activities are not essential for rhythmic contractions of the organoid (line 178-181).

(10) Finally, the manuscript is written to suggest that the focus of the study is to establish a system to interrogate ICC-SMC interactions in gut physiology and peristalsis. However, the organoids designed in this study are derived from the fetal precursors to the adult cell types. Thus, they might not accurately portray the adult cell physiology. I don't believe that this is a downfall, but rather a strength of the study that should be emphasized. That is, the focus could be shifted toward stressing the power of this new system as a reductionist, self-organizing model to examine the developmental emergence of contractile synchronization in the intestine - in particular that arising through ICC-SMC interactions.

We appreciate these advices. In the revised MS, we are careful so that our findings do not necessarily portray the physiological functions in adult gut.

Minor:

More technical information could be used in the methods:

(1) What concentration of Matrigel is used for coating, and what size were the wells that cells were deposited into?

We have added, “14-mm diameter glass-bottom dishes (Matsunami, D11130H)” and “undiluted Matrigel (Corning, 354248) at 38.5°C for 20 min” (line 471473).

(2) How were organoids transferred to the hydrogels? And were the hydrogels coated?

We have added “Organoids were transferred to the hydrogel using a glass capillary” (line 560-561).

(3) Tests for significance and p values should be added where appropriate (e.g. Figure S3B).

We have added these in Figure legend of new Fig. S3.

Reviewer #3 (Recommendations For The Authors):

This is an exciting study, and while the majority of our comments are minor suggestions to improve the clarity and impact of findings, it would be important to verify the effective disruption of GAP junction function with CBX or 18Beta-GA treatments before concluding they are not required for coordination of contractility and initiation by ICCs. It is possible that sufficient contextual support exists in the literature for the nature of treatments used, but this may need to be conveyed within the manuscript to allay concerns that the results could be explained by ineffective inhibition of GAP junctions.

Thank you very much for these advices. In the revised version, we have newly carried out experiments with dissociated embryonic heart cells cultured in vitro, a model widely used for gap junction studies (Fig. S3D). Both CBX or 18b-GA exert efficient inhibiting activity on contractions of heart cells. We have added the following sentence, “The inhibiting activity of the drugs used here was verified using embryonic heart culture (line 237-239)”.

eLife Assessment

The study presents a comprehensive multi-approach and functional investigation of RBMX2 as a host factor involved in Mycobacterium bovis pathogenesis and its potential role in promoting epithelial-mesenchymal transition and lung cancer progression. The findings are valuable since the possible connection between M. bovis and lung cancer and the underlying mechanisms provides a promising direction for future research. The evidence is solid with methods, data, and analyses broadly supporting the claims, albeit with minor weaknesses that, if addressed, will make the evidence stronger. The study remains of great interest to microbiology, oncology, and drug discovery scientists.

Reviewer #1 (Public review):

Summary:

This manuscript presents a compelling study identifying RBMX2 as a novel host factor upregulated during Mycobacterium bovis infection.

The study demonstrates that RBMX2 plays a role in:

(1) Facilitating M. bovis adhesion, invasion, and survival in epithelial cells.

(2) Disrupting tight junctions and promoting EMT.

(3) Contributing to inflammatory responses and possibly predisposing infected tissue to lung cancer development.

By using a combination of CRISPR-Cas9 library screening, multi-omics, coculture models, and bioinformatics, the authors establish a detailed mechanistic link between M. bovis infection and cancer-related EMT through the p65/MMP-9 signaling axis. Identification of RBMX2 as a bridge between TB infection and EMT is novel.

Strengths:

This topic and data are both novel and significant, expanding the understanding of transcriptomic diversity beyond RBM2 in M. bovis responsive functions.

Weaknesses:

(1) The abstract and introduction sometimes suggest RBMX2 has protective anti-TB functions, yet results show it facilitates pathogen adhesion and survival. The authors need to rephrase claims to avoid contradiction.

(2) While p65/MMP-9 is convincingly implicated, the role of MAPK/p38 and JNK is less clearly resolved.

(3) Metabolomics results are interesting but not integrated deeply into the main EMT narrative.

(4) A key finding and starting point of this study is the upregulation of RBMX2 upon M. bovis infection. However, the authors have only assessed RBMX2 expression at the mRNA level following infection with M. bovis and BCG. To strengthen this conclusion, it is essential to validate RBMX2 expression at the protein level through techniques such as Western blotting or immunofluorescence. This would significantly enhance the credibility and impact of the study's foundational observation.

(5) The manuscript would benefit from a more in-depth discussion of the relationship between tuberculosis (TB) and lung cancer. While the study provides experimental evidence suggesting a link via EMT induction, integrating current literature on the epidemiological and mechanistic connections between chronic TB infection and lung tumorigenesis would provide important context and reinforce the translational relevance of the findings.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript presents a compelling study identifying RBMX2 as a novel host factor upregulated during Mycobacterium bovis infection.

The study demonstrates that RBMX2 plays a role in:

(1) Facilitating M. bovis adhesion, invasion, and survival in epithelial cells.

(2) Disrupting tight junctions and promoting EMT.

(3) Contributing to inflammatory responses and possibly predisposing infected tissue to lung cancer development.

By using a combination of CRISPR-Cas9 library screening, multi-omics, coculture models, and bioinformatics, the authors establish a detailed mechanistic link between M. bovis infection and cancer-related EMT through the p65/MMP-9 signaling axis. Identification of RBMX2 as a bridge between TB infection and EMT is novel.

Strengths:

This topic and data are both novel and significant, expanding the understanding of transcriptomic diversity beyond RBM2 in M. bovis responsive functions.

Weaknesses:

(1) The abstract and introduction sometimes suggest RBMX2 has protective anti-TB functions, yet results show it facilitates pathogen adhesion and survival. The authors need to rephrase claims to avoid contradiction.

We sincerely appreciate the reviewer's valuable feedback regarding the need to clarify RBMX2's role throughout the manuscript. We have carefully revised the text to ensure consistent messaging about RBMX2's function in promoting M. bovis infection. Below we detail the specific modifications made:

(1) Introduction Revisions:

Changed "The objective of this study was to elucidate the correlation between host genes and the susceptibility of M.bovis infection" to "The objective of this study was to identify host factors that promote susceptibility to M.bovis infection"

Revised "RBMX2 polyclonal and monoclonal cell lines exhibited favorable phenotypes" to "RBMX2 knockout cell lines showed reduced bacterial survival"

Replaced "The immune regulatory mechanism of RBMX2" with "The role of RBMX2 in facilitating M.bovis immune evasion"

(2) Results Revisions:

Modified "RBMX2 fails to affect cell morphology and the ability to proliferate and promotes M.bovis infection" to "RBMX2 does not alter cell viability but significantly enhances M.bovis infection"

Strengthened conclusion in Figure 4: "RBMX2 actively disrupts tight junctions to facilitate bacterial invasion"

(3) Discussion Revisions:

Revised screening description: "We screened host factors affecting M.bovis susceptibility and identified RBMX2 as a key promoter of infection"

Strengthened concluding statement: "In summary, RBMX2 drives TB pathogenesis by compromising epithelial barriers and inducing EMT"

These targeted revisions ensure that:

All sections consistently present RBMX2 as promoting infection; the language aligns with our experimental finding; potential protective interpretations have been eliminated. We believe these modifications have successfully addressed the reviewer's concern while maintaining the manuscript's original structure and scientific content. We appreciate the opportunity to improve our manuscript and thank the reviewer for this constructive suggestion.

(2) >While p65/MMP-9 is convincingly implicated, the role of MAPK/p38 and JNK is less clearly resolved.

We sincerely appreciate the reviewer's insightful comment regarding the roles of MAPK/p38 and JNK in our study. Our experimental data clearly demonstrated that RBMX2 knockout significantly reduced phosphorylation levels of p65, p38, and JNK (Fig. 5A), indicating potential involvement of all three pathways in RBMX2-mediated regulation.

Through systematic functional validation, we obtained several important findings:

In pathway inhibition experiments, p65 activation (PMA treatment) showed the most dramatic effects on both tight junction disruption (ZO-1, OCLN reduction) and EMT marker regulation (E-cadherin downregulation, N-cadherin upregulation);

p38 activation (ML141 treatment) exhibited moderate effects on these processes;

JNK activation (Anisomycin treatment) displayed minimal impact.

Most conclusively, siRNA-mediated silencing of p65 alone was sufficient to:

Restore epithelial barrier function

Reverse EMT marker expression

Reduce bacterial adhesion and invasion

These results establish a clear hierarchy in pathway importance: p65 serves as the primary mediator of RBMX2's effects, while p38 plays a secondary role and JNK appears non-essential under our experimental conditions. We have now clarified this relationship in the revised Discussion section to strengthen this conclusion.

This refined understanding of pathway hierarchy provides important mechanistic insights while maintaining consistency with all our experimental data. We thank the reviewer for this valuable suggestion that helped improve our manuscript.

(3) Metabolomics results are interesting but not integrated deeply into the main EMT narrative.

Thank you for this constructive suggestion. In this article, we detected the metabolome of RBMX2 knockout and wild-type cells after Mycobacterium bovis infection, which mainly served as supporting evidence for our EMT model. However, we did not conduct an in-depth discussion of these findings. We have now added a detailed discussion of this section to further support our EMT model.

ADD:Meanwhile, metabolic pathways enriched after RBMX2 deletion, such as nucleotide metabolism, nucleotide sugar synthesis, and pentose interconversion, primarily support cell proliferation and migration during EMT by providing energy precursors, regulating glycosylation modifications, and maintaining redox balance; cofactor synthesis and amino sugar metabolism participate in EMT regulation through influencing metabolic remodeling and extracellular matrix interactions; chemokine and cGMP-PKG signaling pathways may further mediate inflammatory responses and cytoskeletal rearrangements, collectively promoting the EMT process.

(4) A key finding and starting point of this study is the upregulation of RBMX2 upon M. bovis infection. However, the authors have only assessed RBMX2 expression at the mRNA level following infection with M. bovis and BCG. To strengthen this conclusion, it is essential to validate RBMX2 expression at the protein level through techniques such as Western blotting or immunofluorescence. This would significantly enhance the credibility and impact of the study's foundational observation.

Thank you for your comment. We have supplemented the experiments in this part and found that Mycobacterium bovis infection can significantly enhance the expression level of RBMX2 protein.

(5) The manuscript would benefit from a more in-depth discussion of the relationship between tuberculosis (TB) and lung cancer. While the study provides experimental evidence suggesting a link via EMT induction, integrating current literature on the epidemiological and mechanistic connections between chronic TB infection and lung tumorigenesis would provide important context and reinforce the translational relevance of the findings.

We sincerely appreciate the valuable comments from the reviewer. We fully agree with your suggestion to further explore the relationship between tuberculosis (TB) and lung cancer. In the revised manuscript, we will add a new paragraph in the Discussion section to systematically integrate the current literature on the epidemiological and mechanistic links between chronic tuberculosis infection and lung cancer development, including the potential bridging roles of chronic inflammation, tissue damage repair, immune microenvironment remodeling, and the epithelial-mesenchymal transition (EMT) pathway. This addition will help more comprehensively interpret the clinical implications of the observed EMT activation in the context of our study, thereby enhancing the biological plausibility and clinical translational value of our findings.

ADD:There is growing epidemiological evidence suggesting that chronic TB infection represents a potential risk factor for the development of lung cancer. Studies have shown that individuals with a history of TB exhibit a significantly increased risk of lung cancer, particularly in areas of the lung with pre-existing fibrotic scars, indicating that chronic inflammation, tissue repair, and immune microenvironment remodeling may collectively contribute to malignant transformation 74. Moreover, EMT not only endows epithelial cells with mesenchymal features that enhance migratory and invasive capacity but is also associated with the acquisition of cancer stem cell-like properties and therapeutic resistance 75. Therefore, EMT may serve as a crucial molecular link connecting chronic TB infection with the malignant transformation of lung epithelial cells, warranting further investigation in the intersection of infection and tumorigenesis.

Reviewer #2 (Public review):

Summary:

I am not familiar with cancer biology, so my review mainly focuses on the infection part of the manuscript. Wang et al identified an RNA-binding protein RBMX2 that links the Mycobacterium bovis infection to the epithelial-Mesenchymal transition and lung cancer progression. Upon mycobacterium infection, the expression of RBMX2 was moderately increased in multiple bovine and human cell lines, as well as bovine lung and liver tissues. Using global approaches, including RNA-seq and proteomics, the authors identified differential gene expression caused by the RBMX2 knockout during M. bovis infection. Knockout of RBMX2 led to significant upregulations of tight-junction related genes such as CLDN-5, OCLN, ZO-1, whereas M. bovis infection affects the integrity of epithelial cell tight junctions and inflammatory responses. This study establishes that RBMX2 is an important host factor that modulates the infection process of M. bovis.

Strengths:

(1) This study tested multiple types of bovine and human cells, including macrophages, epithelial cells, and clinical tissues at multiple timepoints, and firmly confirmed the induced expression of RBMX2 upon M. bovis infection.

(2) The authors have generated the monoclonal RBMX2 knockout cell lines and comprehensively characterized the RBMX2-dependent gene expression changes using a combination of global omics approaches. The study has validated the impact of RBMX2 knockout on the tight-junction pathway and on the M. bovis infection, establishing RBMX2 as a crucial host factor.

Weaknesses:

(1) The RBMX2 was only moderately induced (less than 2-fold) upon M. bovis infection, arguing its contribution may be small. Its value as a therapeutic target is not justified. How RBMX2 was activated by M. bovis infection was unclear.

Thank you for your valuable and constructive comments. In this study, we primarily utilized the CRISPR whole-genome screening approach to identify key factors involved in bovine tuberculosis infection. Through four rounds of screening using a whole-genome knockout cell line of bovine lung epithelial cells infected with Mycobacterium bovis, we identified RBMX2 as a critical factor.

Although the transcriptional level change of RBMX2 was less than two-fold, following the suggestion of Reviewer 1, we examined its expression at the protein level, where the change was more pronounced, and we have added these results to the manuscript.

Regarding the mechanism by which RBMX2 is activated upon M. bovis infection, we previously screened for interacting proteins using a Mycobacterium tuberculosis secreted and membrane protein library, but unfortunately, we did not identify any direct interacting proteins from M. tuberculosis (https://doi.org/10.1093/nar/gkx1173).

(2) Although multiple time points have been included in the study, most analyses lack temporal resolution. It is difficult to appreciate the impact/consequence of M. bovis infection on the analyzed pathways and processes.

We appreciate the valuable comments from the reviewers. Although our study included multiple time points post-infection, in our experimental design we focused on different biological processes and phenotypes at distinct time points:

During the early phase (e.g., 2 hours post-infection), we focused on barrier phenotypes; during the intermediate phase (e.g., 24 hours post-infection), we concentrated more on pathway activation and EMT phenotypes;

And during the later phase (e.g., 48–72 hours post-infection), we focused more on cell death phenotypes, which were validated in another FII article (https://doi.org/10.3389/fimmu.2024.1431207).

We also examined the impact of varying infection durations on RBMX2 knockout EBL cellular lines via GO analysis. At 0 hpi, genes were primarily related to the pathways of cell junctions, extracellular regions, and cell junction organization. At 24 hpi, genes were mainly associated with pathways of the basement membrane, cell adhesion, integrin binding and cell migration By 48 hpi, genes were annotated into epithelial cell differentiation and were negatively regulated during epithelial cell proliferation. This indicated that RBMX2 can regulate cellular connectivity throughout the stages of M. bovis infection.

For KEGG analysis, genes linked to the MAPK signaling pathway, chemical carcinogen-DNA adducts, and chemical carcinogen-receptor activation were observed at 0 hpi. At 24 hpi, significant enrichment was found in the ECM-receptor interaction, PI3K-Akt signaling pathway, and focal adhesion. Upon enrichment analysis at 48 hpi, significant enrichment was noted in the TGF-beta signaling pathway, transcriptional misregulation in cancer, microRNAs in cancer, small cell lung cancer, and p53 signaling pathway.

Reviewer #3 (Public review):

Summary:

This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

Strengths:

The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

Weaknesses:

Although it's a solid study, there are a few weaknesses noted below.

(1) In the transcriptomics analysis, the authors performed (GO/KEGG) to explore biological functions. Did they perform the search locally or globally? If the search was performed with a global reference, then I would recommend doing a local search. That would give more relevant results. What is the logic behind highlighting some of the enriched pathways (in red), and how are they relevant to the current study?

We appreciate the reviewer's thoughtful questions regarding our transcriptomic analysis. In this study, we employed a localized enrichment approach focusing specifically on gene expression profiles from our bovine lung epithelial cell system. This cell-type-specific analysis provides more biologically relevant results than global database searches alone.

Regarding the highlighted pathways, these represent:

(1) Temporally significant pathways showing strongest enrichment at each stage:

• 0h: Cell junction organization (immediate barrier response)

• 24h: ECM-receptor interaction (early EMT initiation)

• 48h: TGF-β signaling (chronic remodeling)

(2) Mechanistically linked to our core findings about RBMX2's role in:

• Epithelial barrier disruption

• Mesenchymal transition

• Chronic infection outcomes

We selected these particular pathways because they:

(1) Showed the most statistically significant changes (FDR <0.001)

(2) Formed a coherent biological narrative across infection stages

(3) Were independently validated in our functional assays

This targeted approach allows us to focus on the most infection-relevant pathways while maintaining statistical rigor.

(2) While the authors show that RBMX2 expression correlates with EMT-related gene expression and barrier dysfunction, the evidence for direct association remains limited in this study. How does RBMX2 activate p65? Does it bind directly to p65 or modulate any upstream kinases? Could ChIP-seq or CLIP-seq provide further evidence for direct RNA or DNA targets of RBMX2 that drive EMT or NF-κB signaling?

We sincerely appreciate the reviewer's in-depth questions regarding the mechanisms by which RBMX2 activates p65 and its association with EMT. Although the molecular mechanism remains to be fully elucidated, our study has provided experimental evidence supporting a direct regulatory relationship between RBMX2 and the p65 subunit of the NF-κB pathway. Specifically, we investigated whether the transcription factor p65 could directly bind to the promoter region of RBMX2 using CHIP experiments. The results demonstrated that the transcription factor p65 can physically bind to the RBMX2 region.

Furthermore, dual-luciferase reporter assays were conducted, showing that p65 significantly enhances the transcriptional activity of the RBMX2 promoter, indicating a direct regulatory effect of RBMX2 on p65 expression.

These findings support our hypothesis that RBMX2 activates the NF-κB signaling pathway through direct interaction with the p65 protein, thereby participating in the regulation of EMT progression and barrier function.

In our subsequent work papers, we will also employ experiments such as CLIP to further investigate the specific mechanisms through which RBMX2 exerts its regulatory functions.

(3) The manuscript suggests that RBMX2 enhances adhesion/invasion of several bacterial species (e.g., E. coli, Salmonella), not just M. bovis. This raises questions about the specificity of RBMX2's role in Mycobacterium-specific pathogenesis. Is RBMX2 a general epithelial barrier regulator or does it exhibit preferential effects in mycobacterial infection contexts? How does this generality affect its potential as a TB-specific therapeutic target?

Thank you for your valuable comments. When we initially designed this experiment, we were interested in whether the RBMX2 knockout cell line could confer effective resistance not only against Mycobacterium bovis but also against Gram-negative and Gram-positive bacteria. Surprisingly, we indeed observed resistance to the invasion of these pathogens, albeit weaker compared to that against Mycobacterium bovis.

Nevertheless, we believe these findings merit publication in eLife. Moreover, RBMX2 knockout does not affect the phenotype of epithelial barrier disruption under normal conditions; its significant regulatory effect on barrier function is only evident upon infection with Mycobacterium bovis.

Importantly, during our genome-wide knockout library screening, RBMX2 was not identified in the screening models for Salmonella or Escherichia coli, but was consistently detected across multiple rounds of screening in the Mycobacterium bovis model.

(4) The quality of the figures is very poor. High-resolution images should be provided.

Thank you for your feedback; we provided higher-resolution images.

(5) The methods are not very descriptive, particularly the omics section.

Thank you for your comments; we have revised the description of the sequencing section.

(6) The manuscript is too dense, with extensive multi-omics data (transcriptomics, proteomics, metabolomics) but relatively little mechanistic integration. The authors should have focused on the key mechanistic pathways in the figures. Improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

Thank you for your valuable comments. We have streamlined the figures and revised the description of the results section accordingly.

Reviewer #2 (Public review):

Summary:

I am not familiar with cancer biology, so my review mainly focuses on the infection part of the manuscript. Wang et al identified an RNA-binding protein RBMX2 that links the Mycobacterium bovis infection to the epithelial-Mesenchymal transition and lung cancer progression. Upon mycobacterium infection, the expression of RBMX2 was moderately increased in multiple bovine and human cell lines, as well as bovine lung and liver tissues. Using global approaches, including RNA-seq and proteomics, the authors identified differential gene expression caused by the RBMX2 knockout during M. bovis infection. Knockout of RBMX2 led to significant upregulations of tight-junction related genes such as CLDN-5, OCLN, ZO-1, whereas M. bovis infection affects the integrity of epithelial cell tight junctions and inflammatory responses. This study establishes that RBMX2 is an important host factor that modulates the infection process of M. bovis.

Strengths:

(1) This study tested multiple types of bovine and human cells, including macrophages, epithelial cells, and clinical tissues at multiple timepoints, and firmly confirmed the induced expression of RBMX2 upon M. bovis infection.

(2) The authors have generated the monoclonal RBMX2 knockout cell lines and comprehensively characterized the RBMX2-dependent gene expression changes using a combination of global omics approaches. The study has validated the impact of RBMX2 knockout on the tight-junction pathway and on the M. bovis infection, establishing RBMX2 as a crucial host factor.

Weaknesses:

(1) The RBMX2 was only moderately induced (less than 2-fold) upon M. bovis infection, arguing its contribution may be small. Its value as a therapeutic target is not justified. How RBMX2 was activated by M. bovis infection was unclear.

(2) Although multiple time points have been included in the study, most analyses lack temporal resolution. It is difficult to appreciate the impact/consequence of M. bovis infection on the analyzed pathways and processes.

Reviewer #3 (Public review):

Summary:

This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

Strengths:

The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

Weaknesses:

Although it's a solid study, there are a few weaknesses noted below.

(1) In the transcriptomics analysis, the authors performed (GO/KEGG) to explore biological functions. Did they perform the search locally or globally? If the search was performed with a global reference, then I would recommend doing a local search. That would give more relevant results. What is the logic behind highlighting some of the enriched pathways (in red), and how are they relevant to the current study?

(2) While the authors show that RBMX2 expression correlates with EMT-related gene expression and barrier dysfunction, the evidence for direct association remains limited in this study. How does RBMX2 activate p65? Does it bind directly to p65 or modulate any upstream kinases? Could ChIP-seq or CLIP-seq provide further evidence for direct RNA or DNA targets of RBMX2 that drive EMT or NF-κB signaling?

(3) The manuscript suggests that RBMX2 enhances adhesion/invasion of several bacterial species (e.g., E. coli, Salmonella), not just M. bovis. This raises questions about the specificity of RBMX2's role in Mycobacterium-specific pathogenesis. Is RBMX2 a general epithelial barrier regulator or does it exhibit preferential effects in mycobacterial infection contexts? How does this generality affect its potential as a TB-specific therapeutic target?

(4) The quality of the figures is very poor. High-resolution images should be provided.

(5) The methods are not very descriptive, particularly the omics section.

(6) The manuscript is too dense, with extensive multi-omics data (transcriptomics, proteomics, metabolomics) but relatively little mechanistic integration. The authors should have focused on the key mechanistic pathways in the figures. Improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

eLife Assessment

This work describes an inference technique for extracting information about relative contributions of excitatory and inhibitory synaptic drive onto single neurons in neural networks. The electrophysiological techniques and results are of high quality, and the analytical work is novel and potentially powerful, yet with several untested assumptions underlying the approach. This is nevertheless solid work that will be valuable to neuroscience labs interested in exploring alternative approaches to studies of integrated synaptic connectivity.

Reviewer #2 (Public review):

Summary:

By measuring intracellular changes in membrane voltage from a single neuron of the medulla the authors attempted to develop a method for determining the balance of excitatory and inhibitory synaptic drive onto a single neuron.

Strengths:

This data-driven approach to explore neural circuits is described well in this study and could be valuable in identifying microcircuits that generate rhythms. Importantly, perhaps, this inference method could enable microcircuits to be studied without the need for time-consuming anatomical tracing or other more involved electrophysiological techniques. Therefore, I can see the value in developing an approach of this type.

Weaknesses:

The implications of several assumptions associated with this inference technique have been considered by the authors.

Most importantly, it is my understanding that this approach assumes a linear I-V when extracting information about the excitatory and inhibitory synaptic conductances (see equations 6 and 7). In Figure 6, the authors explore the impact of varying the reversal potential for the extraction of information about synaptic drive, but this still assumes that the underlying conductance is linear. However, open rectification will be a feature of any conductance generated by asymmetric distributions of ions (see the GHK current equation) and will therefore be a particular issue for the inhibition resulting from asymmetrical Cl- ion gradients across GABA-A receptors as well as the K+ conductance indirectly activated by GABA-B receptor activation. The mixed cation conductance that underlies most synaptic excitation will also generate a non-linear I-V relationship due to the inward rectification associated with polyamine block of AMPA receptors. The authors present evidence that the I-V relationship is linear over most of the voltage range examined, and this is a helpful addition. The authors have discussed the absence of active conductances contributing to the I-V, but I still wonder how the extraction of information concerning the excitatory and inhibitory conductances relies on the assumption of a linear I-V for these conductances.

This approach has similarities to earlier studies undertaken in the visual cortex that estimated the excitatory and inhibitory synaptic conductance changes that contributed to membrane voltage changes during receptive field stimulation. However, these approaches also involved the recording of transmembrane current changes during visual stimulation that were undertaken in voltage-clamp at various command voltages to estimate the underlying conductance changes. Molkov et al have attempted to essentially deconvolve the underlying conductance changes without this information and I am concerned that this simply may not be possible. However, I appreciate the efforts taken by the authors to address this issue.

The current balance equation (1) cited in this study is based upon the parallel conductance model developed by Hodgkin & Huxley. One key element of the HH equations is the inclusion of an estimate of the capacitive current generated due to the change in voltage across the membrane capacitance. While the present study considers the impact of membrane capacitance, a deeper discussion on how variations in capacitance across different neuron types might affect inference accuracy would be useful. Differences in capacitance could introduce variability in inferred conductances, potentially influencing model predictions.

Studies using acute slicing preparations to examine circuit effects have often been limited to the study of small microcircuits, especially feedforward and feedback interneuron circuits. It is widely accepted that any information gained from this approach will always be compromised by the absence of patterned afferent input from outside the brain region being studied. In this study, descending control from the Pons and the neocortex will not be contributing much to the synaptic drive and ascending information from respiratory muscles will also be absent completely. This may not have been such a major concern if this study had been limited to demonstrating the feasibility of a methodological approach. However, this limitation does need to be considered when using an approach of this type to speculate on the prevalence of specific circuit motifs within the medulla (Figure 4). Therefore, I would argue that some discussion of this limitation should be included in this manuscript.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study aims to create a comprehensive repository about the changes in protein abundance and their modification during oocyte maturation in Xenopus laevis.

Strengths:

The results contribute meaningfully to the field.

Weaknesses:

The manuscript could have benefitted from more comprehensive analyses and clearer writing. Nonetheless, the key findings are robust and offer a valuable resource for the scientific community.

We would like to thank the reviewer for his/her positive feedback on our article. The public review points out that "The manuscript could have benefitted from more comprehensive analyses and clearer writing." We have rewritten several sections and provided more detailed explanations of the analysis and interpretation of some data (see below for details). We have also followed all of the reviewer's recommendations, some of which specifically highlighted areas lacking clarity. We would also like to thank the reviewer for pointing out some errors, for which we apologize, and which have now been corrected. We sincerely appreciate the reviewer's thorough work, as it has greatly enhanced the clarity and precision of the manuscript.

Reviewer #2 (Public review):

Summary:

The authors analyzed Xenopus oocytes at different stages of meiosis using quantitative phosphoproteomics. Their advanced methods and analyses revealed changes in protein abundances and phosphorylation states to an unprecedented depth and quantitative detail. In the manuscript they provide an excellent interpretation of these findings putting them in the context of past literature in Xenopus as well as in other model systems.

Strengths:

High quality data, careful and detailed analysis, outstanding interpretation in the context of the large body of the literature.

Weaknesses:

Merely a resource, none of the findings are tested in functional experiments.

I am very impressed by the quality of the data and the careful and detailed interpretation of the findings. In this form the manuscript will be an excellent resource to the cell division community in general, and it presents a very large number of hypotheses that can be tested in future experiments. Xenopus has been and still is a popular and powerful model system that led to critical discoveries around countless cellular processes, including the spindle, nuclear envelope, translational regulation, just to name a few. This also includes a huge body of literature on the cell cycle describing its phosphoregulation. It is indeed somewhat frustrating to see that these earlier studies using phosphomutants and phospho-antibodies were just scratching the surface. The phosphoproteomics analysis presented here reveals much more extensive and much more dynamic changes in phosphorylation states. Thereby, in my opinion, this manuscript opens a completely new chapter in this line of research, setting the stage for more systematic future studies.

We thank the reviewer for his/her extremely positive comments. The public review points out that "none of the findings are tested in functional experiments." This is entirely accurate. We focused our work on obtaining the highest quality proteomic and phosphoproteomic data possible, and then sought to highlight these data by connecting them with existing functional data from the literature. This approach has opened up research avenues with enormous, previously unforeseen potential, in a wide range of biological fields (cell cycle, meiosis, oogenesis, embryonic development, cell biology, cellular physiology, signaling, evolution, etc.). We chose not to delay publication by experimentally investigating the narrow area in which we are specialists (meiotic maturation), while our data offer a vast array of research opportunities across various fields. Our goal was, therefore, to present this extensive dataset as a resource for different scientific communities, who can explore their specific biological questions using our data. This is why we submitted our article to the "Repository" section of eLife. Nevertheless, in the context of the comparative analysis of the mouse and Xenopus phosphoproteomes performed at the reviewer’s request, we felt it was important to complement this new section with functional experiments that not only validate the proteomic data but also provide new insights into certain proteins and their regulation by Cdk1 (new paragraph lines 824-860 and new Figure 9).

We are also grateful to the reviewer for the recommendation to improve the manuscript by including more comparisons between our Xenopus data and those from other systems. We have followed this suggestion (see below), which has significantly enriched the article (new paragraph lines 824-860 and new Figure 9).

Reviewer #3 (Public review):

Summary:

The authors performed time-resolved proteomics and phospho-proteomics in Xenopus oocytes from prophase I through the MII arrest of the unfertilized egg. The data contains protein abundance and phosphorylation sites of a large number set of proteins at different stages of oocyte maturation. The large sets of the data are of high quality. In addition, the authors discussed several key pathways critical for the maturation. The data is very useful for the researchers not only researchers in Xenopus oocytes but also those in oocyte biology in other organisms.

Strengths:

The data of proteomics and phospho-proteomics in Xenopus oocyte maturation is very useful for future studies to understand molecular networks in oocyte maturation.

Weaknesses:

Although the authors offered molecular pathways of the phosphorylation in the translation, protein degradation, cell cycle regulation, and chromosome segregation. The author did not check the validity of the molecular pathways based on their proteomic data by the experimentation.

We thank the reviewer for his/her positive comments. The public review points out that "The author did not check the validity of the molecular pathways based on their proteomic data by the experimentation." This is entirely accurate. We focused our work on obtaining the highest quality proteomic and phosphoproteomic data possible, and then sought to highlight these data by connecting them with existing functional data from the literature. This approach has opened up research avenues with enormous, previously unforeseen potential, in a wide range of biological fields (cell cycle, meiosis, oogenesis, embryonic development, cell biology, cellular physiology, signaling, evolution, etc.). We chose not to delay publication by experimentally investigating the very narrow area in which we are specialists (meiotic maturation), while our data offer a vast array of research opportunities across various fields. Our goal was, therefore, to present this extensive dataset as a resource for different scientific communities, who can explore their specific biological questions using our data. This is why we submitted our article to the "Repository" section of eLife. Nevertheless, in the context of the comparative analysis of the mouse and Xenopus phosphoproteomes performed at the reviewer’s request, we felt it was important to complement this new section with functional experiments that not only validate the proteomic data but also provide new insights into certain proteins and their regulation by Cdk1 (new paragraph lines 824-860 and new Figure 9).

We have also followed all of the reviewer's recommendations and thank him/her, as the suggestions have significantly enhanced the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Fig. 1 -> In the Figure legend "mPRβ" is called "mPRb". In the Figure, it is indicated that PKA substrates are always activated by the phosphorylation. As the relevant substrates and the mode-of-action of the Arpp19 phosphorylation are not clear at the moment, this seems to be preliminary. It could for example also be conceivable that PKA phosphorylation inhibits a translation activator. In addition, the PG-dependent translation of RINGO/Speedy should be included in the model.

We fully agree with the reviewer. PKA substrates can either be activators of the Cdk1 activation pathway, which are inhibited by phosphorylation by PKA, or repressors of the same pathway, which are activated by phosphorylation by PKA. This is now illustrated in the new Fig. 1. In addition, we have also included RINGO/Speedy in the model and in the text (lines 78-79) and corrected "mPRb" in the legend.

(2) Lane 51-52 -> it is questionable if the meiotic divisions can be called "embryonic processes"

We agree with the reviewer comment, and we have removed the word “embryonic”.

(3) Lane 53 and lane 106-107 -> recent data have indicated that transcription already starts during cell cycle 12 and 13 in most cells (e.g. Blitz and Cho: Control of zygotic genome activation in Xenopus (2021))

We apologize for this mistake. The text has been corrected and the reference added (lines 53 and 107).

(4) Lane 61-62 -> "MI" and "MII" are given as abbreviation for "first and second meiotic spindle"

The text has been clarified to explain that MI is referred to metaphase I and MII stands for metaphase II (lines 61-64).

(%) Lane 131-132 -> "single-cell" is mentioned redundantly in this sentence.

The sentence has been corrected (lines 131-132).

(6) Fig. 2B -> it is not explained what is plotted as "Average levels" on the x-Axis. Is it the average of expression over all samples or at a given time point? Are the values given as a concentration or are the values normalized? If so, how were they normalized?

We agree with the reviewer comment that “Average levels” may have been unclear. In the new Fig. 2B, we have re-plotted the graph using the average protein concentration during meiosis, measured as described in the Methods section.

(7) In Fig. 2-supplement 3E -> from the descriptions it is not entirely clear to me what the difference to the data in Fig. 2B is?

We thank the reviewer for his/her question regarding the relationship between the data in Fig. 2B and Fig. 2-supplement 3E. We confirm that the raw data visualized in Fig. 2-supplement 3E are the same as those in Fig. 2B. However, in Fig. 2-supplement 3E, the data are color-coded differently to highlight the number of proteins whose concentrations change during meiotic divisions, based on the threshold adopted. The legend of Fig. 2-supplement 3E has been modified to clarify this point.

(8) Lane 225-226 -> Kifc1 is a minus-end directed motor

This mistake has been corrected (lines 232-233).

(9) Lane 271 -> Serbp1, here mentioned to be involved in stabilization of mRNAs, has also been implicated in the regulation of ribosomes (e.g. Leesch et al. 2023). Regarding the overall topic of this manuscript, this could be mentioned as well.

We agree with the referee that the important role of Serbp1 in the control of ribosome hibernation needs to be mentioned. We have included this point in the revised manuscript together with the reference (lines 277-279).

(10) Lane 360-363 -> it is mentioned that APPL1 and Akt2 act "to induce meiosis". Furthermore, in the Nader et al. 2020 paper, Akt2 phosphorylation is reported to happen within 30min after PG treatment. In the present work, they only seem to get phosphorylated when Cdk1 is activated. Is there an explanation for this discrepancy?

Indeed, Nader et al. (2020) indicate that Akt2 is phosphorylated on Ser473 (actually, they should have mentioned Ser474, which is the phosphorylated residue on Akt2; Ser473 corresponds to the numbering of Akt1) between 5 and 30 minutes post-Pg, which supports their hypothesis of an early role for this kinase. However, these conclusions should be taken with caution, considering that their functional experiment using antisense against Akt2 depletes only 25% of the protein, the antibody used to visualize Akt2 phosphorylation also recognizes phosphorylated Akt1 and Akt3, and they did not analyze phosphorylation of the protein after 30 minutes. Therefore, we cannot determine whether the level observed at 30 minutes represents a maximum or if it is just the onset of the phosphorylation that peaks later, possibly after activation of Cdk1, for example.

Regarding our measurements: we clearly observe phosphorylation of Akt2 following Cdk1 activation on Ser131. We did not detect Akt2 phosphorylation on Ser474, but since our measurements started 1 hour post-Pg, this protein may have returned to a dephosphorylated state on Ser474.

Therefore, the observations of Nader et al. and ours involve different residues and different phosphorylation kinetics, Nader et al. limiting their analysis to the first 30 minutes, whereas we started at 1 hour.

We have revised the manuscript text to make these aspects clearer (lines 387-392).

(11) Fig. 3B -> it could be made clearer in the Figure that all these sites belong to class I

A title “Class I proteins” has been added in Fig. 3B to clarify it.

(12) Lane 433-434 -> the authors write that the proteomic data of this study confirm that PATL1 is accumulating during meiotic maturation. However, in Fig. 2B PATL1 is not among the significantly enriched proteins.

We apologize for this error. Indeed, PATL1 protein is not significantly enriched. The text has been corrected (lines 461-465).

(13) Fig. 4B -> Zar2 is color-coded to increase in abundance. This is clearly different to published results and what is shown in Fig. 2B of this manuscript.

Indeed, our dataset shows that the quantity of Zar2 decreases. This does not appear anymore in Figure 2B since Zar2 average concentration cannot be estimated. We made an error in the color coding, which has now been corrected in Figure 4B.

(14) Lane 442-444 -> it might be worth mentioning that the interaction between CPEB1 and Maskin, and thus probably its role in regulation of translation, could not be reproduced in other studies (Minshall et al.: CPEB interacts with an ovary-specific eIF4E and 4E-T in early Xenopus oocytes (2007) or Duran-Arque et al.: Comparative analyses of vertebrate CPEB proteins define two subfamilies with coordinated yet distinct functions in post-transcriptional gene regulation (2022)).

This clarification is now mentioned in the text, supported by the two references that have been added (lines 471-477).

(15) Lane 483-485 -> The meaning of these sentences is not entirely clear to me. What exactly is the similarity with the function of Emi1? What does "...binding of Cyclin B1..." mean (binding to which other protein?). What is the similarity between Emi1 and CPEB1/BTG4, both of which are regulators of mRNA stability/polyadenylation?

We apologize if these sentences were unclear. Our intention was to emphasize the central role of ubiquitin ligases in regulating multiple events during meiotic divisions. We used SCF^βTrCP, a wellstudied ubiquitin ligase in Xenopus and mouse oocytes during meiosis, as an example. SCF^βTrCP regulates the degradation of several substrates, including Emi1, Emi2, CPEB1, and Btg4, whose degradation or stabilization is essential for the proper progression of meiosis. Lastly, we highlighted that these regulatory processes, mediated by protein degradation, may be conserved in mitosis, as for example the destruction of Emi1. We have rewritten this paragraph for clarity (lines 513-518).

(16) Lane 521-522 and 572-573 -> the authors write that Myt1 was not detected in their proteome. However, in Fig. 6A they list "pkmyt1" as a class II protein. On Xenbase, "pkmyt1" is the Cdk1 kinase, "Myt1" is a transcription factor, so the authors might have been looking for the wrong protein.

We thank the reviewer for this accurate observation. We have modified the text to correct this error (lines 554 and 607).

(17) Lane 564-565 -> The authors state that Cdk1 activity can be measured by analyzing Cdc27 S428 phosphorylation. However, in vivo the net phosphorylation of a site is always depending on the relevant kinase and phosphatase activities. As S428 is a Cdk1 site, it is not unlikely that it is dephosphorylated by PP2A-B55, which by itself is under the control of Cdk1. Do the authors have direct evidence that the change in phosphorylation of S428 can only be attributed to the changes in Cdk1 activity?

There is evidence in the literature that Cdc27 is dephosphorylated by PP2A (Torres et al., 2010). In Xenopus oocytes, PP2A activity is high during prophase (Lemonnier et al., 2021) and decreases at the time of Cdk1 activation, mediated by the Greatwall-ENSA/Arpp19 system, remaining low until MII (Labbé et al., 2021). Therefore, the period where fluctuations in Cdk1 activity are difficult to assess, from NEBD to MII, corresponds to a phase of inhibited PP2A activity. As a result, the phosphorylation level of Cdc27 reflects primarily the activity of Cdk1. We have added this clarification in the text (lines 597-600).

(18) Fig. 7C and 7D -> in 7C, for Nup35/Nup53 there is a phospho-peptide GIMEVRS(60)PPLHSGG. In Fig. 7D phosphorylation of GVMEMRS(59)PLFSGG is analyzed. Is this the same phosphosite/region of Nup35/Nup53? How can there be a slightly different version of the same peptide in one protein? Are these the L- and S-version of Nup35/Nup53? It is also very surprising that the two phosphosites belong to different classes, class III and class II, respectively.

We thank the reviewer for this observation. The peptides GIMEVRS(60)PPLHSGG and GVMEMRS(59)PLFSGG correspond to the same phosphorylation site in the L and S versions of Xenopus laevis Nup35, respectively. The L version peptide was classified as Class III, while the S version was not assigned to any class due to its high phosphorylation level in prophase, which prevented it from meeting the log₂ fold-change threshold of 1 required by our analysis to detect significant differences.

(19) Table 1 -> second last column is headed "Whur, 2014"

The typo has been corrected.

(20) Fig. 8 -> Why are all the traces starting at t=1h after PG?

The labeling of the graphs in Fig. 8 has been corrected, and the traces now begin at t0.

(21) Lane 754 -> Although a minority, there are also some minus-end directed kinesins, e.g. Kifc1

We agree with the reviewer. We should have mentioned that, in addition to dyneins, some kinesins are minus-end directed motors, especially since one of them, Kifc1, is regulated at the level of its accumulation. We have rephrased the relevant sentences to incorporate this observation (lines 790-793).

(22) Section "Assembly of microtubule spindles and microtubule dynamics" -> Although this section clearly has a strong focus on phosphorylation, it might be worth mentioning again that many regulators of the microtubule spindle, e.g. TXP2, are among the upregulated proteins in Fig. 2B/C

We have already discussed that the protein levels of certain key regulators of the mitotic spindle (Tpx2, PRC1, SSX2IP, Kif11/Eg5 among others) are subject to control during meiotic maturation in a previous chapter “Protein accumulation: the machinery of cell division and DNA replication” (lines 230-239). We agree with the reviewer that this important observation can be mentioned again at the beginning of this chapter on phosphorylation control. We have added a sentence regarding this at the start of the paragraph (lines 774-775).

Reviewer #2 (Recommendations for the authors):

While I find the manuscript excellent and detailed already in its current form, I would appreciate including even more comparisons to other systems. In particular, a similar phosphoproteomics experiment has been performed in starfish oocytes undergoing meiosis (Swartz et al, eLife, 2021), and there are several studies on mitosis of diverse mammalian cells. It would be very exciting to see to what extent changes are conserved.

We thank the reviewer for this recommendation, which we have attempted to follow. We have matched our dataset of mass spectrometry using the the phosphor-occupancy_matlab package, available as part of our code repository (https://github.com/elizabeth-van-itallie) previously described in (Van Itallie et al, 2025). Unfortunately, we were unable to match our dataset with the data from Swartz et al. (2021) on starfish oocyte due to the low sequence conservation. However, we have compared our dataset with the dataset from Sun et al. (2024) on mouse oocyte maturation. We identified a total of 408 conserved phosphorylation sites, which mapped to 320 proteins in Xenopus and 277 in mice (refer to a new paragraph: lines 824-860, new Figure 9, Methods: lines 1011-1032 and 1060-1065, and Appendix 7). The phosphorylation patterns during meiosis showed a significant crossspecies correlation (Pearson r = 0.39, p < 0.0001; see new Figure 9A), demonstrating the evolutionary conservation of phosphoproteomic regulation. Important phosphorylation events, including Plk1 at T201, Gwl at S467, and Erk2 at T188, were upregulated in both species, in line with the activation of the Cdk1 and MAPK signaling cascades (Figure 6B, new Figure 9A-B). We validated several of these phosphorylation sites by western blotting and demonstrated their dependency on Cdk1 activation (new Figure 9C). Together, these findings reinforce the notion that fundamental phospho-regulatory pathways are conserved during oocyte maturation in vertebrates.

Reviewer #3 (Recommendations for the authors):

(1) Page 6, the first paragraph of Results section: Please describe the method on how the authors measured and quantified the proteomes in different stages of Xenopus oocyte maturation briefly. Without the experimental design, it is very hard to evaluate the results in the following paragraphs.

As requested by the reviewer, we added a few sentences describing the method of proteomics and phosphoproteomics measurements in oocytes resuming meiosis (lines 151-158).

(2) In the phospho-proteome, it is better to classify the amino acids for the phosphorylation such as Ser, Thr, and Tyr. Particularly how many tyrosine phosphorylations are in the list.

Our phosphosites dataset contains 80% Ser, 19.9% Thr, and 0.01% Tyr. Phospho-Tyr are slightly less abundant than what has been described in the literature (in most cells “roughly 85-90% of protein phosphorylation happens on Ser, ~10% on Thr, and less than 0.05% on Tyr" after Sharma et al., 2014. The same observation was made regarding the distribution of phosphorylated amino acids in mouse oocytes, where phospho-Tyr abundance is relatively diminished in oocytes compared to mouse organs (Sun et al., 2024). These observations are now reported in the manuscript (lines 309-313).

(3) In class II (Figure 3), when Cdk1 (line 326) is a major kinase, how many phosphorylation sites are a target of Cdk1 (with the Cdk1-motif)? Moreover, do the authors find any other consensus sequences for the phosphorylation? Those are either known or unknown. This information would be useful for the readers.

We thank the reviewer for this valuable comment. To address it, we used the kinase prediction server (https://kinase-library.phosphosite.org/kinase-library/score-site) to analyze Class II phosphosites. These new results are mentioned in lines 340-349 and illustrated in a new Figure (Figure 3—figure supplement 1A). We identified 303 sites predicted to be phosphorylated by Cdk1. Of these, 166 were also predicted as Erk1/2 targets, reflecting the similarity between Cdk1 and Erk1/2 consensus motifs.

Cdk1 substrate phosphorylation is governed by more than just the presence of a consensus sequence. In addition to its preference for the (S/T)P×(K/R) motif, Cdk1/cyclin complexes achieve specificity through docking interactions with short linear motifs (SLiMs) recognized by the cyclin subunit (as LxF motifs)(Loog & Morgan, 2005), and via the Cdk-binding subunits Cks1 or Cks2, which interact with phosphorylated threonine residues in primed substrates (Örd et al, 2019). These mechanisms promote processive multisite phosphorylation and allow Cdk1 to target substrates even at non-canonical sites. Our motif-based analysis captures only part of this complexity and may underestimate the number of true Cdk1 targets.

To further explore kinase involvement across phosphosite classes, we extended the analysis to all clusters and identified the most enriched kinase predictions for each (lines 360-365, new Figure 3— figure supplement 1B). In Class II, the most enriched kinases included Cdk1, Erk2, and Plk1, supporting the conclusions derived from the identification of the phosphosites of this Class. But others such as Cdk2, Cdk3, Cdk5, Cdk16, KIS, JNK1, and JNK3 were also identified.

(4) Figure 3B: Why do the authors show this kind of Table only for Class I, not Classes II-V? It would be informative to show candidate proteins in other classes.

We chose to present the candidate proteins from Class I in a table format because the number of phosphosites (136) was too small to allow a meaningful Gene Ontology (GO) enrichment analysis. Therefore, we manually curated the data and highlighted proteins whose Class I phosphosites are associated with specific biological processes. For Classes II–V, the higher number of phosphosites allowed us to perform GO enrichment analyses. Since several of the enriched processes were shared across different classes, and some proteins have phosphosites in multiple classes, we opted to organize the results by biological processes rather than by class. We agree with the reviewer that it is indeed valuable to highlight interesting proteins with Class II–V phosphosites. We have done so in Figures 4 through 8, using graphical representations instead of tables, in order to make the data more accessible and avoid long tables. Additionally, the Supplementary Figures provide detailed phosphorylation trends for many of the proteins discussed in the main figures.

(5) It would be nice if the authors compare this phospho-proteome in Xenopus oocyte maturation with that in mouse oocyte maturation (Sun et al. 2024) in terms of evolutional conservation of the phospho-proteomes.

We thank the reviewer for this suggestion. As now detailed in the manuscript, we compared our Xenopus phosphoproteome with the dataset from Sun et al. (2024) on mouse oocyte maturation using the the phospho_occupancy_matlab package, available as part of our code repository (https://github.com/elizabeth-van-itallie) previously described in (Van Itallie et al, 2025). We identified 408 conserved phosphorylation sites corresponding to 320 Xenopus and 277 mouse proteins (see new paragraph: lines 824-860, new Figure 9, Methods: lines 1011-1032 and 1060-1065, and Appendix 7). Phosphorylation dynamics across meiosis were significantly correlated between the species (Pearson r = 0.39, p < 0.0001; new Figure 9A), highlighting evolutionary conservation of the phosphoproteomes. Key phosphorylation events such as Plk1 at T201, Gwl at S467, and Erk2 at T188 increased in both species, consistent with activation of the Cdk1 and MAPK pathways (Figure 6B, new Figure 9A–B). We validated experimentally several of these phosphorylation sites by western blot (Erk2, Plk1, Fak1 and Akts1) and demonstrated their dependency on Cdk1 activation (new Figure 9C). Together, these new findings support the conservation of key phospho-regulatory mechanisms across vertebrate oocyte maturation.

Minor points:

(1) Reference lists: Please add Sun et al (2024) shown in line 115.

This important reference has been added (lines 115, 134, 313 and 826).

(2) Figure 1, red arrows for the inhibition: This should be "T" shape for a better understanding of these complicated pathways.

We agree with the reviewer’s remark, and we have modified Figure 1.

(3) Line 236-238: The authors referred to the absence of Cdc6 in oocyte maturation in Xenopus. However, Figure 2C shows that Cdc6 belongs to a list of accumulating proteins with Orc1 and Ocr2 etc. and the authors did not discuss this discrepancy in the text. Please clarity the claim.

We apologize for the unclear wording in our text. The section of the manuscript regarding the pre-RC components may have been misleading. The text has been revised to clarify that Cdc6 was not detected in prophase-arrested oocytes by western blot and that it accumulates during meiotic maturation after MI, enabling oocytes to replicate DNA (lines 243-250).

(4) Line 306: Please add the link to phosphosite.org.

The link has been added (line 319).

eLife Assessment

This important paper describes a comprehensive quantitative phospho-proteomic analysis of the meiotic progression of Xenopus oocytes. Using time-resolved proteomic analyses, the authors provide insights into changes in protein levels and phosphorylation states to an unprecedented depth, quality, and quantitative detail. The key findings are compelling and offer a helpful resource for the scientific community.

Reviewer #1 (Public review):

In the revised version of the manuscript, the authors have adequately addressed all our concerns. The authors should spell check their manuscript, e.g., correct phosphor-site to phospho-site etc.

Summary:

The study aims to create a comprehensive repository about the changes in protein abundance and their modification during oocyte maturation in Xenopus laevis.

Reviewer #2 (Public review):

Summary:

The authors analyzed Xenopus oocytes at different stages of meiosis using quantitative phosphoproteomics. Their advanced methods and analyses revealed changes in protein abundances and phosphorylation states to an unprecedented depth and quantitative detail. In the manuscript they provide an excellent interpretation of these findings putting them in the context of past literature in Xenopus as well as in other model systems. The clarity of these explanations improved significantly in the revised version of the manuscript, and several minor imprecisions have been corrected as well.

Strengths:

High-quality data, careful and detailed analysis, and outstanding interpretation in the context of the large body of literature.

Weaknesses:

Merely a resource, none of the findings are tested in functional experiments.

I am very impressed by the quality of the data and the careful and detailed interpretation of the findings. In this form, the manuscript will be an excellent resource to the cell division community in general, and it presents a very large number of hypotheses that can be tested in future experiments. Xenopus has been and still is a popular and powerful model system that led to critical discoveries around countless cellular processes, including the spindle, nuclear envelope, and translational regulation, just to name a few. This also includes a huge body of literature on the cell cycle describing its phosphoregulation. It is indeed somewhat frustrating to see that these earlier studies using phospho-mutants and phospho-antibodies were just scratching the surface. The phosphoproteomics analysis presented here reveals much more extensive and much more dynamic changes in phosphorylation states. Thereby, in my opinion, this manuscript opens a completely new chapter in this line of research, setting the stage for more systematic future studies.

Reviewer #3 (Public review):

Summary:

The authors performed time-resolved proteomics and phospho-proteomics in Xenopus oocytes from prophase I through the MII arrest of the unfertilized egg. The data contains protein abundance and phosphorylation sites of a large number set of proteins at different stages of oocyte maturation. The large sets of data are of high quality. In addition, the authors discussed several key pathways critical for the maturation. The data is very useful for researchers, not only researchers in Xenopus oocytes but also those in oocyte biology in other organisms.

Strengths:

The data of proteomics and phospho-proteomics in Xenopus oocyte maturation is very useful for future studies to understand molecular networks in oocyte maturation.

Weaknesses:

Although the authors offered molecular pathways of the phosphorylation in translation, protein degradation, cell cycle regulation, and chromosome segregation. The authors did not check the validity of the molecular pathways based on their proteomic data by experimentation. But this is not essential since this is a resource paper.

eLife Assessment

The authors quantified intentions and knowledge gaps in scientists' use of sex as a biological variable in their work, and used a workshop intervention to show that while willingness was high, pressure points centered on statistical knowledge and perceived additional monetary costs to research. These important findings demonstrate the difficulty in changing understanding - while interventions can improve knowledge and decrease perceived barriers, the impact was small. The evidence was solid, although the sample size was small for the intervention.

Reviewer #1 (Public review):

Summary:

The authors use the theory of planned behavior to understand whether or not intentions to use sex as a biological variable (SABV), as well as attitude (value), subjective norm (social pressure), and behavioral control (ability to conduct behavior), across scientists at a pharmacological conference. They also used an intervention (workshop) to determine the value of this workshop in changing perceptions and misconceptions. Attempts to understand the knowledge gaps were made.

Strengths:

The use of SABV is limited in terms of researchers using sex in the analysis as a variable of interest in the models (and not a variable to control). To understand how we can improve on the number of researchers examining the data with sex in the analyses, it is vital we understand the pressure points that researchers consider in their work. The authors identify likely culprits in their analyses. The authors also test an intervention (workshop) to address the main bias or impediments for researchers' use of sex in their analyses.

Weaknesses:

There are a number of assumptions the authors make that could be revisited:

(1) that all studies should contain across sex analyses or investigations. It is important to acknowledge that part of the impetus for SABV is to gain more scientific knowledge on females. This will require within sex analyses and dedicated research to uncover how unique characteristics for females can influence physiology and health outcomes. This will only be achieved with the use of female-only studies. The overemphasis on investigations of sex influences limits the work done for women's health, for example, as within-sex analyses are equally important.

(2) It should be acknowledged that although the variability within each sex is not different on a number of characteristics (as indicated by meta-analyses in rats and mice), this was not done on all variables, and behavioral variables were not included. In addition, across-sex variability may very well be different, which, in turn, would result in statistical sex significance. In addition, on some measures, there are sex differences in variability, as human males have more variability in grey matter volume than females. PMID: 33044802.

(3) The authors need to acknowledge that it can be important that the sample size is increased when examining more than one sex. If the sample size is too low for biological research, it will not be possible to determine whether or not a difference exists. Using statistical modelling, researchers have found that depending on the effect size, the sample size does need to increase. It is important to bare this in mind as exploratory analyses with small sample size will be extremely limiting and may also discourage further study in this area (or indeed as seen the literature - an exploratory first study with the use of males and females with limited sample size, only to show there is no "significance" and to justify this as an reason to only use males for the further studies in the work.

Reviewer #2 (Public review):

Summary:

The investigators tested a workshop intervention to improve knowledge and decrease misconceptions about sex inclusive research. There were important findings that demonstrate the difficulty in changing opinions and knowledge about the importance of studying both males and females. While interventions can improve knowledge and decrease perceived barriers, the impact was small.

Strengths:

The investigators included control groups and replicated the study in a second population of scientists. The results appear to be well substantiated. These are valuable findings that have practical implications for fields where sex is included as a biological variable to improve rigor and reproducibility.

Weaknesses:

I found the figures difficult to understand and would have appreciated more explanation of what is depicted, as well as greater space between the bars representing different categories.

Reviewer #3 (Public review):

Summary:

This manuscript aims to determine cultural biases and misconceptions in inclusive sex research and evaluate the efficacy of interventions to improve knowledge and shift perceptions to decrease perceived barriers for including both sexes in basic research.

Overall, this study demonstrates that despite the intention to include both sexes and a general belief in the importance of doing so, relatively few people routinely include both sexes. Further, the perceptions of barriers to doing so are high, including misconceptions surrounding sample size, disaggregation, and variability of females. There was also a substantial number of individuals without the statistical knowledge to appropriately analyze data in studies inclusive of sex. Interventions increased knowledge and decreased perception of barriers.

Strengths:

(1) This manuscript provides evidence for the efficacy of interventions for changing attitudes and perceptions of research.

(2) This manuscript also provides a training manual for expanding this intervention to broader groups of researchers.

Weaknesses:

The major weakness here is that the post-workshop assessment is a single time point, soon after the intervention. As this paper shows, intention for these individuals is already high, so does decreasing perception of barriers and increasing knowledge change behavior, and increase the number of studies that include both sexes?

Similarly, does the intervention start to shift cultural factors? Do these contribute to a change in behavior?

Author response:

Reviewer #1 (Public review):

Summary:

The authors use the theory of planned behavior to understand whether or not intentions to use sex as a biological variable (SABV), as well as attitude (value), subjective norm (social pressure), and behavioral control (ability to conduct behavior), across scientists at a pharmacological conference. They also used an intervention (workshop) to determine the value of this workshop in changing perceptions and misconceptions. Attempts to understand the knowledge gaps were made.

Strengths:

The use of SABV is limited in terms of researchers using sex in the analysis as a variable of interest in the models (and not a variable to control). To understand how we can improve on the number of researchers examining the data with sex in the analyses, it is vital we understand the pressure points that researchers consider in their work. The authors identify likely culprits in their analyses. The authors also test an intervention (workshop) to address the main bias or impediments for researchers' use of sex in their analyses.

Weaknesses:

There are a number of assumptions the authors make that could be revisited:

(1) that all studies should contain across sex analyses or investigations. It is important to acknowledge that part of the impetus for SABV is to gain more scientific knowledge on females. This will require within sex analyses and dedicated research to uncover how unique characteristics for females can influence physiology and health outcomes. This will only be achieved with the use of female-only studies. The overemphasis on investigations of sex influences limits the work done for women's health, for example, as within-sex analyses are equally important.

The Sex and Gender Equity in Research (SAGER) guidelines (1) provide guidance that “Where the subjects of research comprise organisms capable of differentiation by sex, the research should be designed and conducted in a way that can reveal sex-related differences in the results, even if these were not initially expected.”. This is a default position of inclusion where the sex can be determined and analysis assessing for sex related variability in response. This position underpins many of the funding bodies new policies on inclusion.

However, we need to place this in the context of the driver of inclusion. The most common reason for including male and female samples is for those studies that are exploring the effect of a treatment and then the goal of inclusion is to assess the generalisability of the treatment effect (exploratory sex inclusion)(2). The second scenario is where sex is included because sex is one of the variables of interest and this situation will arise because there is a hypothesized sex difference of interest (confirmatory sex inclusion).

We would argue that the SABV concept was introduced to address the systematic bias of only studying one sex when assessing treatment effect to improve the generalisability of the research. Therefore, it isn’t directly to gain more scientific knowledge on females. However, this strategy will highlight when the effect is very different between male and female subjects which will potentially generate sex specific hypotheses.

Where research has a hypothesis that is specific to a sex (e.g. it is related to oestrogen levels) it would be appropriate to study only the sex of interest, in this case females. The recently published Sex Inclusive Research Framework gives some guidance here and allows an exemption for such a scenario classifying such proposals “Single sex study justified” (3).

We plan to add an additional paragraph to the introduction to clarify the objectives behind inclusion and how this assists the research process.

(2) It should be acknowledged that although the variability within each sex is not different on a number of characteristics (as indicated by meta-analyses in rats and mice), this was not done on all variables, and behavioral variables were not included. In addition, across-sex variability may very well be different, which, in turn, would result in statistical sex significance. In addition, on some measures, there are sex differences in variability, as human males have more variability in grey matter volume than females. PMID: 33044802.

The manuscript was highlighting the common argument used to exclude the use of females, which is that females are inherently more variable as an absolute truth. We agree there might be situations, where the variance is higher in one sex or another depending on the biology. We will extend the discussion here to reflect this, and we will also link to the Sex Inclusive Research Framework (3) which highlights that in these situations researchers can utlise this argument provided it is supported with data for the biology of interest.

(3) The authors need to acknowledge that it can be important that the sample size is increased when examining more than one sex. If the sample size is too low for biological research, it will not be possible to determine whether or not a difference exists. Using statistical modelling, researchers have found that depending on the effect size, the sample size does need to increase. It is important to bare this in mind as exploratory analyses with small sample size will be extremely limiting and may also discourage further study in this area (or indeed as seen the literature - an exploratory first study with the use of males and females with limited sample size, only to show there is no "significance" and to justify this as an reason to only use males for the further studies in the work.

The reviewer raises a common problem: where researchers have frequently argued that if they find no sex differences in a pilot then they can proceed to study only one sex. The SAGER guidelines (1), and now funder guidelines (4, 5), challenge that position. Instead, the expectation is for inclusion as the default in all experiments (exploratory inclusion strategy) to allow generalisable results to be obtained. When the results are very different between the male and female samples, then this can be determined. This perspective shift (2) requires a change in mindset and understanding that the driver behind inclusion is of generalisability not exploration of sex differences. This will be added to the introduction as an additional paragraph exploring the drivers behind inclusion.

We agree with the reviewer that if the researcher is interested in sex differences in an effect (confirmatory inclusion strategy, aka sex as a primary variable) then the N will need to be higher. However, in this situation, one, of course, must have male and female samples in the same experiment to allow the simultaneous exploration to assess the dependency on sex.

Reviewer #2 (Public review):

Summary:

The investigators tested a workshop intervention to improve knowledge and decrease misconceptions about sex inclusive research. There were important findings that demonstrate the difficulty in changing opinions and knowledge about the importance of studying both males and females. While interventions can improve knowledge and decrease perceived barriers, the impact was small.

Strengths:

The investigators included control groups and replicated the study in a second population of scientists. The results appear to be well substantiated. These are valuable findings that have practical implications for fields where sex is included as a biological variable to improve rigor and reproducibility.

Thank you for assessment and highlighting these strengths. We appreciate your recognition of the value and practical implications of this work.

Weaknesses:

I found the figures difficult to understand and would have appreciated more explanation of what is depicted, as well as greater space between the bars representing different categories.

We plan to review the figures and figure legends to improve clarity of the data.

Reviewer #3 (Public review):

Summary:

This manuscript aims to determine cultural biases and misconceptions in inclusive sex research and evaluate the efficacy of interventions to improve knowledge and shift perceptions to decrease perceived barriers for including both sexes in basic research.

Overall, this study demonstrates that despite the intention to include both sexes and a general belief in the importance of doing so, relatively few people routinely include both sexes. Further, the perceptions of barriers to doing so are high, including misconceptions surrounding sample size, disaggregation, and variability of females. There was also a substantial number of individuals without the statistical knowledge to appropriately analyze data in studies inclusive of sex. Interventions increased knowledge and decreased perception of barriers. Strengths:

(1) This manuscript provides evidence for the efficacy of interventions for changing attitudes and perceptions of research.

(2) This manuscript also provides a training manual for expanding this intervention to broader groups of researchers.

Thank you for highlighting these strengths. We appreciate your recognition that the intervention was effect in changing attitudes and perception. We deliberately chose to share the material to provide the resources to allow a wider engagement.

Weaknesses:

The major weakness here is that the post-workshop assessment is a single time point, soon after the intervention. As this paper shows, intention for these individuals is already high, so does decreasing perception of barriers and increasing knowledge change behavior, and increase the number of studies that include both sexes? Similarly, does the intervention start to shift cultural factors? Do these contribute to a change in behavior?

Measuring change in behaviour following an intervention is challenging and hence we had implemented an intention score as a proxy for behaviour. We appreciate the benefit of a long-term analysis, but it was beyond the scope of this study and would need a larger dataset size to allow for attrition. We agree that the strategy implemented has weaknesses. We plan to extend the limitation section in the discussion to include these.

References

(1) Heidari S, Babor TF, De Castro P, Tort S, Curno M. Sex and Gender Equity in Research: rationale for the SAGER guidelines and recommended use. Res Integr Peer Rev. 2016;1:2.

(2) Karp NA. Navigating the paradigm shift of sex inclusive preclinical research and lessons learnt. Commun Biol. 2025;8(1):681.

(3) Karp NA, Berdoy M, Gray K, Hunt L, Jennings M, Kerton A, et al. The Sex Inclusive Research Framework to address sex bias in preclinical research proposals. Nat Commun. 2025;16(1):3763.

(4) MRC. Sex in experimental design - Guidance on new requirements https://www.ukri.org/councils/mrc/guidance-for-applicants/policies-and-guidance-for-researchers/sex-in-experimental-design/: UK Research and Innovation; 2022

(5) Clayton JA, Collins FS. Policy: NIH to balance sex in cell and animal studies. Nature. 2014;509(7500):282-3.