Reviewer #1 (Public Review):

Summary:

Using concurrent in vivo whole-cell patch clamp and dendritic calcium imaging, the authors characterized how functional synaptic inputs across dendritic arborizations of mouse primary visual cortex layer 2/3 neurons emerge during the second postnatal week. They were able to identify spatially and functionally separated domains of clustered synapses in these neurons even before eye-opening and characterize how the clustering changes from P8 to P13.

Strengths:

The work is technically challenging and the findings are novel. The results support previous EM and immunostaining studies but really provide in vivo evidence on the time course and the trajectory of how functional synaptic input develop.

Weaknesses:

The authors have provided additional details about the analyses and have adequately addressed all my concerns.

Reviewer #2 (Public Review):

In this study, Leighton et al performed remarkable experiments by combining in-vivo patch-clamp recording with two-photon dendritic Ca2+ imaging. The voltage-clamp mode is a major improvement over the pioneer versions of this combinatorial experiment that had led to major breakthroughs in the neuroscience field for visualizing and understanding synaptic input activities in single cells in-vivo (sharp electrodes: Svoboda et al, Nature 1997, Helmchen et al, Nature Neurosci 1999; whole-cell current-clamp: Jia et al, Nature 2010, Chen et al, Nature 2011. I suggest that these papers would be cited). This is because in voltage-clamp mode, despite a full control of membrane voltage in-vivo is not realistic, is nevertheless most effective in preventing back-propagation action potentials, which would severely confound the measurement of individual synaptically-induced Ca2+ influx events. Furthermore, clamping the cell body at a strongly depolarized potential (here the authors did -30mV) also facilitates the detection of synaptically-induced Ca2+ influx. As a result, the authors successfully recorded high-quality Ca2+ imaging data that can be used for precise analysis. To date, even in view of the rapid progress of voltage-sensitive indicators and relevant imaging technologies in the recent years, this very old 'art' of combining single-cell electrophysiology and two-photon imaging (ordinary, raster-scanned, video-rate imaging) of Ca2+ signals still enable measurements of the best-level precision.

On the other hand, the interpretation of data in this study is a bit narrow-minded and lacks a comprehensive picture. Some suggestions to improve the manuscript are as follows:

(1) The authors made a segregation of 'spine synapse' and 'shaft synapse' based solely on the two-photon images in-vivo. However, caution shall be taken here, because the optical resolution under in-vivo imaging conditions like this cannot reliably tell apart whether a bright spot within or partially overlapping a segment of dendrite is a spine on top (or below) of it. Therefore, what the authors consider as a 'shaft synapse' (by detecting Ca2+ hotspots) has an unknown probability to be just a spine on top or below the dendrite. If there is other imaging data of higher axial resolution to validate or calibrate, the authors shall take some further considerations or analysis to check the consistency of their data, as the authors do need such a segregation between spine and shaft synapses to show how they evolve over the brain development stages.<br /> (2) The use of terminology 'bursts of spontaneous inputs' for describing voltage-clamp data seems improper. Conventionally, 'burst' refers to suprathreshold spike firing events, but here, the authors use 'burst' to refer to inward synaptic currents collected at the cell body. It is obvious that not every excitatory synaptic input (or ensemble of inputs) activation will lead to spike firing under naturalistic conditions, therefore, these two concepts are not equivalent. It is recommended to use 'barrage of inputs' instead of 'burst of inputs'. Imagine a full picture of the entire dendritic tree, the fact that the authors could always capture spontaneous Ca2+ events here and there within a few pieces of dendrites within an arbitrary field-of-view suggest that, the whole dendritic tree must have many more such events going on as a barrage while the author's patch electrode picks up the summed current flow from the whole dendritic tree.<br /> (3) Following the above issue, an analysis of the temporal correlation between synaptic (not segregating 'spine' or 'shaft') Ca2+ events and EPSCs is absent. Again, the authors drew arbitrary time windows to clump the events for statistical analysis. However, the demonstrated example data already show that the onset times of individual synaptic Ca2+ events do not necessarily align with the beginning of a 'barrage' inward current event.<br /> (4) The authors claim that "these observations indicate that the activity patterns investigated here are not or only slightly affected by low-level anesthesia". It would be nice to show some of the recordings in this work without any anesthesia to support this claim.<br /> (5) I suggest the authors should provide the number of cells and mice recorded in the figure legends.<br /> (6) Instead of showing only cartoon illustrations of dendrites in Figure 3-6, I suggest showing the two-photon images as well together with the cartoon.

The authors have addressed most of my issues, but I miss the responses to my points 5 and 6. I have no additional comments.

Reviewer #3 (Public Review):

Summary:

There is a growing body of literature on the clustering of co-active synapses in adult mice, which has important implications for understanding dendritic integration and sensory processing more broadly. However, it has been unclear when this spatial organization of co-active synapses arises during development. In this manuscript, Leighton et al. investigate the emergence of spatially organized, co-active synapses on pyramidal dendrites in the mouse visual cortex before eye opening. They find that some dendrite segments contain highly active synapses that are co-active with their neighbors as early as postnatal day (P) 8-10, and that these domains of co-active synapses increase their coverage of the dendritic arbor by P12-13. Interestingly, Leighton et al. demonstrate that synapses co-active with their neighbors are more likely to increase their activity across a single recording session, compared to synapses that are not co-active with their neighbors, suggesting local plasticity driven by coincident activity before eye opening.

The current manuscript includes some replication of earlier results from the same research group (Winnubst et al., 2015), including the presence of clustered, co-active synapses in the visual cortex of mouse pups, and the finding that synapses co-active with their neighbors show an increase in transmission frequency during a recording session. The main novelty in the current study compared to Winnubst et al. (2015) is the inclusion of younger animals (P8-13 in the current study compared to P10-15 in Winnubst et al., 2015). The current manuscript is the first demonstration that active synapses are clustered on specific dendrite segments as early as P8-10 in the mouse visual cortex, and the first to show the progression in active synapse distribution along the dendrite during the 2nd postnatal week. These results from visual cortex may help inform our understanding of sensory development more broadly.

Strengths:

The authors ask a novel question about the emergence of synaptic spatial organization, and they use well-chosen techniques that directly address their questions despite the challenging nature of these techniques. To capture both structural and functional information from dendrites simultaneously, the authors performed whole-cell voltage clamp to record synaptic currents arriving at the soma while imaging calcium influx at individual synaptic sites on dendrites. The simultaneous voltage clamp and calcium imaging allowed the authors to isolate individual synaptic inputs without their occlusion by widespread calcium influx from back-propagating action potentials. Achieving in vivo dendrite imaging in live mice that are as young as P8 is challenging, and the resulting data provides a unique view of synaptic activity along individual dendrites in the visual cortex at an early stage in development that is otherwise difficult to assess.<br /> The authors provide convincing evidence that synapses are more likely to be co-active with their neighbors compared to synapses located farther away (Fig. 6F-H), and that synapses co-active with their neighbors increase their transmission frequency during a recording session (Fig. 7C). These findings are particularly interesting given that the recordings occur before eye opening, suggesting a relationship between co-activity and local synaptic plasticity even before the onset of detailed visual input. These results replicate previously published findings from P10-15 pups (Winnubst et al., 2015), increasing confidence in the reproducibility of the data.<br /> The authors also provide novel data documenting for the first time spatially organized, co-active synapses in pups as young as P8. Comparing the younger (P8-10) and older (P12-13) pups, provides insight into how clusters of co-active synapses might emerge during development.

Weaknesses:

The P8-10 vs P12-13 age comparisons are the primary novel finding in this manuscript, and it is therefore critical to avoid systematic age differences in the methods and analysis whenever possible. In their rebuttal and revised manuscript the authors have acceptably addressed prior concerns regarding this important point, as well as most of the other methodological issues raised.<br /> One point addressed in the rebuttal, but not corrected in the manuscript relates to the reliable localization of cells to visual cortex.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary: 

Using concurrent in vivo whole-cell patch clamp and dendritic calcium imaging, the authors characterized how functional synaptic inputs across dendritic arborizations of mouse primary visual cortex layer 2/3 neurons emerge during the second postnatal week. They were able to identify spatially and functionally separated domains of clustered synapses in these neurons even before eye-opening and characterize how the clustering changes from P8 to P13. 

Strengths: 

The work is technically challenging and the findings are novel. The results support previous EM and immunostaining studies but provide in vivo evidence on the time course and the trajectory of how functional synaptic input develops. 

Weaknesses: 

There are some missing details about how the experiments were performed, and I also have some questions about the analyses. 

We have now added a more detailed description of the methods and added new supplemental figures and descriptions to clarify our analyses. Please find our responses to the specific points of this reviewer in the section “Recommendations for the authors” below.

Reviewer #2 (Public Review):

In this study, Leighton et al performed remarkable experiments by combining in-vivo patch-clamp recording with two-photon dendritic Ca2+ imaging. The voltage-clamp mode is a major improvement over the pioneer versions of this combinatorial experiment that has led to major breakthroughs in the neuroscience field for visualizing and understanding synaptic input activities in single cells in-vivo (sharp electrodes: Svoboda et al, Nature 1997, Helmchen et al, Nature Neurosci 1999; whole-cell current-clamp: Jia et al, Nature 2010, Chen et al, Nature 2011. I suggest that these papers would be cited). This is because in voltage-clamp mode, despite the full control of membrane voltage in-vivo not being realistic, is nevertheless most effective in preventing back-propagation action potentials, which would severely confound the measurement of individual synaptically-induced Ca2+ influx events. Furthermore, clamping the cell body at a strongly depolarized potential (here the authors did -30mV) also facilitates the detection of synaptically-induced Ca2+ influx. As a result, the authors successfully recorded high-quality Ca2+ imaging data that can be used for precise analysis. To date, even in view of the rapid progress of voltage-sensitive indicators and relevant imaging technologies in recent years, this very old 'art' of combining single-cell electrophysiology and two-photon imaging (ordinary, raster-scanned, video-rate imaging) of Ca2+ signals still enables measurements of the best level precision. 

We thank the reviewer for reminding us of these important previous studies that we cite now in the revised manuscript. 

On the other hand, the interpretation of data in this study is a bit narrow-minded and lacks a comprehensive picture. Some suggestions to improve the manuscript are as follows: 

(1) The authors made a segregation of 'spine synapse' and 'shaft synapse' based solely on the two photon images in-vivo. However, caution shall be taken here, because the optical resolution under in vivo imaging conditions like this cannot reliably tell apart whether a bright spot within or partially overlapping a segment of the dendrite is a spine on top of (or below) it. Therefore, what the authors consider as a 'shaft synapse' (by detecting Ca2+ hotspots) has an unknown probability of being just a spine on top or below the dendrite. If there is other imaging data of higher axial resolution to validate or calibrate, the authors shall take some further considerations or analysis to check the consistency of their data, as the authors do need such a segregation between spine and shaft synapses to show how they evolve over the brain development stages. 

We agree with the reviewer that the differentiation between spine and sha synapses can be difficult for those spines that are located above or below the dendric sha in the z-dimension because of the lower resolution of 2-photon microscopy in the z-dimension compared to the image plane. We have now added a new paragraph to the Methods section to describe in more detail how we identify spine and sha synapses and provide more examples in a new supplementary figure (Fig S5). We believe that we can identify spine and sha synapses reliably in most cases, but added a cautionary note to make the reader aware of potential misidentifications.

(2) The use of terminology 'bursts of spontaneous inputs' for describing voltage-clamp data seems improper. Conventionally, 'burst' refers to suprathreshold spike firing events, but here, the authors use 'burst' to refer to inward synaptic currents collected at the cell body. Not every excitatory synaptic input (or ensemble of inputs) activation will lead to spike firing under naturalistic conditions, therefore, these two concepts are not equivalent. It is recommended to use 'barrage of inputs' instead of 'burst of inputs'. Imagine a full picture of the entire dendritic tree, the fact that the authors could always capture spontaneous Ca2+ events here and there within a few pieces of dendrites within an arbitrary field-of-view suggests that, the whole dendritic tree must have many more such events going on as a barrage while the author's patch electrode picks up the summed current flow from the whole dendritic tree. 

We agree with the reviewer that “barrage” is a clearer term for multiple synaptic inputs occurring simultaneously and therefore we changed the terminology throughout the manuscript.

(3) Following the above issue, an analysis of the temporal correlation between synaptic (not segregating 'spine' or 'shaft') Ca2+ events and EPSCs is absent. Again, the authors drew arbitrary time windows to clump the events for statistical analysis. However, the demonstrated example data already shows that the onset times of individual synaptic Ca2+ events do not necessarily align with the beginning of a 'barrage' inward current event. 

The reviewer writes that “an analysis of the temporal correlation between synaptic calcium events and EPSCs is absent”. We would like to point out that we did determine the percentage of calcium transients that occurred during barrages of synaptic inputs (~60%, page 7). This is important, since the barrages in our patch-clamp recordings most likely reflect spontaneous network events as described in the developing cortex previously by us and many other labs . The time window we chose was not “arbitrary” as the reviewer suggests, but based on the duration of the barrages of synaptic inputs as defined in the Methods section. 

The reason, why we did not perform a more in-depth analysis of the temporal relationship between synaptic calcium transients and synaptic input currents is that it is essentially impossible to relate calcium transients at individual synapses to specific synaptic input events. First, during barrages of synaptic inputs many synapses are active simultaneously, both in the mapped dendrites as well as in the un-observed parts of the dendric arborization as the reviewer notes above. Thus, barrages cannot be broken down into individual synaptic transmission events. Second, since our acquisition frequency is ~10 Hz, we can identify the onset of individual synaptic calcium transients with 100-200 ms precision (1 or 2 frames). However, throughout any 100-200 ms period of recording, several synapses are active across the entire dendric arborization such that we cannot assign a given calcium transient to a specific EPSC within a 100-200 ms epoch. Third, due to the limited clamping capacity of in vivo patch recordings, we cannot be certain that individual transmission events in distal dendrites can be resolved in the patch recording.

(4) The authors claim that "these observations indicate that the activity patterns investigated here are not or only slightly affected by low-level anesthesia". It would be nice to show some of the recordings in this work without any anesthesia to support this claim. 

Indeed, the conclusion that the patterns of activity are only slightly affected by low levels of anesthesia is based on our previous recordings on the network level. Unfortunately, we are still not able to record calcium imaging with single synapse resolution in unanesthezed developing mice (and no one else is as far as we know), because the skull of these young animals is not firm, yet. As a consequence, movements cannot be reduced sufficiently for patching and imaging with single synapse resolution. Our previously published (Siegel et al., 2012) and unpublished work on the cellular level suggests that activity patterns during light anesthesia are very similar to those during sleep in mouse pups at this age.

Reviewer #3 (Public Review):

Summary: 

There is a growing body of litterature on the clustering of co-active synapses in adult mice, which has important implications for understanding dendritic integration and sensory processing more broadly. However, it has been unclear when this spatial organization of co-active synapses arises during development. In this manuscript, Leighton et al. investigate the emergence of spatially organized, coactive synapses on pyramidal dendrites in the mouse visual cortex before eye-opening. They find that some dendrite segments contain highly active synapses that are co-active with their neighbors as early as postnatal day (P) 8-10, and that these domains of co-active synapses increase their coverage of the dendritic arbor by P12-13. Interestingly, Leighton et al. demonstrate that synapses co-active with their neighbors are more likely to increase their activity across a single recording session, compared to synapses that are not co-active with their neighbors, suggesting local plasticity driven by coincident activity before eye-opening. 

The current manuscript includes some replication of earlier results from the same research group (Winnubst et al., 2015), including the presence of clustered, co-active synapses in the visual cortex of mouse pups, and the finding that synapses co-active with their neighbors show an increase in transmission frequency during a recording session. The main novelty in the current study compared to Winnubst et al. (2015) is the inclusion of younger animals (P8-13 in the current study compared to P10-15 in Winnubst et al., 2015). The current manuscript is the first demonstration that active synapses are clustered on specific dendrite segments as early as P8-10 in the mouse visual cortex, and the first to show the progression in active synapse distribution along the dendrite during the 2nd postnatal week. These results from the visual cortex may help inform our understanding of sensory development more broadly. 

Strengths: 

The authors ask a novel question about the emergence of synaptic spatial organization, and they use well-chosen techniques that directly address their questions despite the challenging nature of these techniques. To capture both structural and functional information from dendrites simultaneously, the authors performed a whole-cell voltage clamp to record synaptic currents arriving at the soma while imaging calcium influx at individual synaptic sites on dendrites. The simultaneous voltage clamp and calcium imaging allowed the authors to isolate individual synaptic inputs without their occlusion by widespread calcium influx from back-propagating action potentials. Achieving in vivo dendrite imaging in live mice that are as young as P8 is challenging, and the resulting data provides a unique view of synaptic activity along individual dendrites in the visual cortex at an early stage in development that is otherwise difficult to assess. 

The authors provide convincing evidence that synapses are more likely to be co-active with their neighbors compared to synapses located farther away (Fig. 6F-H), and that synapses co-active with their neighbors increase their transmission frequency during a recording session (Figure 7C). These findings are particularly interesting given that the recordings occur before eye-opening, suggesting a relationship between co-activity and local synaptic plasticity even before the onset of detailed visual input. These results replicate previously published findings from P10-15 pups (Winnubst et al., 2015), increasing confidence in the reproducibility of the data. 

The authors also provide novel data documenting for the first time spatially organized, co-active synapses in pups as young as P8. Comparing the younger (P8-10) and older (P12-13) pups, provides insight into how clusters of co-active synapses might emerge during development. 

Weaknesses: 

This manuscript provides insufficient detail for assessing the rigor and reproducibility of the methods, particularly for age comparisons. The P8-10 vs P12-13 age comparisons are the primary novel finding in this manuscript, and it is, therefore, critical to avoid systematic age differences in the methods and analysis whenever possible. Specific concerns related to the age comparisons are listed below: 

(1) Given that the same research group previously published P12-13 data (Winnubst et al., 2015), it is unclear whether both age groups in the current study were imaged/analyzed in parallel by the same researcher(s), or whether previous data was used for the P12-13 group. 

While indeed the approach in the present study is similar to that of our previous study (Winnubst et al. 2015), the data set presented here is entirely new. The current study was made possible by a new microscope that allows combining resonant scanning with piezo-focusing to image large fractions of the dendric arborization. In fact, we could now image almost 10 times larger dendric segments including branch points than in our previous study. One author contributed to the experiments in both studies. Image analysis of all experiments was performed by the first author of the present study who was not involved in the Winnubst et al. work.

(2) The authors mention that they used 2 different microscopes, and used a fairly wide range of imaging frame rates (5-15 Hz). It is unclear from the current manuscript whether the same imaging parameters were used across the two age groups. If data for the two experimental groups was collected separately, perhaps at different times, by a different person, or on a different microscope, there is a concern that some differences between the groups may not necessarily be due to age. 

The reviewer mentions that the experimental settings are not identical across the experiments of this study. In the original manuscript we erroneously reported in the Methods section that 2 different setups were used for this study; however, all experiments were performed on the same microscope. We have corrected this in the new manuscript. We took timelapse recordings of small stacks of varying depth to cover as many dendrites as possible in each recording, therefore, we needed to adjust the rate of acquired stacks within a certain range as the reviewer points out. The data were acquired by two scientists during an overlapping period. And while the different ages were not recorded in a strictly randomized fashion, they were not acquired in sequence according to ages, but rather involved many attempts on animals of different ages from many different litters. For each litter a small percentage of animals would generate successful recordings, and the ages of these successes were random. Therefore, we believe that neither the collection of data nor the analysis (see point above) affected the differences we describe here for the two age groups.

(3) It is unclear whether the image analysis was performed blind to age. Blinding to age during analysis is particularly important for this study, in which it was not possible to blind to age during imaging due to visible differences in size and developmental stage between younger and older pups. 

The analysis was not setup to be performed blind to age. Not only is the age of the animal apparent at the stage (as the reviewer points out), also the number of spines and the activity levels clearly show differences between neurons only a few days apart. However, all age-related findings reported in this study - except the increase in synapse density and activity - became apparent to us only after the full set of synaptic transmission events was determined and the analysis was performed on the entire data set, making it very unlikely that event detection was biased.

(4) The relatively low N (where N is the number of dendrites or the number of mice) in this study is acceptable due to the challenging nature of the techniques used, but unintentional sampling bias is a concern. For example, if higher-order dendrites from the apical tuft were imaged at P12-13, while more segments of the apical trunk were imaged at P8-10, this could inadvertently create apparent age differences that were in fact due to dendrite location on the arbor or dendrite depth. 

The reviewer points out that sampling bias with respect to synapse location along dendrites in the dataset could lead to falsely apparent age differences. In all experiments we imaged dendrites of layer 2/3 neurons that were relatively close to the cortical surface to optimize image quality. In addition, we confirmed that the mean distance of the imaged dendric stretches from the cell body was similar between the dendrites of each age group (Young: 392 +/-  104 µm, Old: 323 +/- 118 µm; mean +/- STD). Therefore, we do not think that sampling bias affected these results.

Additional general methodological concerns, which are not specifically related to the age comparisons, are listed below: 

(5) The authors assert that clustered, co-active synapses emerge in the visual cortex before eye-opening, which is an important finding in that it suggests this phenomention is driven by spontaneous activity rather than visual input. However, this finding hinges on the imaged cells being reliably located in the visual cortex, which is difficult to identify with certainty in animals that have not yet opened their eyes and therefore cannot undergo intrinsic signal imaging to demarcate the boundaries of the visual cortex. If the imaged cells were in, for example, nearby somatosensory cortex, then the observed spatial organization could be due to sensory input rather than spontaneous activity. 

The reviewer argues that if the neurons included in our analysis were located in non-visual sensory cortex, e.g. the somatosensory cortex, sensory experience might have shaped clustered inputs instead of spontaneous activity. We are, however, certain that the neurons were located inside the primary visual cortex. In previous experiments where we performed the same craniotomies, we mapped spontaneous activity across the sensory areas in the occipital neocortex and we know the exact location of V1 which is already very consistent during the second postnatal week. (See for example Supplemental Figure 4 in Leighton et al., 2021).  

(6) It is unclear how the authors defined a synaptic transmission event in the GCaMP signal (e.g. whether there was a quantitative deltaF/F threshold). 

In the revised manuscript, we describe the procedure of identifying synaptic calcium transients in more detail and added a new supplemental figure to clarify this aspect of the analysis. In short, we use an automated detection with a 2x standard deviation threshold and a subsequent manual control and selection step. Please, find all details in the Methods section and Figure S4 of the revised manuscript.

(7) The authors' division of synapses into spine vs shaft is unconvincing due to the difficulty of identifying Z-projecting spines in images from 2-photon microscopy, where the Z resolution is insufficient to definitively identify Z-projecting spines, and the fact that spines in young animals may be thin and dim. The authors' examples of spine synapses (e.g. in Fig. 2A) are convincing, but some of the putative shaft synapses may in fact be on spines. 

We agree with the reviewer that the differentiation between spine and sha synapses can be difficult for those spines that are located above or below the dendric sha in the z-dimension because of the lower resolution of 2-photon microscopy in the z-dimension compared to the image plane (see also response to Reviewer 2, point 1). We have now added a new paragraph to the Methods section to describe in more detail how we identify spine and sha synapses and provide more examples in a new supplementary figure (Fig S5). We believe that we can identify spine and sha synapses reliably in most cases, but added a cautionary note to make the reader aware of potential misidentifications.

Reviewer #1 (Recommendations For The Authors):

I think the experiments performed were very technically challenging (probably one of the few labs that can do this in the field), and the findings provide in vivo evidence on how structured synaptic inputs are assembled during development that has never been reported. 

I suggest improving the writing and presentation and really explaining how they conducted the experiments and how they defined shaft synapses. 

Line 96: 12 dendritic areas from 11 mice at ages between postnatal day 8 to 13. 

- Do the authors know how many neurons were imaged? It is unclear if the authors patch on all the imaged neurons and only imaged (or analyzed) the dendrites of those patched neurons. If yes, how sparse are the neurons labelled from IUE? From 1B, it looks like there are two cells adjacent to each other. Can the authors really distinguish whether the imaged dendrites are from the patched neuron? 

The reviewer wonders whether we can tell apart dendrites of patched cells from those of neighboring neurons that were not patched. This is actually very straight forward: the experiment included a depolarization step (see Methods section) which leads to an immediate, but temporary, increase in fluorescence in all of the patched neurons’ dendrites, but none of the neighboring dendrites. We have added this information to the Methods section of the new manuscript and provide now an example (Fig S3). Furthermore, as these cells normally fire frequently, it would immediately become clear that an unpatched cell is being imaged if backpropagating action potentials are predominantly observed rather than synaptic signals. The visualization of these synaptic signals is only possible due to the blockade of Na+ channels with QX314 in the intracellular solution (see Methods). 

- In the methods section, it says 'dendrites were imaged in single plane or small stacks with plane...'. How do the authors do calcium imaging with small stacks of plane using Nikon MP scope? 

Small stacks were acquired by using the piezo focusing device of our Nikon A1 microscope. Since we combined this fast focusing approach with resonant scanning, we were able to acquire z-stacks of 3-5 frames at a rate of up to 15 Hz (per stack).

- I also assume this is not chronic imaging, and there are different mice for each postnatal day. If it's true, this is somewhat important for all the correlation analysis as there are only 2 mice for each postnatal day (other than day 12) and day 13 only has 1 animal. 

Yes, indeed these are not chronic experiments and dendrites imaged on different days are from different neurons and different mice. We agree with the reviewer that if it had been possible to image the same neurons across these developmental stages, we would have detected even clearer correlations. Therefore, we see our results as conservative estimates of the developmental trajectory of the analyzed parameters.

Line 104 - 109: I don't understand why the authors need to hold at -30mV to facilitate calcium influx through NMDA receptors? I assume this helps them to visualize as many synapses as possible? but wouldn't that also make the 'event frequency' not reflect the true value? 

Indeed depolarizing the imaged neurons to -30 mV was necessary to get sufficient calcium influx to map synaptic inputs. We don’t think that this affects the frequency of inputs, because the frequency of synaptic inputs is determined by the presynaptic firing rate and the release probability of the presynaptic terminal, which are not affected by the depolarization of the dendrite.

Figure 2A - It says in the method section that ROIs are manually selected. However, it's not explained what the criteria are. For spine synapses, it's easy to define but for shaft synapses like in Fig 2B, why are there 2 synapses on the shaft? And in Fig 4a, 5a, Fig S1 P13, some of the dendrites are packed with ROIs. What's the distance between those shaft synapses? Can the imaging resolution really separate them? 

The reviewer asks for a better description of how we identified individual ROIs and thus synapse locations and whether this is actually feasible. We have now added a more detailed description of how we select synaptic sites based on the occurrence of synaptic calcium transients. In addition, we have added a new supplemental Figure (S4) to give the reader an impression of the image quality and the ability to locate individual synapses reliably. We find that separating sha synapses was possible for inter-synapse distances of ~4 µm or more. The mean sha synapse distance in our data set is 21 µm.

- Similar issue applies to Figure 4A that I'm not sure what's the resolution of each 'hot spot'. They all seem very close together. Maybe additional raw dendrite images with fluorescence changes like 1C or 2A could be helpful (or movies in the supplementary?) 

As the reviewer suggests, we have added now additional supplemental figures to illustrate better how we identify synaptic transmission events as well as spine and sha synapses.

- Also for line 164, it says that 76% of high-activity synapses were located on spines. This could also maybe support that only the spine synapses are real synapses and many shaft synapses are actually not synapses and they were just categorized as shaft synapses from manual ROI? 

We are actually quite sure that sha synapses are real synapses based on our analysis, since they show repeated synaptic calcium transients that co-occur with barrages of synaptic inputs as measured by patch-clamp recordings. Indeed one would expect to see a number of excitatory synapses on dendric shas of pyramidal neurons at these ages based on previous EM studies (Miller and Peters, 1981; Wildenberg et al., 2023).

- While this might not impact the overall novelty of the paper, I would be curious to know if the authors can still observe the same findings if they only analyze spine synapses. 

We repeated several analyses with a dataset that contained only spine synapses. For most analyses we observed the expected result: the effect sizes were similar compared to the entire data set, but the power was reduced. For example the effect of distance to closest high-activity neighbor and own activity (Fig 5E, F) was similar, but p-values were around 0.1 (Similar results for Figure 7B). In contrast, the co-activity with synapses within a domain was significantly higher than the co-activity with synapses in other domains also for the spine-synapse only data set. 

Fig 6 - Does the domain co-activity also contribute to the synaptic current recorded (related to Fig 4). 

Yes, the synaptic activity measured by calcium imaging contributes to the recorded EPSCs. However, the exact relationship between synaptic inputs measured by calcium imaging and those measured by patch-clamping is complicated by 3 factors: first, during barrages of synaptic inputs many synapses are active simultaneously, both in the mapped dendrites as well as in the un-observed parts of the dendric arborization. Thus, barrages cannot be broken down into individual events. Second, since our acquisition frequency is ~10 Hz, we can identify the onset of individual synaptic calcium transients with 100-200 ms precision (1 or 2 frames). However, throughout any 100-200 ms period of recording several synapses are active across the entire dendric arborization such that we cannot assign a given calcium transient to a specific EPSC within a 100-200 ms epoch. Third, due to the limited clamping capacity of in vivo patch recordings, we cannot be certain that individual transmission events in distal dendrites can be resolved in the patch recording as EPSCs.

Reviewer #2 (Recommendations For The Authors):

(1) I suggest the authors should provide the number of cells and mice recorded in the figure legends. 

The number of dendrites and mice is the same across all analyses: 12 dendrites from 11 mice for all experiments, 6/6 for P8-10 and 6/5 for P12-13. All dendrites and synapses (and their ages) are shown in the supplemental figures S1 and S2. We mention the number of imaged dendrites now at the beginning of the Results section and when we split ages for the first me.

(2) Instead of showing only cartoon illustrations of dendrites in Figures 3-6, I suggest showing the two photon images as well together with the cartoon. 

The 2-photon images of all dendrites of the dataset are available in Figure S1. To allow the reader to compare the cartoon representations in the main figures and the 2-photon images of each neuron, we have now labeled each dendrite in the dataset (D1-D12, see figures S1 and S2). For every figure, where we show example neurons (cartoons or zoom ins) we now provide this identifier.

Reviewer #3 (Recommendations For The Authors):

To address the weaknesses outlined above, we recommend that the authors do the following: 

• To address concerns about the rigor and reproducibility of the methods specifically related to age comparisons, please confirm the following: 

- Both age groups were run in parallel by the same researcher(s). 

Experiments were run partly overlapping and experiments from different age groups were performed in parallel by both researchers.

- Both age groups were imaged on the same microscope, or animals from each age group were imaged on both microscopes. If it was necessary to use different microscopes for the different age groups for biological or practical reasons, please explain. 

All experiments were run on the same microscope, a Nikon A1 2-photon microscope. In the original methods description we erroneously mentioned two microscopes (copy and paste error from a previous publication). We corrected that in the revised manuscript.

- There was no difference in imaging frame rates or other imaging parameters between age groups. If it was necessary to use different parameters for different age groups for biological reasons, please explain. 

We varied the frame rates somewhat to allow larger z-stacks for some experiments where dendrites traversed different depths; however the mean frame rates were similar between the experiments in P8-10 vs P12-13 dendrites, 8.5 vs 10 Hz, respectively.

- Images were analyzed blind to age. 

The analysis was not setup to be performed blind to age. The number of spines and the activity levels clearly show obvious differences between neurons only a few days apart. However, all findings reported in this study related to age - except the increase in synapse density and activity - became apparent to us only after the full set of synaptic transmission events was determined and the analysis was performed on the entire data set, making it unlikely that event detection was biased.

- There was no difference in the location of analyzed dendrites (e.g. depth from the pia, branch order) between age groups. 

In all experiments we imaged dendrites of layer 2/3 neurons that were relatively close to the cortical surface to optimize image quality. In addition, we determined the mean distance of the imaged dendric stretches from the cell body and found that this distance was similar between the dendrites of each age group (Young: 392 +/-  104 µm, Old: 323 +/- 118 µm; mean +/- STD). Therefore, we do not think that sampling bias affected these results.

• To address general methodological concerns, please provide additional description of the following points: 

- Please clarify how the visual cortex was identified in P8-13 pups. If there was ambiguity about identifying the visual cortex in these pups, please discuss the implications of this ambiguity. 

The reviewer asks how we identified V1 in these experiments. We are indeed certain that the neurons were located inside the primary visual cortex. We have ample experience with mapping V1 in these animals based on patterns of spontaneous activity as well as post-hoc stainings. V1 is quite large already at these ages (> 2 mm long and > 1 mm wide) and its extent very consistent across animals. Thus, we would argue it is actually hard to miss.

- Please clarify how synaptic transmission events were identified in the GCaMP signal. 

We have now added a more detailed description of how we identify synaptic calcium transients. In addition, we have added a new supplemental Figure (S3) to give the reader an impression of the image quality and the ability to locate individual synapses reliably. 

- It is acceptable to use the spine vs shaft analysis despite the inevitable difficulty resolving Z-projecting spines, but this caveat should be mentioned in the discussion of the spine vs shaft results. 

We added a more detailed description of spine and sha synapse identification, a new supplemental figure (S5) and we now mention the caveat related to the limited z-resolution of 2-photon microscopy in the revised manuscript.

• Two additional minor details should be clarified in the text of the manuscript: 

- Please specify the volume of DNA solution injected into each embryo. 

The injected volume was 1 µl. We added this information in the Methods section of the revised manuscript.

- In Fig S1, please specify whether the scale bar applies to all images. 

The scale bar applies to all images. This information was added to the figure legend.

References

Leighton AH, Cheyne JE, Houwen GJ, Maldonado PP, De Winter F, Levelt CN, Lohmann C. 2021. Somatostatin interneurons restrict cell recruitment to renally driven spontaneous activity in the developing cortex. Cell Rep 36:109316. doi:10.1016/j.celrep.2021.109316

Miller M, Peters A. 1981. Maturation of rat visual cortex. II. A combined Golgi-electron microscope study of pyramidal neurons. JComp Neurol 203:555–573.

Siegel F, Heimel JA, Peters J, Lohmann C. 2012. Peripheral and central inputs shape network dynamics in the developing visual cortex in vivo. Current Biology 22:253–258.

Wildenberg G, Li H, Sampathkumar V, Sorokina A, Kasthuri N. 2023. Isochronic development of cortical synapses in primates and mice. Nat Commun 14:8018. doi:10.1038/s41467-02343088-3

eLife assessment

This important study reports a novel approach to studying cerebellar function based on the idea of selective recruitment using fMRI. It provides convincing evidence for task-dependent gating of neocortical input to the cerebellum during a motor task and a working memory task. The study will be of interest to a broad cognitive neuroscience audience.

Reviewer #1 (Public Review):

This is an interesting and well-written paper reporting on a novel approach to studying cerebellar function based on the idea of selective recruitment using fMRI. The study is well-designed and executed. Analyses are sound and results are properly discussed. The paper makes a significant contribution to broadening our understanding of the role of cerebellum in human behavior.

In the revision, the authors did an excellent job in addressing my concerns.

Reviewer #2 (Public Review):

Summary:

Shahshahani and colleagues used a combination of statistical modelling and whole-brain fMRI data in an attempt to separate the contributions of cortical and cerebellar regions in different cognitive contexts.

Strengths:

* The manuscript uses a sophisticated integration of statistical methods, cognitive neuroscience and systems neurobiology.<br /> * The authors use multiple statistical approaches to ensure robustness in their conclusions.<br /> * The consideration of the cerebellum as not a purely 'motor' structure is excellent and important.

Weaknesses:

* The assumption that cortical BOLD responses in cognitive tasks should be matched irrespective of cerebellar involvement does not cohere directly with the notion of 'forcing functions' introduced by Houk and Wise, suggesting the need for future work.

Author response:

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

Reviewer #1 (Public Review):

This is an interesting and well-written paper reporting on a novel approach to studying cerebellar function based on the idea of selective recruitment using fMRI. The study is well-designed and executed. Analyses are sound and results are properly discussed. The paper makes a significant contribution to broadening our understanding of the role of the cerebellum in human behavior.

We thank the reviewer for the positive assessment of our paper.

(1) While the authors provide a compelling case for the link between BOLD and the cerebellar cortical input layer, there remains considerable unexplained variance. Perhaps the authors could elaborate a bit more on the assumption that BOLD signals mainly reflect the input side of the cerebellum (see for example King et al., elife. 2023 Apr 21;12:e81511).

Our paper is based on the assumption that the cerebellar BOLD signal reflects solely the input to the cerebellum and does not reflect the changes in firing rates of Purkinje cells. This assumption relies on two lines of arguments: Studies that have directly looked at the mechanism of vasodilation in the cerebellum, and studies that try to infer the contributions of different neurophysiological mechanisms to overall cerebellar metabolism (Attwell and Iadecola, 2002).

Vasodilatory considerations: The mechanisms that causes vasodilation in the cerebellum, and hence BOLD signal increases, has been extensively studied: Electrical stimulation of mossy fibers (Gagliano et al., 2022; Mapelli et al., 2017), as well as parallel fibers (Akgören et al., 1994; Iadecola et al., 1996; Mathiesen et al., 1998; Yang and Iadecola, 1997) lead to robust increases in cerebellar blood flow. In contrast to the neocortex, the regulation of blood flow in the cerebellum depends nearly purely on the vasodilator Nitric Oxide (NO) (Akgören et al., 1994; Yang and Iadecola, 1997) with stellate cells playing a key role in the signaling cascade (Yang et al., 2000).

Electrical (Mathiesen et al., 2000) and pharmacological (Yang and Iadecola, 1998) stimulation of climbing fibers also leads to robust increases in blood flow. Simultaneous parallel and climbing fiber stimulation seems to combine sub-additively to determine the blood flow changes (K. Caesar et al., 2003).

Importantly, even dramatic changes in spiking rate of Purkinje cells do not lead to changes in vasodilation. For starters, parallel fiber stimulation leads to blood flow increases, even though the net effect on Purkinje cell firing is inhibitory (Mathiesen et al., 1998). More importantly, complete inhibition of the Purkinje cell using a GABA agonist does not change baseline cerebellar blood flow (Kirsten Caesar et al., 2003). Conversely, even a 200-300% increase in simple (and complex) spike firing rate through application of a GABA antagonist does not show any measurable consequences for blood flow, even though it clearly increases the metabolic rate of oxygen consumption in the tissue (Thomsen et al., 2009, 2004).

In sum, this extensive set of studies clearly argues that the cerebellar blood flow response is mostly dictated by synaptic input, and that the firing rate of Purkinje cells does not influence vasodilation. Because the BOLD signal is caused by an supply of oxygen over and above the level of oxygen consumption, this would argue that increases in Purkinje cell firing would not lead to BOLD increases. What is less clear is the degree to which changes in BOLD signal during normal activity are determined by changes in mossy fiber or climbing fiber input. Disruption of either pathway leads to 60-70% reductions in the evoked blood flow response during whisker stimulation (Yang et al., 2000; Zhang et al., 2003) – but it remains unclear to what degree this reflects the distribution of contributions in the healthy animal, as these powerful disruptions may have a number of side-effects.

Metabolic considerations: To estimate the relative contributions climbing fiber / mossy fiber input to the variations in BOLD signal under natural conditions, it is useful to consider the contributions of different cerebellar processes to the overall metabolism of the cerebellum. Assuming an average firing rate of 40Hz for mossy fibers, ~3Hz for Granule cells, and 1Hz for climbing fibers, Howarth et al. (Howarth et al., 2012, 2010) estimated that the transmission from mossy fibers to granular cells, dominates the energy budget with 53%. The subsequent stage, encompassing the transfer of information from Granular cells to Purkinje cells, accounts for 32% of energy expenditure. In contrast, integration within Purkinje cells and the spiking (simple and complex) of these cells represents only 15% of the total energy consumption.

More important for the BOLD signal, however, are the activity-induced variations in metabolic consumption: Purkinje cells fire relatively constantly at a very high frequency (~50Hz) both during awake periods and during sleep (Shin et al., 2007). When providing a signal to the neocortex, firing rate decreases, actually lowering the metabolic demand. Climbing fibers normally fire at ~0.5 Hz and even during activity rarely fire much above 2Hz (Streng et al., 2017). In contrast, granule cells show a low firing rates during rest (typically <1hz) and can spike during activity well above 100Hz. Combined with the sheer number of granule cells, these considerations would suggest that the vast majority of the variation in metabolic demand are due to mossy fiber input and granule cell activity.

Overall, we therefore think it is likely that the main determinant of the cerebellar cortical BOLD signal is mossy fiber input and the transmission of information from mossy fibers to granule cells to Purkinje cells. We admit that the degree to which climbing fiber input contribute to BOLD signal changes is much less clear. We can be quite certain, however, that the firing rate of Purkinje cells does not contribute to the cerebellar BOLD signal, as even dramatic changes in the firing rate do not cause any changes in vasodilation.  We have clarified our line of reasoning in the paper, and hope this more extensive response here will give the reader a better overview over the pertaining literature.

(2) The current approach does not appear to take the non-linear relationships between BOLD and neural activity into account.

Thank you for raising this concern. We did not stress this point in the paper, but one big advantage of our selective recruitment approach is that it is – to some degree- robust against non-linearities in the relationship between neural activity and BOLD signal. This is the case, as long as the shape of the non-linearity is similar in the cerebellum and the neocortex. The results of our motor task (Figure 3) provide a clear example of this: The BOLD signal both in the neocortex and cerebellum incases non-linearly as a function of force – the increase from 2.5N to 6N (a 3.5N increase) is larger than the increase from 6N to 10N (a 4N increase). A similar non-linearity can be observed for tapping speed (6, 10 to 18 taps / s). However, within each condition, the relationship between cortical and cerebellar activity is nearly perfectly linear, reflecting the fact that the shape of the non-linearity for the cerebellum and cortex is very similar.

Most importantly, even if the non-linearity across the two structures is different, any non-linear relationship between neural activity and BOLD signal (of vasodilatory nature) should apply to different conditions (here force and speed increases) similarly. Therefore, if two conditions show overlapping activity levels (as observed for force and speed across medium and high levels, Figure 3), a offset between conditions cannot be caused by a non-linearity in the relationship of cortical and cerebellar activity. Because all conditions are subject to the same non-linearity, all points should lie on a single (likely monotonically increasing) non-linear function. Both for the motor and working memory task, the pattern of results clearly violates this assumption.

(3) The authors may want to address a bit more the issue of closed loops as well as the underlying neuroanatomy including the deep cerebellar nuclei and pontine nuclei in the context of their current cerebello-cortical correlational approach. But also the contribution of other brain areas such as the basal ganglia and hippocampus. 

Cortical-cerebellar communication is of course bi-directional. As discussed in King at al., (2023), however, we are restricting our model to the connections from the neocortex to the cerebellum for the following reasons: First, cerebellar BOLD activity likely reflects mostly neocortical input (see our answer to pt. 1), whereas neocortical activity is determined by a much wider array of projections, including striato-thalamo-cortical and cortico-cortical connections. Secondly, the output of the cerebellum cannot be predicted from the BOLD signal of the cerebellar cortex, as it is unlikely that the firing rate of Purkinje cells contribute to cerebellar BOLD signal (see pt. 1). For these reasons we believe that the relationship between neocortical and cerebellar activity patterns is mostly dictated by the connectivity from cortex to cerebellum, and is therefore best modelled as thus. This is now more clearly discussed in a new paragraph (line 318-323) of the revised manuscript.

We are also ignoring other inputs to the cerebellum, including the spinal chord, the basal ganglia (Bhuvanasundaram et al., 2022; Bostan and Strick, 2018) hippocampus (Froula et al., 2023; Watson et al., 2019), and amygdala (Farley et al., 2016; Jung et al., 2022; Terburg et al., 2024). In humans, however, the neocortex remains the primary source of input to pontine nuclei. Consequently, it stands as the main structure shaping activity within the cerebellar cortex. While it is an interesting question to what degree the consideration of subcortical structures can improve the prediction of cerebellar activity patterns, we believe that considering the neocortex provides a good first approximation.

Reviewer #1 (Recommendations):

(4)  A few sentences to clarify the used models as was done in the King et al. (2024) paper may improve readability.

We have now added the sentences in the introduction (line 25ff):

To approach this problem, we have recently developed and tested a range of cortical-cerebellar connectivity models (King et al., 2023), designed to capture fixed, or task-invariant, transmission between neocortex and cerebellum. For each cerebellar voxel, we estimated a regularized multiple regression model to predict its activity level across a range of task conditions (King et al., 2019) from the activity pattern observed in the neocortex for the same conditions. The models were then evaluated in their ability to predict cerebellar activity in novel tasks, again based only on the corresponding neocortical activity pattern. Two key results emerged from this work. First, while rs-FC studies (Buckner et al., 2011; Ji et al., 2019; Marek et al., 2018) have assumed a 1:1 mapping between neocortical and cerebellar networks, models which allowed for convergent input from multiple neocortical regions to a single cerebellar region performed better in predicting cerebellar activity patterns for novel tasks. Second, when given a cortical activation pattern, the best performing model could predict about 50% of the reliable variance in the cerebellar cortex across tasks (King et al., 2023).

(5) To what extent does this paper demonstrate the limitations of BOLD in neuroscientific research? 

The primary objective of this study was to shed light on the problems of interpreting BOLD activation within the cerebellum. The problem that the BOLD signal mostly reflect input to a region is not unique to the cerebellum, but also applies (albeit likely to a lesser degree) to other brain structures. However, the solution we propose here critically hinges on three features of the cerebellar circuitry: a) the mossy fiber input for the cerebellar hemispheres mostly arise from the neocortex, b) the BOLD signal is likely dominated by this mossy fiber input (see pt. 1), and c) there is very little excitatory recurrent activity in the cerebellum, so output activity in the cerebellum does not cause direct activity in other parts of the cerebellum.

These features motivate us to use a directed cortex->cerebellum connectivity model, which does not allow for any direct connectivity within the cerebellum. While the same approach can also be applied to other brain structures, it is less clear that the approach would yield valid results here. For example, due the local excitatory recurrent connectivity within neocortical columns, the activity here will also relate to local processing.

(6) What if the authors reversed their line of reasoning as in that cerebellum activity is matched to map changes in cerebral cortical activity? Perhaps this could provide further evidence for the assumed directional specificity of the task-dependent gating of neocortical inputs. 

Given (a) that the cerebellar BOLD signal tells us very little about cerebellar output signals (b) that there are many other input signals to the neocortex that are more powerful than cerebellar inputs, and c) that there strong cortical-cortical connections, we believe that this model would be hard to interpret (see also our answer to pt. 3).

Therefore, while the inversion of the linear task-invariant mapping between cortical and cerebellar activity is a potentially interesting exercise, it is unclear to us at this point what strong predictions we would be able to test with this approach.

(7) The statement that cerebellar fMRI activity may simply reflect the transmission of neocortical activity through fixed connections can be better explained. Also in the context of using the epiphenomenon (on page 11) in the paper. To what extent is the issue of epiphenomenon not a general problem of fMRI research?

We have rephrased the introduction of this idea (line 17):

This means that increases in the cerebellar BOLD signal could simply reflect the automatic transmission of neocortical activity through fixed anatomical connections. As such, whenever a task activates a neocortical region, the corresponding cerebellar region would also be activated, regardless of whether the cerebellum is directly involved in the task or not.

Epiphemonal activity: This is indeed a general problem in fMRI research (and indeed research that uses neurophysiological recordings, rather than manipulations of activity). Indeed, we have discussed similar issues in the context of motor activity in ipsilateral motor cortex (Diedrichsen et al., 2009). However, given that we only offer a possible approach to address this issue for the cerebellum (see pt. 5), we thought it best to keep the scope of the discussion focused on this structure.

Reviewer #2 (Public Review):

Summary:

Shahshahani and colleagues used a combination of statistical modelling and whole-brain fMRI data in an attempt to separate the contributions of cortical and cerebellar regions in different cognitive contexts.

Strengths:

The manuscript uses a sophisticated integration of statistical methods, cognitive neuroscience, and systems neurobiology.

The authors use multiple statistical approaches to ensure robustness in their conclusions.

The consideration of the cerebellum as not a purely 'motor' structure is excellent and important. <br />

We thank the reviewer for their positive evaluation.

Weaknesses:

(1) Two of the foundation assumptions of the model - that cerebellar BOLD signals reflect granule cells > purkinje neurons and that corticocerebellar connections are relatively invariant - are still open topics of investigation. It might be helpful for the reader if these ideas could be presented in a more nuanced light.

Please see response to the comment 1 of Reviewer 1 for a more extensive and detailed justification of this assumption. We have now also clarified our rationale for this assumption better in the paper on line 10-14. Finally, we now also raise explicitly the possibility that some of the violations of the task-invariant model could be caused by selectively increase of climbing fiber activity in some tasks (line 340).

(2) The assumption that cortical BOLD responses in cognitive tasks should be matched irrespective of cerebellar involvement does not cohere with the idea of 'forcing functions' introduced by Houk and Wise. 

We are assuming that you refer to the idea that cerebellar output is an important determinant of the dynamics (and likely also of the magnitude) of neocortical activity. We agree most certainly here. However, we also believe that in the context of our paper, it is justified to restrict the model to the connectivity between the neocortex and the cerebellum only (see reviewer 1, comment 3).

Furthermore, if increased cerebellar output indeed occurs during the conditions for which we identified unusually high cerebellar activity, it should increase neocortical activity, and bring the relationship of the cerebellar and cortical activity again closer to the predictions of the linear model. Therefore, the identification of functions for which cerebellar regions show selective recruitment is rather conservative.

Reviewer #2 (Recommendations):

(3) One of the assumptions stated in the abstract -- that the inputs to the cerebellum may simply be a somewhat passive relay of the outputs of the cerebral cortex -- has been challenged recently by work from Litwin-Kumar (Muscinelli et al., 2023 Nature Neuroscience), which argues for complex computational relationships between cortical pyramidal neurons, pontine nuclei and granule cells, which in turn would have a non-linear impact on the relationship between cortical and cerebellar BOLD. The modelling is based on empirical recordings from Wagner (2019, Cell) which show that the synaptic connections between the cortex and granule cells change as a function of learning, further raising concerns about the assumption that the signals inherent within these two systems should be identical. Whether these micro-scale features are indicative of the macroscopic patterns observed in BOLD is an interesting question for future research, but I worry that the assumption of direct similarity is perhaps not reflective of the current literature. The authors do speak to these cells in their discussion, but I believe that they could also help to refine the authors' hypotheses in the manuscript writ large.

We absolutely agree with your point. However, we want to make extremely clear here that our hypothesis (that the inputs to the cerebellum are a linear task-invariant function of the outputs of the cerebral cortex) is the Null-hypothesis that we are testing in our paper. In fact, our results show the first empirical evidence that task-dependent gating may indeed occur. In this sense, our paper is consistent with the theoretical suggestion of (Muscinelli et al., 2023).

You may ask whether a linear task-invariant model of cortical-cerebellar connectivity is not a strawman, given that is most likely incorrect. However, as we stress in the discussion (line 298-), a good Null-model is a useful model, even if it is (as all models) ultimately incorrect. Without it, we would not be able to determine which cerebellar activity outstrips the linear prediction. The fact that this Null-model itself can predict nearly 50% of the variance in cerebellar activity patterns across tasks at a group level, means that it is actually a very powerful model, and hence is a much more stringent criterion for evidence for functional involvement than just the presence of activity.

(4) Further to this point, I didn't follow the authors' logic that the majority of the BOLD response in the cerebellum is reflective of granule cells rather than Purkinje cells. I read through each of the papers that were cited in defense of the comment: "The cerebellar BOLD signal is dominated by mossy fiber input with very little contribution from the output of the cerebellar cortex, the activity of Purkinje cells" and found that none of these studies made this same direct conclusion. As such, I suggest that the authors soften this statement, or provide a different set of references that directly confirm this hypothesis. 

Please see response to the comment 1, Reviewer 1. We hope the answer provides a more comprehensive overview over the literature, which DOES show that spiking behavior of Purkinje cells does not influence vasodilation (as opposed to mossy fiber input). We have now clarified our rationale and the exact cited literature on line 9-14 of the paper.

(5) Regarding the statement: "As such, whenever a task activates a neocortical region, we might observe activity in the corresponding cerebellar regions regardless of whether the cerebellum is directly involved in the task or not." -- what if this is a feature, rather than a bug? That is, the organisation of the nervous system has been shaped over phylogeny such that every action, via efference copies of motor outputs, is filtered through the complex architecture of the cerebellum in order to provide a feed-forward signal to the thalamus/cortex (and other connected structures). Houk and Wise made compelling arguments in their 1995 Cerebral Cortex paper arguing that these outputs (among other systems) could act as 'forcing functions' on the kinds of dynamics that arise in the cerebral cortex. I am inclined to agree with their hypothesis, where the implication is that there are no tasks that don't (in some way) depend on cerebellar activity, albeit to a lesser or greater extent, depending on the contexts/requirements of the task. I realise that this is a somewhat philosophical point, but I do think it is important to be clear about the assumptions that form the basis of the reasoning in the paper. 

This is an interesting point. Our way of thinking about cerebellar function does indeed correspond quite well to the idea of forcing functions- the idea that cerebellar output can “steer” cortical dynamics in a particular way. However, based on patient and lesion data, it is also clear that some cortical functions rely much more critically on cerebellar input than others. We hypothesize here that cerebellar activity is higher (as compared to the neocortical activity) when the functions require cerebellar computation.

We also agree with the notion that cerebellar contribution is likely not an all-or-none issue, but rather a matter of gradation (line 324ff).

(6) Regarding the logic of expecting the cortical patterns for speed vs. force to be matched -- surely if the cerebellum was involved more in speed than force production, the feedback from the cerebellum to the cortex (via thalamus) could also contribute to the observed differences? How could the authors control for this possibility? 

Our model currently indeed does not attempt to quantify the contributions of cerebellar output to cortical activity. However, given that cerebellar output is not visible in the BOLD signal of the cerebellum (see reviewer 1, comment 1), we believe that this is a rational approach. As argued in our response to your comment 2, increased cerebellar output in the speed compared to the force condition should bring the activity relationship closer to the linear model prediction. The fact that we find increased cerebellar (as compared to neocortical) activity in the speed conditions, suggests that there is indeed task-dependent gating of cortical projections to the cerebellum.

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eLife assessment

This important study presents an original and promising approach to combine convolutional neural networks of visual processing with evidence accumulation models of decision-making. While the methodological approach itself is strong and technically sophisticated, the evidence supporting the conclusions is currently incomplete. The study will be of interest to researchers working in the fields of machine learning and cognitive modeling.

Reviewer #1 (Public Review):

Summary:

This paper introduces a new approach to modeling human behavioral responses using image-computable models. They create a model (VAM) that is a combination of a standard CNN coupled with a standard evidence accumulation model (EAM). The combined model is then trained directly on image-level data using human behavioral responses. This approach is original and can have wide applicability. However, many of the specific findings reported are less compelling.

Strengths:

(1) The manuscript presents an original approach to fitting an image-computable model to human behavioral data. This type of approach is sorely needed in the field.<br /> (2) The analyses are very technically sophisticated.<br /> (3) The behavioral data are large both in terms of sample size (N=75) and in terms of trials per subject.

Weaknesses:

Major

(1) The manuscript appears to suggest that it is the first to combine CNNs with evidence accumulation models (EAMs). However, this was done in a 2022 preprint (https://www.biorxiv.org/content/10.1101/2022.08.23.505015v1) that introduced a network called RTNet. This preprint is cited here, but never really discussed. Further, the two unique features of the current approach discussed in lines 55-60 are both present to some extent in RTNet. Given the strong conceptual similarity in approach, it seems that a detailed discussion of similarities and differences (of which there are many) should feature in the Introduction.

(2) In the approach here, a given stimulus is always processed in the same way through the core CNN to produce activations v_k. These v_k's are then corrupted by Gaussian noise to produce drift rates d_k, which can differ from trial to trial even for the same stimulus. In other words, the assumption built into VAM appears to be that the drift rate variability stems entirely from post-sensory (decisional) noise. In contrast, the typical interpretation of EAMs is that the variability in drift rates is sensory. This is also the assumption built into RTNet where the core CNN produces noisy evidence. Can the authors comment on the plausibility of VAM's assumption that the noise is post-sensory?

(3) Figure 2 plots how well VAM explains different behavioral features. It would be very useful if the authors could also fit simple EAMs to the data to clarify which of these features are explainable by EAMs only and which are not.

(4) VAM is tested in two different ways behaviorally. First, it is tested to what extent it captures individual differences (Figure 2B-E). Second, it is tested to what extent it captures average subject data (Figure 2F-J). It wasn't clear to me why for some metrics only individual differences are examined and for other metrics only average human data is examined. I think that it will be much more informative if separate figures examine average human data and individual difference data. I think that it's especially important to clarify whether VAM can capture individual differences for the quantities plotted in Figures 2F-J.

(5) The authors look inside VAM and perform many exploratory analyses. I found many of these difficult to follow since there was little guidance about why each analysis was conducted. This also made it difficult to assess the likelihood that any given result is robust and replicable. More importantly, it was unclear which results are hypothesized to depend on the VAM architecture and training, and which results would be expected in performance-optimized CNNs. The authors train and examine performance-optimized CNNs later, but it would be useful to compare those results to the VAM results immediately when each VAM result is first introduced.

(6) The authors don't examine how the task-optimized models would produce RTs. They say in lines 371-2 that they "could not examine the RT congruency effect since the task-optimized models do not generate RTs." CNNs alone don't generate RTs, but RTs can easily be generated from them using the same EAM add-on that is part of VAM. Given that the CNNs are already trained, I can't see a reason why the authors can't train EAMs on top of the already trained CNNs and generate RTs, so these can provide a better comparison to VAM.

(7) The Discussion felt very long and mostly a summary of the Results. I also couldn't shake the feeling that it had many just-so stories related to the variety of findings reported. I think that the section should be condensed and the authors should be clearer about which explanations are speculations and which are air-tight arguments based on the data.

(8) In one of the control analyses, the authors train different VAMs on each RT quantile. I don't understand how it can be claimed that this approach can serve as a model of an individual's sensory processing. Which of the 5 sets of weights (5 VAMs) captures a given subject's visual processing? Are the authors saying that the visual system of a given subject changes based on the expected RT for a stimulus? I feel like I'm missing something about how the authors think about these results.

Reviewer #2 (Public Review):

In an image-computable model of speeded decision-making, the authors introduce and fit a combined CCN-EAM (a 'VAM') to flanker-task-like data. They show that the VAM can fit mean RTs and accuracies as well as the congruency effect that is present in the data, and subsequently analyze the VAM in terms of where in the network congruency effects arise.

Overall, combining DNNs and EAMs appears to be a promising avenue to seriously model the visual system in decision-making tasks compared to the current practice in EAMs. Some variants have been proposed or used before (e.g., doi.org/10.1016/j.neuroimage.2017.12.078 , doi.org/10.1007/s42113-019-00042-1), but always in the context of using task-trained models, rather than models trained on behavioral data. However, I was surprised to read that the authors developed their model in the context of a conflict task, rather than a simpler perceptual decision-making task. Conflict effects in human behavior are particularly complex, and thereby, the authors set a high goal for themselves in terms of the to-be-explained human behavior. Unfortunately, the proposed VAM does not appear to provide a great account of conflict effects that are considered fundamental features of human behavior, like the shape of response time distributions, and specifically, delta plots (doi.org/10.1037/0096-1523.20.4.731). The authors argue that it is beyond the scope of the presented paper to analyze delta plots, but as these are central to studies of human conflict behavior, models that aim to explain conflict behavior will need to be able to fit and explain delta plots.

Theories on conflict often suggest that negative/positive-trending delta plots arise through the relative timing of response activation related to relevant and irrelevant information. Accumulation for relevant and irrelevant information would, as a result, either start at different points in time or the rates vary over time. The current VAM, as a feedforward neural network model, does not appear to be able to capture such effects, and perhaps fundamentally not so: accumulation for each choice option is forced to start at the same time, and rates are a static output of the CNN.

The proposed solution of fitting five separate VAMs (one for each of five RT quantiles) is not satisfactory: it does not explain how delta plots result from the model, for the same reason that fitting five evidence accumulation models (one per RT quantile) does not explain how response time distributions arise. If, for example, one would want to make a prediction about someone's response time and choice based on a given stimulus, one would first have to decide which of the five VAMs to use, which is circular. But more importantly, this way of fitting multiple models does not explain the latent mechanism that underlies the shape of the delta plots.

As such, the extensive analyses on the VAM layers and the resulting conclusions that conflict effects arise due to changing representations across layers (e.g., "the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations") - while inspiring, they remain hard to weigh, as they are contingent on the assumption that the VAM can capture human behavior in the conflict task, which it struggles with. That said, the promise of combining CNNs and EAMs is clearly there. A way forward could be to either adjust the proposed model so that it can explain delta plots, which would potentially require temporal dynamics and time-varying evidence accumulation rates, or perhaps to start simpler and combine CCNs-EAMs that are able to fit more standard perceptual decision-making tasks without conflict effects.

Reviewer #3 (Public Review):

Summary:

In this article, the authors combine a well-established choice-response time (RT) model (the Linear Ballistic Accumulator) with a CNN model of visual processing to model image-based decisions (referred to as the Visual Accumulator Model - VAM). While this is not the first effort to combine these modeling frameworks, it uses this combination of approaches uniquely. Specifically, the authors attempt to better understand the structure of human information representations by fitting this model to behavioral (choice-RT) data from a classic flanker task. This objective is made possible by using a very large (by psychological modeling standards) industry data set to jointly fit both components of this VAM model to individual-level data. Using this approach, they illustrate (among other results) (1) how the interaction between target and flanker representations influence the presence and strength of congruency effects, (2) how the structure of representations changes (distributed versus more localized) with depth in the CNN model component, and (3) how different model training paradigms change the nature of information representations. This work contributes to the ML literature by demonstrating the value of training models with richer behavioral data. It also contributes to cognitive science by demonstrating how ML approaches can be integrated into cognitive modeling. Finally, it contributes to the literature on conflict modeling by illustrating how information representations may lead to some of the classic effects observed in this area of research.

Strengths:

(1) The data set used for this analysis is unique and is made publicly available as part of this article. Specifically, they have access to data for 75 participants with >25,000 trials per participant. This scale of data/individual is unusual and is the foundation on which this research rests.

(2) This is the first time, to my knowledge, that a model combining a CNN with a choice-RT model has been jointly fit to choice-RT data at the level of individual people. This type of model combination has been used before but in a more restricted context. This joint fitting, and in particular, learning a CNN through the choice-RT modeling framework, allows the authors to probe the structure of human information representations learned directly from behavioral data.

(3) The analysis approaches used in this article are state-of-the-art. The training of these models is straightforward given the data available. The interesting part of this article (opinion of course) is the way in which they probe what CNN has learned once trained. I find their analysis of how distractor and target information interfere with each other particularly compelling as well as their demonstration that training on behavioral data changes the structure of information representations when compared to training models on standard task-optimized data.

Weaknesses:

(1) Just as the data in this article is a major strength, it is also a weakness. This type of modeling would be difficult, if not impossible to do with standard laboratory data. I don't know what the data floor would be, but collecting tens of thousands of decisions for a single person is impractical in most contexts. Thus this type of work may live in the realm of industry. I do want to re-iterate that the data for this study was made publicly available though!

2) While this article uses choice-RT data it doesn't fully leverage the richness of the RT data itself. As the authors point out, this modeling framework, the LBA component in particular, does not account for some of the more nuanced but well-established RT effects in this data. This is not a big concern given the already nice contributions of this article and it leads to an opportunity for ongoing investigation.

Reviewer #3 (Public Review):

Summary:

Pyruvate kinase M2 (PKM2) is a rate-limiting enzyme in glycolysis and its translocation to the nucleus in astrocytes in various nervous system pathologies has been associated with a metabolic switch to glycolysis which is a sign of reactive astrogliosis. The authors investigated whether this occurs in experimental autoimmune encephalomyelitis (EAA), an animal model of multiple sclerosis (MS). They show that in EAA, PKM2 is ubiquitinated by TRIM21 and transferred to the nucleus in astrocytes. Inhibition of TRIM21-PKM2 axis efficiently blocks reactive gliosis and partially alleviates symptoms of EAA. Authors conclude that this axis can be a potential new therapeutic target in the treatment of MS.

Strengths:

The study is well-designed, controls are appropriate and a comprehensive battery of experiments has been successfully performed. Results of in vitro assays, single-cell RNA sequencing, immunoprecipitation, RNA interference, molecular docking, and in vivo modeling etc. complement and support each other.

Weaknesses:

Though EAA is a valid model of MS, a proposed new therapeutic strategy based on this study needs to have support from human studies.

Reviewer #1 (Public Review):

Summary:

Yang, Hu et al. examined the molecular mechanisms underlying astrocyte activation and its implications for multiple sclerosis. This study shows that the glycolytic enzyme PKM2 relocates to astrocyte nuclei upon activation in EAE mice. Inhibiting PKM2's nuclear import reduces astrocyte activation, as evidenced by decreased proliferation, glycolysis, and inflammatory cytokine release. Crucially, the study identifies TRIM21 as pivotal in regulating PKM2 nuclear import via ubiquitination. TRIM21 interacts with PKM2, promoting its nuclear translocation and enhancing its activity, affecting multiple signaling pathways. Confirmatory analyses using single-cell RNA sequencing and immunofluorescence demonstrate TRIM21 upregulation in EAE astrocytes. Modulating TRIM21 expression in primary astrocytes impacts PKM2-dependent glycolysis and proliferation. In vivo experiments targeting this mechanism effectively mitigate disease severity, CNS inflammation, and demyelination in EAE.

The authors supported their claims with various experimental approaches, however, some results should be supported with higher-quality images clearly depicting the conclusions and additional quantitative analyses of Western blots.

Strength:

This study presents a comprehensive investigation into the function and molecular mechanism of metabolic reprogramming in the activation of astrocytes, a critical aspect of various neurological diseases, especially multiple sclerosis. The study uses the EAE mouse model, which closely resembles MS. This makes the results relevant and potentially translational. The research clarifies how TRIM21 regulates the nuclear import of PKM2 through ubiquitination by integrating advanced techniques. Targeting this axis may have therapeutic benefits since lentiviral vector-mediated knockdown of TRIM21 in vivo significantly reduces disease severity, CNS inflammation, and demyelination in EAE animals.

Weaknesses:

The authors reported that PKM2 levels are elevated in the nucleus of astrocytes at different EAE phases compared to cytoplasmic localization. However, Figure 1 also shows elevated cytoplasmic expression of PKM2. The authors should clarify the nuclear localization of PKM2 by providing zoomed-in images. An explanation for the increased cytoplasmic PKM2 expression should provided. Similarly, while PKM2 translocation is inhibited by DASA-58, in addition to its nuclear localization, a decrease in the cytoplasmic localization of PKM2 is also observed. This situation brings to mind the possibility of a degradation mechanism being involved when its nuclear translocation of PKM2 is inhibited.

In Figure 3D, the authors claim that PKM2 expression causes nuclear retention of STAT3, p65, and p50, and inhibiting PKM2 localization with DASA-58 suppresses this retention. The western blot results for the MOG-stimulated group show high levels of STAT3, p50, and p65 in nuclear localization. However, in the MOG and DASA-58 treated group, one would expect high levels of p50, p65, and STAT3 proteins in the cytoplasm, while their levels decrease in the nucleus. These western blot results could be expanded. Additionally, intensity quantification for these results would be beneficial to see the statistical difference in their expressions, especially to observe the nuclear localization of PKM2.

The discrepancy between Figure 7A and its explaining text is confusing. The expectation from the knocking down of TRIM21 is the amelioration of activated astrocytes, leading to a decrease in inflammation and the disease state. The presented results support these expectations, while the images showing demyelination in EAE animals are not highly supportive. Clearly labeling demyelinated areas would enhance readers' understanding of the important impact of TRIM21 knockdown on reducing the disease severity.

Reviewer #2 (Public Review):

This study significantly advances our understanding of the metabolic reprogramming underlying astrocyte activation in neurological diseases such as multiple sclerosis. By employing an experimental autoimmune encephalomyelitis (EAE) mouse model, the authors discovered a notable nuclear translocation of PKM2, a key enzyme in glycolysis, within astrocytes.

Preventing this nuclear import via DASA 58 substantially attenuated primary astrocyte activation, characterized by reduced proliferation, glycolysis, and inflammatory cytokine secretion.<br /> Moreover, the authors uncovered a novel regulatory mechanism involving the ubiquitin ligase TRIM21, which mediates PKM2 nuclear import. TRIM21 interaction with PKM2 facilitated its nuclear translocation, enhancing its activity in phosphorylating STAT3, NFκB, and c-myc. Single-cell RNA sequencing and immunofluorescence staining further supported the upregulation of TRIM21 expression in astrocytes during EAE.

Manipulating this pathway, either through TRIM21 overexpression in primary astrocytes or knockdown of TRIM21 in vivo, had profound effects on disease severity, CNS inflammation, and demyelination in EAE mice. This comprehensive study provides invaluable insights into the pathological role of nuclear PKM2 and the ubiquitination-mediated regulatory mechanism driving astrocyte activation.

The author's use of diverse techniques, including single-cell RNA sequencing, immunofluorescence staining, and lentiviral vector knockdown, underscores the robustness of their findings and interpretations. Ultimately, targeting this PKM2-TRIM21 axis emerges as a promising therapeutic strategy for neurological diseases involving astrocyte dysfunction.

While the strengths of this piece of work are undeniable, some concerns could be addressed to refine its impact and clarity further; as outlined in the recommendations for the authors.

Reviewer #4 (Public Review):

Summary:

The authors report the role of the Pyruvate Kinase M2 (PKM2) enzyme nuclear translocation as fundamental in the activation of astrocytes in a model of autoimmune encephalitis (EAE). They show that astrocytes, activated through culturing in EAE splenocytes medium, increase their nuclear PKM2 with consequent activation of NFkB and STAT3 pathways. Prevention of PKM2 nuclear translocation decreases astrocyte counteracts this activation. The authors found that the E3 ubiquitin ligase TRIM21 interacts with PKM2 and promotes its nuclear translocation. In vivo, either silencing of TRIM21 or inhibition of PKM2 nuclear translocation ameliorates the severity of the disease in the EAE model.

Strengths:

This work contributes to the knowledge of the complex action of the PKM2 enzyme in the context of an autoimmune-neurological disease, highlighting its nuclear role and a novel partner, TRIM21, and thus adding a novel rationale for therapeutic targeting.

Weaknesses:

Despite the relevance of the work and its goals, some of the conclusions drawn would require more thorough proof:

I believe that the major weakness is the fact that TRIM21 is known to have per se many roles in autoimmune and immune pathways and some of the effects observed might be due to a PKM2-independent action. Some of the experiments to link the two proteins, besides their interaction, do not completely clarify the issue. On top of that, the in vivo experiments address the role of TRIM21 and the nuclear localisation of PKM2 independently, thus leaving the matter unsolved.

Some experimental settings are not described to a level that is necessary to fully understand the data, especially for a non-expert audience: e.g. the EAE model and MOG treatment; action and reference of the different nuclear import inhibitors; use of splenocyte culture medium and the possible effect of non-EAE splenocytes.

The statement that PKM2 is a substrate of TRIM21 ubiquitin ligase activity is an overinterpretation. There is no evidence that this interaction results in ubiquitin modification of PKM2; the ubiquitination experiment is minimal and is not performed in conditions that would allow us to see ubiquitination of PKM2 (e.g. denaturing conditions, reciprocal pull-down, catalytically inactive TRIM21, etc.).

eLife assessment

The manuscript establishes a sophisticated mouse model for acute retinal artery occlusion (RAO) by combining unilateral pterygopalatine ophthalmic artery occlusion (UPOAO) with a silicone wire embolus and carotid artery ligation, generating ischemia-reperfusion injury upon removal of the embolus. This clinically relevant model is useful for studying the cellular and molecular mechanisms of RAO. The data overall are solid, presenting a novel tool for screening pathogenic genes and promoting further therapeutic research in RAO.

Reviewer #1 (Public Review):

Summary:

Wang, Y. et al. used a silicone wire embolus to definitively and acutely clot the pterygopalatine ophthalmic artery in addition to carotid artery ligation to completely block the blood supply to the mouse inner retina, which mimics clinical acute retinal artery occlusion. A detailed characterization of this mouse model determined the time course of inner retina degeneration and associated functional deficits, which closely mimic human patients. Whole retina transcriptome profiling and comparison revealed distinct features associated with ischemia, reperfusion, and different model mechanisms. Interestingly and importantly, this team found a sequential event including reperfusion-induced leukocyte infiltration from blood vessels, residual microglial activation, and neuroinflammation that may lead to neuronal cell death.

Strengths:

Clear demonstration of the surgery procedure with informative illustrations, images, and superb surgical videos.

Two-time points of ischemia and reperfusion were studied with convincing histological and in vivo data to demonstrate the time course of various changes in retinal neuronal cell survivals, ERG functions, and inner/outer retina thickness.

The transcriptome comparison among different retinal artery occlusion models provides informative evidence to differentiate these models.

The potential applications of the in vivo retinal ischemia-reperfusion model and relevant readouts demonstrated by this study will certainly inspire further investigation of the dynamic morphological and functional changes of retinal neurons and glial cell responses during disease progression and before and after treatments.

Weaknesses:

It would be beneficial to the manuscript and the readers if the authors could improve the English of this manuscript by correcting obvious grammar errors, eliminating many of the acronyms that are not commonly used by the field, and providing a reason why this complicated but clever surgery procedure was designed and a summary table with the time course of all the morphological, functional, cellular, and transcriptome changes associated with this model.

Reviewer #2 (Public Review):

Summary:

The authors of this manuscript aim to develop a novel animal model to accurately simulate the retinal ischemic process in retinal artery occlusion (RAO). A unilateral pterygopalatine ophthalmic artery occlusion (UPOAO) mouse model was established using silicone wire embolization combined with carotid artery ligation. This manuscript provided data to show the changes in major classes of retinal neural cells and visual dysfunction following various durations of ischemia (30 minutes and 60 minutes) and reperfusion (3 days and 7 days) after UPOAO. Additionally, transcriptomics was utilized to investigate the transcriptional changes and elucidate changes in the pathophysiological process in the UPOAO model post-ischemia and reperfusion. Furthermore, the authors compared transcriptomic differences between the UPOAO model and other retinal ischemic-reperfusion models, including HIOP and UCCAO, and revealed unique pathological processes.

Strengths:

The UPOAO model represents a novel approach to studying retinal artery occlusion. The study is very comprehensive.

Weaknesses:

Some statements are incorrect and confusing. It would be helpful to review and clarify these to ensure accuracy and improve readability.

Author response:

eLife assessment:

The manuscript establishes a sophisticated mouse model for acute retinal artery occlusion (RAO) by combining unilateral pterygopalatine ophthalmic artery occlusion (UPOAO) with a silicone wire embolus and carotid artery ligation, generating ischemia-reperfusion injury upon removal of the embolus. This clinically relevant model is useful for studying the cellular and molecular mechanisms of RAO. The data overall are solid, presenting a novel tool for screening pathogenic genes and promoting further therapeutic research in RAO.

Thank you for recognizing the sophistication and clinical relevance of our mouse model for acute retinal artery occlusion. We are grateful for your supportive feedback.

Public Reviews:

Reviewer #1:

Summary:

Wang, Y. et al. used a silicone wire embolus to definitively and acutely clot the pterygopalatine ophthalmic artery in addition to carotid artery ligation to completely block the blood supply to the mouse inner retina, which mimics clinical acute retinal artery occlusion. A detailed characterization of this mouse model determined the time course of inner retina degeneration and associated functional deficits, which closely mimic human patients. Whole retina transcriptome profiling and comparison revealed distinct features associated with ischemia, reperfusion, and different model mechanisms. Interestingly and importantly, this team found a sequential event including reperfusion-induced leukocyte infiltration from blood vessels, residual microglial activation, and neuroinflammation that may lead to neuronal cell death.

Strengths:

Clear demonstration of the surgery procedure with informative illustrations, images, and superb surgical videos.

Two-time points of ischemia and reperfusion were studied with convincing histological and in vivo data to demonstrate the time course of various changes in retinal neuronal cell survivals, ERG functions, and inner/outer retina thickness.

The transcriptome comparison among different retinal artery occlusion models provides informative evidence to differentiate these models.

The potential applications of the in vivo retinal ischemia-reperfusion model and relevant readouts demonstrated by this study will certainly inspire further investigation of the dynamic morphological and functional changes of retinal neurons and glial cell responses during disease progression and before and after treatments.

We sincerely appreciate your detailed and positive feedback. These evaluations are invaluable in highlighting the significance and impact of our work. Thank you for your thoughtful and supportive review.

Weaknesses:

It would be beneficial to the manuscript and the readers if the authors could improve the English of this manuscript by correcting obvious grammar errors, eliminating many of the acronyms that are not commonly used by the field, and providing a reason why this complicated but clever surgery procedure was designed and a summary table with the time course of all the morphological, functional, cellular, and transcriptome changes associated with this model.

Thank you for your thorough review of the manuscript. We sincerely apologize for any grammatical errors resulting from our English language proficiency and have taken the necessary steps to polish the article. Additionally, we have heeded your advice and reduced the use of field-specific acronyms to enhance readability for both the manuscript and its readers.

Regarding the rationale behind the design of the UPOAO model, we have provided a description in Introduction section. Our group focuses on the research of pathogenesis and clinical treatment for RAO. The absence of an accurate mouse model simulating the retinal ischemic process has hampered progress in developing neuroprotective agents for RAO. To better simulate the retinal ischemic process and possible ischemia-reperfusion injury following RAO, we developed a novel vascular-associated mouse model called the unilateral pterygopalatine ophthalmic artery occlusion (UPOAO) model. We drew inspiration from the widely employed middle cerebral artery occlusion (MCAO) model, commonly used in cerebral ischemic injury research, which guided the development of the UPOAO model.

We appreciate your valuable suggestion regarding the inclusion of a summary table outlining the time course of morphological, functional, cellular, and transcriptome changes associated with this model. To address this, we intend to include a supplementary table at the end of the article, which will offer a comprehensive overview of the experimental results, thereby aiding in clarity and interpretation.

Once again, we thank you for your insightful comments and suggestions, which have greatly contributed to the improvement of our manuscript.

Reviewer #2:

Summary:

The authors of this manuscript aim to develop a novel animal model to accurately simulate the retinal ischemic process in retinal artery occlusion (RAO). A unilateral pterygopalatine ophthalmic artery occlusion (UPOAO) mouse model was established using silicone wire embolization combined with carotid artery ligation. This manuscript provided data to show the changes in major classes of retinal neural cells and visual dysfunction following various durations of ischemia (30 minutes and 60 minutes) and reperfusion (3 days and 7 days) after UPOAO. Additionally, transcriptomics was utilized to investigate the transcriptional changes and elucidate changes in the pathophysiological process in the UPOAO model post-ischemia and reperfusion. Furthermore, the authors compared transcriptomic differences between the UPOAO model and other retinal ischemic-reperfusion models, including HIOP and UCCAO, and revealed unique pathological processes.

Strengths:

The UPOAO model represents a novel approach to studying retinal artery occlusion. The study is very comprehensive.

We greatly appreciate your positive assessment of our work and are encouraged by your recognition of its significance.

Weaknesses:

Some statements are incorrect and confusing. It would be helpful to review and clarify these to ensure accuracy and improve readability.

We sincerely appreciate your meticulous review of the manuscript. Taking into account your valuable feedback, we will thoroughly address the inaccuracies identified in the revised version. Additionally, we will commit to polishing the article to ensure improved readability. We apologize for any confusion caused by these inaccuracies and genuinely thank you for bringing them to our attention.

eLife assessment

This study describes useful mouse models of knock-ins of human STING1 variants and an assessment of these variants' action in mouse immune cells. While the data included in the manuscript are solid, because of the authors' interpretation and conclusions made, the work is currently considered incomplete.

Reviewer #1 (Public Review):

Summary:

This manuscript by Aybar-Torres et al investigated the effect of common human STING1 variants on STING-mediated T cell phenotypes in mice. The authors previously made knock-in mice expressing human STING1 alleles HAQ or AQ, and here they established a new knock-in line Q293. The authors stimulated cells isolated from these mice with STING agonists and found that all three human mutant alleles resist cell death, leading to the conclusion that R293 residue is essential for STING-mediated cell death (there are several caveats with this conclusion, more below). The authors also bred HAQ and AQ alleles to the mouse Sting1-N153S SAVI mouse and observed varying levels of rescue of disease phenotypes with the AQ allele showing more complete rescue than the HAQ allele. The Q293 allele was not tested in the SAVI model. They conclude that the human common variants such as HAQ and AQ have a dominant negative effect over the gain-of-function SAVI mutants.

Strengths:

The authors and Dr. Jin's group previously made important observations of common human STING1 variants, and these knock-in mouse models are essential for understanding the physiological function of these alleles.

Weaknesses:

However, although some of the observations reported here are interesting, the data collectively does not support a unified model. The authors seem to be drawing two sets of conclusions from in vitro and in vivo experiments, and neither mechanism is clear. Several experiments need better controls, and these knock-in mice need more comprehensive functional characterization.

(1) In Figure 1, the authors are trying to show that STING agonist-induced splenocytes cell death is blocked by HAQ, AQ and Q alleles. The conclusion at line 134 should be splenocytes, not lymphocytes. Most experiments in this figure were done with mixed population that may involve cell-to-cell communication. Although TBK1-dependence is likely, a single inhibitor treatment of a mixed population is not sufficient to reach this conclusion.<br /> (2) Q293 knock-in mouse needs to be characterized and compared to HAQ and AQ. Is this mutant expressed in tissues? Does this mutant still produce IFN and other STING activities? Does the protein expression level altered on Western blot? Is the mutant protein trafficking affected? In the authors' previous publications and some of the Western blot here, expression levels of each of these human STING1 protein in mice are drastically different. HAQ and AQ also have different effects on metabolism (pmid: 36261171), which could complicate interoperation of the T cell phenotypes.<br /> (3) HAQ/WT and AQ/WT splenocytes are protected from STING agonist-induced cell death equally well (Figure 1G). HAQ/SAVI shows less rescue compared to AQ/SAVI. These are interesting observations, but mechanism is unclear and not clearly discussed. E.g., how does AQ protect disease pathology better than HAQ (that contains AQ)? Does Q293 allele also fully rescue SAVI?<br /> (4) Figure 2 feels out of place. First of all, why are the authors using human explant lung tissues? PBMCs should be a better source for lymphocytes. In untreated conditions, both CD4 and B cells show ~30% dying cells, but CD8 cells show 0% dying cells. This calls for technical concerns on the CD8 T cell property or gating strategy because in the mouse experiment (Figure 1A) all primary lymphocytes show ~30% cell death at steady-state. Second, Figure 2C, these type of partial effect needs multiple human donors to confirm. Three, the reconstitution of THP1 cells seems out of place. STING-mediated cell death mechanism in myeloid and lymphoid cells are likely different. If the authors want to demonstrate cell death in myeloid cells using THP1, then these reconstituted cell lines need to be better validated. Expression, IFN signaling, etc. The parental THP1 cells is HAQ/HAQ, how does that compare to the reconstitutions? There are published studies showing THP1-STING-KO cells reconstituted with human variants do not respond to STING agonists as expected. The authors need to be scientifically rigorous on validation and caution on their interpretations.<br /> (5) Figure 2G, H, I are confusing. AQ is more active in producing IFN signaling than HAQ and Q is the least active. How to explain this?<br /> (6) The overall model is unclear. If HAQ, AQ and Q are loss-of-function alleles and Q is the key residue for STING-mediated cell death, then why AQ is the most active in producing IFN signaling and AQ/SAVI rescues disease most completely? If these human variants act as dominant negatives, which would be consistent with the WT/het data, then how do you explain AQ is more dominant negative than HAQ?<br /> (7) As a general note, SAVI disease phenotypes involve multiple cell types. Lymphocyte cell death is only one of them. The authors' characterization of SAVI pathology is limited and did not analyze immunopathology of the lung.<br /> (8) Line 281, the discussion on HIV T cell death mechanism is not relevant and over-stretching. This study did not evaluate viral infection in T cells at all. The original finding of HAQ/HAQ enrichment in HIV/AIDS was 2/11 in LTNP vs 0/11 in control, arguably not the strongest statistics.

Reviewer #2 (Public Review):

Aybar-Torres and colleagues utilize common human STING alleles to dissect the mechanism of SAVI inflammatory disease. The authors demonstrate that these common alleles alleviate SAVI pathology in mice, and perhaps more importantly use the differing functionality of these alleles to provide insight into requirements of SAVI disease induction. Their findings suggest that it is residue A230 and/or Q293 that are required for SAVI induction, while the ability to induce an interferon-dependent inflammatory response is not. This is nicely exemplified by the AQ/SAVI mice that have an intact inflammatory response to STING activation, yet minimal disease progression. As both mutants seem to be resistant STING-dependent cell death, this manuscript also alludes to the importance of STING-dependent cell death, rather than STING-dependent inflammation, in the progression of SAVI pathology. While I have some concerns, I believe this manuscript makes some important connections between STING pathology mouse models and human genetics that would contribute to the field.

Some points to consider:

(1) While the CD4+ T cell counts from HAQ/SAVI and AQ/SAVI mice suggest that these T cells are protected from STING-dependent cell death, an assay that explores this more directly would strengthen the manuscript. This is also supported by Fig 2C, but I believe a strength of this manuscript is the comparison between the two alleles. Therefore, if possible, I would recommend the isolation of T cells from these mice and direct stimulation with diABZI or other STING agonist with a cell death readout.<br /> (2) Related to the above point - further exemplifying that the Q293 locus is essential to disease, even in human cells, would also strengthen the paper. It seems that CD4 T cell loss is a major component of human SAVI. While not completely necessary, repeating the THP1 cell death experiments from Fig 2 with a human T cell line would round out the study nicely.<br /> (3) While I found the myeloid cell counts and BMDM data interesting, I think some more context is needed to fully loop this data into the story. Is myeloid cell expansion exemplified by SAVI patients? Do we know if myeloid cells are the major contributors to the inflammation these patients experience? Why should the SAVI community care about the Q293 locus in myeloid cells?<br /> (4) The functional assays in Figure 4 are exciting and really connect the alleles to disease progression. To strengthen the manuscript and connect all the data, I would recommend additional readouts from these mice that address the inflammatory phenotype shown in vitro in Figure 5. For example, measuring cytokines from these mice via ELISA or perhaps even Western blots looking for NFkB or STING activation would be supportive of the story. This would also allow for some tissue specificity. I believe looking for evidence of inflammation and STING activation in the lungs of these mice, for example, would further connect the data to human SAVI pathology.

Author response:

We deeply appreciate the editors’ and reviewers’ invaluable time and effort. We would also like to extend our gratitude to eLife for its unwavering commitment to a transparent review and publication model. Below, we present our point-by-point responses to the comments.  

Besides the WT allele, equivalent to the mouse TMEM173 gene, the human TMEM173 gene has two common alleles: the HAQ and AQ alleles carried by billions of people. The main conclusions and interpretation, summarized in the Title and Abstract, are (i) Different from the WT TMEM173 allele, the HAQ or AQ alleles are resistant to STING activation-induced cell death; (ii) STING residue 293 is critical for cell death; (iii) HAQ, AQ alleles are dominant to the SAVI allele; iv) One copy of the AQ allele rescues the SAVI disease in mice. We propose that STING research and STING-targeting immunotherapy should consider human TMEM173 heterogeneity. These interpretations and conclusions were based on Data and Logic. We welcome alternative, logical interpretations from our peers and potential collaborations to advance the human TMEM173 research.  

Reviewer #1 (Public Review):

Responses to Comment 1: We greatly appreciate Reviewer 1's insights. We will change the “lymphocytes” to “splenocytes” (line 134) as suggested. We respectfully disagree with Reviewer 1’s comments on TBK1 (lines 129 – 134). First, we used two different TBK1 inhibitors: BX795 and GSK8612. Second, because BX795 also inhibits PDK1, we used a PDK1 inhibitor GSK2334470; Third, both BX795 and GSK8612 completely inhibited diABZI-induced splenocyte cell death (Figure 1B). The logical conclusion is “TBK1 activation is required for STING-mediated mouse spleen cell death ex vivo”. (line 118). 

This manuscript uncovers a significant aspect of the interplay between the common human TMEM173 alleles and the rare SAVI mutation (lines 23-26). Our discovery that the common human TMEM173 alleles are resistant to STING activation-induced cell death is a substantial finding. It further strengthens the argument that the HAQ and AQ alleles are functionally distinct from the WT allele 1-3. We wish to underscore the crucial message of this study-that 'STING research and STING-targeting immunotherapy should consider TMEM173 heterogeneity in humans' (line 37), which has been largely overlooked in current STING clinical trials 4.  

Regarding STING-Cell death, as we stated in the Introduction (lines 62-79). (i) STING-mediated cell death is cell type-dependent 5-7 and type I IFNs-independent 5,7,8. (ii) The in vivo biological significance of STING-mediated cell death is not clear 7,8. (iii) The mechanisms of STING-Cell death remain controversial. Multiple cell death pathways, i.e., apoptosis, necroptosis, pyroptosis, ferroptosis, and PANoptosis, are proposed 7,9,10. SAVI patients (WT/SAVI) and mouse models had CD4 T cellpenia 8,11. SAVI/HAQ, SAVI/AQ restored T cells in mice. Thus, the manuscript provides some answers to the biological significance of STING-cell death. Next, splenocytes from Q293/Q293 mice are resistant to STING cell death. The logical conclusion is that the amino acid 293 is critical for STING cell death. How aa293 mediates this function needs future investigation. Similarly, how TBK1 mediates STING cell death, independent of type I IFNs and NFκB induction, needs future investigation.

Responses to Comment 2: These are all very interesting questions that we will address in future studies. This manuscript, titled “The common TMEM173 HAQ, AQ alleles rescue CD4 T cellpenia, restore T-regs, and prevent SAVI (N153S) inflammatory disease in mice” does not focus on Q293 mice. We have been researching the common human TMEM173 alleles since 2011 from the discovery12 , mouse model1,3, human clinical trial2, and human genetics studies 3. This manuscript is another step towards understanding these common human TMEM173 alleles with the new discovery that HAQ, AQ are resistant to STING cell death. 

Responses to Comment 3: We aim to address these worthy questions in future studies. In this manuscript, Figure 6 shows AQ/SAVI had more T-regs than HAQ/SAVI (lines 246 – 256). In our previous publication on HAQ, AQ knockin mice, we showed that AQ T-regs have more IL-10 and mitochondria activity than HAQ T-regs 3. We propose that increased IL-10+

Tregs in AQ mice may contribute to an improved phenotype in AQ/SAVI compared to

HAQ/SAVI. However, we are not excluding other contributions (e.g. metabolic difference) by the AQ allele. We will explore these possibilities in future research.   

Responses to Comment 4: Figure 2 is necessary because it reveals the difference between mouse and human STING cell death. Figure 2A-2B showed that STING activation killed human CD4 T cells, but not human CD8 T cells or B cells. This observation is different from Figure 1A, where STING activation killed mouse CD4, CD8 T cells, and CD19 B cells, revealing the species-specific STING cell death responses. Regarding human CD8 T cells, as we stated in the Discussion (lines 318-320), human CD8 T cells (PBMC) are not as susceptible as the CD4 T cells to STING-induced cell death 8. We used lung lymphocytes that showed similar observations (Figure 2A). For Figure 2C, we used 2 WT/HAQ and 3 WT/WT individuals (lines 738-739). We generate HAQ, AQ THP-1 cells in STING-KO THP-1 cells (Invivogen,, cat no. thpd-kostg) (lines 740-741). 

A recent study found that STING agonist SHR1032 induces cell death in STING-KO THP-1 cells expressing WT(R232) human STING 10 (line 182) independent of type I IFNs. SHR1032 suppressed THP1-STING-WT(R232) cell growth at GI50: 23 nM while in the parental THP1STING-HAQ cells, the GI50 of SHR1032 was >103 nM 10. Cytarabine was used as an internal control where SHR1032 killed more robustly than cytarabine in the THP1-STING-WT(R232) cells but much less efficiently than cytarabine in the THP-1-STING-HAQ cells 10.   

This manuscript rigorously uses mouse splenocytes, human lung lymphocytes, THP-1 reconstituted with HAQ, AQ, and HAQ/SAVI, AQ/SAVI mice, to demonstrate that the common human HAQ, AQ alleles are resistant to STING cell death in vitro and in vivo.

We agree with reviewer 1 that STING-mediated cell death mechanisms in myeloid and lymphoid cells may be different and likely contribute to the different mechanisms proposed in STING cell death research 7,9,10. Our study focuses on the in vivo mechanism of T cellpenia.  

Responses to Comment 5: We stated in the Introduction that “AQ responds to CDNs and produce type I IFNs in vivo and in vitro 3,13,14 ”(line 94, 95). We reported that the AQ knock in mice responded to STING activation 3. We previously showed that there was a negative natural selection on the AQ allele in individuals outside of Africa 3. 28% of Africans are WT/AQ but only 0.6% East Asians are WT/AQ 3. Future research on the AQ allele will address this interesting question that may shed new mechanistic light on STING action.

Responses to Comment 6: The comment here is similar to comment 3. In this manuscript, Figure 6 shows AQ/SAVI had more T-regs than HAQ/SAVI (lines 246 – 256). In our previous publication on HAQ, AQ knockin mice, we showed that AQ T-regs have more IL-10 and mitochondria activity than HAQ T-regs 3. We propose that increased IL-10+ Tregs in AQ mice may contribute to an improved phenotype in AQ/SAVI compared to HAQ/SAVI. However, we are not excluding other contributions (e.g. metabolic difference) by the AQ allele.

Responses to Comment 7: Both radioresistant parenchymal and/or stromal cells and hematopoietic cells influence SAVI pathology in mice 15,16. Nevertheless, the lack of CD 4 T cells, including the anti-inflammatory T-regs, likely contributes to the inflammation in SAVI mice and patients. We characterized lung function, lung inflammation (Figure 4), lung neutrophils, and inflammatory monocyte infiltration (Figure S4). 

Responses to Comment 8: Several publications have linked STING to HIV pathogenesis 17-22  (line 271). The manuscript studies STING activation-induced cell death. It is not stretching to ask, for example, does preventing STING cell death, without affecting type I IFNs production, restore CD4 T cell counts and improve care for AIDS patients?

Reviewer #2 (Public Review):

Response to Comment 1: Please see the Figure below for cell death by diABZI, DMXAA in Splenocytes from WT/WT, WT/HAQ, HAQ/SAVI, AQ/SAVI mice. The HAQ/SAVI and AQ/SAVI splenocytes showed similar partial resistance to STING activationinduced cell death. 

Responses to Comment 2: We examined HAQ, AQ mouse splenocytes, HAQ human lung lymphocytes, THP-1 reconstituted with HAQ, AQ, and HAQ/SAVI, AQ/SAVI mice, to demonstrate that the common human HAQ, AQ alleles are resistant to STING cell death in vitro and in vivo. Additional human T cell line work does not add too much. 

Responses to Comment 3: This is possibly a misunderstanding. We use BMDM for the purpose of comparing STING signaling (TBK1, IRF3, NFκB, STING activation) by WT/SAVI, HAQ/SAVI, AQ/SAVI. Ideally, we would like to compare STING signaling in CD4 T cells from WT/SAVI to HAQ/SAVI, AQ/SAVI mice. However, WT/SAVI has no CD4 T cells. Here, we are making the assumption that the basic STING signaling (TBK1, IRF3, NFκB, STING activation) is conserved between T cells and macrophages. 

Responses to Comment 4: Reviewer 2 suggests looking for evidence of inflammation and STING activation in the lungs of HAQ/SAVI, AQ/SAVI. We would like to elaborate further. First, anti-inflammatory treatments, e.g. steroids, DMARDs, IVIG, Etanercept, rituximab, Nifedipine, amlodipine, et al., all failed in SAVI patients 11. Second, Figure S4 examined lung neutrophils and inflammatory monocyte infiltration. Interestingly, while AQ/SAVI mice had a better lung function than HAQ/SAVI mice (Figure 4D, 4E vs 4H, 4I), HAQ/SAVI and AQ/SAVI lungs had comparable neutrophils and inflammatory monocyte infiltration. Last, SAVI is classified as type I interferonopathy 11, but the lung diseases of SAVI are mainly independent of type I IFNs 23-26. The AQ allele suppresses SAVI in vivo.  Understanding the mechanisms by which AQ rescues SAVI can generate curative care for SAVI patients.  

Author response image 1.

(A-B). Flow cytometry of HAQ/SAVI, AQ/SAVI, WT/WT or WT/HAQ splenocytes treated with diABZI (100ng/ml) or DMXAA (20µg/ml) for 24hrs. Cell death was determined by PI staining. Data are representative of three independent experiments. Graphs represent the mean with error bars indication s.e.m. p values are determined by one-way ANOVA Tukey’s multiple comparison test. * p<0.05. n.s: not significant.

References.

(1)             Patel, S. et al. The Common R71H-G230A-R293Q Human TMEM173 Is a Null Allele. J Immunol 198, 776-787 (2017). 

(2)             Sebastian, M. et al. Obesity and STING1 genotype associate with 23-valent pneumococcal vaccination efficacy. JCI Insight 5 (2020). 

(3)             Mansouri, S. et al. MPYS Modulates Fatty Acid Metabolism and Immune Tolerance at Homeostasis Independent of Type I IFNs. J Immunol 209, 2114-2132 (2022). 

(4)             Sivick, K. E. et al. Comment on "The Common R71H-G230A-R293Q Human TMEM173 Is a Null Allele". J Immunol 198, 4183-4185 (2017). 

(5)             Gulen, M. F. et al. Signalling strength determines proapoptotic functions of STING. Nat Commun 8, 427 (2017). 

(6)             Kabelitz, D. et al. Signal strength of STING activation determines cytokine plasticity and cell death in human monocytes. Sci Rep 12, 17827 (2022). 

(7)             Murthy, A. M. V., Robinson, N. & Kumar, S. Crosstalk between cGAS-STING signaling and cell death. Cell Death Differ 27, 2989-3003 (2020). 

(8)             Kuhl, N. et al. STING agonism turns human T cells into interferon-producing cells but impedes their functionality. EMBO Rep 24, e55536 (2023). 

(9)             Li, C., Liu, J., Hou, W., Kang, R. & Tang, D. STING1 Promotes Ferroptosis Through MFN1/2-Dependent Mitochondrial Fusion. Front Cell Dev Biol 9, 698679 (2021). 

(10)         Song, C. et al. SHR1032, a novel STING agonist, stimulates anti-tumor immunity and directly induces AML apoptosis. Sci Rep 12, 8579 (2022). 

(11)         Liu, Y. et al. Activated STING in a vascular and pulmonary syndrome. N Engl J Med 371, 507-518 (2014). 

(12)         Jin, L. et al. Identification and characterization of a loss-of-function human MPYS variant. Genes Immun 12, 263-269 (2011). 

(13)         Yi, G. et al. Single nucleotide polymorphisms of human STING can affect innate immune response to cyclic dinucleotides. PLoS One 8, e77846 (2013). 

(14)         Patel, S. et al. Response to Comment on "The Common R71H-G230A-R293Q Human TMEM173 Is a Null Allele". J Immunol 198, 4185-4188 (2017). 

(15)         Gao, K. M. et al. Endothelial cell expression of a STING gain-of-function mutation initiates pulmonary lymphocytic infiltration. Cell Rep 43, 114114 (2024). 

(16)         Gao, K. M., Motwani, M., Tedder, T., Marshak-Rothstein, A. & Fitzgerald, K. A. Radioresistant cells initiate lymphocyte-dependent lung inflammation and IFNgammadependent mortality in STING gain-of-function mice. Proc Natl Acad Sci U S A 119, e2202327119 (2022). 

(17)         Monroe, K. M. et al. IFI16 DNA sensor is required for death of lymphoid CD4 T cells abortively infected with HIV. Science 343, 428-432 (2014). 

(18)         Doitsh, G. et al. Cell death by pyroptosis drives CD4 T-cell depletion in HIV-1 infection. Nature 505, 509-514 (2014). 

(19)         Jakobsen, M. R., Olagnier, D. & Hiscott, J. Innate immune sensing of HIV-1 infection. Curr Opin HIV AIDS 10, 96-102 (2015). 

(20)         Silvin, A. & Manel, N. Innate immune sensing of HIV infection. Curr Opin Immunol 32, 54-60 (2015). 

(21)         Altfeld, M. & Gale, M., Jr. Innate immunity against HIV-1 infection. Nat Immunol 16, 554-562 (2015). 

(22)         Krapp, C., Jonsson, K. & Jakobsen, M. R. STING dependent sensing - Does HIV actually care? Cytokine Growth Factor Rev 40, 68-76 (2018). 

(23)         Luksch, H. et al. STING-associated lung disease in mice relies on T cells but not type I interferon. J Allergy Clin Immunol 144, 254-266 e258 (2019). 

(24)         Stinson, W. A. et al. The IFN-gamma receptor promotes immune dysregulation and disease in STING gain-of-function mice. JCI Insight 7 (2022). 

(25)         Warner, J. D. et al. STING-associated vasculopathy develops independently of IRF3 in mice. J Exp Med 214, 3279-3292 (2017). 

(26)         Fremond, M. L. et al. Overview of STING-Associated Vasculopathy with Onset in Infancy (SAVI) Among 21 Patients. J Allergy Clin Immunol Pract 9, 803-818 e811 (2021).

eLife assessment

This valuable study reveals how a rhizobial effector protein cleaves and inhibits a key plant receptor for symbiosis signaling, while the host plant counters by phosphorylating the effector. The molecular evidence for the protein-protein interaction and modification is solid, though biological evidence directly linking effector cleavage to rhizobial infection is incomplete. With additional functional data, this work could have implications for understanding intricate plant-microbe dynamics during mutualistic interactions.

Reviewer #1 (Public Review):

Bacterial effectors that interfere with the inner molecular workings of eukaryotic host cells are of great biological significance across disciplines. On the one hand they help us to understand the molecular strategies that bacteria use to manipulate host cells. On the other hand they can be used as research tools to reveal molecular details of the intricate workings of the host machinery that is relevant for the interaction/defence/symbiosis with bacteria. The authors investigate the function and biological impact of a rhizobial effector that interacts with and modifies, and curiously is modified by, legume receptors essential for symbiosis. The molecular analysis revealed a bacterial effector that cleaves a plant symbiosis signaling receptor to inhibit signaling and the host counterplay by phosphorylation via a receptor kinase. These findings have potential implications beyond bacterial interactions with plants.

Bao and colleagues investigated how rhizobial effector proteins can regulate the legume root nodule symbiosis. A rhizobial effector is described to directly modify symbiosis-related signaling proteins, altering the outcome of the symbiosis. Overall, the paper presents findings that will have a wide appeal beyond its primary field.

Out of 15 identified effectors from Sinorhizobium fredii, they focus on the effector NopT, which exhibits proteolytic activity and may therefore cleave specific target proteins of the host plant. They focus on two Nod factor receptors of the legume Lotus japonicus, NFR1 and NFR5, both of which were previously found to be essential for the perception of rhizobial nod factor, and the induction of symbiotic responses such as bacterial infection thread formation in root hairs and root nodule development (Madsen et al., 2003, Nature; Tirichine et al., 2003; Nature). The authors present evidence for an interaction of NopT with NFR1 and NFR5. The paper aims to characterize the biochemical and functional consequences of these interactions and the phenotype that arises when the effector is mutated.

Evidence is presented that in vitro NopT can cleave NFR5 at its juxtamembrane region. NFR5 appears also to be cleaved in vivo. and NFR1 appears to inhibit the proteolytic activity of NopT by phosphorylating NopT. When NFR5 and NFR1 are ectopically over-expressed in leaves of the non-legume Nicotiana benthamiana, they induce cell death (Madsen et al., 2011, Plant Journal). Bao et al., found that this cell death response is inhibited by the coexpression of nopT. Mutation of nopT alters the outcome of rhizobial infection in L. japonicus. These conclusions are well supported by the data.

The authors present evidence supporting the interaction of NopT with NFR1 and NFR5. In particular, there is solid support for cleavage of NFR5 by NopT (Figure 3) and the identification of NopT phosphorylation sites that inhibit its proteolytic activity (Figure 4C). Cleavage of NFR5 upon expression in N. benthamiana (Figure 3A) requires appropriate controls (inactive mutant versions) that have been provided, since Agrobacterium as a closely rhizobia-related bacterium, might increase defense related proteolytic activity in the plant host cells.

Key results from N. benthamiana appear consistent with data from recombinant protein expression in bacteria. For the analysis in the host legume L. japonicus transgenic hairy roots were included. To demonstrate that the cleavage of NFR5 occurs during the interaction in plant cells the authors build largely on western blots. Regardless of whether Nicotiana leaf cells or Lotus root cells are used as the test platform, the Western blots indicate that only a small proportion of NFR5 is cleaved when co-expressed with nopT, and most of the NFR5 persists in its full-length form (Figures 3A-D). It is not quite clear how the authors explain the loss of NFR5 function (loss of cell death, impact on symbiosis), as a vast excess of the tested target remains intact. It is also not clear why a large proportion of NFR5 is unaffected by the proteolytic activity of NopT. This is particularly interesting in Nicotiana in the absence of Nod factor that could trigger NFR1 kinase activity.

It is also difficult to evaluate how the ratios of cleaved and full-length protein change when different versions of NopT are present without a quantification of band strengths normalized to loading controls (Figure 3C, 3D, 3F). The same is true for the blots supporting NFR1 phosphorylation of NopT (Figure 4A).

It is clear that mutation of nopT results in a quantitative infection phenotype. Nodule primordia and infection threads are still formed when L. japonicus plants are inoculated with ∆nopT mutant bacteria, but it is not clear if these primordia are infected or develop into fully functional nodules (Figure 5). A quantification of the ratio of infected and non-infected nodules and primordia would reveal whether NopT is only active at the transition from infection focus to thread or perhaps also later in the bacterial infection process of the developing root nodule.

Reviewer #2 (Public Review):

Summary:

This manuscript presents data demonstrating NopT's interaction with Nod Factor Receptors NFR1 and NFR5 and its impact on cell death inhibition and rhizobial infection. The identification of a truncated NopT variant in certain Sinorhizobium species adds an interesting dimension to the study. These data try to bridge the gaps between classical Nod-factor-dependent nodulation and T3SS NopT effector-dependent nodulation in legume-rhizobium symbiosis. Overall, the research provides interesting insights into the molecular mechanisms underlying symbiotic interactions between rhizobia and legumes.

Strengths:

The manuscript nicely demonstrates NopT's proteolytic cleavage of NFR5, regulated by NFR1 phosphorylation, promoting rhizobial infection in L. japonicus. Intriguingly, authors also identify a truncated NopT variant in certain Sinorhizobium species, maintaining NFR5 cleavage but lacking NFR1 interaction. These findings bridge the T3SS effector with the classical Nod-factor-dependent nodulation pathway, offering novel insights into symbiotic interactions.

Weaknesses:

(1) In the previous study, when transiently expressed NopT alone in Nicotiana tobacco plants, proteolytically active NopT elicited a rapid hypersensitive reaction. However, this phenotype was not observed when expressing the same NopT in Nicotiana benthamiana (Figure 1A). Conversely, cell death and a hypersensitive reaction were observed in Figure S8. This raises questions about the suitability of the exogenous expression system for studying NopT proteolysis specificity.

(2)NFR5 Loss-of-function mutants do not produce nodules in the presence of rhizobia in lotus roots, and overexpression of NFR1 and NFR5 produces spontaneous nodules. In this regard, if the direct proteolysis target of NopT is NFR5, one could expect the NGR234's infection will not be very successful because of the Native NopT's specific proteolysis function of NFR5 and NFR1. Conversely, in Figure 5, authors observed the different results.

(3) In Figure 6E, the model illustrates how NopT digests NFR5 to regulate rhizobia infection. However, it raises the question of whether it is reasonable for NGR234 to produce an effector that restricts its own colonization in host plants.

(4) The failure to generate stable transgenic plants expressing NopT in Lotus japonicus is surprising, considering the manuscript's claim that NopT specifically proteolyzes NFR5, a major player in the response to nodule symbiosis, without being essential for plant development.

Author response:

eLife assessment

This valuable study reveals how a rhizobial effector protein cleaves and inhibits a key plant receptor for symbiosis signaling, while the host plant counters by phosphorylating the effector. The molecular evidence for the protein-protein interaction and modification is solid, though biological evidence directly linking effector cleavage to rhizobial infection is incomplete. With additional functional data, this work could have implications for understanding intricate plant-microbe dynamics during mutualistic interactions.

Thank you for this helpful comment. In the revised manuscript version, we will be more prudent with directly linking cleavage of Nod factor receptors by NopT and rhizobial infection.

We plan to modify the Title, the One-Sentence Summary, Abstract, and Discussion regarding this point.

Public Reviews:

Reviewer #1 (Public Review):

Bacterial effectors that interfere with the inner molecular workings of eukaryotic host cells are of great biological significance across disciplines. On the one hand they help us to understand the molecular strategies that bacteria use to manipulate host cells. On the other hand they can be used as research tools to reveal molecular details of the intricate workings of the host machinery that is relevant for the interaction/defence/symbiosis with bacteria. The authors investigate the function and biological impact of a rhizobial effector that interacts with and modifies, and curiously is modified by, legume receptors essential for symbiosis. The molecular analysis revealed a bacterial effector that cleaves a plant symbiosis signaling receptor to inhibit signaling and the host counterplay by phosphorylation via a receptor kinase. These findings have potential implications beyond bacterial interactions with plants.

Thank you for highlighting the broad significance of rhizobial effectors in understanding legume-rhizobium interactions. We fully agree with your assessment and will emphasize these points in the revised Introduction and Discussion sections of our manuscript. Specifically, we will expand our Discussion regarding the potential impact of the NopT interaction with symbiotic receptor kinases on plant immune signaling and regarding the general significance of our work.

Bao and colleagues investigated how rhizobial effector proteins can regulate the legume root nodule symbiosis. A rhizobial effector is described to directly modify symbiosis-related signaling proteins, altering the outcome of the symbiosis. Overall, the paper presents findings that will have a wide appeal beyond its primary field.

Out of 15 identified effectors from Sinorhizobium fredii, they focus on the effector NopT, which exhibits proteolytic activity and may therefore cleave specific target proteins of the host plant. They focus on two Nod factor receptors of the legume Lotus japonicus, NFR1 and NFR5, both of which were previously found to be essential for the perception of rhizobial nod factor, and the induction of symbiotic responses such as bacterial infection thread formation in root hairs and root nodule development (Madsen et al., 2003, Nature; Tirichine et al., 2003; Nature). The authors present evidence for an interaction of NopT with NFR1 and NFR5. The paper aims to characterize the biochemical and functional consequences of these interactions and the phenotype that arises when the effector is mutated.

Thank you for your positive feedback on our manuscript. In the revised Introduction and Discussion sections, we plan to better emphasize the interdisciplinary significance of our work. We will show how the knowledge gained from our study can contribute to a better understanding of microbial interactions with eukaryotic hosts in general, which may have a stimulating effect on future research in various research areas such as pathogenesis and immunity.

To ensure that the readers can easily follow the rationale behind our experiments, we will improve the Results section and provide more detailed explanations of how NopT among 15 examined effectors was selected. Additionally, we will provide more background information on NopT and the roles of NFR1 and NFR5 in symbiotic signaling in the Introduction section. As suggested, we will include the references Madsen et al. (2003) and Tirichine et al. (2003) as well as additional references on rhizobial NopT proteins into our revised manuscript version.

Evidence is presented that in vitro NopT can cleave NFR5 at its juxtamembrane region. NFR5 appears also to be cleaved in vivo. and NFR1 appears to inhibit the proteolytic activity of NopT by phosphorylating NopT. When NFR5 and NFR1 are ectopically over-expressed in leaves of the non-legume Nicotiana benthamiana, they induce cell death (Madsen et al., 2011, Plant Journal). Bao et al., found that this cell death response is inhibited by the coexpression of nopT. Mutation of nopT alters the outcome of rhizobial infection in L. japonicus. These conclusions are well supported by the data.

We appreciate that you recognize the value of our data.

The authors present evidence supporting the interaction of NopT with NFR1 and NFR5. In particular, there is solid support for cleavage of NFR5 by NopT (Figure 3) and the identification of NopT phosphorylation sites that inhibit its proteolytic activity (Figure 4C). Cleavage of NFR5 upon expression in N. benthamiana (Figure 3A) requires appropriate controls (inactive mutant versions) that have been provided, since Agrobacterium as a closely rhizobia-related bacterium, might increase defense related proteolytic activity in the plant host cells.

Thank you for recognizing the use of an inactive NopT variant in Figure 3A. In fact, increased activity of plant proteases induced by Agrobacterium is an important point that should not be neglected. We plan to mention this aspect in our revised Discussion.

In the context of your comments, we are planning to make the following improvements to the manuscript:

(1) We will add a more detailed description of the experimental conditions under which the cleavage of NFR5 by NopT was observed in vitro and in vivo.

(2) We plan to provide more comprehensive data on the phosphorylation of NopT by NFR1, including phosphorylation assays and mass spectrometry results. These additional data support the proposed mechanism by which NFR1 inhibits the proteolytic activity of NopT.

(3) We will expand the Discussion on the cell death response induced by ectopic expression of NFR1 and NFR5 in Nicotiana benthamiana. We will include more details from Madsen et al. (2011) to contextualize our findings with published literature.

We believe these additions and clarifications will enhance the clarity and impact of our findings.

Key results from N. benthamiana appear consistent with data from recombinant protein expression in bacteria. For the analysis in the host legume L. japonicus transgenic hairy roots were included. To demonstrate that the cleavage of NFR5 occurs during the interaction in plant cells the authors build largely on western blots. Regardless of whether Nicotiana leaf cells or Lotus root cells are used as the test platform, the Western blots indicate that only a small proportion of NFR5 is cleaved when co-expressed with nopT, and most of the NFR5 persists in its full-length form (Figures 3A-D). It is not quite clear how the authors explain the loss of NFR5 function (loss of cell death, impact on symbiosis), as a vast excess of the tested target remains intact. It is also not clear why a large proportion of NFR5 is unaffected by the proteolytic activity of NopT. This is particularly interesting in Nicotiana in the absence of Nod factor that could trigger NFR1 kinase activity.

Thank you for your comments regarding the cleavage of NFR5 and its functional implications. In the revised version, we will change our manuscript taking into account the following considerations:

(1) We acknowledge that the Western blots indicate only a small proportion of NFR5 is cleaved when co-expressed with NopT. It is worth noting in this context that the proteins were expressed at high levels which likely do not reflect the natural situation in L. japonicus. Low production of cleaved NFR5 in our Western blots with transformed N. benthamiana or L. japonicus cells thus may simply reflect an experimental effect due to high NFR5 protein synthesis. We suggest that the presence of high amounts of intact NFR5 does not have a significant functional impact on plant responses (cell death in N. benthamiana, rhizobial infection of L. japonicus) whereas NFR5 cleavage (or formation of NFR5 cleavage products) may be crucial for the observation of the observed phenotypic changes. The fraction of cleaved NFR5, although small, may be sufficient to disrupt crucial signaling pathways, leading to observable phenotypic changes. We will address possible differences between experimental and natural protein levels in our revised Discussion.

(2) We studied in our work three biochemical aspects of NopT: (i) physical binding of NopT to NFR1 and NFR5 (ii) proteolytical cleavage of NFR5 by NopT and (iii) phosphorylation of NopT by NFR1. These three biochemical properties appear to influence each other. Phosphorylation of NopT by NFR1 appears to reduce its proteolytic activity, thereby counteracting NFR5 degradation by NopT (NFR5 homeostasis). Moreover, as NopT is a phosphorylation substrate for NFR1, NopT probably interferes with kinase mediated downstream responses of NFR1. Thus, NFR5 cleavage activity of NopT appears to be only one feature of NopT. We plan to mention these considerations in our revised Discussion.

It is also difficult to evaluate how the ratios of cleaved and full-length protein change when different versions of NopT are present without a quantification of band strengths normalized to loading controls (Figure 3C, 3D, 3F). The same is true for the blots supporting NFR1 phosphorylation of NopT (Figure 4A).

Thank you for pointing out this aspect. Following your recommendation, we will quantify the band intensities for cleaved and full-length NFR5 in the experiments with different versions of NopT. These values will be normalized to loading controls. Similarly, the Western blots supporting NFR1 phosphorylation of NopT will be quantified. The data for normalized band intensities will be included into the revised figures. The quantifications will provide a clearer understanding of how the ratios of cleaved to full-length proteins change with different NopT variants and also will provide information to which extent NopT is phosphorylated by NFR1.

It is clear that mutation of nopT results in a quantitative infection phenotype. Nodule primordia and infection threads are still formed when L. japonicus plants are inoculated with ∆nopT mutant bacteria, but it is not clear if these primordia are infected or develop into fully functional nodules (Figure 5). A quantification of the ratio of infected and non-infected nodules and primordia would reveal whether NopT is only active at the transition from infection focus to thread or perhaps also later in the bacterial infection process of the developing root nodule.

Thank you for pointing this out. In the revised version of our manuscript, we will provide data showing that there are no obvious differences in nodule formation in plants inoculated with ∆nopT and wild-type NGR234, respectively. However, quantification of infection threads containing our GFP-labeled rhizobia in primordia and nodules would be difficult to perform due to strong autofluorescence signals in these tissues. The main goal of our study was to identify and characterize the interaction between NopT and Nod factor receptors. We therefore believe that an in-depth analysis of the bacterial infection process at later symbiotic stages is out of the scope of the present work.

Reviewer #2 (Public Review):

Summary:

This manuscript presents data demonstrating NopT's interaction with Nod Factor Receptors NFR1 and NFR5 and its impact on cell death inhibition and rhizobial infection. The identification of a truncated NopT variant in certain Sinorhizobium species adds an interesting dimension to the study. These data try to bridge the gaps between classical Nod-factor-dependent nodulation and T3SS NopT effector-dependent nodulation in legume-rhizobium symbiosis. Overall, the research provides interesting insights into the molecular mechanisms underlying symbiotic interactions between rhizobia and legumes.

Strengths:

The manuscript nicely demonstrates NopT's proteolytic cleavage of NFR5, regulated by NFR1 phosphorylation, promoting rhizobial infection in L. japonicus. Intriguingly, authors also identify a truncated NopT variant in certain Sinorhizobium species, maintaining NFR5 cleavage but lacking NFR1 interaction. These findings bridge the T3SS effector with the classical Nod-factor-dependent nodulation pathway, offering novel insights into symbiotic interactions.

We appreciate that you recognize the value of our manuscript.

Weaknesses:

(1) In the previous study, when transiently expressed NopT alone in Nicotiana tobacco plants, proteolytically active NopT elicited a rapid hypersensitive reaction. However, this phenotype was not observed when expressing the same NopT in Nicotiana benthamiana (Figure 1A). Conversely, cell death and a hypersensitive reaction were observed in Figure S8. This raises questions about the suitability of the exogenous expression system for studying NopT proteolysis specificity.

We appreciate your attention to these plant-specific differences. In view of your comments, we plan to revise the Discussion and explain the different expression systems used for studying NopT effects in planta. Previous studies showed that NopT expressed in tobacco (N. tabacum) or in specific Arabidopsis thaliana ecotypes (with PBS1/RPS5 genes) causes rapid cell death (Dai et al. 2008; Khan et al. 2022). Our data shown in Fig. S8 confirm these findings. As cell death (effector triggered immunity) is usually associated with induction of protease activities, we considered N. tabacum and A. thaliana plants as not suitable for testing NFR5 cleavage by NopT. In fact, no NopT/NFR5 experiments were performed with these plants in our study. In contrast, the expression of NopT in Nicotiana benthamiana did not lead to cell death in our experiments. Khan et al. 2022 also reported that cell death does not occur in N. benthamiana unless the cells were transformed with PBS1/RPS5 constructs. Thus, N. benthamiana is a suitable expression system to analyze NopT protease activity on co-expressed substrates. Our revision aims to better understand the advantages of the N. benthamiana expression system for studying NopT mediated proteolysis of NFR5.

(2) NFR5 Loss-of-function mutants do not produce nodules in the presence of rhizobia in lotus roots, and overexpression of NFR1 and NFR5 produces spontaneous nodules. In this regard, if the direct proteolysis target of NopT is NFR5, one could expect the NGR234's infection will not be very successful because of the Native NopT's specific proteolysis function of NFR5 and NFR1. Conversely, in Figure 5, authors observed the different results.

Our inoculation experiments clearly show that NopT of NGR234 has a negative effect on formation of infection foci (Fig. 5A) and nodule primordia (Fig. 5E). Our biochemical analysis indicates that NopT targets the NFR1/NFR5 complex, which most likely impairs activation of downstream responses such as NIN gene expression. Accordingly, NIN promoter activity was found to be higher in roots inoculated with the Δ_nopT_ mutant as compared to the NGR234 wild-type (Fig. 5B and 5D). It is therefore plausible that NopT impairs rhizobial infection of L. japonicus due to inhibition of NFR1/NFR5 functions. We agree with this Reviewer that it can be expected that “NGR234's infection will not be very successful”. Fig. 5 confirms that Δ_nopT_ mutant is indeed a better symbiont and we do not think that we obtained “unexpectedly different results”. In the revised version, we will try to formulate our discussion text better in order to avoid any misunderstandings. Furthermore, will write as figure title “NopT dampens rhizobial infection…” instead of “NopT regulates rhizobial infection…”. We are also considering changing the title of our manuscript.  

(3) In Figure 6E, the model illustrates how NopT digests NFR5 to regulate rhizobia infection. However, it raises the question of whether it is reasonable for NGR234 to produce an effector that restricts its own colonization in host plants.

We acknowledge the potential paradox of NGR234 producing an effector that appears to restrict its own colonization in host plants. In fact, depending on the host plant, most rhizobial effectors are “double-edged swords” that play either a positive or negative role in the symbiosis. In response to your comment, we will discuss the possibility that NopT may confer selective advantages in interactions between NGR234 and host plants where NopT plays a positive symbiotic role (Dai et al. 2008; Kambara et al. 2009). Inhibition of NFR1/NFR5 functions by NopT in these host plants could be a feedback response in cells in which symbiotic signaling has already started. It is tempting speculate that the interaction between NopT and Nod factor receptors reduces Nod factor perception and downstream signaling to avoid a possible overreaction of symbiotic signaling, which may result in hypernodulation or formation of empty nodules without bacteria. Furthermore, it is tempting to speculate that NopT targets not only Nod factor receptors but also other host proteins to promote symbiosis, e.g. by suppressing excessive immune responses triggered by hyperinfection of rhizobia. In our revised manuscript, we will highlight the need for further investigations to elucidate the precise mechanisms underlying the observed infection phenotype and the role of NopT in modulating symbiotic signaling pathways.  

(4) The failure to generate stable transgenic plants expressing NopT in Lotus japonicus is surprising, considering the manuscript's claim that NopT specifically proteolyzes NFR5, a major player in the response to nodule symbiosis, without being essential for plant development.

Thank you for your comments. The failure to obtain L. japonicus plants constitutively expressing NopT was indeed surprising and suggests that NopT targets not only NFR5 but also other proteins in L. japonicus. The number of NopT substrates in plants could be greater than assumed. For example, we show in our work that NopT can cleave AtLYK5 and LjLYS11. In our manuscript, we don’t provide protocols and data on our efforts to construct L. japonicus plants stably expressing NopT. Indeed, it cannot be completely ruled out that the observed failure is not due to NopT expression, but rather to other factors that influence the transformation and regeneration of explants into whole plants. Our results should therefore not be over-interpreted. We consider a discussion of our failed transformation experiments to be somewhat preliminary and not central to this manuscript. herefore, we plan to modify our Discussion and delete the sentence reporting that stable transgenic plants expressing NopT have not been successfully generated.

Author response:

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

We thank the reviewers for their overall careful evaluation of our work, the constructive criticism, and their many helpful suggestions. We feel that our revision built on the strengths identified by the reviewers, and addressed all the concerns they have raised. Both reviewers recognize that our revisions have improved the paper.  Since the first submission we have:

• Rewritten large parts of the papers to improve clarity and make it more concise where possible

• Simulated an alternative working memory model, as recommended by Reviewer 1

• Included 4 new/revised supplementary figures, following the reviewer’s suggestions for additional analysis.

Below we provide a brief response to the Reviewers’ comments on our manuscript revision.

Reviewer #1: Public Review:

Strengths:

Overall, the work offers a very interesting approach of a topic which is hard to accomplish experimentally --therefore the computational take is entirely justified and extremely useful. The authors carefully designed the computational experiments to shed light into the demyelination effects on working memory from multiple levels of description, increasing the reliability of their conclusions. I think this work provides now convincing evidence and has the potential to be influential in future studies of myelin alterations (and related disorders such as multiple sclerosis).

Weaknesses:

In its current form, the authors have improved the clarity of the results and the model details, and have provided a new set of simulations to complement and reinforce the original ones (including the development of a new spatial working memory model based on silent working memory principles). I do not appreciate any significant weaknesses at this point.

We thank the reviewer for these positive comments on our revision and for the suggestion of adding the silent memory model, as we feel this has strengthened our findings.

Reviewer #2: Public Review:

This paper analyzes the effect of axon de-myelination and re-myelination on action potential speed, and propagation failure. Next, the findings are then incorporated in a standard spiking ring attractor model of working memory.

I think the results are not very surprising or solid and there are issues with method and presentation.

The authors did many simulations with random parameters, then averaged the result, and found for instance that the Conduction Velocity drops in demyelination. It gives the reader little insight into what is really going on. My personal preference is for a well understood simple model rather than a poorly understood complex model. The link between the model outcome of WM and data remains qualitative and is further weakened by the existence of known other age-related effects in PFC circuits.

Comments on revised version:

The paper has improved in the revision, although I still think a reduced model would have been nice.

As noted above, in addition to our spiking bump attractor model, our revision includes a second network-level model:  an activity-silent working memory model for continuous features.  We found qualitatively similar effects as in our bump attractor network model, showing that our main conclusions do not critically depend on the exact working memory mechanism (active vs. activity-silent).  This new model was described in two new supplementary figures and a new paragraph in the Results section.

We did not add a reduced model in our revision to this paper, since neither reviewer explicitly recommended that we add one.  As we noted in our private response to reviewers that accompanied our revision: we share the view that understanding simple models can provide critical insights into brain function (and we believe that many of our papers related to attractor dynamics in working memory and decision-making fall into this category, e.g. Wimmer et al. 2014, Esnaola-Acebes et al. 2022, Ibañez et al 2020). We disagree with the reviewer on an important point: we feel that the model complexity that we have chosen is appropriate and necessary to study the phenomenon at hand. Our modeling efforts are principled, with complexity added as necessary. We started with a biophysical single neuron model with firing dynamics fit to empirical data in pyramidal neurons of rhesus monkey dlPFC (Rumbell et al. 2016) – the same type of neurons and cortical region analyzed in the Peters et al. work on structural changes to myelin seen during aging (e.g., Figure 1).  Because simple models do not accurately capture the CV along thin axons like those in the PFC, we attached a multicompartment axon with detailed myelinated segments, and constructed a cohort of feasible models. We then used this cohort to get quantitative estimates of the effects of variable degrees of demyelination and remyelination. This would not be possible with a simpler model. We then study the consequences of de- and re-myelination in a spiking neural network model. Again, we could not use a simpler model (e.g. a firing rate attractor model) without making gross assumptions about how demyelination affects circuit function. In sum, we believe that our models are relatively simple but comprehensive given the phenomenon that we are studying.

The reviewer is correct in that there exist “known other age-related effects in PFC circuits”. These are reviewed in the introduction and we discuss future extensions of our model that would incorporate those effects as well. It is important to note that this is the first comprehensive study of demyelination effects in aging PFC, demonstrating that myelin changes alone predict working memory changes associated with aging.

While we agree that averaging results about different parameter sets provide a limited understanding of the system, we persist in our belief that such analyses provide an important baseline.  We acknowledge that results vary across our model cohort; this is why we included the heatmaps of our single cell model perturbation results (Figure 3 and Supplementary Figure 3), and simulated network models representing a heterogeneity of neuronal axons with healthy and altered myelin sheaths in different degrees, as likely occurs in the aging brain (Figures 7 and 8).  The model framework we present here is well-suited for more targeted analyses and better insights, including those which we are pursuing currently.

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

We thank the reviewers for their careful evaluation of our work, the constructive criticism, and their many helpful suggestions. We feel that our revision builds on the strengths identified by the reviewers, and addresses all the concerns they have raised. We have:

• Rewritten large parts of the papers to improve clarity and make it more concise where possible

• Simulated an alternative working memory model

• Included 4 new/revised supplementary figures, following the reviewer’s suggestions for additional analysis

Reviewer #1 (Public Review):

Summary:

The authors study the effects of myelin alterations in working memory via the complementary use of two computational approaches: one based on the de- and re-myelination in multicompartmental models of pyramidal neurons, and one based on synaptic changes in a spiking bump attractor model for spatial working memory. The first model provides the most precise angle (biophysically speaking) of the different effects (loss of myelin lamella or segments, remyelination with thinner and shorter nodes, etc), while the second model allows to infer the consequences of myelin alterations in working memory performance, including memory stability, duration, and bump diffusion. The results indicate (i) a slowing down and failure of propagation of spikes with demyelination and partial recovery with remyelination, with detailed predictions on the role of nodes and myelina lamella, and (ii) a decrease in memory duration and an increase in memory drift as a function of the demyelination, in agreement with multiple experimental studies.

Strengths:

Overall, the work offers a very interesting approach of a topic which is hard to accomplish experimentally --therefore the computational take is entirely justified and extremely useful. The authors carefully designed the computational experiments to shed light into the demyelination effects on working memory from multiple levels of description, increasing the reliability of their conclusions. I think this work is solid and has the potential to be influential in future studies of myelin alterations (and related disorders such as multiple sclerosis).

We thank the reviewer for these positive comments on our manuscript.

Weaknesses:

In its current form, the study still presents several issues which prevent it from achieving a higher potential impact. These can be summarized in two main items. First, the manuscript is missing some important details about how demyelination and remyelination are incorporated in both models (and what is the connection between both implementations). For example, it is unclear whether an unperturbed axon and a fully remyelinated axon would be mathematically equivalent in the multicompartment model, or how the changes in the number of nodes, myelin lamella, etc, are implemented in the spiking neural network model.

We thank the reviewer for these suggestions to improve the clarity of our manuscript. A ‘fully remyelinated’ axon is not mathematically equivalent to the unperturbed axon: it has shorter and thinner myelinated segments, and additional nodes in between. This is consistent with empirical observations in rhesus monkey dlPFC, as reviewed in Peters et al. (2009): a 90% increase in paranode profiles, and myelin sheaths that were thinner than expected for the size of the enclosed axon. With no empirical observations of fewer numbers of nodes (but rather, the opposite) or bare sections of axon, we assumed that the remyelination process also creates new nodes (which are identical to existing nodes), as also modeled in Scurfield & Latimer (2018). We have added two new sentences to the results to clarify this fact, before presenting the first set of results for the single cell model: (starting at line 137):

“To simulate demyelination, we removed lamellae from selected myelinated segments; for remyelination we replaced a fraction of myelinated segments by two shorter and thinner segments with a node in between. As such, a ‘fully remyelinated axon’ had all the demyelinated segments subsequently remyelinated, but with fewer lamellae and additional nodes compared to the unperturbed control case, consistent with empirical observations (Peters, 2009).”

We also state the maximal amount of remyelination more explicitly in the Results, starting on lines 164-165: "We next examined the extent to which remyelination with shorter and thinner segments, occurring after demyelination, restored axonal AP propagation (Figure 4).”

Also on line 192-193: “Remyelinating all affected segments with 75% of lamellae (the maximal amount of remyelination) nearly eliminated AP failures (1.8 ± 1.1%).”

Finally, in Methods we also clarified the structure of the added node (starting at line 634): “Remyelination was performed by replacing an affected (previously demyelinated) segment with two shorter segments, each including paranodes, juxtaparanodes, and an internode, and a new node between them that was identical to existing nodes.”

We have also provided further details describing how myelin dystrophy was simulated in the network model in Results (lines 243 - 249) and in Methods (lines 722 - 747). How myelin alterations have been implemented in the network model is one of the questions of the reviewer (Question 5 in Reviewer #1: Recommendations for the Authors_)._ We have addressed this question by describing in detail how we adjusted CV and AP failure rate to the values produced by the multicompartment neuron model. Please see our answer to Question 5 for the details.

Second, it is unclear whether some of the conclusions are strong computational predictions or just a consequence of the model chosen. For example, the lack of effect of decreasing the conduction velocity on working memory performance could be due to the choice of considering a certain type of working memory model (continuous attractor), and therefore be absent under other valid assumptions (i.e. a silent working memory model, which has a higher dependence on temporal synaptic dynamics).

Whether some conclusions are strong predictions or just a consequence of the model chosen is an important concern and indeed a general problem of computational modeling of working memory. For example, Stein et al. (Stein et al. Towards biologically constrained attractor models of schizophrenia, Curr. Opin. Neurobiol. 2021) showed that opposed manipulations of E/I ratio can produce the same behavioral pattern in different alternative, plausible biological network models. As long as we do not fully understand the neural mechanisms underlying working memory, modeling studies of how alterations (e.g. in E/I ratio or in the reliability and timing of axonal transmission, as we did here) affect circuit function need to be interpreted critically and tested against new experimental data.

One way to strengthen model predictions is by showing that different computational models make similar predictions. To do this, we implemented an activity-silent working memory model for continuous features, as suggested by the reviewer, and we found qualitatively similar effects as in our bump attractor network model. Thus, our main conclusions do not critically depend on the exact working memory mechanism (active vs. activity-silent).

In the revised manuscript, we have added two new supplementary figures (Supplementary Figure 8 and 9, see the next page) and a new paragraph in the Results section about activity silent working memory (starting at line 319):

“Alternative working memory mechanisms. Working memory in our neural network is maintained in an attractor state with persistent neural activity (Compte et al., 2000; Hansel and Mato, 2013). Other mechanisms have been proposed, including that working memory maintenance may rely on activity-silent memory traces (Mongillo et al., 2008; Stokes, 2015; Barbosa et al., 2020). In activity-silent models, a slowly decaying transient of synaptic efficacy preserves information without the need for persistent ongoing activity. We implemented an activity-silent model, to our knowledge the first one for continuous spatial locations, and tested how working memory performance is affected by AP failures and propagation delays. We found that AP failures corresponding to demyelination caused working memory errors qualitatively similar to the delay-active network (Supplementary Figure 8). On the other hand, increasing propagation delays did not lead to additional working memory errors, unless we include unrealistically high values (uniform distribution in the range of 0 to 100 ms; Supplementary Figure 9). These results are qualitatively similar to the delay active network model. Thus, our main findings do not critically depend on the exact working memory mechanism (active vs. activity-silent).”

Author response image 1.

Action potential failures impair working memory performance in a network model with activity-silent memory traces. (A) Spiking and synaptic activity in an unperturbed, activity-silent working memory model. Top: Raster plot showing the activity for each excitatory neuron (labeled by its preferred direction) in a single trial with a cue stimulus presented at 180°. We modified our spiking neural network model such that it does not show elevated persistent firing throughout the delay period (see Figure 5B for comparison). In particular, we reduced the external background input to excitatory neurons by a factor of 3.61% and we increased the cue stimulus amplitude by 12.5%. Even though spiking activity decays to baseline (close to 0 Hz), a memory trace is imprinted in enhanced synaptic strength due to short-term synaptic facilitation (Mongillo et al., 2008). Selective spiking activity is recovered by a non-selective constant input applied during 300 ms to all excitatory neurons during the two reactivation periods (marked by yellow and green rectangles in the raster plot). The amplitude of the input was 11 mV during the first and 13 mV during the second reactivation period. Reactivation periods are marked in light gray shading in the remaining panels below and the cue period is indicated by dark gray shading. Firing rates (second row), synaptic facilitation variable u (third row), and synaptic depression variable x (bottom row) for the same trial, averaged for 500 neurons around the neuron with 180° as preferred direction (solid lines) and around the neuron with 0° as preferred direction (dashed lines). Note that reactivation recovers the activity bump (C) but also causes elevated firing and subsequent enhancement of synapses at all positions in the networks. (B) Activity in a network with demyelination of 50% of the myelinated segments by removing 60% of the myelin lamellae. AP failures lead to reduced firing rates in the cue and early delay periods and consequently to weaker synaptic enhancement. (C) Average spike counts of the excitatory neurons during the cue period (black lines), and the two reactivation periods indicated in the raster plots in A and B (yellow and green lines). Solid lines correspond to the control network and dashed lines to the perturbed network. (D) Memory strength as a function of time for the control and perturbed networks. (E-F) Trajectories of the bump center (i.e., remembered cue location) read out from the neural activity across the cue and delay periods using a population vector (see Methods). Cue position was 180° in all trials. The perturbed network (F) shows larger working memory errors towards the end of the delay period compared to the control network (E).

Author response image 2.

Effect of propagation delays on control and perturbed activity-silent network models. (A) Memory strength during the whole simulation time for the young, control networks relying on activity-silent working memory (Supplementary Figure 8) with zero propagation delays (blue line), and with propagation delays from a uniform distribution with a range between 0 and 40 ms (yellow line) and between 0 and 100 ms (orange line). (B) Memory strength for perturbed networks when demyelinating 25% of the myelinated segments by removing 50% of the myelin lamellae, without delays (red line), and with uniformly distributed delays between 0 and 40 ms (light gray line) and between 0 and 100 ms (black line). The cue period is indicated by dark gray shading and reactivation periods are marked in light gray. Memory strength was calculated by averaging across 280 trials for one network. Shaded areas indicate SEM for each case. For the young, control networks (A), working memory was not affected by including delays of up to 40 ms. Unrealistically long delays ranging up to 100 ms did cause an impairment (the longest delays found for the most extreme perturbation condition – demyelination of 75% of the segments by removing 100% of the myelin lamellae – were of 49.9 ms on average). When also incorporating AP failures to the networks (B), we observed a similar trend. For this perturbation condition, delays of up to 40 ms were already much larger than the delays quantified in the single neuron model (for the case of 25% of the segments demyelinated by removing 50% of the myelin lamellae, the average delay in the cohort was 3.75 ms).

With additional simulations to address these issues, I consider that the present study would become a convincing milestone in the computational modeling of myelin-related models, and an important study in the field of working memory.

Again, we would like to thank the reviewer for the positive comments. We have addressed all the main issues raised (see below our response to the “recommendations for the authors”).

Reviewer #2 (Public Review):

This paper analyzes the effect of axon de-myelination and re-myelination on action potential speed, and propagation failure. Next, the findings are then incorporated in a standard spiking ring attractor model of working memory.

I think the results are not very surprising or solid and there are issues with method and presentation.

The authors did many simulations with random parameters, then averaged the result, and found for instance that the Conduction Velocity drops in demyelination. It gives the reader little insight into what is really going on. My personal preference is for a well understood simple model rather than a poorly understood complex model. The link between the model outcome of WM and data remains qualitative, and is further weakened by the existence of known other age-related effects in PFC circuits.

We thank the reviewer for the critical assessment of our work. We share the view that understanding simple models can provide critical insights into brain function (and we believe that many of our papers related to attractor dynamics in working memory and decision making fall into this category, e.g. Wimmer et al. 2014, Esnaola-Acebes et al. 2022, Ibañez et al 2020). However, we respectfully disagree with the reviewer on an important point: the model complexity that we have chosen is appropriate and necessary to study the phenomenon at hand. Our modeling efforts are principled, with complexity added as necessary. We started with a biophysical single neuron model with firing dynamics fit to empirical data in pyramidal neurons of rhesus monkey dlPFC (Rumbell et al. 2016) – the same type of neurons and cortical region analyzed in the Peters et al. work on structural changes to myelin seen during aging (e.g., Figure 1). Because simple models do not accurately capture the CV along thin axons like those in the PFC, we attached a multicompartment axon with detailed myelinated segments, and constructed a cohort of feasible models. We then used this cohort to get quantitative estimates of the effects of variable degrees of demyelination and remyelination. This would not be possible with a simpler model. We then study the consequences of de- and re-myelination in a spiking neural network model. Again, we could not use a simpler model (e.g. a firing rate attractor model) without making gross assumptions about how demyelination affects circuit function. In sum, we believe that our models are relatively simple but comprehensive given the phenomenon that we are studying.

The reviewer is correct in that there exist “known other age-related effects in PFC circuits”. These are reviewed in the introduction and we discuss future extensions of our model that would incorporate those effects as well. It is important to note that this is the first comprehensive study of demyelination effects in aging PFC, demonstrating that myelin changes alone predict working memory changes associated with aging.

The specific issues about modeling choices and interpretation of the results are discussed below.

Both for the de/re myelination the spatial patterns are fully random. Why is this justified?

We agree that myelin dystrophy during aging could be non-random, that is, localized to certain regions of an axon. Our collaborators (Drs Jennifer Luebke, Maya Medalla, and Patrick Hof) are currently addressing this question using 3D electron microscopy and immunohistochemistry on axons of individual neurons and their associated myelin, but results are not available yet. Early on in this study we examined how the location of myelin alterations affected AP propagation. Focusing demyelination along a section of axon led to more AP slowing and failure than when spatially randomized. Likewise, remyelination of such spatially localized dystrophy led to greater recovery, as there were fewer transitions between long and short internodes (Supplemental Figure 4). Since otherwise the effects in the localized cases were largely similar to those in the spatially random case (see Author response image 3 below), for brevity in this paper we assumed myelin alterations were randomly distributed. Our next paper, extending this study to collateralized axons and which was presented as a poster at the 2023 Society for Neuroscience meeting, will include an examination of localized myelin dystrophy.

Author response image 3.

Effect of localized myelin alterations on CV change. Myelin alterations were either focused on the third of myelinated segments closest to the initial segment (‘proximally clustered’), the third of myelinated segments furthest from the initial segment (‘distally clustered’), or distributed according to a uniform distribution as in the current study. For demyelination, all lamellae were removed from 25% of myelinated segments (showing mean +/- SEM of all 50 cohort models, 30 randomized trials each). For remyelination, affected segments were replaced by two shorter segments with 75% of the original lamellae thickness and a node in between.

We have added two sentences in Methods to justify this assumption more clearly (line 510): “Evidence suggests that aging affects oligodendrocytes in several ways, including the ability for oligodendrocyte precursor cells to mature (Dimovasili et al., 2022). Knowing that individual oligodendrocytes myelinate axons of many different neurons, but without data quantifying how oligodendrocyte dystrophy affects myelination in individual axons, we assumed that myelin alterations were randomly distributed.”

We have also added a sentence in the Discussion alluding to our upcoming study (line 434): “Our model can also be extended to explore interactions between spatially localized myelin perturbations (such as those seen in multiple sclerosis) and axon collateralization (Sengupta et al., 2023), which would affect the distance-dependence of AP failures.”

Similarly, to model the myelin parameters were drawn from uniform distributions, Table 1 (I guess). Again, why is this reasonable?

The reviewer is correct that our initial Latin hypercube sample generated a uniform distribution. However, parameters of the random sample of models selected as biologically feasible were not uniformly distributed. We have added a new figure (Supplementary Figure 1A) to illustrate the parameter distributions, and have added two sentences in Methods (starting on line 596):

“Of the 1600 simulated models, 138 met these criteria; for the present study, we randomly selected 50 models to comprise the young, control model cohort. Along most dimensions, the chosen cohort was approximately normally distributed (Supplementary Figure 1). The g-ratio (ratio of axon to fiber diameter) among models in the cohort was 0.71 ± 0.02, with total axon lengths of 1.2 ± 0.1 cm.”

Author response image 4.

Distribution of parameters and conduction velocities in the single neuron model cohort. (A) Histograms of axon morphology parameters of models selected for the single neuron cohort. Top: axon diameter: middle, length of unperturbed myelin segments; bottom: total myelin thickness in unperturbed segments, computed as the product of lamella thickness and number of lamellae. (B) Histograms of the CV for the 50 axons of the unperturbed model cohort (top), and representative demyelination and remyelination perturbations: mild demyelination (removing 25% of lamellae from 25% of the myelinated segments, second row); severe demyelination (removing all lamellae from 75% of the myelinated segments, third row); and complete (100%) remyelination (where the demyelinated segments from the third row were remyelinated by two shorter segments with 75% of lamellae). CVs averaged over 30 trials in each case. (C) Changes in CV (measured in %) in response to demyelination and remyelination versus the magnitude of current clamp step (+180, +280, or +380 pA). Shown are mean +/- SEM for demyelinating 50% of myelinated segments (removing all lamellae), and subsequent remyelination of those segments by shorter segments with 75% of lamellae.

The focus of most analysis is on the conduction velocity but in the end, this has no effect on WM, so the discussion of CV remains sterile.

CV delays likely do affect brain functions that rely on neuronal oscillations and synchrony, as mentioned in the Discussion. As such, we feel that our single neuron model results on CV delays as well as AP failures are valuable for the scientific community. Yet, given the results of our network models here, the reviewer has a valid point. We have clarified in the introduction that AP failures but not CV delays affected the network output (line 115):

“Higher degrees of demyelination led to slower propagation and eventual failure of APs along the axons of the multicompartment models. In the network models, an increase in AP failure rate resulted in progressive working memory impairment, whereas slower conduction velocities, in the range observed in the multicompartment models, had a negligible effect.”

We have also revised the single neuron section of the Results throughout, to better highlight the effects of myelin dystrophy on AP failures. Revisions to address this in the demyelination section start on line 148:

“AP propagation was progressively impaired as demyelination increased (Figure 3): CV became slower, eventually leading to AP failure. Removing 25% of lamellae had a negligible effect on CV, regardless of how many segments were affected. However, when all lamellae were removed, CV slowed drastically – by 38 ± 10% even when just 25% of the segments were demyelinated in this way, and 35 ± 13% of APs failed. When 75% of segments lost all their lamellae, CV slowed by 72 ± 8% and 45 ± 13% of APs failed.”

Similiarly, we have added several sentences about AP failures that remain after remyelination of the single neuron model (starting on line 190):

“Results for the percentage of AP failures (Figure 4C,F) were consistent with those for CV recovery. Remyelinating all previously demyelinated segments, even adding just 10% of lamellae, brought AP failure rates down to 14.6 ± 5.1%. Remyelinating all affected segments with 75% of lamellae (the maximal amount of remyelination) nearly eliminated AP failures (1.8 ± 1.1%). Incomplete remyelination, where some segments were still demyelinated, still had relatively high AP failure rates. For example, when one eighth of segments were remyelinated with the maximal amount of lamellae and one eighth were left bare, 25.7 ± 11.5% of APs failed across the cohort (Figure 4C, red dashed line and arrow). AP failure rates were slightly lower when starting with partial demyelination: 10.6 ± 7.6% of APs failed in the analogous paradigm (Figure 4F, red dashed line and arrow). In short: combinations of demyelinated and remyelinated segments often led to sizable CV delays and AP failures.”

The more important effect of de/re myelination is on failure. However, the failure is, AFAIK, just characterized by a constant current injection of 380pA. From Fig 2 it seems however that the first spike is particularly susceptible to failure. In other words, it has not been justified that it is fine to use the failure rates from this artificial protocol in the I&F model. I would expect the temporal current trace to affect whether the propagation fails or not.

In general, we did not find the first spike to be more susceptible to failure than latter spikes; the trace in Figure 2 is a representative snapshot intended to illustrate CV slowdown, AP failure, and recovery. Regarding the constant current injection: while the reviewer is correct that neurons do not receive such inputs in vivo, the applied current injections were designed to match in vitro current clamp protocols for these rhesus monkey neurons. While our future studies will include responses to more realistic synaptic inputs, we focused on somatic current injections here. We have added a new panel (C) to Supplementary Figure 1 (see previous response above) showing that the current step magnitude had little effect on the CV change after myelin perturbations; there was little effect on AP failure rates too. We now also state this finding more explicitly in Methods (starting on line 561):

“As done during in vitro electrophysiological experiments (Chang et al., 2005; Ibanez et al., 2020) and past modeling studies (Coskren et al., 2015; Rumbell et al., 2016), we first applied a holding current to stabilize the somatic membrane potential at -70 mV, then injected a current step into the somatic compartment for 2 seconds. …The CV changes in response to myelin alterations were relatively insensitive to variations in the magnitude of suprathreshold somatic current steps (Supplementary Figure 1C), and whether the current was constant or included Gaussian noise. Therefore, here we quantified CV changes and AP failures from responses to constant +380 pA current steps only.”

I don't know if there are many axon-collaterals in the WM circuits and or distance dependence in the connectivity, but if so, then the current implementation of failure would be questionable.

We agree that axon collaterals may affect our results; our unpublished morphological analyses of individual neuron axons indicate that there is a high degree of local axon collateralization in Layer 3 pyramidal neurons in LPFC. In this first study from our group on myelin perturbations, we chose to focus here on unbranched axons. There was some distance dependence of AP failure along the length of the axon. For example, in our most extreme demyelination case (75% of segments losing all their lamellae), about 14% of the axons showed more AP failure at their distal ends relative to the middle (mean difference 6.33%). We are examining this distance dependence more broadly in our next study, now cited in the Discussion (line 434): “Our model can also be extended to explore interactions between spatially localized myelin perturbations (such as those seen in multiple sclerosis) and axon collateralization (Sengupta et al., 2023), which would affect the distance-dependence of AP failures.”

I would also advise against thresholding at 75% failure in Fig3C. Why don't the authors not simply plot the failure rate?

We thank the reviewer for this suggestion, and have made this change. As suggested by the reviewer, we now show the AP failure rate in Figure 3 and Figure 4. The trends shown are nearly identical to those from the high failure trials.

Regarding the presentation, there are a number of dead-end results that are not used further on. The paper is rather extensive, and it would be clearer if written up in half the space. In addition, much information is really supplementary. The issue of the CV I already mentioned, also the Lasso regression for instance remains unused.

We understand the reviewer’s perspective, and we do value brevity when possible. During the revision process we examined the paper carefully, and made things more concise when it was feasible. As mentioned above, reporting CV results is important, though these revisions increased emphasis on results for AP failures in our revision. We combined the two Supplementary Figures about remyelination in the single neuron model into one (Supplementary Figure 3). We also moved the Lasso figure and associated methods to the Supplementary Material (Supplementary Figure 2), and have separated the Lasso results for demyelination and remyelination into their respective paragraphs (lines 154-160 and lines 200-204 respectively). While we do not use the Lasso explicitly later in Results, we cite them in the Discussion when comparing our findings to previous work (starting on line 417):

“Since our single neuron cohort sampled a wide range of parameter space, we used Lasso regression to identify which of the complex, interacting parameters contributed most to CV delays (which preceded AP failures). Parameters including axon diameter, node length, length of myelinated segments, and nodal ion channel densities predicted how our models responded to demyelination and remyelination; these findings are consistent with past modeling studies over more limited parameter ranges (e.g., Goldman and Albus, 1968; Moore et al., 1978; Babbs and Shi, 2013; Young et al., 2013; Schmidt and Knösche, 2019).”

We hope that our revision has struck an appropriate balance between clear and concise writing, and addressing concerns from both reviewers. We greatly value the time you have given to help us to improve our manuscript.

Response to Recommendations for the Authors:

Reviewer #1 (Recommendations for the Authors):

As I mentioned above, I consider that this study is well designed and it offers very interesting results. I have detailed below some of the issues that should be addressed to improve its potential impact in the field:

(1) Across the manuscript, it is not entirely clear how the results of the multicompartmental model compare to existing modeling results on demyelination and CV changes (such as in the papers cited by the authors). Is this section confirming previous results with a new (more accurate) computational model, or are there any new insights previously unreported? A new paragraph in the Discussion putting these results in context would be very useful for the reader.

We thank the reviewer for this suggestion. We have added two new subheadings to organize the Discussion better, and have expanded the single neuron section to three paragraphs. We feel this now clarifies how our model fits in with previous work while stating its novelty more explicitly. Starting on line 391:

“Myelin changes affect AP propagation in a cohort of model neurons

The novelty of our neuron model lies in its systematic exploration of a combination of different myelin perturbation types known to occur in myelin dystrophies, across a wide range of biologically feasible models. Our single neuron model assumed that age-related myelin dystrophies (e.g., Figure 1) alter the insulative properties of lamellae analogously to demyelination, and examined interactions between demyelination and remyelination. Past studies of myelin dystrophy examined how either demyelination or remyelination of all segments affected AP propagation for a few representative axon morphologies. For example, Scurfield and Latimer (2018) explored how remyelination affected CV delays, finding that axons with more transitions between long and short myelinated segments had slower CV (Supplementary Figure 4), and was first to explore how remyelination interacts with tight junctions. However, their study did not couple remyelination and demyelination together or examine AP failures. Other basic findings from our single neuron cohort are consistent with past modeling studies, including that demyelination caused CV slowing and eventual AP failures (Stephanova et al., 2005; Stephanova and Daskalova, 2008; Naud and Longtin, 2019), and, separately, that remyelination with shorter and thinner myelinated segments led to CV slowing (Lasiene et al., 2008; Powers et al., 2012; Scurfield and Latimer, 2018). However, by assuming that some previously demyelinated segments were remyelinated while others were not, we found that models could have much higher AP failure rates than previously reported. Such a scenario, in which individual axons have some segments that are normal, some demyelinated, and some remyelinated, is likely to occur. We also found a few neurons in our cohort showing a CV increase after remyelination, which has not generally been reported before and is likely due to an interplay between ion channels in the new nodes and altered electrotonic lengths in the perturbed myelinated segments (e.g., Waxman, 1978; Naud and Longtin, 2019).

Since our single neuron cohort sampled a wide range of parameter space, we used Lasso regression to identify which of the complex, interacting parameters contributed most to CV delays (which preceded AP failures). Parameters including axon diameter, node length, length of myelinated segments, and nodal ion channel densities predicted how our models responded to demyelination and remyelination; these findings are consistent with past modeling studies over more limited parameter ranges (e.g., Goldman and Albus, 1968; Moore et al., 1978; Babbs and Shi, 2013; Young et al., 2013; Schmidt and Knösche, 2019). Better empirical measurements of these parameters in monkey dlPFC, for example from 3-dimensional electron microscopy studies or single neuron axon studies combined with markers for myelin, would help predict the extent to which myelin dystrophy and remyelination along individual axons with aging affect AP propagation.

Another important feature of our multicompartment model is that it was constrained by morphologic and physiological data in rhesus monkey dlPFC —an extremely valuable dataset from an animal model with many similarities to humans (Upright and Baxter, 2021; Tarantal et al., 2022). While beyond the scope of the current study, this computational infrastructure –with a detailed axon, initial segment, soma, and apical and basal dendrites– enables simultaneous investigations of signal propagation through the dendritic arbor and axon. Our model can also be extended to explore interactions between spatially localized myelin perturbations (such as those seen in multiple sclerosis) and axon collateralization (Sengupta et al., 2023), which would affect the distance-dependence of AP failures. Integrating such results from single neuron models into network models of working memory, as we have done here, is a powerful way to connect empirical data across multiple scales.”

(2) Although the authors provide a well-designed study for the multi-compartmental model, it would be useful to add more details about how an unperturbed model and a completely remyelinated model differ in practice, perhaps right before the first results on the single cell model are presented. Are the new myelin sheaths covering the same % of axon as in the original case? Are there the same number of nodes? It is hard to distinguish which of these results are due to a compensation by the new myelin sheaths and which ones are just the model coming back to its original (and mathematically equivalent) starting point.

A ‘fully remyelinated’ axon is not mathematically equivalent to the unperturbed axon. Newly remyelinated segments had at most 75% of the original number of myelin wraps, with a new node in between, consistent with empirical observations in rhesus monkey dlPFC. Our manuscript changes in response to this recommendation are described in detail above in our response to the public review of the same reviewer.

(3) The authors observe a directed component in the bias that is known to be caused by heterogeneities in network connectivity, as stated in the text. It occurs to me that similar effects could be also caused by an heterogeneous demyelination in parts of the network. Inducing these biases could be another potential effect of demyelination in practice, and could be easily revealed by the author's current model (and displayed in a supplementary figure).

As suggested by the reviewer, we have tested heterogeneous demyelination in parts of the network and the results confirm the reviewer’s intuition. We have included these new results as new Supplementary Figure 7 (see below) and we have added the following sentences in the Legend of Figure 5, line 1265: “When demyelination is restricted to a part of the network, diffusion only increases in the perturbed zone (Supplementary Figure 7).” and in the Discussion (line 457): “In addition to age-related changes in memory duration and precision, our network model predicts an age-related increase in systematic errors (bias) due to an increased drift of the activity bump (Supplementary Figure 11). Moreover, if demyelination is spatially localized in a part of the network, the model predicts a repulsive bias away from the memories encoded in the affected zone (Supplementary Figure 7).”

Author response image 5.

Effect of spatially heterogeneous demyelination of the model neurons according to their preferred angle. We also tested working memory performance in the network when demyelination affects only parts of the network. The figure shows the decoded bump center position during the cue and delay period for the eight possible cue directions when a fraction of neurons was perturbed and the rest of the neurons in the circuit were unaltered (Figure 5B). We perturbed 10% of the neurons around the neuron with preferred direction 90° (left panel), 25% of the neurons around -90° (middle panel), and 50% of the neurons around 180° (right panel). Bump traces for cues that lie inside the perturbed portion of the circuit are shown in blue. Network perturbation in the three cases consisted in demyelinating 25% of the segments along the axons of model neurons, by removing 70% of the myelin lamellae. In each case, 280 trials were simulated for one network. These simulations show an increased drift and diffusion inside the perturbed zone, consistent with the increased drift and diffusion when perturbing the entire network (Figure 6B and Supplementary Figure 11). In particular, spatially heterogeneous demyelination in our network leads to a bias away from the affected zone and to increased trial-to-trial variability. Note that this is a model prediction, but we are not aware of empirical data showing heterogeneous demyelination with aging. Further, note that while our network model has a topological ring structure, neurons in PFC are not anatomically arranged depending on their preferred features. Thus, spatially heterogeneous demyelination would likely affect neurons with different feature preferences (i.e., neurons throughout our ring model).

(4) The bump attractor model of WM relies on a continuous attractor dynamics to encode the information stored in memory --a fixed point dynamics that can only vary via the slow noise-driven drift. This means, as the authors mention, that changes in CV won't affect the performance of WM in their model. This seems to be a limitation of the model, or at least an effect which is highly dependent on the modeler's choice, rather than an accurate prediction. While testing the effects of oscillations (as the authors argue in the Discussion) might be out of the scope of this work, there are other WM models which are more sensitive to temporal differences in activity. The authors should test whether the same (lack of) effects are also found in other WM models. A silent WM model seems to be the ideal candidate for this, as the authors already have the key dynamics of that model incorporated in their computational framework (namely, short-term synaptic facilitation in excitatory synapses).

We fully agree that considering the effects of demyelination in networks with alternative mechanisms would strengthen our manuscript. As suggested by the reviewer, we have simulated demyelination effects (AP failures and changes in CV) in an activity silent working memory model. The results are described in detail above in our response to the public review of the same reviewer.

We also would like to mention that we have now also tested larger conduction delays in the bump attractor model, revealing additional working memory errors. This is shown in the revised version of Supplementary Figure 6 (see below). However, those delays are unrealistically large and thus the main effect in both the bump attractor and the activity-silent model is due to AP failures.

Author response image 6.

Effect of propagation delays on control and perturbed networks. (A) Memory strength (left panels) and diffusion (right panels) for the young, control networks with zero propagation delays (blue solid line), as in Figure 5, and with propagation delays from a uniform distribution with a range between 0 and 100 ms (yellow dashed line). (B) Memory strength and diffusion for perturbed networks when demyelinating 50% of the segments along the axons of model neurons, by removing 60% of the myelin lamellae without delays (red solid line), and with delays from a uniform distribution with a range between 0 and 40 ms (gray dashed line) and between 0 and 85 ms (black dash-dotted line). The measures of working memory performance were calculated by averaging across 20 networks and 280 trials for each network. Shaded areas indicate SEM for each case. For the young, control networks, there was no difference with and without propagation delays, even though the delays used in the network simulations were much larger than the delays quantified in the single neuron model (the longest delays found for the most extreme perturbation condition –demyelination of 75% of the segments by removing 100% of the myelin lamellae– were of 49.9 ms on average; A). Working memory performance was also unaffected in the perturbed network with AP failures for delays ranging between 0 and 40 ms, also larger than the ones quantified in the single neuron model (for the case of 50% of the segments demyelinated by removing 60% of the myelin lamellae, the average delay in the cohort was 4.6 ms and the maximum delay was 15.7 ms; B). However, including extremely long delays of up to 85 ms did further impair memory compared to the impairment level introduced by AP failures alone (B).

(5) Impact of demyelination and remyelination on working memory: Could the authors explain here how these biologically detailed alterations are implemented in the bump attractor model? Is the CV and AP failure rate adjusted to the values produced by the multicompartment neuron model with these myelin alterations?

Yes, the reviewer is right, the CV and AP failure rate have been adjusted to the values produced by the multicompartment neuron model. To clarify this in the manuscript, we have restated the text as follows:

Lines 243 - 249 (Results):

To investigate how myelin alterations affect working memory maintenance, we explored in the network model the same demyelination and remyelination conditions as we did in the single neuron model. Because our network model consists of point neurons (i.e., without detailed axons), we incorporated CV slowing as an effective increase in synaptic transmission delays (see Methods). To simulate AP failures, we adjusted the AP failure rate to the values given by the single neuron model, by creating a probabilistic model of spike transmission from the excitatory presynaptic neurons to both the excitatory and inhibitory postsynaptic neurons (see Methods).

Lines 722 - 747 (Methods):

Modeling action potential propagation failures in the network. The network model is composed of point neurons without an explicit model of the axon. To effectively model the action potential failures at the distal end of the axons quantified with the single neuron model under the different demyelination and remyelination conditions, the AP failure rate was adjusted to the values produced by the single neuron model. To do this, we perturbed the 10 control networks by designing a probabilistic model of spike transmission from the excitatory presynaptic neurons to both the excitatory and inhibitory postsynaptic neurons. From the single neuron model, for each demyelination/remyelination condition, we quantified the probability of AP failure for each of the neurons in the control cohort, as well as the percentage of those neurons that shared the same probabilities of failure. That is, the percentage of neurons that had probability of failure = 0, probability of failure = 1 or any other probability. Then, we computed the probability of transmission, , and we specified for the corresponding percentages of excitatory neurons in the networks. Thus, in the network model, we took into account the heterogeneity observed in the single neuron model under each demyelination/remyelination condition.

Modeling conduction velocity slowing in the network. To explore the effect of CV slowing along the axons of model neurons, we simulated 20 young, control networks and 20 perturbed networks with AP failure rates adjusted for the case of single model neurons with 50% of the segments demyelinated along the axons by removing 60% of the myelin lamellae (we ran 280 trials for each network). Then, we added random delays uniformly distributed with a minimum value of 0 ms in both cases, a maximum value of 100 ms in the control networks, and a maximum values of 40 ms and 85 ms in the perturbed networks, in both the AMPA and NMDA excitatory connections to both E and I neurons (Supplementary Figure 6). These large values were chosen because we wanted to illustrate the potential effect of CV slowing in our network and smaller, more realistic, values did not have any effect.

(6) "We also sought to reveal the effect on working memory performance of more biologically realistic network models with AP transmission probabilities matched to both axons with intact and with altered myelin sheaths, as likely occurs in the aging brain (Figure 1). Thus, we ran network model simulations combining AP failure probabilities corresponding to groups of neurons containing intact axons and axons presenting different degrees of demyelination." I fail to see the difference with respect to the results in previous sections. Is it that now we have subnetworks in which axons are intact and subnetworks with significant AP failures, while before there was no topological separation between both cases? Please clarify.

In Figures 5 and 6 the AP failure rate of the neural population in the network simulations was matched to the AP failure rate of the cohort of single model neurons for each demyelination/remyelination condition. Since not all model neurons have equal features, a given condition produces different levels of impairment in its neuron. Thus, we quantified the probability of AP failure for each neuron in the control cohort, as well as the percentage of those neurons that shared the same probabilities of failure. Then, we computed the probability of AP transmission for the corresponding percentages of excitatory neurons in the networks. Thus, in the network model, we took into account the heterogeneity observed in the single neuron model under each demyelination/remyelination condition.

However, In Figures 7 and 8, we consider additional heterogeneity due to a different degree of demylination/remyelination of different neurons. Here, excitatory neurons in the network model are not perturbed according to a single demyelination/remyelination condition. Instead, we allowed that different percentages of excitatory neurons had AP failure rates corresponding to different demyelination/remyelination conditions: some were unperturbed, while others had different degrees of demyelination (Figure 7) and different degrees of remyelination (Figure 8). We have modified the text for clarification in several places.

First, when we describe the impact of demyelination on working memory, we already mention that (line 271): “In each of the 10 networks, we set the AP failure rate of the excitatory neurons according to the distribution of failure probabilities of the neurons in the single neuron cohort for the given demyelination or remyelination condition. Thus, we took into account the heterogeneity of demyelination and remyelination effects from our single neuron cohort (Figure 3A; Supplementary Figure 3). Note that this heterogeneity originates from differences in axon properties, but probabilities of failure for all neurons in the network correspond to the same degree of demyelination (Figure 6). We will also consider networks that contain different combinations of axons with either intact or perturbed myelin (Figure 7 and Figure 8).”

Second, we have combined the text describing Figures 7 and 8 under a single section title, which reads “Simulated heterogenous myelin alterations match empirical data” (line 334) and start this section with (line 337): “Up to this point we have studied network models with AP failure probabilities corresponding to a single degree of myelin alterations (i.e., with all excitatory neurons in the network having AP failure rates matched to those of the single neuron cohort for one particular demyelination or remyelination condition). Next, we sought to reveal the effect on working memory performance of more biologically realistic network models, where excitatory neurons in the networks were perturbed according to a combination of different demyelination or remyelination conditions. That is, we simulated networks with excitatory neurons having AP failure probabilities matched to both neuronal axons with intact and with altered myelin sheaths in different degrees, as likely occurs in the aging brain (Figure 1).”

(7) "Unexpectedly, our model indicates that compared to the performance of networks composed of neurons possessing axons with intact myelin sheaths, both demyelination and remyelination leads to an impaired performance." This conclusion is quite interesting, but I lack intuition from the paper as of why it is happening. In fact, the authors say in the Discussion that "complete remyelination of all the previously demyelinated segments with sufficient myelin, with fewer transitions between long and short segments, recovered working memory function." Would we then see a minimum and then an increase in memory duration in Figure 9B if we extended the X-axis until we hit 100% of new myelin sheaths?

This is a very important question that we have carefully addressed in Results and Discussion. We distinguish between two remyelination cases in the models. Complete remyelination: when all (100%) the previously demyelinated segments have been subsequently remyelinated, and incomplete remyelination: when less than 100% (25%, 50% or 75%) of the demyelinated segments have been remyelinated. Figure 6 (middle and right columns) shows the two cases (black lines for any percentage of lamellae added vs. colored lines): for 100% of the segments remyelinated, the network performance is nearly or completely (when enough lamellae are added) recovered to the young network performance. In fact, with the single neuron model we observe that (lines 192 - 193 in Results): “Remyelinating all affected segments with 75% of lamellae (the maximal amount of remyelination) nearly eliminated AP failures (1.8 ± 1.1%)”. However, incomplete remyelination recovers the performance compared to demyelination (middle and right columns in Figure 6 vs left column), but this performance is worse than the performance of the young networks. The single neuron model shows that (lines 194 - 197 in Results): “Incomplete remyelination, where some segments were still demyelinated, still had relatively high AP failure rates. For example, when one eighth of segments were remyelinated with the maximal amount of lamellae and one eighth were left bare, 25.7 ± 11.5% of APs failed across the cohort (Figure 4C, red dashed line and arrow).”

In Figure 9B (now Figure 8B), we combine intact axons with axons that are only partially remyelinated (i.e., incomplete remyelination). Extending the X-axis in Figure 8B until 100% of new myelin sheaths would not imply a minimum and a subsequent increase, but a continuous impairment: the more axons we perturb (remyelinate) the higher is the impairment compared to the young cases where all the axons are intact.

The sentence "Unexpectedly, our model indicates that compared to the performance of networks composed of neurons possessing axons with intact myelin sheaths, both demyelination and remyelination leads to an impaired performance.", now reads as (lines 379 380 in Results): “Therefore, both demyelination and incomplete remyelination lead to impaired performance in our networks, compared to networks with intact myelin sheaths”. We have also rewritten the corresponding section in Discussion (lines 486 - 489) as follows: “Therefore, it is reasonable to assume that ineffective remyelination may lead to working memory impairment. In fact, complete remyelination of all previously demyelinated segments with sufficient myelin, with fewer transitions between long and short segments, led to full recovery of working memory function.”

(8) [minor] "Our recent network model found that age-related changes in firing rates and synapse numbers in individual neurons can lead to working memory impairment (Ibañez et al., 2020), but did not consider myelin dystrophy." Could you be more precise about which age-related changes were studied in Ibanez et al. 2020? From the paper it seems like it was mostly cellular excitability and synaptic density, so this should be added here for more context.

To clarify this, we have added the following sentences in the Introduccion (line 105):

“Our recent network model revealed that the empirically observed age-related increase in AP firing rates in prefrontal pyramidal neurons (modeled through an increased slope of the f-I curve) and loss of up to 30% of both excitatory and inhibitory synapses (modeled as a decrease in connectivity strength) can lead to working memory impairment (Ibañez et al., 2020), but this model did not incorporate the known changes to myelin structure that occur during normal

aging.”

(9) [minor] "Recurrent excitatory synapses are facilitating, which promotes robust and reliable persistent activity despite spatial heterogeneities in the connectivity or in the intrinsic properties of the neurons." It would be great to add a reference here to justify the inclusion of this type of plasticity in the excitatory circuit (for example Wang, Markram et al. Nat Neuro 2006).

We have added the references suggested by the reviewer and a further one in the Results (line 216):

“Recurrent excitatory synapses are facilitating, as has been empirically observed in PFC (Hempel et al., 2000; Wang et al., 2006), which promotes robust and reliable persistent activity despite spatial heterogeneities in the connectivity or in the intrinsic properties of the neurons.”

References:

Hempel, C. M., Hartman, K. H., Wang, X. J., Turrigiano, G. G., and Nelson, S. B. (2000). Multiple forms of short-term plasticity at excitatory synapses in rat medial prefrontal cortex. J. Neurophysiol. 83, 3031–3041. doi: 10.1152/jn.2000.83.5.3031

Wang, Y., Markram, H., Goodman, P. H., Berger, T. K., Ma, J., and Goldman- Rakic, P. S.(2006). Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat.Neurosci. 9, 534–542. doi: 10.1038/nn1670

eLife assessment

This manuscript reports a valuable computational study of the effects of axon de-myelination and re-myelination on action potential speed and propagation failure. The manuscript presents solid evidence for the effects of de- and re-myelination in different models of working memory, with potential implications in disorders such as multiple sclerosis. The exposition of the manuscript is targeted for researchers interested in biophysical models of cognitive deficits.

Reviewer #1 (Public Review):

Summary:

The authors study the effects of myelin alterations in working memory via the complementary use of two computational approaches: one based on the de- and re-myelination in multicompartmental models of pyramidal neurons, and one based on synaptic changes in a spiking bump attractor model for spatial working memory. The first model provides the most precise angle (biophysically speaking) of the different effects (loss of myelin lamella or segments, remyelination with thinner and shorter nodes, etc), while the second model allows to infer the consequences of myelin alterations in working memory performance, including memory stability, duration, and bump diffusion, while also exploring the case of myeling alterations in a novel silent working memory model. The results indicate (i) a slowing down and failure of propagation of spikes with demyelination and partial recovery with remyelination, with detailed predictions on the role of nodes and myelina lamella, and (ii) a decrease in memory duration and an increase in memory drift as a function of the demyelination, in agreement with multiple experimental studies.

Strengths:

Overall, the work offers a very interesting approach of a topic which is hard to accomplish experimentally --therefore the computational take is entirely justified and extremely useful. The authors carefully designed the computational experiments to shed light into the demyelination effects on working memory from multiple levels of description, increasing the reliability of their conclusions. I think this work provides now convincing evidence and has the potential to be influential in future studies of myelin alterations (and related disorders such as multiple sclerosis).

Weaknesses:

In its current form, the authors have improved the clarity of the results and the model details, and have provided a new set of simulations to complement and reinforce the original ones (including the development of a new spatial working memory model based on silent working memory principles). I do not appreciate any significant weaknesses at this point.

Reviewer #2 (Public Review):

This paper analyzes the effect of axon de-myelination and re-myelination on action potential speed, and propagation failure. Next, the findings are then incorporated in a standard spiking ring attractor model of working memory.

I think the results are not very surprising or solid and there are issues with method and presentation.<br /> The authors did many simulations with random parameters, then averaged the result, and found for instance that the Conduction Velocity drops in demyelination. It gives the reader little insight into what is really going on. My personal preference is for a well understood simple model rather than a poorly understood complex model. The link between the model outcome of WM and data remains qualitative and is further weakened by the existence of known other age-related effects in PFC circuits.

Comments on revised version:

The paper has improved in the revision, although I still think a reduced model would have been nice.

eLife assessment

The authors develop a self-returning self-avoiding polymer model of chromosome organization and show that their framework can recapitulate at the same time local density and large-scale contact structural properties observed experimentally by various technologies. The presented theoretical framework and the results are valuable for the community of modelers working on 3D genomics. The work provides solid evidence that such a framework can be used, is reliable in describing chromatin organization at multiple scales, and could represent an interesting alternative to standard molecular dynamics simulations of chromatin polymer models.

Reviewer #1 (Public Review):

Carignano et al propose an extension of the self-returning random walk (SRRW) model for chromatin to include excluded volume aspects and use it to investigate generic local and global properties of the chromosome 3D organization inside eukaryotic nuclei. In particular, they focus on chromatin volumic density, contact probability, and domain size and suggest that their framework can recapitulate several experimental observations and predict the effect of some perturbations.

Strengths:

• The developed methodology is convincing and may offer an alternative - less computationally demanding - framework to investigate the single-cell and population structural properties of 3D genome organization at multiple scales.

• Compared to the previous SRRW model, it allows for investigation of the role of excluded volume locally.

• They perform some experiments to compare with model predictions and show consistency between the two.

Weaknesses:

• The model is a homopolymer model and currently cannot fully account for specific mechanisms that may shape the heterogeneous, complex organization of chromosomes (TAD at specific positions, A/B compartmentalization, promoter-enhancer loops, etc.).

• By construction of their framework, the effect of excluded volume is only local and larger-scale properties for which excluded volume could be a main actor (formation of chromosome territories [Rosa & Everaers, PLoS CB 2009], bottle-brush effects due to loop extrusion [Polovnikov et al, PRX 2023], etc.) cannot be captured.

• Apart from being a computationally interesting approach to generating realistic 3D chromosome organization, the method offers fewer possibilities than standard polymer models (eg, MD simulations) of chromatin (no dynamics, no specific mechanisms, etc.) with likely the same predictive power under the same hypotheses. In particular, authors often claim the superiority of their approach to describing the local chromatin compaction compared to previous polymer models without showing it or citing any relevant references that would show it.

• Comparisons with experiments are solid but are not quantified.

Impact:

Building on the presented framework in the future to incorporate TAD and compartments may offer an interesting model to study the single-cell heterogeneity of chromatin organization. But currently, in this reviewer's opinion, standard polymer modeling frameworks may offer more possibilities.

Reviewer #2 (Public Review):

Summary:

The authors introduce a simple Self Returning Excluded Volume (SR-EV) model to investigate the 3D organization of chromatin. This is a random walk with a probability to self-return accounting for the excluded volume effects. The authors use this method to study the statistical properties of chromatin organization in 3D. They compute contact probabilities, 3D distances, and packing properties of chromatin and compare them with a set of experimental data.

Strengths:

(1) Typically, to generate a polymer with excluded volume interactions, one needs to run long simulations with computationally expensive repulsive potentials like the Weeks-Chanlder-Anderson potential. However, here, instead of performing long simulations, the authors have devised a method where they can grow polymer, enabling quick generation of configurations.

(2) Authors show that the chromatin configurations generated from their models do satisfy many of the experimentally known statistical properties of chromatin. Contact probability scalings and packing properties are comparable with Chromatin Scanning Transmission Electron Microscopy (ChromSTEM)  experimental data from some of the cell types.

Weaknesses:

This can only generate broad statistical distributions. This method cannot generate sequence-dependent effects, specific TAD structures, or compartments without a prior model for the folding parameter alpha. It cannot generate a 3D distance between specific sets of genes. This is an interesting soft-matter physics study. However, the output is only as good as the alpha value one provides as input.

eLife assessment

The study by Kim et al. is a valuable contribution to the topic of obtaining good channel conductance parameters from electrophysiological recordings. While promising in its ability to rapidly construct newly fitted models using generative adversarial networks, the approach is incompletely described and the generated models often substantially deviate from the dynamics observed empirically. The comparison with existing multi-objective optimization methods is also incomplete.

Reviewer #1 (Public Review):

The manuscript describes a GAN-based approach that generates parameters for HH-like channels for multiple C. Elengans neurons. The network is trained on generated data to produce parameter sets that, on the one hand, reproduce voltage responses and IV curves, and on the other hand, are indistinguishable from the ground truth parameters, as tested by the discriminator. It is then shown that these generated parameter sets lead to reasonable reproductions of the recorded responses (but see the section "weaknesses" below for some reservations).

Strengths:

In itself, I find the methodology of high interest, particularly in that it can generate parameter sets to construct models of new recordings at a very low computational cost.

Weaknesses:

Nevertheless, I believe there are some weaknesses in the evaluation of the models that should be addressed before the quality of the methodology can be fully assessed. Firstly, at the methodological level, the authors should provide more clarity on the inverse gradient operation they use, as opposed to just simulating the models, as such an inversion depends not only on the parameters but also on the state of the model. How the state is obtained remains unclear here. Secondly, in the evaluation of their models, the authors could provided more information such as IV curves, as whether these would be accurate is difficult to visually infer from their figures. Thirdly, the authors do not address the question of whether all obtained parameter sets are stable when simulated over longer times, while their figures do include hints that this might not be the case for at least some of their models (e.g. voltage traces that do not converge back to the equilibrium after the stimulus, but rather seem to diverge).

Reviewer #2 (Public Review):

Summary:

Generating biophysically detailed computational models that capture the characteristic physiological properties of biological neurons for diverse cell types is an important and difficult problem in computational neuroscience. One major challenge lies in determining the large number of parameters of such models, which are notoriously difficult to fit into experimental data. Thereby, the computational and energy costs can be significant. The study 'ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses' by Kim et al. describes a computationally efficient approach for predicting model parameters of Hodgkin-Huxley neuron models using Generative Adversarial Networks (GANs) trained on simulation data. The method is applied to generate models for 9 non-spiking neurons in C. elegans based on electrophysiological recordings. While the generated models capture the responses of these neurons to some degree, they generally show significant deviations from the empirically observed responses in important features. While interesting, in its current form, the method has not been demonstrated to generate models that faithfully capture empirically observed responses.

Strengths:

The authors work on an important and difficult problem. A noteworthy strength of their approach is that once trained, the GANs can generate models from new empirical data with very little computational effort. The generated models reproduce the average voltage during current injections reasonably well.

Weaknesses:

Major 1: While the models generated with EP-GAN reproduce the average voltage during current injections reasonably well, the dynamics of the response are not well captured. For example, for the neuron labeled RIM (Figure 2), the most depolarized voltage traces show an initial 'overshoot' of depolarization, i.e. they depolarize strongly within the first few hundred milliseconds but then fall back to a less depolarized membrane potential. In contrast, the empirical recording shows no such overshoot. Similarly, for the neuron labeled AFD, all empirically recorded traces slowly ramp up over time. In contrast, the simulated traces are mostly flat. Furthermore, all empirical traces return to the pre-stimulus membrane potential, but many of the simulated voltage traces remain significantly depolarized, far outside of the ranges of empirically observed membrane potentials. While these deviations may appear small in the Root mean Square Error (RMSE), the only metric used in the study to assess the quality of the models, they likely indicate a large mismatch between the model and the electrophysiological properties of the biological neuron.

Major 2: Other metrics than the RMSE should be incorporated to validate simulated responses against electrophysiological data. A common approach is to extract multiple biologically meaningful features from the voltage traces before, during and after the stimulus, and compare the simulated responses to the experimentally observed distribution of these features. Typically, a model is only accepted if all features fall within the empirically observed ranges (see e.g. https://doi.org/10.1371/journal.pcbi.1002107). However, based on the deviations in resting membrane potential and the return to the resting membrane potential alone, most if not all the models shown in this study would not be accepted.

Major 3: Abstract and introduction imply that the 'ElectroPhysiome' refers to models that incorporate both the connectome and individual neuron physiology. However, the work presented in this study does not make use of any connectomics data. To make the claim that ElectroPhysiomeGAN can jointly capture both 'network interaction and cellular dynamics', the generated models would need to be evaluated for network inputs, for example by exposing them to naturalistic stimuli of synaptic inputs. It seems likely that dynamics that are currently poorly captured, like slow ramps, or the ability of the neuron to return to its resting membrane potential, will critically affect network computations.

eLife assessment

This valuable study by Wu and Zhou combines neurophysiological recordings and computational modelling to address an interesting question regarding the sequence of events from sensing to action. Neurophysiological evidence remains incomplete: explicit mapping of saccade-related activity in the same neurons and a better understanding of the influence of the spatial configuration of stimulus and targets would be required to pinpoint whether such activity might contribute, even partially, to the observed results and interpretations. These results are of interest for neuroscientists investigating decision-making.

Reviewer #1 (Public Review):

Summary:

This valuable study by Wu and Zhou combined neurophysiological recordings and computational modelling to investigate the neural mechanisms that underpin the interaction between sensory evaluation and action selection. The neurophysiological results suggest non-linear modulation of decision-related LIP activity by action selection, but some further analysis would be helpful in order to understand whether these results can be generalised to LIP circuitry or might be dependent on specific spatial task configurations. The authors present solid computational evidence that this might be due to projections from choice target representations. These results are of interest for neuroscientists investigating decision-making.

Strengths:

Wu and Zhou combine awake behaving neurophysiology for a sophisticated, flexible visual-motion discrimination task and a recurrent network model to disentangle the contribution of sensory evaluation and action selection to LIP firing patterns. The correct saccade response direction for preferred motion direction choices is randomly interleaved between contralateral and ipsilateral response targets, which allows the dissociation of perceptual choice from saccade direction.<br /> The neurophysiological recordings from area LIP indicate non-linear interaction between motion categorisation decisions and saccade choice direction.

The careful investigation of a recurrent network model suggests that feedback from choice target representations to an earlier sensory evaluation stage might be the source for this non-linear modulation and that it is an important circuit component for behavioural performance.

The paper presents a possible solution to a central controversy about the role of LIP in perceptual decision-making, but see below.

Weaknesses:

The paper presents a possible solution to a central controversy about the role of LIP in perceptual decision-making. However, the authors could be more clear and upfront about their interpretational framework and potential alternative interpretations.<br /> Centrally, the authors' model and experimental data appears to test only that LIP carries out sensory evaluation in its RFs. The model explicitly parks the representation of choice targets outside the "LIP" module receiving sensory input. The feedback from this separate target representation provides then the non-linear modulation that matches the neurophysiology. However, they ignore the neurophysiological results that LIP neurons can also represent motor planning to a saccade target.<br /> The neurophysiological results with a modulation of the direction tuning by choice direction (contralateral vs ipsilateral) are intriguing. However, the evaluation of the neurophysiological results are difficult, because some of the necessary information is missing to exclude alternative explanations. It would be good to see the actual distributions and sizes of the RF, which were determined based on visual responses not with a delayed saccade task. There might be for example a simple spatial configuration, for example, RF and preferred choice target in the same (contralateral) hemifield, for which there is an increase in firing. It is a shame that we do not see what these neurons would do if only a choice target would be put in the RF, as has been done in so many previous LIP experiments. The authors exclude also some spatial task configurations (vertical direction decisions), which makes it difficult to judge whether these data and models can be generalised. The whole section is difficult to follow, partly also because it appears to mix reporting results with interpretation (e.g. "feedback").

The model and its investigation is very interesting and thorough, but given the neurophysiological literature on LIP, it is not clear that the target module would need to be in a separate brain area, but could be local circuitry within LIP between different neuron types.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors recorded activity in the posterior parietal cortex (PPC) of monkeys performing a perceptual decision-making task. The monkeys were first shown two choice dots of two different colors. Then, they saw a random dot motion stimulus. They had to learn to categorize the direction of motion as referring to either the right or left dot. However, the rule was based on the color of the dot and not its location. So, the red dot could either be to the right or left, but the rule itself remained the same. It is known from past work that PPC neurons would code the learned categorization. Here, the authors showed that the categorization signal depended on whether the executed saccade was in the same hemifield as the recorded PPC neuron or in the opposite one. That is, if a neuron categorized the two motion directions such that it responded stronger for one than the other, then this differential motion direction coding effect was amplified if the subsequent choice saccade was in the same hemifield. The authors then built a computational RNN to replicate the results and make further tests by simulated "lesions".

Strengths:

Linking the results to RNN simulations and simulated lesions.

Weaknesses:

Potential interpretational issues due to a lack of evidence on what happens at the time of the saccades.

eLife assessment

This paper makes a valuable contribution to our understanding of the tradeoffs in eye design - specifically between improvements in optics and in photoreceptor performance. The authors successfully build a formal theory that enables comparisons across a wide range of species and eye types. The conclusion from the modeling is that resources are split relatively evenly between optics and photoreceptors, and hence that both must be considered in eye design. Evidence for this conclusion is solid, and could be strengthened with a more complete comparison with the experiment.

Reviewer #1 (Public Review):

Summary:

Two important factors in visual performance are the resolving power of the lens and the signal-to-noise ratio of the photoreceptors. These both compete for space: a larger lens has improved resolving power over a smaller one, and longer photoreceptors capture more photons and hence generate responses with lower noise. The current paper explores the tradeoff of these two factors, asking how space should be allocated to maximize eye performance (measured as encoded information).

Strengths:

The topic of the paper is interesting and not well studied. The approach is clearly described and seems appropriate (with a few exceptions - see weaknesses below). In most cases, the parameter space of the models are well explored and tradeoffs are clear.

Weaknesses:

- Light level<br /> The calculations in the paper assume high light levels (which reduces the number of parameters that need to be considered). The impact of this assumption is not clear. A concern is that the optimization may be quite different at lower light levels. Such a dependence on light level could explain why the model predictions and experiment are not in particularly good agreement. The paper would benefit from exploring this issue.

- Discontinuities<br /> The discontinuities and non-monotonicity of the optimal parameters plotted in Figure 4 are concerning. Are these a numerical artifact? Some discussion of their origin would be quite helpful.

- Discrepancies between predictions and experiment<br /> As the authors clearly describe, experimental measurements of eye parameters differ systematically from those predicted. This makes it difficult to know what to take away from the paper. The qualitative arguments about how resources should be allocated are pretty general, and the full model seems a complex way to arrive at those arguments. Could this reflect a failure of one of the assumptions that the model rests on - e.g. high light levels, or that the cost of space for photoreceptors and optics is similar? Given these discrepancies between model and experiment, it is also hard to evaluate conclusions about the competition between optics and photoreceptors (e.g. at the end of the abstract) and about the importance for evolution (end of introduction).

Reviewer #2 (Public Review):

Summary:

In short, the paper presents a theoretical framework that predicts how resources should be optimally distributed between receptors and optics in eyes.

Strengths:

The authors build on the principle of resource allocation within an organism and develop a formal theory for optimal distribution of resources within an eye between the receptor array and the optics. Because the two parts of eyes, receptor arrays and optics, share the same role of providing visual information to the animal it is possible to isolate these from resource allocation in the rest of the animal. This allows for a novel and powerful way of exploring the principles that govern eye design. By clever and thoughtful assumptions/constraints, the authors have built a formal theory of resource allocation between the receptor array and the optics for two major types of compound eye as well as for camera-type eyes. The theory is formalized with variables that are well characterized in a number of different animal eyes, resulting in testable predictions.

The authors use the theory to explain a number of design features that depend on different optimal distribution of resources between the receptor array and the optics in different types of eyes. As an example, they successfully explain why eye regions with different spatial resolution should be built in different ways. They also explain differences between different types of eyes, such as long photoreceptors in apposition compound eyes and much shorter receptors in camera type eyes. The predictive power in the theory is impressive.

To keep the number of parameters at a minimum, the theory was developed for two types of compound eye (neural superposition, and apposition) and for camera-type eyes. It is possible to extend the theory to other types of eyes, although it would likely require more variables and assumptions/constraints to the theory. It is thus good to introduce the conceptual ideas without overdoing the applications of the theory.

The paper extends a previous theory, developed by the senior author, that develops performance surfaces for optimal cost/benefit design of eyes. By combining this with resource allocation between receptors and optics, the theoretical understanding of eye design takes a major leap and provides entirely new sets of predictions and explanations for why eyes are built the way they are.

The paper is well written and even though the theory development in the Results may be difficult to take in for many biologists, the Discussion very nicely lists all the major predictions under separate headings, and here the text is more tuned for readers that are not entirely comfortable with the formalism of the Results section. I must point out though that the Results section is kept exemplary concise. The figures are excellent and help explain concepts that otherwise may go above the head of many biologists.

Reviewer #3 (Public Review):

Summary:

This is a proposal for a new theory for the geometry of insect eyes. The novel cost-benefit function combines the cost of the optical portion with the photoreceptor portion of the eye. These quantities are put on the same footing using a specific (normalized) volume measure, plus an energy factor for the photoreceptor compartment. An optimal information transmission rate then specifies each parameter and resource allocation ratio for a variable total cost. The elegant treatment allows for comparison across a wide range of species and eye types. Simple eyes are found to be several times more efficient across a range of eye parameters than neural superposition eyes. Some trends in eye parameters can be explained by optimal allocation of resources between the optics and photoreceptors compartments of the eye.

Strengths:

Data from a variety of species roughly align with rough trends in the cost analysis, e.g. as a function of expanding the length of the photoreceptor compartment.

New data could be added to the framework once collected, and many species can be compared.

Eyes of different shapes are compared.

Weaknesses:

Detailed quantitative conclusions are not possible given the approximations and simplifying assumptions in the models and poor accounting for trends in the data across eye types.

eLife assessment

This important study provides solid evidence that both psychiatric dimensions (e.g. anhedonia, apathy, or depression) and chronotype (i.e., being a morning or evening person) influence effort-based decision-making. Notably, the current study does not elucidate whether there may be interactive effects of chronotype and psychiatric dimensions on decision-making. This work is of importance to researchers and clinicians alike, who may make inferences about behaviour and cognition without taking into account whether the individual may be tested or observed out-of-sync with their phenotype.

Reviewer #1 (Public Review):

Summary:

This study uses an online cognitive task to assess how reward and effort are integrated in a motivated decision-making task. In particular the authors were looking to explore how neuropsychiatric symptoms, in particular apathy and anhedonia, and circadian rhythms affect behavior in this task. Amongst many results, they found that choice bias (the degree to which integrated reward and effort affects decisions) is reduced in individuals with greater neuropsychiatric symptoms, and late chronotypes (being an 'evening person').

Strengths:

The authors recruited participants to perform the cognitive task both in and out of sync with their chronotypes, allowing for the important insight that individuals with late chronotypes show a more reduced choice bias when tested in the morning.<br /> Overall, this is a well-designed and controlled online experimental study. The modelling approach is robust, with care being taken to both perform and explain to the readers the various tests used to ensure the models allow the authors to sufficiently test their hypotheses.

Weaknesses:

This study was not designed to test the interactions of neuropsychiatric symptoms and chronotypes on decision making, and thus can only make preliminary suggestions regarding how symptoms, chronotypes and time-of-assessment interact.

Reviewer #2 (Public Review):

Summary:

The study combines computational modeling of choice behavior with an economic, effort-based decision-making task to assess how willingness to exert physical effort for a reward varies as a function of individual differences in apathy and anhedonia, or depression, as well as chronotype. They find an overall reduction in effort selection that scales with apathy and anhedonia and depression. They also find that later chronotypes are less likely to choose effort than earlier chronotypes and, interestingly, an interaction whereby later chronotypes are especially unwilling to exert effort in the morning versus the evening.

Strengths:

This study uses state-of-the-art tools for model fitting and validation and regression methods which rule out multicollinearity among symptom measures and Bayesian methods which estimate effects and uncertainty about those estimates. The replication of results across two different kinds of samples is another strength. Finally, the study provides new information about the effects not only of chronotype but also chronotype by timepoint interactions which are previously unknown in the subfield of effort-based decision-making.

Weaknesses:

The study has few weaknesses. One potential concern is that the range of models which were tested was narrow, and other models might have been considered. For example, the Authors might have also tried to fit models with an overall inverse temperature parameter to capture decision noise. One reason for doing so is that some variance in the bias parameter might be attributed to noise, which was not modeled here. Another concern is that the manuscripts discuss effort-based choice as a transdiagnostic feature - and there is evidence in other studies that effort deficits are a transdiagnostic feature of multiple disorders. However, because the present study does not investigate multiple diagnostic categories, it doesn't provide evidence for transdiagnosticity, per se.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Mehrhof and Nord study a large dataset of participants collected online (n=958 after exclusions) who performed a simple effort-based choice task. They report that the level of effort and reward influence choices in a way that is expected from prior work. They then relate choice preferences to neuropsychiatric syndromes and, in a smaller sample (n<200), to people's circadian preferences, i.e., whether they are a morning-preferring or evening-preferring chronotype. They find relationships between the choice bias (a model parameter capturing the likelihood to accept effort-reward challenges, like an intercept) and anhedonia and apathy, as well as chronotype. People with higher anhedonia and apathy and an evening chronotype are less likely to accept challenges (more negative choice bias). People with an evening chronotype are also more reward sensitive and more likely to accept challenges in the evening, compared to the morning.

Strengths:

This is an interesting and well-written manuscript which replicates some known results and introduces a new consideration related to potential chronotype relationships which have not been explored before. It uses a large sample size and includes analyses related to transdiagnostic as well as diagnostic criteria. I have some suggestions for improvements.

Weaknesses:

(1) The novel findings in this manuscript are those pertaining to transdiagnostic and circadian phenotypes. The authors report two separate but "overlapping" effects: individuals high on anhedonia/apathy are less willing to accept offers in the task, and similarly, individuals tested off their chronotype are less willing to accept offers in the task. The authors claim that the latter has implications for studying the former. In other words, because individuals high on anhedonia/apathy predominantly have a late chronotype (but might be tested early in the day), they might accept less offers, which could spuriously look like a link between anhedonia/apathy and choices but might in fact be an effect of the interaction between chronotype and time-of-testing. The authors therefore argue that chronotype needs to be accounted for when studying links between depression and effort tasks.<br /> The authors argue that, if X is associated with Y and Z is associated with Y, X and Z might confound each other. That is possible, but not necessarily true. It would need to be tested explicitly by having X (anhedonia/apathy) and Z (chronotype) in the same regression model. Does the effect of anhedonia/apathy on choices disappear when accounting for chronotype (and time-of-testing)? Similarly, when adding the interaction between anhedonia/apathy, chronotype, and time-of-testing, within the subsample of people tested off their chronotype, is there a residual effect of anhedonia/apathy on choices or not?<br /> If the effect of anhedonia/apathy disappeared (or got weaker) while accounting for chronotype, this result would suggest that chronotype mediates the effect of anhedonia/apathy on effort choices. However, I am not sure it renders the direct effect of anhedonia/apathy on choices entirely spurious. Late chronotype might be a feature (induced by other symptoms) of depression (such as fatigue and insomnia), and the association between anhedonia/apathy and effort choices might be a true and meaningful one. For example, if the effect of anhedonia/apathy on effort choices was mediated by altered connectivity of the dorsal ACC, we would not say that ACC connectivity renders the link between depression and effort choices "spurious", but we would speak of a mechanism that explains this effect. The authors should discuss in a more nuanced way what a significant mediation by the chronotype/time-of-testing congruency means for interpreting effects of depression in computational psychiatry.

(2) It seems that all key results relate to the choice bias in the model (as opposed to reward or effort sensitivity). It would therefore be helpful to understand what fundamental process the choice bias is really capturing in this task. This is not discussed, and the direction of effects is not discussed either, but potentially quite important. It seems that the choice bias captures how many effortful reward challenges are accepted overall which maybe captures general motivation or task engagement. Maybe it is then quite expected that this could be linked with questionnaires measuring general motivation/pleasure/task engagement. Formally, the choice bias is the constant term or intercept in the model for p(accept), but the authors never comment on what its sign means. If I'm not mistaken, people with higher anhedonia but also higher apathy are less likely to accept challenges and thus engage in the task (more negative choice bias). I could not find any discussion or even mention of what these results mean. This similarly pertains to the results on chronotype. In general, "choice bias" may not be the most intuitive term and the authors may want to consider renaming it. Also, given the sign of what the choice bias means could be flipped with a simple sign flip in the model equation (i.e., equating to accepting more vs accepting less offers), it would be helpful to show some basic plots to illustrate the identified differences (e.g., plotting the % accepted for people in the upper and lower tertile for the SHAPS score etc).

(3) None of the key effects relate to effort or reward sensitivity which is somewhat surprising given the previous literature and also means that it is hard to know if choice bias results would be equally found in tasks without any effort component. (The only analysis related to effort sensitivity is exploratory and in a subsample of N=56 per group looking at people meeting criteria for MDD vs matched controls.) Were stimuli constructed such that effort and reward sensitivity could be separated (i.e., are uncorrelated/orthogonal)? Maybe it would be worth looking at the % accepted in the largest or two largest effort value bins in an exploratory analysis. It seems the lowest and 2nd lowest effort level generally lead to accepting the challenge pretty much all the time, so including those effort levels might not be sensitive to individual difference analyses?

(4) The abstract and discussion seem overstated (implications for the school system and statements on circadian rhythms which were not measured here). They should be toned down to reflect conclusions supported by the data.

eLife assessment

This important work advances our understanding of microglial aging trajectory and heterogeneity. The authors provide an in-depth characterization of microglia in aging and aim to identify molecular checkpoints, that while solid are also deemed incomplete to support all the authors' claims. The study should be of interest to neuroimmunologists and biologists interested in aging.

Reviewer #1 (Public Review):

Summary:

This manuscript by Shea and Villeda furnishes the field with a valuable scRNAseq data set detailing microglial aging in the mouse hippocampus. They provide clear evidence that changes in microglial attributes begin in mid-life, well before time points when mice are traditionally considered to be "aging." It also adds to a growing body of data in the field demonstrating that there is substantial heterogeneity in microglial responses to aging. Using in vitro experiments and transgenic manipulations in mice, the authors show that transforming growth factor beta (TGFb1)-based signaling can potently impact microglial state, consistent with previous findings in the field. They also demonstrate that manipulation of microglial TGFb1-based signaling can impact hippocampus-dependent behaviors.

Limitations of the study lie primarily in reaching too far with interpretations of the data. The authors argue that changes in microglial transcriptome during midlife represent a type of "checkpoint," after which microglial aging can progress along distinct trajectories depending on the status of TGFb1 signaling. They also posit that a specific intermediate "stress response" state in midlife is mechanistically linked to a translational burst that drives the subsequent progression of microglia to an "inflammatory state." Unequivocal data to support these causal links is lacking, however. similarly, key additional experiments would be needed to demonstrate that TGFb1 signaling and microglial progression through these identified intermediate states are causally linked to cognitive decline.

Guidance for readers along with study strengths and caveats:

The present manuscript provides valuable strengthening and expansion to a growing body of data showing prominent changes in the microglial state during aging. Microarray(1), bulkRNAseq(2-5), scRNAseq(6,7), snRNAseq(8,9), and spatial transcriptomic(10) approaches have been leveraged to map changes in microglial transcriptome during aging in rodents, non-human primates, and humans. A number of these studies include the hippocampus (1,8,9,11) and have highlighted variation across brain regions in microglial transcriptomic changes during aging (1,11). They have also revealed differences across sex (7) as well as increased cell-to-cell heterogeneity (6-10), consistent with the idea that individual microglia can follow distinct aging trajectories. Several of these studies revealed that changes in microglial attributes begin in middle age (1,7,11), supporting similar observations from studies that did not use omics (12-14). The present manuscript utilizes scRNAseq of hippocampal microglia at adulthood (6mo), middle age (12mo), late middle age (18mo) and aging (24mo) to show that aging-induced changes in microglia begin in middle age and that microglia exhibit ample phenotypic heterogeneity during the progression of aging.

To gain further insight into the dynamics of microglial aging in the hippocampus, the authors used a bioinformatics method known as "pseudotime" or "trajectory inference" to understand how cells may progress through different functional states, as defined by cellular transcriptome (15,16). These bioinformatics approaches can reveal key patterns in scRNAseq / snRNAseq datasets and, in the present study, the authors conclude that a "stress response" module characterized by expression of TGFb1 represents a key "checkpoint" in microglial aging in midlife, after which the cells can move along distinct transcriptional trajectories as aging progresses. This is an intriguing possibility. However, pseudotime analyses need to be validated via additional bioinformatics as well as follow-up experiments. Indeed, Heumos et al, in their Nature Genetics "Expert Guidelines" Review, emphasize that "inferred trajectories might not necessarily have biological meaning." They recommend that "when the expected topology is unknown, trajectories and downstream hypotheses should be confirmed by multiple trajectory inference methods using different underlying assumptions."(15) Numerous algorithms are available for trajectory inference (e.g. Monocle, PAGA, Sligshot, RaceID/StemID, among many others) and their performance and suitability depends on the individual dataset and nature of the trajectories that are to be inferred. It is recommended to use dynGuidelines(16) for the selection of optimal pseudotime analysis methods. In the present manuscript, the authors do not provide any justification for their use of Monocle 3 over other trajectory inference approaches, nor do they employ a secondary trajectory inference method to confirm observations made with Monocle 3. Finally, follow-up validation experiments that the authors carry out have their own limitations and caveats (see below). Hence, while the microglial aging trajectories identified by this study are intriguing, they remain hypothetical trajectories that need to be proven with additional follow-up experiments.

To follow up on the idea that TGFb1 signaling in microglia plays a key role in determining microglial aging trajectories, the authors use RNAscope to show that TGFb1 levels in microglia peak in middle age. They also treat primary LPS-activated microglia with TGFb1 and show that this restores expression of microglial homeostatic gene expression and dampens expression of stress response and, potentially, inflammatory genes. Finally, they utilize transgenic approaches to delete TGFb1 from microglia around 8-10mo of age and scRNAseq to show that homeostatic signatures are lost and inflammatory signatures are gained. Hence, findings in this study support the idea that TGFb1 can strongly regulate microglial phenotype. Loss of TGFb1 signaling to microglia in adulthood has already been shown to cause decreased microglial morphological complexity and upregulation of genes typically associated with microglial responses to CNS insults(17-19). TGFb1 signaling to microglia has also been implicated in microglial responses to disease and manipulations to increase this signaling can improve disease progression in some cases(19). In this light, the findings in the present study are largely confirmatory of previous findings in the literature. They also fall short of unequivocally demonstrating that TGFb1 signaling acts as a "checkpoint" for determining subsequent microglial aging trajectory. To show this clearly, one would need to perturb TGFb1 signaling around 12mo of age and carry out sequencing (bulkRNAseq or scRNAseq) of microglia at 18mo and 24mo. Such experiments could directly demonstrate whether the whole microglial population has been diverted to the TGFb1-low aging trajectory (that progresses through a translational burst state to an inflammation state as proposed). Future development of tools to tag TGFb1 high or low microglia could also enable fate tracing type experiments to directly show whether the TGFb1 state in middle age predicts cell state at later phases of aging.

The present study would also like to draw links between features of microglial aging in the hippocampus and a decline in hippocampal-dependent cognition during aging. To this end, they carry out behavioral testing in 8-10mo old mice that have undergone microglial-specific TGFb1 deletion and find deficits in novel object recognition and contextual fear conditioning. While this provides compelling evidence that TGFb1 signaling in microglia can impact hippocampus-dependent cognition in midlife, it does not demonstrate that this signaling accelerates or modulates cognitive decline (see below). Age-associated cognitive decline refers to cognitive deficits that emerge as a result of the normative brain aging process(20-21). For a cognitive deficit to be considered age-associated cognitive decline, it must be shown that the cognitive operation under study was intact at some point earlier in the adult lifespan. This requires longitudinal study designs that determine whether a manipulation impacts the relationship between brain status and cognition as animals age (22-24). Alternatively, cross-sectional studies with adequate sample sizes can be used to sample the variability in cognitive outcomes at different points of the adult lifespan(22-24) and show that this is altered by a particular manipulation. For this specific study, one would ideally demonstrate that hippocampal-based learning/memory was intact at some point in the lifespan of mice with microglial TGFb1 KO but that this manipulation accelerated or exacerbated the emergence of deficits in hippocampal-dependent learning/memory during aging. In the absence of these types of data, the authors should tone down their claims that they have identified a cellular and molecular mechanism that contributes to cognitive decline.

A final point of clarification for the reader pertains to the mining of previously generated data sets within this study. The language in the results section, methods, and figure legends causes confusion about which experiments were actually carried out in this study versus previous studies. Some of the language makes it sound as though parabiosis experiments and experiments using mouse models of Alzheimer's Disease were carried out in this study. However, parabiosis and AD mouse model experiments were executed in previous studies (25,26), and in the present study, RNAseq datasets were accessed for targeted data mining. It is fantastic to see further mining of datasets that already exist in the field. However, descriptions in the results and methods sections need to make it crystal clear that this is what was done.

References:

(1) Grabert, K. et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat. Neurosci. (2016). doi:10.1038/nn.4222<br /> (2) Hickman, S. E. et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. (2013). doi:10.1038/nn.3554<br /> (3) Deczkowska, A. et al. Mef2C restrains microglial inflammatory response and is lost in brain ageing in an IFN-I-dependent manner. Nat. Commun. (2017). doi:10.1038/s41467-017-00769-0<br /> (4) O'Neil, S. M., Witcher, K. G., McKim, D. B. & Godbout, J. P. Forced turnover of aged microglia induces an intermediate phenotype but does not rebalance CNS environmental cues driving priming to immune challenge. Acta Neuropathol. Commun. (2018). doi:10.1186/s40478-018-0636-8<br /> (5) Olah, M. et al. A transcriptomic atlas of aged human microglia. Nat. Commun. (2018). doi:10.1038/s41467-018-02926-5<br /> (6) Hammond, T. R. et al. Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes. Immunity 50, 253-271 (2019).<br /> (7) Li, X. et al. Transcriptional and epigenetic decoding of the microglial aging process. Nat. aging 3, 1288-1311 (2023).<br /> (8) Zhang, H. et al. Single-nucleus transcriptomic landscape of primate hippocampal aging. Protein Cell 12, 695-716 (2021).<br /> (9) Su, Y. et al. A single-cell transcriptome atlas of glial diversity in the human hippocampus across the postnatal lifespan. Cell Stem Cell 29, 1594-1610.e8 (2022).<br /> (10) Allen, W. E., Blosser, T. R., Sullivan, Z. A., Dulac, C. & Zhuang, X. Molecular and spatial signatures of mouse brain aging at single-cell resolution. Cell 186, 194-208.e18 (2023).<br /> (11) Soreq, L. et al. Major Shifts in Glial Regional Identity Are a Transcriptional Hallmark of Human Brain Aging. Cell Rep. 18, 557-570 (2017).<br /> (12) Hefendehl, J. K. et al. Homeostatic and injury-induced microglia behavior in the aging brain. Aging Cell (2014). doi:10.1111/acel.12149<br /> (13) Nikodemova, M. et al. Microglial numbers attain adult levels after undergoing a rapid decrease in cell number in the third postnatal week. J. Neuroimmunol. 0, 280-288 (2015).<br /> (14) Moca, E. N. et al. Microglia Drive Pockets of Neuroinflammation in Middle Age. J. Neurosci. 42, 3896-3918 (2022).<br /> (15) Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550-572 (2023).<br /> (16) Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods: towards more accurate and robust tools. (2018). doi:10.1101/276907<br /> (17) Zöller, T. et al. Silencing of TGFβ signalling in microglia results in impaired homeostasis. Nat. Commun. 9, (2018).<br /> (18) Bedolla, A. et al. Microglia-derived TGF-β1 ligand maintains microglia homeostasis via autocrine mechanism and is critical for normal cognitive function in adult mouse brain. bioRxiv Prepr. Serv. Biol. (2023). doi:10.1101/2023.07.05.547814<br /> (19) Spittau, B., Dokalis, N. & Prinz, M. The Role of TGFβ Signaling in Microglia Maturation and Activation. Trends Immunol. 41, 836-848 (2020).<br /> (20) L. Nyberg, M. Lövdén, K. Riklund, U. Lindenberger, L. Bäckman, Memory aging and brain maintenance. Trends Cogn. Sci. 16, 292-305 (2012).<br /> (21) L. Luo, F. I. M. Craik, Aging and memory: A cognitive approach. Can. J. Psychiatry 53, 346-353 (2008).<br /> (22) Y. Stern, M. Albert, C. Barnes, R. Cabeza, A. Pascual-Leone, P. Rapp.<br /> A framework for concepts of reserve and resilience in aging. Neurobiol. Aging, 124 (2022), pp. 100-103, 10.1016/j.neurobiolaging.2022.10.015<br /> (23) Y. Stern, C.A. Barnes, C. Grady, R.N. Jones, N. Raz. Brain reserve, cognitive reserve, compensation, and maintenance: operationalization, validity, and mechanisms of cognitive resilience. Neurobiol. Aging, 83 (2019), pp. 124-129, 10.1016/j.neurobiolaging.2019.03.022<br /> (24) R. Cabeza, M. Albert, S. Belleville, F.I.M. Craik, A. Duarte, C.L. Grady, U. Lindenberger, L. Nyberg, D.C. Park, P.A. Reuter-Lorenz, M.D. Rugg, J. Steffener, M.N. Rajah. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat. Rev. Neurosci., 19 (11) (2018), Article 11, 10.1038/s41583-018-0068-2<br /> (25) Palovics, R. et al molecular hallmarks of heterochronic parabiosis at single-cell resolution. Nature 603, 309-314 (2022)<br /> (26) Sala Frigerio, C. et al. The major risk factors for Alzheimer's Disease: age, sex, and genes modulate the microglial response to Abeta plaques. Cell Rep, 27, 1293-1306 (2019)

Reviewer #2 (Public Review):

Summary:

The goal of the paper was to trace the transitions hippocampal microglia undergo along aging. ScRNA-seq analysis allowed the authors to predict a trajectory and hypothesize about possible molecular checkpoints, which keep the pace of microglial aging. E.g. TGF1b was predicted as a molecule slowing down the microglial aging path and indeed, loss of TGF1 in microglia led to premature microglia aging, which was associated with premature loss of cognitive ability. The authors also used the parabiosis model to show how peripheral, blood-derived signals from the old organism can "push" microglia forward on the aging path.

Strengths:

A major strength and uniqueness of this work is the in-depth single-cell dataset, which may be a useful resource for the community, as well as the data showing what happens to young microglia in heterochronic parabiosis setting and upon loss of TGFb in their environment.

Weaknesses:

That said, given what we recently learned about microglia isolation for RNA-seq analysis, there is a danger that some of the observations are a result of not age, but cell stress from sample preparation (enzymatic digestion 10min at 37C; e.g. PMID: 35260865). Changes in cell state distribution along aging were made based on scRNA-seq and were not corroborated by any other method, such as imaging of cluster-specific marker expression in microglia at different ages. This analysis would allow confirming the scRNA-seq data and would also give us an idea of where the subsets are present within the hippocampus, and whether there is any interesting distribution of cell states (e.g. some are present closer to stem cells?). Since TGFb is thought to be crucial to microglia biology, it would be valuable to include more analysis of the mice with microglia-specific Tgfb deletion e.g. what was the efficiency of recombination in microglia? Did their numbers change after induction of Tgfb deletion in Cx3cr1-creERT2::Tgfb-flox mice.

Overall:

In general, I think the authors did a good job following the initial observations and devised clever ways to test the emerging hypotheses. The resulting data are an important addition to what we know about microglial aging and can be fruitfully used by other researchers, e.g. those working on microglia in a disease context.

eLife assessment

This valuable study showing that sleep deprivation increases functional synapses while depleting silent synapses supports previous findings that excitatory signaling, in particular via AMPA receptors, increases during wakefulness. The consistency with the literature increases confidence in the conclusions, which otherwise are supported by incomplete evidence. An interesting aspect of this manuscript is the inclusion of a model for the accumulation of sleep need that is based upon the MEF2C transcription factor but also links to the sleep-regulating SIK3-HDAC4/5 pathway. As such, the manuscript is as much of a perspective as a primary research paper.

Reviewer #1 (Public Review):

Summary:

This manuscript by Vogt et al examines how the synaptic composition of AMPA and NMDA receptors changes over sleep and wake states. The authors perform whole-cell patch clamp recordings to quantify changes in silent synapse numbers across conditions of spontaneous sleep, sleep deprivation, and recovery sleep after deprivation. They also perform single nucleus RNAseq to identify transcriptional changes related to AMPA/NMDA receptor composition following spontaneous sleep and sleep deprivation. The findings of this study are consistent with a decrease in silent synapse number during wakefulness and an increase during sleep. However, these changes cannot be conclusively linked to sleep/wake states. Measurements were performed in the motor cortex, and sleep deprivation was achieved by forced locomotion, raising the possibility that recent levels of neuronal activity/induction of plasticity, rather than sleep/wake states, are responsible for the observed results.

Strengths:

This study examines an important question. Glutamatergic synaptic transmission has been a focus of studies in the sleep field, but AMPA receptor function has been the primary target of these studies. Silent synapses, which contain NMDA receptors but lack AMPA receptors, have important functional consequences for the brain. Exploring the role of sleep in regulating silent synapse numbers is important to understanding the role of sleep in brain function. The electrophysiological approach of measuring the failure rate ratio, supported by AMPA/NMDA ratio measurements, is a rigorous tool to evaluate silent synapse numbers.

The authors also perform snRNAseq to identify genes differentially expressed in the spontaneous sleep and sleep deprivation groups. This analysis reveals an intriguing pattern of upregulated genes controlled by HDAC4 and Mef2c, along with synaptic shaping component genes and genes associated with autism spectrum disorder, across cell types in the sleep deprivation group. This unbiased approach identifies candidate genes for follow-up studies.

Weaknesses:

A major weakness of this study is the experimental design. Measurements are made from the motor cortex, and sleep deprivation was achieved using forced locomotion on a treadmill. Therefore, the effects observed could be due to recent high levels of activity or plasticity induction in the motor cortex from locomotion, rather than lack of sleep per se. In support of this interpretation, other groups have failed to find a difference in AMPA/NMDA ratio in mice with different spontaneous sleep/wake histories, although sleep deprivation was not performed (Bridi et al., Neuron 2020).

The electrophysiological measurements are problematic in several ways. First, the methods lack crucial details such as inclusion/exclusion criteria for each cell based on input and series resistance, stability of input/series resistance, polysynaptic responses, etc. that make it difficult to interpret the data. The holding potential (-90mV) used for AMPA receptor current recordings is much more hyperpolarized than typically used for these measurements. The statistical analysis of these experiments is also problematic. The number of mice used is low (3/group) and more should be added to account for inter-animal variability. Comparing the raw data with the statistical tests in supplementary table 1 (FR ratio), it appears that a data point has been dropped from the analysis, but it is unclear why. In addition, a false discovery rate (FDR) correction for multiple comparisons is used to evaluate group differences following the ANOVAs. Correcting for the FDR is less stringent and is typically used when a large number of hypotheses are tested and false positives are more acceptable. In this analysis, few comparisons are made, and the standard approach of correcting for the family-wise error rate is more appropriate.

The snRNAseq data are intriguing, but a more thorough discussion of the candidate genes and pathways that are upregulated during sleep deprivation is warranted. Several genes relevant to the AMPA/NMDA ratio are mentioned, but upregulation of most of these genes would not be expected to increase the AMPA/NMDA ratio based on the literature cited. The model presented in Figure 4C is not consistent with the data (e.g. many candidate genes could alter NMDAR function without receptor insertion/removal), and it is unclear how the current study fits into the model presented in 4D.

Reviewer #2 (Public Review):

Summary:

Here Vogt et al., provide new insights into the need for sleep and the molecular and physiological response to sleep loss. The authors expand on their previously published work (Bjorness et al., 2020) and draw from recent advances in the field to propose a neuron-centric molecular model for the accumulation and resolution of sleep need and the basis of restorative sleep function. While speculative, the proposed model successfully links important observations in the field and provides a framework to stimulate further research and advances on the molecular basis of sleep function. In my review, I highlight the important advances of this current work, and the clear merits of the proposed model, and indicate areas of the model that can serve to stimulate further investigation.

Strengths:

Reviewer comment on new data in Vogt et al., 2024<br /> Using classic slice electrophysiology, the authors conclude that wakefulness (sleep deprivation (SD)) drives a potentiation of excitatory glutamate synapses, mediated in large part by "un-silencing" of NMDAR-active synapses to AMPAR-active synapses. Using a modern single nuclear RNAseq approach the authors conclude that SD drives changes in gene expression primarily occurring in glutamatergic neurons. The two experiments combined highlight the accumulation and resolution of sleep need centered on the strength of excitatory synapses onto excitatory neurons. This view is entirely consistent with a large body of extant and emerging literature and provides important direction for future research.

Consistent with prior work, wakefulness/SD drives an LTP-type potentiation of excitatory synaptic strength on principle cortical neurons. It has been proposed that LTP associated with wake, leads to the accumulation of sleep need by increasing neuronal excitability, and by the "saturation" of LTP capacity. This saturation subsequently impairs the capacity for further ongoing learning. This new data provides a satisfying mechanism of this saturation phenomenon by introducing the concept of silent synapses. The new data show that in mice well rested, a substantial number of synapses are "silent", containing an NMDAR component but not AMPARs. Silent synapses provide a type of reservoir for learning in that activity can drive the un-silencing, increasing the number of functional synapses. SD depletes this reservoir of silent synapses to essentially zero, explaining how SD can exhaust learning capacity. Recovery sleep led to restoration of silent synapses, explaining how recovery sleep can renew learning capacity. In their prior work (Bjorness et al., 2020) this group showed that SD drives an increase in mEPSC frequency onto these same cortical neurons, but without a clear change in pre-synaptic release probability, implying a change in the number of functional synapses. This prediction is now born out in this new dataset.

The new snRNAseq dataset indicates the sleep need is primarily seen (at the transcriptional level) in excitatory neurons, consistent with a number of other studies. First, this conclusion is corroborated by an independent, contemporary snRNAseq analysis recently available as a pre-print (Ford et al., 2023 BioRxiv https://doi.org/10.1101/2023.11.28.569011). A recently published analysis on the effects of SD in drosophila imaged synapses in every brain region in a cell-type dependent manner (Weiss et al., PNAS 2024), concluding that SD drives brain wide increases in synaptic strength almost exclusively in excitatory neurons. Further, Kim et al., Nature 2022, heavily cited in this work, show that the newly described SIK3-HDAC4/5 pathway promotes sleep depth via excitatory neurons and not inhibitory neurons.

The new experiments provided in Fig1-3 are expertly conducted and presented. This reviewer has no comments of concern regarding the execution and conclusions of these experiments.

Reviewer comment on the model in Vogt et al., 2024

In the view of this reviewer the new model proposed by Vogt et al., is an important contribution. The model is not definitively supported by new data, and in this regard should be viewed as a perspective, providing mechanistic links between recent molecular advances, while still leaving areas that need to be addressed in future work. New snRNAseq analysis indicates that SD drives the expression of synaptic shaping components (SSCs) consistent with the excitatory synapse as a major target for the restorative basis of sleep function. SD-induced gene expression is also enriched for autism spectrum disorder (ASD) risk genes. As pointed out by the authors, sleep problems are commonly reported in ASD, but the emphasis has been on sleep amount. This new analysis highlights the need to understand the impact on sleep's functional output (synapses) to fully understand the role of sleep problems in ASD.

Importantly, SD-induced gene expression in excitatory neurons overlaps with genes regulated by the transcription factor MEF2C and HDAC4/5 (Figure 4). In their prior work, the authors show loss of MEF2C in excitatory neurons abolished the SD transcriptional response and the functional recovery of synapses from SD by recovery sleep. Recent advances identified HDAC4/5 as major regulators of sleep depth and duration (in excitatory neurons) downstream of the recently identified sleep-promoting kinase SIK3. In Zhou et al., and Kim et al., Nature 2022, both groups propose a model whereby "sleep-need" signals from the synapse activate SIK3, which phosphorylates HDAC4/5, driving cytoplasmic targeting, allowing for the de-repression and transcriptional activation of "sleep genes". Prior work shows that HDAC4/5 are repressors of MEF2C. Therefore, the "sleep genes" derepressed by HDAC4/5 may be the same genes activated in response to SD by MEF2C. The new model thereby extends the signaling of sleep need at synapses (through SIK3-HDAC4/5) to the functional output of synaptic recovery by expression of synaptic/sleep genes by MEF2C. The model thereby links aspects of the expression of sleep need with the resolution of sleep need by mediating sleep function: synapse renormalization.

Weaknesses:

Areas for further investigation

In the discussion section Vogt et al., explore the links between excitatory synapse strength, arguably the major target of "sleep function", and NREM slow-wave activity (SWA), the most established marker of sleep need. SIK3-HDAC4/5 have major effects on the "depth" of sleep by regulating NREM-SWA. The effects of MEF2C loss of function on NREM SWA activity are less obvious, but clearly impact the recovery of glutamatergic synapses from SD. The authors point out how adenosine signaling is well established as a mediator of SWA, but the links between adenosine and glutamatergic strength are far from clear. The mechanistic links between SIK3/HDAC4/5, adenosine signaling, and MEF2C, are far from understood. Therefore, the molecular/mechanistic links between a synaptic basis of sleep need and resolution with NREM-SWA activity require further investigation.

Additional work is also needed to understand the mechanistic links between SIK3-HDAC4/5 signaling and MEF2C activity. The authors point out that constitutively nuclear (cn) HDAC4/5 (acting as a repressor) will mimic MEF2C loss of function. This is reasonable, however, there are notable differences in the reported phenotypes of each. Notably, cnHDAC4/5 suppresses NREM amount and NREM SWA but had no effect on the NREM-SWA increase following SD (Zhou et al., Nature 2022). Loss of MEF2C in CaMKII neurons had no effect on NREM amount and suppressed the increase in NREM-SWA following SD (Bjorness et al., 2020). These instances indicate that cnHDAC4/5 and loss of MEF2C do not exactly match suggesting additional factors are relevant in these phenotypes. Likely HDAC4/5 have functionally important interactions with other transcription factors, and likewise for MEF2C, suggesting areas for future analysis.

One emerging theme may be that the SIK3-HDAC4/5 axis is a major regulator of the sleep state, perhaps stabilizing the NREM state once the transition from wakefulness occurs. MEF2C is less involved in regulating sleep per se, and more involved in executing sleep function, by promoting restorative synaptic modifications to resolve sleep need.

Finally, advances in the roles of the respective SIK3-HDAC4/5 and MEF2C pathways point towards transcription of "sleep genes", as clearly indicated in the model of Figure 4. Clearly, more work is needed to understand how the expression of such genes ultimately leads to the resolution of sleep need by functional changes at synapses. What are these sleep genes and how do they mechanistically resolve sleep need? Thus, the current work provides a mechanistic framework to stimulate further advances in understanding the molecular basis for sleep need and the restorative basis of sleep function.

eLife assessment

The important study by Ding and colleagues identifies subpopulations of neurons recorded in the monkey subthalamic nucleus (STN) with distinct activity profiles and causal contributions during perceptual decision-making. The combination of neuronal recording, microstimulation, and computational methods provides convincing evidence for a heterogenous neural population that could support multifaceted roles in decision formation. This study should be of wide interest to computational and experimental neuroscientists interested in cognitive function.

Reviewer #1 (Public Review):

The study reports that STN neurons recorded while monkeys performed a random-dot motion task show diverse activation timecourses relative to task events and dependencies on coherence, reaction time, and saccade-choice direction. Different neuron types could be grouped into functional subpopulations, e.g., coherence sensitivity emerging early only in choice-coding neurons. Clustering techniques identified three functionally defined neuron clusters whose dynamic activity profiles related to computational predictions of different decision models in the literature. Microstimulation at different STN recording sites affected behavioral performance in varying but well-conceptualized ways that were captured by the parameters of drift-diffusion models and related to the presence of STN functional clusters at recording sites. The authors conclude that their results validate key aspects of decision models and identify novel aspects of decision-related STN activity.

This is an interesting and high-quality paper that will be of interest across computational and decision neuroscience fields. The recordings and data analyses seem carefully conducted. The study has an attractive theoretical starting point of three specific computational signals that are then mapped onto identified neuron clusters. The combination of single-cell recordings, microstimulation, and computational modelling is a distinct strength of the paper. I only have a few questions and suggestions for clarification.

(1) It would be helpful to explain the criteria for choosing a given number of clusters and for accepting the final clustering solution more clearly. The quantitative results (silhouette plots, Rand index) in Supplementary Figure 2 should perhaps be included in the main figure to justify the parameter choices and acceptance of specific clustering solutions.

(2) It would be helpful to show how the activity profiles in Figure 3 would look like for 3 or 5 (or 6) clusters, to give the reader an impression of how activity profiles recovered using different numbers of clusters would differ.

(3) The authors attempt to link the microstimulation effects to the presence of functional neuron clusters at the stimulation site. How can you rule out that there were other, session-specific factors (e.g., related to the animal's motivation) that affected both neuronal activity and behavior? For example, could you incorporate aspects of the monkey's baseline performance (mean reaction time, fixation breaks, error trials) into the analysis?

(4) Line 84: What was the rationale for not including both coherence and reaction time in one multiple regression model?

Reviewer #2 (Public Review):

This study uses single-unit recordings in the monkey STN to examine the evidence for three theoretical models that propose distinct roles for the STN in perceptual decision-making. Importantly, the proposed functional roles are predictive of unique patterns of neural activity. Using k-means clustering with seeds informed by each model's predictions, the current study identified three neural clusters with activity dynamics that resembled those predicted by the described theoretical models. The authors are thorough and transparent in reporting the analyses used to validate the clustering procedure and the stability of the clustering results. To further establish a causal role for the STN in decision-making, the researchers applied microstimulation to the STN and found effects on response times, choice preferences, and latent decision parameters estimated with a drift diffusion model. Overall, the study provides strong evidence for a functionally diverse population of STN neurons that could indeed support multiple roles involved in perceptual decision-making. The manuscript would benefit from stronger evidence linking each neural cluster to specific decision roles in order to strengthen the overall conclusions.

The interpretation of the results, and specifically, the degree to which the identified clusters support each model, is largely dependent on whether the artificial vectors used as model-based clustering seeds adequately capture the expected behavior under each theoretical model. The manuscript would benefit from providing further justification for the specific model predictions summarized in Figure 1B. Further, although each cluster's activity can be described in the context of the discussed models, these same neural dynamics could also reflect other processes not specific to the models. That is, while a model attributing the STN's role to assessing evidence accumulation may predict a ramping up of neural activity, activity ramping is not a selective correlate of evidence accumulation and could be indicative of a number of processes, e.g., uncertainty, the passage of time, etc. This lack of specificity makes it challenging to infer the functional relevance of cluster activity and should be acknowledged in the discussion.

Additionally, although the effects of STN microstimulation on behavior provide important causal evidence linking the STN to decision processes, the stimulation results are highly variable and difficult to interpret. The authors provide a reasonable explanation for the variability, showing that neurons from unique clusters are anatomically intermingled such that stimulation likely affects neurons across several clusters. It is worth noting, however, that a substantial body of literature suggests that neural populations in the STN are topographically organized in a manner that is crucial for its role in action selection, providing "channels" that guide action execution. The authors should comment on how the current results, indicative of little anatomical clustering amongst the functional clusters, relate to other reports showing topographical organization.

Overall, the association between the identified clusters and the function ascribed to the STN by each of the models is largely descriptive and should be interpreted accordingly. For example, Figure 3 is referenced when describing which cluster activity is choice/coherence dependent, yet it is unclear what specific criteria and measures are being used to determine whether activity is choice/coherence "dependent." Visually, coherence activity seems to largely overlap in panel B (top row). Is there a statistically significant distinction between low and high coherence in this plot? The interpretation of these plots and the methods used to determine choice/coherence "dependence" needs further explanation.

In general, the association between cluster activity and each model could be more directly tested. At least two of the models assume coordination with other brain regions. Does the current dataset include recordings from any of these regions (e.g., mPFC or GPe) that could be used to bolster claims about the functional relevance of specific subpopulations? For example, one would expect coordinated activity between neural activity in mPFC and Cluster 2 according to the Ratcliff and Frank model. Additionally, the reported drift-diffusion model (DDM) results are difficult to interpret as microstimulation appears to have broad and varied effects across almost all the DDM model parameters. The DDM framework could, however, be used to more specifically test the relationships between each neural cluster and specific decision functions described in each model. Several studies have successfully shown that neural activity tracks specific latent decision parameters estimated by the DDM by including neural activity as a predictor in the model. Using this approach, the current study could examine whether each cluster's activity is predictive of specific decision parameters (e.g., evidence accumulation, decision thresholds, etc.). For example, according to the Ratcliff and Frank model, activity in cluster 2 might track decision thresholds.

Reviewer #3 (Public Review):

Summary:

The authors provide compelling evidence for the causal role of the subthalamic nucleus (STN) in perceptual decision-making. By recording from a large number of STN neurons and using microstimulation, they demonstrate the STN's involvement in setting decision bounds, scaling evidence accumulation, and modulating non-decision time.

Strengths:

The study tested three hypotheses about the STN's function and identified distinct STN subpopulations whose activity patterns support predictions from previous computational models. The experiments are well-designed, the analyses are rigorous, and the results significantly advance our understanding of the STN's multi-faceted role in decision formation.

Weaknesses:

While the study provides valuable insights into the STN's role in decision-making, there are a few areas that could be improved. First, the interpretation of the neural subpopulations' activity patterns in relation to the computational models should be clarified, as the observed patterns may not directly correspond to the specific signals predicted by the models. Second, the authors could consider using a supervised learning method to more explicitly model the pattern correlations between the three profiles. Third, a neural population model could be employed to better understand how the STN population jointly contributes to decision-making dynamics. Finally, the added value of the microstimulation experiments should be more directly addressed in the Results section, as the changes in firing patterns compared to the original patterns are not clearly evident.

eLife assessment

This important study uses calcium imaging to show an increase in the selectivity of the sensory-evoked response in the apical dendritic tuft of layer 5 barrel cortex neurons as mice learn a whisker-dependent discrimination task. The evidence supporting the conclusions is compelling, and this work will be of great interest to neuroscientists working on reward-based learning and sensory processing.

Reviewer #1 (Public Review):

What neurophysiological changes support the learning of new sensorimotor transformations is a key question in neuroscience. Many studies have attempted to answer this question at the neuronal population level - with varying degrees of success - but few, if any, have studied the change in activity of the apical dendrites of layer 5 cortical neurons. Neurons in layer 5 of the sensory cortex appear to play a key role in sensorimotor transformations, showing important decision and reward-related signals, and being the main source of cortical and subcortical projections from the cortex. In particular, pyramidal track (PT) neurons project directly to subcortical regions related to motor activity, such as the striatum and brainstem, and could initiate rapid motor action in response to given sensory inputs. Additionally, layer 5 cortical neurons have large apical dendrites that extend to layer 1 where different neuromodulatory and long-range inputs converge, providing motor and contextual information that could be used to modulate layer 5 neurons output and/or to establish the synaptic plasticity required for learning a new association.

In this study, the authors aimed to test whether the learning of a new sensorimotor transformation could be supported by a change in the evoked response of the apical dendrites of layer 5 neurons in the mouse whisker primary somatosensory cortex. To do this, they performed longitudinal functional calcium imaging of the apical dendrites of layer 5 neurons while mice learned to discriminate between two multi-whisker stimuli. The authors used a simple conditioning task in which one whisker stimulus (upward or backward air puff, CS+) is associated with a reward after a short delay, while the other whisker stimulus (CS-) is not. They found that task learning (measured by the probability of anticipatory licking just after the CS+) was not associated with a significant change in the average population response evoked by the CS+ or the CS-, nor a change in the average population selectivity. However, when considering individual dendritic tufts, they found interesting changes in selectivity, with approximately equal numbers of dendrites becoming more selective for CS+ and dendrites becoming more selective for CS-.

One of the major challenges when assessing changes in neural representation during the learning of such Go/NoGo tasks is that the movements and rewards themselves may elicit strong neural responses that may be a confounding factor, that is, inexperienced mice do not lick in response to the CS+, while trained mice do. In this study, the authors addressed this issue in three ways: first, they carefully monitored the orofacial movements of mice and showed that task learning is not associated with changes in evoked whisker movements. Second, they show that whisking or licking evokes very little activity in the dendritic tufts compared to whisker stimuli (CS+ and CS-). Finally, the authors introduced into the design of their task a post-conditioning session after the last conditioning session during which the CS+ and the CS- are presented but no reward is delivered. During this post-session, the mice gradually stopped licking in response to the CS+. A better design might have been to perform the pre-conditioning and post-conditioning sessions in non-water-restricted, unmotivated mice to completely exclude any lick response, but the fact that the change in selectivity persists after the mice stopped licking in the last blocks of the post-conditioning session (in mice relying only on their whiskers to perform the task) is convincing.

The clever task design and careful data analysis provide compelling evidence that learning this whisker discrimination task does not result in a massive change in sensory representation in the apical dendritic tufts of layer 5 neurons in the primary somatosensory cortex on average. Nevertheless, individual dendritic tufts do increase their selectivity for one or the other sensory stimulus, likely enhancing the ability of S1 neurons to accurately discriminate the two stimuli and trigger the appropriate motor response (to lick or not to lick).

One limitation of the present study is the lack of evidence for the necessity of the primary somatosensory cortex in the learning and execution of the task. As the authors have strongly emphasized in their previous publications, the primary somatosensory cortex may not be necessary for the learning and execution of simple whisker detection tasks, especially when the stimulus is very salient. Although this new task requires the discrimination between two whisker stimuli, the simplicity and salience of the whisker stimuli used could make this task cortex-independent. Especially when considering that some mice seem to not rely entirely on their whiskers to execute the task.

Nevertheless, this is an important result that shows for the first time changes in the selectivity to sensory stimuli at the level of individual apical dendritic tufts in correlation with the learning of a discrimination task. This study sheds new light on the cortical cellular substrates of reward-based learning and opens interesting perspectives for future research in this area. In future studies, it will be important to determine whether the change in selectivity of dendritic calcium spikes is causally involved in the learning of the task or whether it simply correlates with learning, as a consequence of changes in synaptic inputs caused by reward. The dendritic calcium spikes may be involved in the establishment of synaptic plasticity required for learning and impact the output of layer 5 pyramidal neurons to trigger the appropriate motor response. It would be important also to study the changes in selectivity in the apical dendrite of the identified projection neurons.

Reviewer #2 (Public Review):

Summary:

The authors did not find an increased representation of CS+ throughout reinforcement learning in the tuft dendrites of Rbp4-positive neurons from layer 5B of the barrel cortex, as previously reported for soma from layer 2/3 of the visual cortex.

Alternatively, the authors observed an increased selectivity to both stimuli (CS+ and CS-) during reinforcement learning. This feature:

(1) was not present in repeated exposures (without reinforcement),<br /> (2) was not explained by the animal's behaviour (choice, licking, and whisking), and<br /> (3) was long-lasting, being present even when the mice disengaged from the task.

Importantly, increased selectivity was correlated with learning (% correct choices), and neural discriminability between stimuli increased with learning.

In conclusion, the authors show that tuft dendrites from layer 5B of the barrel cortex increase the representation of conditioned (CS+) and unconditioned stimuli (CS-) applied to the whiskers, during reinforcement learning.

Strengths:

The results presented are very consistent throughout the entire study, and therefore very convincing:

(1) The results observed are very similar using two different imaging techniques (2-photon -planar imaging- and SCAPE-volumetric imaging). Figure 3 and Figure 4 respectively.

(2) The results are similar using "different groups" of tuft dendrites for the analysis (e.g. initially unresponsive and responsive pre- and post-learning). Figure 5.

(3) The results are similar from a specific set of trials (with the same sensory input, but different choices). Figure 7.

(4) Additionally, the selectivity of tuft dendrites from layer 5B of the barrel cortex was higher in the mice that exclusively used the whisker to respond to the stimuli (CS+ and CS-).<br /> The results presented are controlled against a group of mice that received the same stimuli presentation, except for the reinforcement (reward).

Additionally, the behaviour outputs, such as choice, whisking, and licking could not account for the results observed.

Although there are no causal experiments, the correlation between selectivity and learning (percentage of correct choices), as well as the increased neural discriminability with learning, but not in repeated exposure, are very convincing.

Weaknesses:

The biggest weakness is the absence of causality experiments. Although inhibiting specifically tuft dendritic activity in layer 1 from layer 5 pyramidal neurons is very challenging, tuft dendritic activity in layer 1 could be silenced through optogenetic experiments as in Abs et al. 2018. By manipulating NDNF-positive neurons the authors could specifically modify tuft dendritic activity in the barrel cortex during CS presentations, and test if silencing tuft dendritic activity in layer 1 would lead to the lack of selectivity and an impairment of reinforcement learning. Additionally, this experiment will test if the selectivity observed during reinforcement learning is due to changes in the local network, namely changes in local synaptic connectivity, or solely due to changes in the long-range inputs.

eLife assessment:

This study reported that cold exposure induced mRNA expression of genes related to lipid metabolism in the paraventricular nucleus of the hypothalamus (PVH). The authors provide useful data highlighting the potential role of lipid metabolism in the brain during cold exposure. However, the study is incomplete and would require specific experiments to solidify the claims being made.

Reviewer #1 (Public Review):

Summary:

This study focuses on metabolic changes in the paraventricular hypothalamic (PVH) region of the brain during acute periods of cold exposure. The authors point out that in comparison to the extensive literature on the effects of cold exposure in peripheral tissues, we know relatively little about its effects on the brain. They specifically focus on the hypothalamus, and identify the PVH as having changes in Atgl and Hsl gene expression changes during cold exposure. They then go on to show accumulation of lipid droplets, increased Fos expression, and increased lipid peroxidation during cold exposure. Further, they show that neuronal activation is required for the formation of lipid droplets and lipid peroxidation.

Strengths:

A strength of the study is trying to better understand how metabolism in the brain is a dynamic process, much like how it has been viewed in other organs. The authors also use a creative approach to measuring in vivo lipid peroxidation via delivery of a BD-C11 sensor through a cannula to the region in conjunction with fiber photometry to measure fluorescence changes deep in the brain.

Weaknesses:

Although the topic and findings are of interest, there are a few key weaknesses in the study that would improve the work if addressed. One weakness was many of the experiments were done in a manner that could not distinguish between the contributions of neurons and glial cells, limiting the extent of conclusions that could be made. While this is not easily doable for all experiments, it can be done for some. For example, the Fos experiments in Figure 3 would be more conclusive if done with the labeling of neuronal nuclei with NeuN, as glial cells can also express Fos. To similarly show more conclusively that neurons are being activated during cold exposure, the calcium imaging experiments in Figure S3 can be done with cold exposure. Additionally, many experiments are only done with the minimal three animals required for statistics and could be more robust with additional animals included. Another weakness is that the authors do not address whether manipulating lipid droplet accumulation or lipid peroxidation has any effect on PVH function (e.g. does it change neuronal activity in the region?).

Reviewer #2 (Public Review):

Summary:

Cold-induced lipid metabolism is well-established in adipose tissues. The authors set out to determine whether cold could alter brain lipid metabolism. By QPCR analysis of brain punches after acute cold, they found that mRNA expressions of several lipolysis-related genes were upregulated compared to RT controls. By combining fluorescent sensors and in vivo fiberphotometry, they observed cold-induced lipid peroxidation/lipolysis, which could be blocked by pharmacological inhibitors of neuronal activity (muscimol and kynurenic acid). The brain is not traditionally considered an organ with high lipid metabolism (vs carbohydrate); therefore, the observation and hypothesis proposed by the authors are unexpected and can be interesting. However, the experiments and data were rather preliminary and superficial and did not support the authors' conclusions. In addition, the main hypothesis, in relationship to the role of cold/temperature, remains incoherent and needs a major update.

Strengths:

A set of relatively novel and interesting observations.

Creative use of several in vivo sensors and techniques.

Weaknesses:

(1) The physiological relevance of lipolysis and thermogenesis genes in the PVH. The authors need to provide quantitative and substantial characterizations of lipid metabolism in the brain beyond a panel of qPCRs, especially considering these genes are likely expressed at very low levels. mRNA and protein level quantification of genes in Fig 1, in direct comparison to BAT/iWAT, should be provided. Besides bulk mRNA/protein, IHC/ISH-based characterization should be added to confirm to cellular expression of these genes.

(2) The fiberphotometry work they cited (Chen 2022, Andersen 2023, Sun 2018) used well-established, genetically encoded neuropeptide sensors (e.g., GRABs). The authors need to first quantitatively demonstrate that adapting BD-C11 and EnzCheck for in vivo brain FP could effectively and accurately report peroxidation and lipolysis. For example, the sensitivity, dynamic range, and off-time should all be calibrated with mass spectrometry measurements before any conclusions can be made based on plots in Figures 4, 5, and 6. This is particularly important because the main hypothesis heavily relies on this unvalidated technique.

(3) Generally, the histology data need significant improvement. It was not convincing, for example, in Figure 3, how the Fos+ neurons can be quantified based on the poor IF images where most red signals were not in the neurons.

(4) The hypothesis regarding the direct role of brain temperature in cold-induced lipid metabolism is puzzling. From the introduction and discussion, the authors seem to suggest that there are direct brain temperature changes in responses to cold, which could be quite striking. However, this was not supported by any data or experiments. The authors should consolidate their ideas and update a coherent hypothesis based on the actual data presented in the manuscript.

eLife assessment

This is an important study to reveal local circuit mechanisms in the POA that control body temperature and also highlight how neurotransmitter GABA and neuropeptide NTS from the same neurons differentially modulate temperature. This study was carefully executed, providing convincing evidence for the conclusions in this paper. The findings have emphasized the importance of considering multiple diverse functions of the same neuron populations and will be of interest to neuroscientists working on central regulations of energy metabolism and temperature homeostasis.

Reviewer #2 (Public Review):

Summary:

The study has demonstrated how two neurotransmitters and neuromodulators from the same neurons can be regulated and utilized in thermoregulation.

The study utilized electrophysiological methods to examine the characteristics and thermoregulation of Neurotensin (Nts)-expressing neurons in the medial preoptic area (MPO). It was discovered that GABA and Nts may be co-released by neurons in MPO when communicating with their target neurons.

Strengths:

The study has leveraged optogenetic, chemogenetic, knockout, and pharmacological inhibitors to investigate the release process of Nts and GABA in controlling body temperature.

The findings are relevant to those interested in the various functions of specific neuron populations and their distinct regulatory mechanisms on neurotransmitter/neuromodulator activities

Weaknesses:

Key points for consideration include:

(1) The co-release of GABA and Nts is primarily inferred rather than directly proven. Providing more direct evidence for the release of GABA and the co-release of GABA and Nts would strengthen the argument. Further in vitro analysis could strengthen the conclusion regarding this co-releasing process.

(2) The differences between optogenetic and chemogenetic methods were not thoroughly investigated. A comparison of in vitro results and direct observation of release patterns could clarify the mechanisms of GABA release alone or in conjunction with Nts under different stimulation techniques.

(3) Neuronal transcripts were mainly identified through PCR, and alternative methods like single-cell sequencing could be explored.

(4) In Figure 6, the impact of GABA released from Nts neurons in MPO on CBT regulation appears to vary with ambient temperatures, requiring a more detailed explanation for better comprehension.

(5) The model should emphasize the key findings of the study.

Reviewer #3 (Public Review):

Summary:

Understanding the central neural circuits regulating body temperature is critical for improving health outcomes in many disease conditions and in combating heat stress in an ever-warming environment. The authors present important and detailed new data that characterizes a specific population of POA neurons with a relationship to thermoregulation. The new insights provided in this manuscript are exactly what is needed to assemble a neural network model of the central thermoregulatory circuitry that will contribute significantly to our understanding of regulating the critical homeostatic variable of body temperature. These experiments were conducted with the expertise of an investigator with career-long experience in intracellular recordings from POA neurons. They were interpreted conservatively in the appropriate context of current literature.

The Introduction begins with "Homeotherms, including mammals, maintain core body temperature (CBT) within a narrow range", but this ignores the frequent hypothermic episodes of torpor that mice undergo triggered by cold exposure. Although the author does mention torpor briefly in the Discussion, since these experiments were carried out exclusively in mice, greater consideration (albeit speculative) of the potential for a role of MPO Nts neurons in torpor initiation or recovery is warranted. This is especially the case since some 'torpor neurons' have been characterized as PACAP-expressing and a population of PACAP neurons represent the target of MPO Nts neurons.

eLife assessment

This technical study presents a novel sampling strategy for detecting synaptic coupling between neurons from dual pipette patch-clamp recordings in acute slices of mammalian brain tissue in vitro. The authors present solid evidence that this strategy, which incorporates automated patch clamp electrode positioning and cleaning for reuse with strategic neuron targeting, has the potential to substantially improve the efficiency of neuronal sampling with paired recordings. This technique and the extensions discussed will be useful for neuroscientists wanting to apply or already conducting automated multi-pipette patch clamp recording electrophysiology experiments in vitro for neuron connectivity analyses.

Reviewer #1 (Public Review):

Summary:

In this technical paper, the authors introduce a useful variation on the fully automated multi-electrode patch-clamp recording technique for probing synaptic connections that they term "patch-walking". The patch-walking approach involves coordinated pipette route-planning and automated pipette cleaning procedures for pipette reuse to improve recording throughput efficiency, which the authors argue can theoretically yield almost twice the number of connections to be probed by paired recordings on a multi-patch electrophysiology setup for a given number of cells compared to conventional manual patch-clamping approaches used in brain slices in vitro. The authors show solid results from recordings in mouse in vitro cortical slices, demonstrating the efficient recording of dozens of paired neurons with a two-patch pipette configuration for paired recordings and detection of synaptic connections. This approach will be of interest and valuable to neuroscientists conducting automated multi-patch in vitro electrophysiology experiments and seeking to increase the efficiency of neuron connectivity detection while avoiding the more complex recording configurations (e.g., 8-10 pipette multi-patch recording configurations) used by several laboratories that are not readily implementable by most of the neuroscience community.

Strengths:

(1) The authors introduce the theory and methods and show experimental results for a fully automated electrophysiology dual patch-clamp recording approach, which uses coordinated patch-clamp pipette route-planning and automated pipette cleaning procedures to "patch-walk" across an in vitro brain slice.

(2) The patch-walking approach improves throughput efficiency over manual patch clamp recording approaches, especially for investigators looking to utilize paired patch electrode recordings in electrophysiology experiments in vitro.

(3) Experimental results are presented from in vitro mouse cortical slices demonstrating the efficiency of recording dozens of paired neurons with a two-patch pipette configuration for paired recordings and detecting synaptic connections, demonstrating the feasibility and efficiency of the patch-walking approach.

(4) The authors suggest extensions of their technique while keeping the number of recording pipettes employed and recording rig complexity low, which are important practical technical considerations for investigators wanting to avoid the more complex recording configurations (e.g., 8-10 pipette multi-patch recording configurations) used by several laboratories that are not readily implementable by most of the neuroscience community.

Reviewer #2 (Public Review):

Summary:

In this study, the authors aim to combine automated whole-cell patch clamp recording simultaneously from multiple cells. Using a 2-electrode approach, they are able to sample as many cells (and connections) from one slice, as would be achieved with a more technically demanding and materially expensive 8-electrode patch clamp system. They provide data to show that this approach is able to successfully record from 52% of attempted cells, which was able to detect 3 pairs in 71 screened neurons. The authors state that this is a step forward in our ability to record from randomly connected ensembles of neurons.

Strengths:

The conceptual approach of recording multiple partner cells from another in a stepwise manner indeed increases the number of tested connections. An approach that is widely applicable to both automated and manual approaches. Such a method could be adopted for many connectivity studies using dual recording electrodes.

The implementation of automated robotic whole-cell patch-clamp techniques from multiple cells simultaneously is a useful addition to the multiple techniques available to ex vivo slice electrophysiologists.

The approach using 2 electrodes, which are washed between cells is economically favourable, as this reduces equipment costs for recording multiple cells, and limits the wastage of capillary glass that would otherwise be used once.

Weaknesses:

(1) The premise of this article is based upon the fact that even a "skilled" whole-cell electrophysiologist is only capable of recording ~10 cells per day are flawed. Many studies have shown that capable electrophysiologists can record upwards of 50 cells a day, given adequate slice quality and reliable recording conditions with multiple electrodes (e.g. Pastoll et al., 2020 eLife, Booker et al., 2014, JoVE, Peng et al., 2017); often with over 80% success rates for recording. It is not convincing that this approach is a dramatic improvement on such approaches - except when a less skilled researcher is beginning recordings.

Importantly, could the patch walk protocol not be alternatively implemented using manual recording approaches? Yes, the use of a semi-automated robotic system aids recording from many cells by a less experienced colleague, but the inferences about the number of connections tested are common to the approach, not the technique used. This seems like a crucial conceptual point to include.

(2) A key omission of this study is the absence of brain area, cell type, and layer recorded from. It is mentioned in Figure 2 that this is the somatosensory and visual cortices. Which were these, and how were they confirmed?

(3) A comparison of measurements shown in Figure 2 to other methods - e.g. conventional dual patch, 8-electrode patch, single electrode. How do the values obtained for cell quality measurements compare to those expected for the cell population recorded (which is unclear - see point 2)?

(4) What is the reliability of performing outside-out patch configuration to obtain sealed and biocytin-filled cells under these conditions? A key tenet of performing high-throughput paired recordings is the ability to identify the cell types involved in the local microcircuit, and if their axon has been preserved in the slice configuration (which varies between cell types). Not having confirmation of morphological identity and integrity likely leads to a dramatic underestimation of connection probability, given that main axon collaterals could be severed during acute brain slice preparation.

(5) The quality control criteria used in this manuscript require further clarification. An upper limit of 50 MΩ access resistance is extremely high (i.e. 20-30 MΩ is a more typical and stringent cut-off), which is worsened as no real information is given to the degree of resistance change that could be accepted. This is simply listed as "If the seal quality decreased during recording, the cell is excluded from analysis". Indeed, the range of access resistances plotted in Figure 2 is from 10-100 MΩ, which implies that some neurons included in this data did not meet recording criteria. Also, it is widely accepted in the field that a 10-20% change in access during recording is acceptable - within a more defined range. I would consider re-assessing the recorded cells to only include cells with access resistances <30MΩ and those that did not fluctuate by more than 20%.

Appraisal of aims:

The authors certainly established a system that is useful for interrogating synaptic connectivity in an automated manner. However, it remains unclear how widely used this would be in the field, and whether this truly represents an advancement from manual recordings or >4 electrode recordings.

Discussion of impact:

This approach, particularly the conceptual approach to paired testing, is of use to the field. However, in practice, many researchers using conventional dual-electrode paired recording likely implement similar approaches - especially when targeting specific cell types (see Booker et al., 2014 JoVE, Qi et al., 2020 Front Synaptic Neurosci.). This may pave the way for greater implementation of dual and multi-electrode recordings using robotic patch-clamp techniques.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Yip and colleagues incorporated the pipette cleaning technique into their existing dual-patch robotic system, "the PatcherBot", to allow sequential patching of more cells for synaptic connection detection in living brain slices. During dual-patching, instead of retracting all two electrodes after each recording attempt, the system cleaned just one of the electrodes and reused it to obtain another recording while maintaining the other. With one new patch clamp recording attempt, new connections can be probed. By placing one pipette in front of the other in this way, one can "walk" across the tissue, termed "patch-walking." This application could allow for probing additional neurons to test the connectivity using the same pipette in the same preparation.

Strengths:

Compared to regular dual-patch recordings, this new approach could allow for probing more possible connections in brain slices with dual-patch recordings, thus having the potential to improve the efficiency of identifying synaptic connections

Weaknesses:

While this new approach offers the potential to increase efficiency, it has several limitations that could curtail its widespread use.

Loss of Morphological Information: Unlike traditional multi-patch recording, this approach likely loses all detailed morphology of each recorded neuron. This loss is significant because morphology can be crucial for cell type verification and understanding connectivity patterns by morphological cell type.

Spatial Restrictions: The robotic system appears primarily suited to probing connections between neurons with greater spatial separation (~100µm ISD). This means it may not reliably detect connections between neurons in close proximity, a potential drawback given that the connectivity is much higher between spatially close neurons. This limitation could help explain the low connectivity rate (5%) reported in the study.

Limited Applicability: While the approach might be valuable in specific research contexts, its overall applicability seems limited. It's important to consider scenarios where the trade-off between efficiency and specific questions that are asked.

Scalability Challenges: Scaling this method beyond a two-pipette setup may be difficult. Additional pipettes would introduce significant technical and logistical complexities.

eLife assessment

In this work, the authors put forward a valuable methodological advancement for imaging deeper in the intact spinal cord of anaesthetized mice. The authors measured blood flow across different vessel types within the spinal cord and observed the cellular responses following venule occlusion. The demonstration is solid, although, a more quantitative comparison with state-of-the-art two-photon excited fluorescence microscopy and a discussion about applicability to functional imaging (e.g., calcium imaging) would have strengthened the study.

Reviewer #1 (Public Review):

Cheng, Yu-Ting, et al. demonstrate the capabilities of three-photon excited fluorescence (3PEF) microscopy for in vivo imaging of the mouse spinal cord. It enables imaging up to ~550 µm in depth, overcoming the limitations of two-photon excited fluorescence (2PEF) microscopy. The authors used 3PEF to visualize and quantify blood flow across different vessel types within the spinal cord and observed the cellular responses following venule occlusion. They showed depth-dependent structural changes in neurites and the behavior of microglia with a high contrast. The findings show that 3PEF can provide high-resolution, multicolor imaging of dynamic cellular interactions and vascular architecture, helping studies of spinal cord physiology and pathology.

The experiments are well done and supported by data but some points need to be clarified:

(1) For the two vs three-photon comparison, the authors should provide more information about how they performed the 2PEF: power and pulse duration. This comparison is primarily focused on imaging depth and signal-to-background ratio (SBR), but imaging speed should also be discussed.

(2) A comparison with state-of-the-art 2PEF would have been more convincing. For instance, the use of adaptive optics, or red-shifted fluorophores allowing better 2PEF SBR, or deeper imaging.

(3) The study focuses on structural imaging and does not provide extensive data on real-time dynamic processes, which could be crucial for understanding rapid cellular responses in the spinal cord.<br /> By addressing these weaknesses, future studies could enhance the applicability and reliability of 3PEF microscopy for spinal cord research.

Reviewer #2 (Public Review):

Summary:

In this work, the authors attempt to advance our capacity to image the intact spinal cord in living mice, with the ultimate goal of allowing optical access to all spinal layers, from the dorsal (sensory-related) to the ventral (motor-related) laminae. They demonstrate the potency of 3-photon excited fluorescence imaging (3PEF) to collect fluorescent signals in anesthetized adult mice to depths of up to 450 µm from the dorsal surface.

Strengths:

• 3PEF is convincingly demonstrated as a significant improvement over previously used 2-photon imaging.

• The images show very good spatial resolution and stable signal-to-noise ratio up to 450 µm from the dorsal surface, providing unprecedented access to intermediate ventral laminae.

Weaknesses:

• The paper in its current form lacks a detailed description of the experimental apparatus used, including its invasiveness (removal of vertebrae and muscles) and its impact on animal behavior. One can hope that, in the future, a similar implantation chamber may be used for awake, freely-moving animals.

• In general, non-optic specialists may find it difficult to appreciate some of the findings due to technical writing at times, and minimally described metrics.

• The possibility that the 3-photon illumination may cause tissue damage, notably by heat induction, is not evaluated or discussed.

• At this stage, no attempt has been made to image cellular activity. The reader should keep in mind that motor neurons, as well as most of their upstream circuits, are located between 500 and 900 µm from the dorsal surface. Hence, although the method is a significant advancement, it still does not allow for the evaluation of morphological (or possibly, activity) changes in the whole spinal cord, particularly excluding motor-related laminae."

eLife assessment

This valuable study describes an apparatus, workflow, and proof-of-concept data for a system to study social cooperation in marmosets, an increasingly popular primate model for neuroscience. The apparatus and methodology have clear and convincing advantages over conventional methods based on manual approaches. However, the claims of faster social learning or of finer-grained behavioural analysis in their setup require further corroboration.

Reviewer #1 (Public Review):

Summary:

This manuscript by Meissner and colleagues described a novel take on a classic social cognition paradigm developed for marmosets. The classic pull task is a powerful paradigm that has been used for many years across numerous species, but its analog approach has several key limitations. As such, it has not been feasible to adopt the task for neuroscience experiments. Here the authors capture the spirit of the classic task but provide several fundamental innovations that modernize the paradigm - technically and conceptually. By developing the paradigm for marmosets, the authors leverage the many advantages of this primate model for studies of social brain functions and their particular amenability to freely-moving naturalistic approaches.

Strengths:

The current manuscript describes one of the most exciting paradigms in primate social cognition to be developed in many years. By allowing for freely-moving marmosets to engage in high numbers of trials, while precisely quantifying their visual behavior (e.g. gaze) and recording neural activity this paradigm has the potential to usher in a new wave of research on the cognitive and neural mechanisms underlying primate social cognition and decision-making. This paradigm is an elegant illustration of how naturalistic questions can be adapted to more rigorous experimental paradigms. Overall, I thought the manuscript was well written and provided sufficient details for others to adopt this paradigm. I did have a handful of questions and requests about topics and information that could help to further accelerate its adoption across the field.

Weaknesses:

LN 107 - Otters have also been successful at the classic pull task (https://link.springer.com/article/10.1007/s10071-017-1126-2)

LN 151 - Can you provide a more precise quantification of timing accuracy than the 'sub-second level'. This helps determine synchronization with other devices.

Using this paradigm, the marmosets achieved more trials than in the conventional task (146 vs 10). While this is impressive, given that only ~50 are successful Mutual Cooperation trials it does present some challenges for potential neurophysiology experiments and particular cognitive questions. The marmosets are only performing the task for 20 minutes, presumably because they become sated and are no longer motivated. This seems a limitation of the task and is something worth discussing in the manuscript. Did the authors try other food rewards, reduce the amount of reward, food/water restrict the animals for more than the stated 1-3 hours? How might this paradigm be incorporated into in-cage approaches that have been successful in marmosets? Any details on this would help guide others seeking to extend the number of trials performed each day.

Can you provide more details on the DLC/Anipose procedure? How were the cameras synchronized? What percentage of trials needed to be annotated before the model could be generalized? Did each monkey require its own model, or was a single one applied to all animals?

Will the schematics and more instructions on building this system be made publicly available? A number of the components listed in Table 1 are custom-designed. Although it is stated that CAD files will be made available upon request, sharing a link to these files in an accessible folder would significantly add to the potential impact of this paradigm by making it easier for others to adopt.

In the Discussion, it would be helpful to have some discussion of how this paradigm might be used more broadly. The classic pulling paradigm typically allows one to ask a specific question about social cognition, but this task has the potential to be more widely applied to other social decision-making questions. For example, how might this task be adopted to ask some of the game-theory-type approaches common in this literature? Given the authors' expertise in this area, this discussion could serve to provide a roadmap for the broader field to adopt.

Although this paradigm was developed specifically for marmosets, it seems to me that it could readily be adopted in other species with some modifications. Could the authors speak to this and their thoughts on what may need to be changed to be used in other species? This is particularly important because one of the advantages of the classic paradigm is that it has been used in so many species, providing the opportunity to compare how different species approach the same challenge. For example, though both chimps and bonobos are successful, their differences are notably illuminating about the nuances of their respective social cognitive faculties.

Reviewer #2 (Public Review):

Summary:

This important work by Meisner et al., developed an automated apparatus (MarmoAPP) to collect a wide array of behavioral data (lever pulling, gaze direction, vocalizations) in marmoset monkeys, with the goal of modernizing collection of behavioral data to coincide with the investigation of neurological mechanisms governing behavioral decision making in an important primate neuroscience model. The authors show a variety of "proof-of-principle" concepts that this apparatus can collect a wide range of behavioral data, with higher behavioral resolution than traditional methods. For example, the authors highlight that typical behavioral experiments on primate cooperation provide around 10 trials per session, while using their approach the authors were able to collect over 100 trials per 20-minute session with the MarmoAAP.

Overall the authors argue that this approach has a few notable advantages:<br /> (1) it enhances behavioral output which is important for measuring small or nuanced effects/changes in behavior;<br /> (2) allows for more advanced analyses given the higher number of trials per session;<br /> (3) significantly reduces the human labor of manually coding behavioral outcomes and experimenter interventions such as reloading apparatuses for food or position;<br /> (4) allows for more flexibility and experimental rigor in measuring behavior and neural activity simultaneously.

Strengths:

The paper is well-written and the MarmoAPP appears to be highly successful at integrating behavioral data across many important contexts (cooperation, gaze, vocalizations), with the ability to measure significantly many more behavioral contexts (many of which the authors make suggestions for).

The authors provide substantive information about the design of the apparatus, how the apparatus can be obtained via a long list of information Apparatus parts and information, and provide data outcomes from a wide number of behavioral and neurological outcomes. The significance of the findings is important for the field of social neuroscience and the strength of evidence is solid in terms of the ability of the apparatus to perform as described, at least in marmoset monkeys. The advantage of collecting neural and freely-behaving behavioral data concurrently is a significant advantage.

Weaknesses:

While this paper has many significant strengths, there are a few notable weaknesses in that many of the advantages are not explicitly demonstrated within the evidence presented in the paper. There are data reported (as shown in Figures 2 and 3), but in many cases, it is unclear if the data is referenced in other published work, as the data analysis is not described and/or self-contained within the manuscript, which it should be for readers to understand the nature of the data shown in Figures 2 and 3.

(1) There is no data in the paper or reference demonstrating training performance in the marmosets. For example, how many sessions are required to reach a pre-determined criterion of acceptable demonstration of task competence? The authors reference reliably performing the self-reward task, but this was not objectively stated in terms of what level of reliability was used. Moreover, in the Mutual Cooperation paradigm, while there is data reported on performance between self-reward vs mutual cooperation tasks, it is unclear how the authors measured individual understanding of mutual cooperation in this paradigm (cooperation performance in the mutual cooperation paradigm in the presence or absence of a partner; and how, if at all, this performance varied across social context). What positive or negative control is used to discern gained advantages between deliberate cooperation vs two individuals succeeding at self-reward simultaneously?

(2) One of the notable strengths of this approach argued by the authors is the improved ability to utilize trials for data analysis, but this is not presented or supported in the manuscript. For example, the paper would be improved by explicitly showing a significant improvement in the analytical outcome associated with a comparison of cooperation performance in the context of ~150 trials using MarmoAAP vs 10-12 trials using conventional behavioral approaches beyond the general principle of sample size. The authors highlight the dissection of intricacies of behavioral dynamics, but more could be demonstrated to specifically show these intricacies compared to conventional approaches. Given the cost and expertise required to build and operate the MarmoAAP, it is critical to provide an important advantage gained on this front. The addition of data analysis and explicit description(s) of other analytical advantages would likely strengthen this paper and the advantages of MarmoAAP over other behavioral techniques.

Reviewer #3 (Public Review):

Summary:

The authors set out to devise a system for the neural and behavioral study of socially cooperative behaviors in nonhuman primates (common marmosets). They describe instrumentation to allow for a "cooperative pulling" paradigm, the training process, and how both behavioral and neural data can be collected and analyzed. This is a valuable approach to an important topic, as the marmoset stands as a great platform to study primate social cognition. Given that the goals of such a methods paper are to (a) describe the approach and instrumentation, (b) show the feasibility of use, and (c) quantitatively compare to related approaches, the work is easily able to meet those criteria. My specific feedback on both strengths and weaknesses is therefore relatively limited in scope and depth.

Strengths:

The device is well-described, and the authors should be commended for their efforts in both designing this system but also in "writing it up" so that others can benefit from their R&D.

The device appears to generate more repetitions of key behavior than other approaches used in prior work (with other species).

The device allows for quantitative control and adjustment to control behavior.

The approach also supports the integration of markerless behavioral analysis as well as neurophysiological data.

Weaknesses:

A few ambiguities in the descriptions are flagged below in the "Recommendations for authors".

The system is well-suited to marmosets, but it is less clear whether it could be generalized for use in other species (in which similar behaviors have been studied with far less elegant approaches). If the system could impact work in other species, the scope of impact would be significantly increased, and would also allow for more direct cross-species comparisons. Regardless, the future work that this system will allow in the marmoset will itself be novel, unique, and likely to support major insights into primate social cognition.

Author response:

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

Reviewers’ Comments:

Reviewer #1 (Remarks to the Author):

Summary:

Fang Huang et al found that RBM7 deficiency promotes metastasis by coordinating MFGE8 splicing switch and NF-kB pathway in breast cancer by utilizing clinical samples as well as cell and tail vein injection models.

Strengths:

This study uncovers a previously uncharacterized role of MFGE8 splicing alteration in breast cancer metastasis, and provides evidence supporting RBM7 function in splicing regulation. These findings facilitate the mechanistic understanding of how splicing dysregulation contributes to metastasis in cancer, a direction that has increasingly drawn attention recently, and provides a potentially new prognostic and therapeutic target for breast cancer.

We thank the reviewer for appreciating the novelty and importance of this study, and have provided new data to address the following concerns raised by the reviewer.

Weaknesses:

This study can be strengthened in several aspects by additional experiments or at least by further discussions. First, how RBM7 regulates NF-kB, and how it coordinates splicing and canonical function as a component of NEXT complex should be clarified. Second, although the roles of MFGE8 splicing isoforms in cell migration and invasion have been demonstrated in transwell and wound healing assays, it would be more convincing to explore their roles in vivo such as the tail vein injection model. Third, the clinical significance would be considerably improved, if the therapeutic value of targeting MFGE8 splicing could be demonstrated.

We’re thankful for the constructive suggestions. A preliminary study on the mechanism by which RBM7 regulates NF-kB pathway is already underway. We found RBM7 depletion remarkably promoted the expression of IL-1β as judged by qPCR and ELISA assays (new Figure S5G- S5I, also see below). IL-1β, commonly known as a pro-inflammatory cytokine, could bind to IL-1R and initiate a multistage enzymatic reaction that triggers the activation of NF-κB pathway (Axel Weber, 2010) (Qing Guo, 2024). Thus we speculated that the upregulation of IL-1β might be a causal factor in RBM7-depletion-induced activation of NF-kB signaling. It will be interesting to determine the complete molecular mechanism in our future study. In addition, we performed a co-IP experiment and found that RBM7 could interact with RNA splicing factor SF3B2, a component of spliceosomal U2 snRNP complex (new Figure S6B, also see below). Consistent with the AS regulation of MFGE8 by RBM7, the depletion of SF3B2 also promoted exon7 skipping, implying a cooperative effect of the two proteins in regulating MFGE8 splicing (new Figure S6C-6D, also see below). This is in concert with a previous study that RRM domain of RBM7 could bind a proline-rich segment within SF3B2 (Falk, Finogenova et al., 2016). The interaction mode with strong similarity to RBM7RRM–ZCCHC8Proline interaction in the NEXT complex indicated mutually exclusive binding of SF3B2 and ZCCHC8 to RBM7. Thus, RBM7 appears to play dual, but not conflicting, roles during RNA processes depending on its interaction with the spliceosome or exosome (see line 427-437 in the new manuscript).

Author response image 1.

The mRNA levels of IL-1β in MDA-MB-231 or BT549 cells with stable RBM7 knockdown or control vector were examined by qRT-PCR approach.

Author response image 2.

Supernatants from RBM7-knockdown MDA-MB-231 or BT549 cells were collected and protein expression of IL-1β was measured by ELISA kit.

Author response image 3.

The knockdown efficiency of RBM7 in two breast cancer cell lines were determined by qRT-PCR approach.

Author response image 4.

Immunoprecipitation assay was performed in breast cancer cells expressing HA-RBM7 and Flag-SF3B2 or empty vector. The Flag-tagged precipitated complexes and lysates were analyzed through western blotting.

Author response image 5.

The splicing shift of MFGE8 upon SF3B2 knockdown in breast cancer cells was examined by RT-PCR approach. The mean ± SD of PSI values derived from three independent replicates is shown.

Author response image 6.

The SF3B2 knockdown efficiency was examined by qRT-PCR.

To further corroborate the roles of two MFGE8 isoforms in cell invasion, we have performed Fluorescent Gelatin Degradation Assays for investigating invadopodia formation. Consistent with the transwell assay results, MFGE8-L up-regulation suppressed breast cancer cells invasion through a layer of extracellular matrix, whereas breast cancer cells with ectopic expression of MFGE8-S acquired enhanced ability to degrade matrix and invasion (new Figure 5B, also see below). In addition, to determine the therapeutic value of targeting MFGE8 splicing, we transfected triple-negative breast cancer cells with ASOs targeting RBM7-binding motif and examined the potential impact on cell aggressiveness. The results showed an obvious increase in exon7-skipped variant of MFGE8 as compared to the scramble negative control ASOs, meanwhile, the migrative and invasive ability of breast cancer cells treated with splice-targeting ASOs was significantly boosted (new Figure 6B and S5B, also see below), further suggesting that RBM7-knockdown stimulated aggressiveness of breast cancer at least partially relies on splicing switch of MFGE8.

Author response image 7.

Gelatin degradation assay was performed to test the effect of RBM7 knockdown on invadopodia function. 10000 cells were plated onto FITC-gelatin substrates (Green) and cultured for 48 h. Representative images are shown (red, Cy3-phalloidin; blue, DAPI) and the degraded areas were quantified by Image J software. Scar bars= 50 μm. P values were determined by one-way ANOVA with Tukey's multiple comparison test (n = 3).

Author response image 8.

Representative transwell analysis of migrative/invasive capability of breast cancer cells transfected with 500 nM ASO directed against RBM7-binding region in MFGE8 pre-mRNA. P values were determined by one-way ANOVA with Tukey's multiple comparison test.

Author response image 9.

RT-PCR quantification of two MFGE8 isoforms after transfecting breast cancer cells with 500 nM ASO directed against RBM7-binding region in MFGE8 pre-mRNA. P values were calculated by one-way ANOVA with Tukey's multiple comparison test.

The minor concerns

(1) Several figure legends do not match with the images, for example, Figure 2K, Figure 4, Figure 7D, and 7E, and the description of Fiure 7F is missing in the text.

As suggested by the reviewer, we have checked all of the figure legends carefully and corrected all of the misinterpretation.

(2) The statistical methods for Figure1A and Figure1B should be indicated.

As suggested by the reviewer, we have included the statistical methods for Figure1A and 1B in Figure1 legend. Data in Figure 1A and 1B are presented as means ± SD and P values were obtained by Mantel-Cox log-rank test.

(3) The molecular weight of the proteins in the Western Blot images should be marked.

As suggested by the reviewer, we have added the molecular weight of proteins in all of the western blot images.

(4) The sequences where RBM7 binds on MFGE8 RNA should be clearly indicated.

We thank the reviewer for this question. We analyzed the sequence of alternative exon 7 and the motifs nearby its 5’ or 3’ splice sites, and found two RBM7 potentially binding motifs are positioned in proximal to the pseudo 3’ splice site. Subsequent RT-PCR for the precipitation in RIP assays confirmed RBM7 could bind to the upstream sequence containing 5’-UUUCUU-3’ motifs adjacent to intron6/exon7 junction of MFGE8 cassette exon, but not another region nearby it. To pinpoint the location for the potential cis-element for AS regulation by RBM7, we designed antisense oligonucleotides (ASOs) to block RBM7 potentially binding sites (UUUCUU). As shown in revised Figure 4F, when compared to scramble ASO, targeting ASOs contributed to the exclusion of exon7. Additionally, we constructed an exogenous MFGE8 splicing reporter containing exon 6-8 and partial intron sequences to determine the binding site for AS regulation by RBM7. The depletion of RBM7 still induced the splicing shift of the minigene reporter by elevating MFGE8-S variant. While the binding motif UUUCUU was removed or mutated, RBM7 failed to affect the splicing outcomes of MFGE8 (new Figure S3C, also see below). Due to its close proximity to 3’ splice site, UUUCUU residues bound by RBM7 is very likely to participate in spliceosome assembly at the upstream 3’ splice site of exon7, which may explain why disruption of the motif led to almost complete exon7 skipping. The above data suggested that RBM7 regulated the exon skipping of MFGE8 by binding to UUUCUU located six nucleotides upstream of the 3’ splice-site of exon7.

Author response image 10.

Upper: the red line in diagram indicates ASOs targeting region which contains UUUCUU; down: MCF7 and MDA-MB-231 cells were transfected with ASOs targeting MFGE8 pre-mRNA for 48h and then applied for RT-PCR identification. P values were determined by one-way ANOVA with Tukey's multiple comparison test.

Author response image 11.

Upper: MFGE8 min-splicing reporters with mutation in the RBM7 binding site or a non-specific binding were generated and shown in cartoon; down: RT-PCR assays were performed to identify the splicing outcomes of MFGE8 reporter while RBM7 was depleted in breast cancer cells.

(5) Some typos, graphic errors, and sentences are hard to understand and need to be corrected, such as lines 80-81, 249-250, line 221 "motfs", line 319 "RBM4". Please carefully proofread and revise the entire manuscript.

As suggested by the reviewer, we have corrected typos and graphic errors mentioned above. In addition, this manuscript was also extensively edited to improve grammar and sentence structure.

(6) Define the abbreviations when they first appear, such as MFGE8-L, RBM, etc.

We thank the reviewer for raising this point. We have defined the abbreviations when firstly presented in the manuscript.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors reported the biological role of RBM7 deficiency in promoting metastasis of breast cancer. They further used a combination of genomic and molecular biology approaches to discover a novel role of RBM7 in controlling alternative splicing of many genes in cell migration and invasion, which is responsible for the RBM7 activity in suppressing metastasis. They conducted an in-depth mechanistic study on one of the main targets of RBM7, MFGE8, and established a regulatory pathway between RBM7, MFGE8-L/MFGE8-S splicing switch, and NF-κB signaling cascade. This link between RBM7 and cancer pathology was further supported by analysis of clinical data.

Strengths:

Overall, this is a very comprehensive study with lots of data, and the evidence is consistent and convincing. Their main conclusion was supported by many lines of evidence, and the results in animal models are pretty impressive.

Weaknesses:

However, there are some controls missing, and the data presentation needs to be improved. The writing of the manuscript needs some grammatical improvements because some of the wording might be confusing.

We thank the reviewer for the positive comments on this work, and have addressed all the concerns raised by the reviewer.

Specific comments:

(1) Figure 2. The figure legend is missing for Figure 2C, which caused many mislabels in the rest of the panels. The labels in the main text are correct, but the authors should check the figure legend more carefully. Also in Figure 2C, it is not clear why the authors choose to examine the expression of this subset of genes. The authors only refer to them as "a series of metastasis-related genes", but it is not clear what criteria they used to select these genes for expression analysis.

We thank the reviewer for raising this question. We have included the figure legend for Figure 2C and improved other figure legends throughout the article. For the second question, since gene ontology analysis of RNA-seq data in RBM7-depleted breast cancer cells showed that a series of differentially expressed genes were enriched in metastasis-associated processe, we identified the expression of this subset of genes in breast cancer cells in the presence or absence of RBM7 by heatmap differential analysis based on qRT-PCR results. To clarify this point and address the reviewer’s concern, we have improved the relevant description of this part (see line 174-180 in the new manuscript).

(2) Line 218-220. The comparison of PSI changes in different types of AS events is misleading. Because these AS events are regulated in different mechanisms, they cannot draw the conclusion that "the presence of RBM7 may promote the usage of alternative splice sites". For example, the regulators of SE and IR may even be opposite, and thus they should discuss this in different contexts. If they want to conclude this point, they should specifically discuss the SE and A5SS rather than draw an overall conclusion.

We are thankful for the reviewer’s valuable comment. According to the suggestion, we have removed the overall conclusion and corrected to discuss in SE and A5SS.

(3) In the section starting at line 243, they first referred to the gene and isoforms as "EFG-E8" or "EFG-E8-L", but later used "EFGE8" and "EFGE8-L". Please be consistent here. In addition, it will be more informative if the authors add a diagram of the difference between two EFGE8 isoforms in terms of protein structure or domain configuration.

As suggested by the reviewer, we keep using the name “MFGE8-L” for the canonical MFGE8 isoform and “MFGE8-S” for the truncated isoform in this manuscript. In addition, to clarify the structural basis for the different tumor invasion-related functions of two MFGE8 isoforms, we have included a diagram of their domain configuration in new Figure S4F and predicted protein structure in new Figure S4G. The details in the revised manuscript are given below:

Author response image 12.

Schematic diagram of the domain composition of two MFGE8 isoforms. Upper: the full-length variant with exon7 indicated by yellow square; down: the truncated variant with exon7 skipping.

Author response image 13.

The model structure of two MFGE8 isoforms was implemented using SwissModel software. The F5/8 type C2 protein domain excluded from MFGE8-S variant was marked in red.

(4) Figure 7B and 7C. The figures need quantification of the inclusion of MFGE exon7 (PSI value) in addition to the RT-PCR gel. The difference seems to be small for some patients.

As suggested by the reviewer, we have included the relative quantification of PSI for endogenous MFGE8 in breast cancer patients and found increased proportion of exon7 exclusion in most tumor samples when compared to normal tissues (case#1: 86:94; case#2: 84:86; case#3: 79:85; case#4: 63:75; case#5: 69:93; case#6: 71:80) (new Figure 7B, also see below). On the other hand, we have expanded the number of metastatic breast cancer cases and quantified the the AS events within MFGE8 by analyzing the PSI values. The lymph node metastases contain a higher proportion of MFGE8 variant with skipped exon7 in comparison with paired primary tumor tissues (case#1: 80:95; case#2: 86:97; case#3: 84:90; case#4: 70:78; case#5: 83:89) (Figure 7C). This is coherent with decreased RBM7 expression levels found in breast cancer with lymph node metastasis.

Author response image 14.

The splicing alteration of MFGE8 in 6 pairs of primary breast cancer tissues and adjacent normal tissues was examined using RT-PCR. The quantification of PSI vales was based on relative band intensities using Image J software.

Author response image 15.

The splicing alteration of MFGE8 in primary breast cancer tissues and corresponding lymph node metastases was identified by RT-PCR assays. The quantification of PSI vales wa determined by Image J software.

Minor comments:

The writing in many places is a little odd or somewhat confusing, I am listing some examples, but the authors need to polish the whole manuscript more to improve the writing. 1. Line 169-170, "...followed by profiling high-throughput transcriptome by RNA sequencing", should be "followed by high-throughput transcriptome profiling with RNA sequencing". 2. Line 170, "displayed a wide of RBM7-regulated genes were enriched...", they should add a "that" after the "displayed" as the sentence is very long. 3. Line 213, "PSI (percent splicing inclusion)" is not correct, PSI stands for "percent spliced in". 4. Line 216-217, the sentence is long and fragmented, they should break it into two sentences. 5. Line 224, the "tethering" should be changed to "recognizing". There is a subtle difference in the mechanistic implication between these two words. 6. Line 250, should be changed to "...in the ratio of two MFGE8 isoforms".

We thank the detailed comments from the reviewer. The points mentioned above has been addressed one by one and this manuscript was also extensively edited to improve grammar and sentence structure for better understanding.

References

Axel Weber PW, Michael Kracht* (2010) Interleukin-1 (IL-1) Pathway. SCIENCESIGNALING.

Qing Guo1, Yizi Jin1,2, Xinyu Chen3, Xiaomin Ye4, Xin Shen5, Mingxi Lin1,2, Cheng Zeng1,2, Teng Zhou1,2 and Jian Zhang1,2 (2024) NF-κB in biology and targeted therapy: new insights and translational implications. Signal Transduction and Targeted Therapy.

Falk S, Finogenova K, Melko M, Benda C, Lykke-Andersen S, Jensen TH, Conti E (2016) Structure of the RBM7–ZCCHC8 core of the NEXT complex reveals connections to splicing factors. Nature Communications.

eLife assessment

This study presents a rather valuable finding on the RBM7 function in spicing regulation and uncharacterized role of MFGE8 splicing alteration in breast cancer metastasis. The evidence supporting the claims of the authors is solid. The work will be of broad interest to clinicians, medical researchers and scientists working on breast cancer.

Reviewer #1 (Public Review):

Fang Huang et al found that RBM7 deficiency promotes metastasis by coordinating MFGE8 splicing switch and NF-kB pathway in breast cancer by utilizing clinical samples as well as cell and tail vein injection models.

This study uncovers a previously uncharacterized role of MFGE8 splicing alteration in breast cancer metastasis, and provides evidence supporting RBM7 function in splicing regulation. These findings facilitate the mechanistic understanding of how splicing dysregulation contributes to metastasis in cancer, a direction that has increasingly drawn attention recently, and provides a potentially new prognostic and therapeutic target for breast cancer.

Reviewer #2 (Public Review):

In this manuscript, the authors reported the biological role of RBM7 deficiency in promoting metastasis of breast cancer. They further used a combination of genomic and molecular biology approaches to discover a novel role of RBM7 in controlling alternative splicing of many genes in cell migration and invasion, which is responsible for the RBM7 activity in suppressing metastasis. They conducted an in-depth mechanistic study on one of the main targets of RBM7, MFGE8, and established a regulatory pathway between RBM7, MFGE8-L/MFGE8-S splicing switch, and NF-κB signaling cascade. This link between RBM7 and cancer pathology was further supported by analysis of clinical data.

Overall, this is a very comprehensive study with lots of data, and the evidence is consistent and convincing. Their main conclusion was supported by many lines of evidence, and the results in animal models are pretty impressive.

Author response:

eLife assessment

This useful study shows how genetic variation is associated with fecundity following a period of reproductive diapause in female Drosophila. The work identifies the olfactory system as central to successful diapause with associated changes in longevity and fecundity. While the genetic screening and methods used are solid, the approach to assessing diapause is incomplete and could benefit from additional orthogonal experiments.

Response: We agree that, as with most studies, additional follow-up work will be informative.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The paper begins with phenotyping the DGRP for post-diapause fecundity, which is used to map genes and variants associated with fecundity. There are overlaps with genes mapped in other studies and also functional enrichment of pathways including most surprisingly neuronal pathways. This somewhat explains the strong overlap with traits such as olfactory behaviors and circadian rhythm. The authors then go on to test genes by knocking them down effectively at 10 degrees. Two genes, Dip-gamma and sbb, are identified as significantly associated with post-diapause fecundity, and they also find the effects to be specific to neurons. They further show that the neurons in the antenna but not the arista are required for the effects of Dip-gamma and sbb. They show that removing the antenna has a diapause-specific lifespan-extending effect, which is quite interesting. Finally, ionotropic receptor neurons are shown to be required for the diapause-associated effects.

Strengths and Weaknesses:

Overall I find the experiments rigorously done and interpretations sound. I have no further suggestions except an ANOVA to estimate the heritability of the post-diapause fecundity trait, which is routinely done in the DGRP and offers a global parameter regarding how reliable phenotyping is. A minor point is I cannot find how many DGRP lines are used.

Response: Thank you for the suggestions. We screened 193 lines and we will add that information to the methods.

Additionally, we will add the heritability estimate of the post-diapause fecundity trait.

Reviewer #2 (Public Review):

Summary

In this study, Easwaran and Montell investigated the molecular, cellular, and genetic basis of adult reproductive diapause in Drosophila using the Drosophila Genetic Reference Panel (DGRP). Their GWAS revealed genes associated with variation in post-diapause fecundity across the DGRP and performed RNAi screens on these candidate genes. They also analyzed the functional implications of these genes, highlighting the role of genes involved in neural and germline development. In addition, in conjunction with other GWAS results, they noted the importance of the olfactory system within the nervous system, which was supported by genetic experiments. Overall, their solid research uncovered new aspects of adult diapause regulation and provided a useful reference for future studies in this field.

Strengths:

The authors used whole-genome sequenced DGRP to identify genes and regulatory mechanisms involved in adult diapause. The first Drosophila GWAS of diapause successfully uncovered many QTL underlying post-diapause fecundity variations across DGRP lines. Gene network analysis and comparative GWAS led them to reveal a key role for the olfactory system in diapause lifespan extension and post-diapause fecundity.

Weaknesses:

(1) I suspect that there may be variation in survivorship after long-term exposure to cold conditions (10ºC, 35 days), which could also be quantified and mapped using genome-wide association studies (GWAS). Since blocking Ir21a neuronal transmission prevented flies from exiting diapause, it is possible that natural genetic variation could have a similar effect, influencing the success rate of exiting diapause and post-diapause mortality. If there is variation in this trait, could it affect post-diapause fecundity? I am concerned that this could be a confounding factor in the analysis of post-diapause fecundity. However, I also believe that understanding phenotypic variation in this trait itself could be significant in regulating adult diapause.

Response: We agree that it is possible that the ability to endure cool temperatures per se may influence post-diapause fecundity. However, cool temperature is the essential diapause-inducing condition in Drosophila, so it is not obvious how to separate those effects experimentally, and we agree that phenotypic variation in the cool-sensitivity trait itself could be significant in regulating diapause.

(2) On p.10, the authors conclude that "Dip-𝛾 and sbb are required in neurons for successful diapause, consistent with the enrichment of this gene class in the diapause GWAS." While I acknowledge that the results support their neuronal functions, I remain unconvinced that these genes are required for "successful diapause". According to the RNAi scheme (Figure 4I), Dip-γ and sbb are downregulated only during the post-diapause period, but still show a significant effect, comparable to that seen in the nSyb Gal4 RNAi lines (Figure 4K).

Response: Our definition of successful diapause is the ability to produce viable adult progeny post-diapause, which requires that the flies enter, maintain, and exit diapause, alive and fertile. We will restate our conclusion to say that Dip-γ and sbb are required for post-diapause fecundity.

In addition, two other RNAi lines (SH330386, 80461) that did not show lethality did not affect post-diapause fecundity.

Response: We interpret those results to mean that those RNAi lines were not effective since Dip-γ and sbb are known to be essential.

Notably, RNAi (27049, KK104056) substantially reduced non-diapause fecundity, suggesting impairment of these genes affects fecundity in general regardless of diapause experience. Therefore, the reduced post-diapause fecundity observed may be a result of this broader effect on fecundity, particularly in a more "sensitized" state during the post-diapause period, rather than a direct regulation of adult diapause by these genes.

Response: Ubiquitous expression of RNAi lines #27049 or #KK104056 was lethal, so we included the tubGAL80ts repressor to prevent RNAi from taking effect during development. Flies had to be shifted to 30 °C to inactivate the repressor and thereby activate the RNAi. At 30 °C, fecundity of the controls (GFP RNAi lines #9331, KK60102) were also lower (average non-diapause fecundity = 12 and 19 respectively) and similar to #27049 or #KK104056. We also assessed the knockdown using Repo GAL4 and nSyb GAL4 and did not find a significant difference/decline in the non diapause fecundity for #27049 and #KK104056 as compared to a nonspecific RNAi control (#54037).

(3) The authors characterized 546 genetic variants and 291 genes associated with phenotypic variation across DGRP lines but did not prioritize them by significance. They did prioritize candidate genes with multiple associated variants (p.9 "Genes with multiple SNPs are good candidates for influencing diapause traits."), but this is not a valid argument, likely due to a misunderstanding of LD among variants in the same gene. A gene with one highly significantly associated variant may be more likely to be the causal gene in a QTL than a gene with many weakly associated variants in LD. I recommend taking significance into account in the analysis.

We agree with the reviewer, and in Supplemental Table S3 we list top-associated SNPs in order from the lowest (most significant) p-value. Most of the top-associated genes from this analysis were uncharacterized CG numbers for which there were insufficient tools available for validation purposes. Nevertheless, there is overlap amongst the highly significant genes by p-value and those with multiple SNPs. Amongst the top 15 genes with multiple associated SNPsCG18636 & CR15280 ranked 3rd by p-value, CG7759 ranked 4th, CG42732 ranked 10th, and Drip ranked 30th (all above the conservative Bonferroni threshold of 4.8e-8) while three Sbb-associated SNPs also appear in Table 3 above the standard e-5 threshold.

Reviewer #3 (Public Review):

Summary:

Drosophila melanogaster of North America overwinters in a state of reproductive diapause. The authors aimed to measure 'successful' D. melanogaster reproductive diapause and reveal loci that impact this quantitative trait. In practice, the authors quantified the number of eggs produced by a female after she exited 35 days of diapause. The authors claim that genes involved with olfaction in part contribute to some of the variation in this trait.

Strengths:

The work used the power platform of the fly DRGP/GWAS. The work tried to verify some of the candidate loci with targeted gene manipulations.

Weaknesses:

Some context is needed. Previous work from 2001 established that D. melanogaster reproductive diapause in the laboratory suspends adult aging but reduces post-diapause fecundity. The work from 2001 showed the extent fecundity is reduced is proportional to diapause duration. As well, the 2001 data showed short diapause periods used in the current submission reduce fecundity only in the first days following diapause termination; after this time fecundity is greater in the post-diapause females than in the non-diapause controls.

Response: The 2001 paper by Tatar et al. reports the number of eggs laid after 3, 6, or 9 weeks in diapause conditions. Thus the diapause conditions used in this study (35 days or 5 weeks) are neither short nor long, rather intermediate. Does the reviewer have a specific concern?

In this context, the submission fails to offer a meaningful concept for what constitutes 'successful diapause'. There is no biological rationale or relationship to the known patterns of post-diapause fecundity. The phenotype is biologically ambiguous.

Response: We have unambiguously defined successful diapause as the ability to produce viable adult progeny post-diapause. Other groups have measured % of flies that arrest ovarian development or % of post-diapause flies with mature eggs in the ovary, or # eggs laid post-diapause; however we suggest that # of viable adult progeny produced post-diapause is more meaningful than the other measurements from the point of view of perpetuating the species.

I have a serious concern about the antenna-removal design. These flies were placed on cool/short days two weeks after surgery. Adults at this time will not enter diapause, which must be induced soon after eclosion. Two-week-old adults will respond to cool temperatures by 'slowing down', but they will continue to age on a time scale of day-degrees. This is why the control group shows age-dependent mortality, which would not be seen in truly diapaused adults. Loss of antennae increases the age-dependent mortality of these cold adults, but this result does not reflect an impact on diapause.

Response: The reviewer has a point. We carried out the lifespan study under two different conditions: either by removing the antenna and moving the flies directly to 10 °C or by removing the antenna and allowing a “wound healing” period prior to moving the flies to 10 °C (out of concern that the flies might have died quickly because wound healing may be impaired at 10 °C). In both cases, lifespan was shortened. We will add a discussion of the technical limitations of this experiment.

• Appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

The work falls well short of its aim because the concept of 'successful diapause' is not biologically established. The paper studies post-diapause fecundity, and we don't know what that means. The loci identified in this analysis segregate for a minimally constructed phenotype. The results and conclusions are orthogonal.

Response: It is unclear to us why the reviewer has such a negative opinion of measuring post-diapause fecundity, specifically the ability to produce viable progeny post-diapause. The value of this measurement seems obvious from the point of view of perpetuating the species.

• The likely impact of the work on the field, and the utility of the methods and data to the community.

The work will have little likely impact. Its phenotype and operational methods are weakly developed. It lacks insight based on the primary literature on post-diapause. The community of insect diapause investigators are not likely to use the data or conclusions to understand beneficial or pest insects, or the impact of a changing climate on how they over-winter.

Response: The reviewer has not explained why his/her opinion is so negative.

Reviewer #1 (Public Review):

Summary:

The paper begins with phenotyping the DGRP for post-diapause fecundity, which is used to map genes and variants associated with fecundity. There are overlaps with genes mapped in other studies and also functional enrichment of pathways including most surprisingly neuronal pathways. This somewhat explains the strong overlap with traits such as olfactory behaviors and circadian rhythm. The authors then go on to test genes by knocking them down effectively at 10 degrees. Two genes, Dip-gamma and sbb, are identified as significantly associated with post-diapause fecundity, and they also find the effects to be specific to neurons. They further show that the neurons in the antenna but not the arista are required for the effects of Dip-gamma and sbb. They show that removing the antenna has a diapause-specific lifespan-extending effect, which is quite interesting. Finally, ionotropic receptor neurons are shown to be required for the diapause-associated effects.

Strengths and Weaknesses:

Overall I find the experiments rigorously done and interpretations sound. I have no further suggestions except an ANOVA to estimate the heritability of the post-diapause fecundity trait, which is routinely done in the DGRP and offers a global parameter regarding how reliable phenotyping is. A minor point is I cannot find how many DGRP lines are used.

eLife assessment

This useful study shows how genetic variation is associated with fecundity following a period of reproductive diapause in female Drosophila. The work identifies the olfactory system as central to successful diapause with associated changes in longevity and fecundity. While the genetic screening and methods used are solid, the approach to assessing diapause is incomplete and could benefit from additional orthogonal experiments.

Reviewer #2 (Public Review):

Summary

In this study, Easwaran and Montell investigated the molecular, cellular, and genetic basis of adult reproductive diapause in Drosophila using the Drosophila Genetic Reference Panel (DGRP). Their GWAS revealed genes associated with variation in post-diapause fecundity across the DGRP and performed RNAi screens on these candidate genes. They also analyzed the functional implications of these genes, highlighting the role of genes involved in neural and germline development. In addition, in conjunction with other GWAS results, they noted the importance of the olfactory system within the nervous system, which was supported by genetic experiments. Overall, their solid research uncovered new aspects of adult diapause regulation and provided a useful reference for future studies in this field.

Strengths:

The authors used whole-genome sequenced DGRP to identify genes and regulatory mechanisms involved in adult diapause. The first Drosophila GWAS of diapause successfully uncovered many QTL underlying post-diapause fecundity variations across DGRP lines. Gene network analysis and comparative GWAS led them to reveal a key role for the olfactory system in diapause lifespan extension and post-diapause fecundity.

Weaknesses:

(1) I suspect that there may be variation in survivorship after long-term exposure to cold conditions (10ºC, 35 days), which could also be quantified and mapped using genome-wide association studies (GWAS). Since blocking Ir21a neuronal transmission prevented flies from exiting diapause, it is possible that natural genetic variation could have a similar effect, influencing the success rate of exiting diapause and post-diapause mortality. If there is variation in this trait, could it affect post-diapause fecundity? I am concerned that this could be a confounding factor in the analysis of post-diapause fecundity. However, I also believe that understanding phenotypic variation in this trait itself could be significant in regulating adult diapause.

(2) On p.10, the authors conclude that "Dip-𝛾 and sbb are required in neurons for successful diapause, consistent with the enrichment of this gene class in the diapause GWAS." While I acknowledge that the results support their neuronal functions, I remain unconvinced that these genes are required for "successful diapause". According to the RNAi scheme (Figure 4I), Dip-γ and sbb are downregulated only during the post-diapause period, but still show a significant effect, comparable to that seen in the nSyb Gal4 RNAi lines (Figure 4K). In addition, two other RNAi lines (SH330386, 80461) that did not show lethality did not affect post-diapause fecundity. Notably, RNAi (27049, KK104056) substantially reduced non-diapause fecundity, suggesting impairment of these genes affects fecundity in general regardless of diapause experience. Therefore, the reduced post-diapause fecundity observed may be a result of this broader effect on fecundity, particularly in a more "sensitized" state during the post-diapause period, rather than a direct regulation of adult diapause by these genes.

(3) The authors characterized 546 genetic variants and 291 genes associated with phenotypic variation across DGRP lines but did not prioritize them by significance. They did prioritize candidate genes with multiple associated variants (p.9 "Genes with multiple SNPs are good candidates for influencing diapause traits."), but this is not a valid argument, likely due to a misunderstanding of LD among variants in the same gene. A gene with one highly significantly associated variant may be more likely to be the causal gene in a QTL than a gene with many weakly associated variants in LD. I recommend taking significance into account in the analysis.

Reviewer #3 (Public Review):

Summary:

Drosophila melanogaster of North America overwinters in a state of reproductive diapause. The authors aimed to measure 'successful' D. melanogaster reproductive diapause and reveal loci that impact this quantitative trait. In practice, the authors quantified the number of eggs produced by a female after she exited 35 days of diapause. The authors claim that genes involved with olfaction in part contribute to some of the variation in this trait.

Strengths:

The work used the power platform of the fly DRGP/GWAS. The work tried to verify some of the candidate loci with targeted gene manipulations.

Weaknesses:

Some context is needed. Previous work from 2001 established that D. melanogaster reproductive diapause in the laboratory suspends adult aging but reduces post-diapause fecundity. The work from 2001 showed the extent fecundity is reduced is proportional to diapause duration. As well, the 2001 data showed short diapause periods used in the current submission reduce fecundity only in the first days following diapause termination; after this time fecundity is greater in the post-diapause females than in the non-diapause controls.

In this context, the submission fails to offer a meaningful concept for what constitutes 'successful diapause'. There is no biological rationale or relationship to the known patterns of post-diapause fecundity. The phenotype is biologically ambiguous.

I have a serious concern about the antenna-removal design. These flies were placed on cool/short days two weeks after surgery. Adults at this time will not enter diapause, which must be induced soon after eclosion. Two-week-old adults will respond to cool temperatures by 'slowing down', but they will continue to age on a time scale of day-degrees. This is why the control group shows age-dependent mortality, which would not be seen in truly diapaused adults. Loss of antennae increases the age-dependent mortality of these cold adults, but this result does not reflect an impact on diapause.

• Appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

The work falls well short of its aim because the concept of 'successful diapause' is not biologically established. The paper studies post-diapause fecundity, and we don't know what that means. The loci identified in this analysis segregate for a minimally constructed phenotype. The results and conclusions are orthogonal.

• The likely impact of the work on the field, and the utility of the methods and data to the community.

The work will have little likely impact. Its phenotype and operational methods are weakly developed. It lacks insight based on the primary literature on post-diapause. The community of insect diapause investigators are not likely to use the data or conclusions to understand beneficial or pest insects, or the impact of a changing climate on how they over-winter.

eLife assessment

The neurotrophic factor Neuritin can moderate T-cell tolerance and immunity through both regulatory T (Treg) and effector T cells, promoting Treg cell expansion and suppression while dampening effector T cells to mediate the inflammatory response. Neuritin expression influences the membrane potential, ion channels, and nutrient transporter expression patterns of CD4+ T cells, contributing to differential metabolic states in Treg and effector T cells. These findings are solid and important for understanding immune regulation involving Treg cells and effector T cells.

Reviewer #1 (Public Review):

The manuscript by Yu et al seeks to investigate the role of neuritin (Nrn1), identified as a marker of anergic cells, in the biology of regulatory (Tregs) and conventional (Tconv) T cells. Although the role of Nrn1 expressed by Tregs has already been explored (Gonzalez-Figueroa 2021 cited in the manuscript), this manuscript shows original new data suggesting that this molecule would be important in promoting Treg function and inhibiting Tconv effector function by acting at the level of membrane potential and molecule transport across the plasma membrane. However, it is disappointing that reading this manuscript leaves an impression of incomplete work done too quickly. Multiple models have been used, but none has been studied thoroughly enough to provide really conclusive and unambiguous data. For example, 5 different models were used to study T cells in vivo. It would have been preferable to use fewer, but to go further in the study of mechanisms. In the absence of a more in-depth study, the conclusions drawn by the authors are often open to question. Major points concern the fact that there are enough biological replicates for most experiments, some critical controls and data are lacking, and the authors have used iTregs rather than nTregs for many experiments (see below). This is unfortunate because the role of neuritin in T cell biology studied here is new and interesting.

Major points (in the order in which they appear in the text):

(1) A real weakness of this work is the fact that in most of the results shown, there are few biological replicates with differences that are often small between Ctrl and Nrn1 -/-. The systematic use of student's t-test may lead to thinking that the differences are significant, which is often misleading given the small number of samples, which makes it impossible to know whether the distributions are Gaussian and whether a parametric test can be used. RNAseq bulk data are based on biological duplicates, which is open to criticism.

(2) The authors use Nrn1+/+ and Nrn1+/- cells indiscriminately as control cells on the basis of similar biology between Nrn1+/+ and Nrn1+/- cells at homeostasis. However, it is quite possible that the Nrn1+/- cells have a phenotype in situations of in vitro activation or in vivo inflammation (cancer, EAE). It would be important to discriminate Nrn1+/- and Nrn1+/+ cells in the data or to show that both cell types have the same phenotype in these conditions too.

(3) Figure 1A-D. Since the authors are using the Nrp1 KO mice, it would be important to confirm the specificity of the anti-Nrn1 mAb by FACS. Once verified, it would be important to add FACS results with this mAb in Figures 1A-C to have single-cell and quantitative data as well.

(4) Figure 1E-H. The authors assume that this immunization protocol induces anergic cells, but they provide no experimental evidence for this. It would be useful to show that T cells are indeed anergic in this model, especially those that are OVA-specific. The lack of IL-2 production by Cltr cells could be explained by the presence of fewer OVA-specific cells, rather than by an anergic status.

(5) Figure 2A-C and Figure 3. The use of iTregs to try to understand what is happening in vivo is problematic. iTregs are cells that have probably no equivalent in vivo, and so may have no physiological relevance. In any case, they are different from pTreg cells generated in vivo. Working with pTreg may be challenging, that is why I would suggest generating data with purified nTreg. Moreover, it was shown in the article of Gonzalez-Figueroa 2021 that Nrn1-/- nTreg retained a normal suppressive function, which would not be what is concluded by the authors of this manuscript. Moreover, we do not even know what the % of Foxp3 cells is in the iTreg used (after differentiation and 20h of re-stimulation) and whether this % is the same between Ctlr and Nrn1 KO cells.

(6) Figure 2D-L. The model is designed to study the role of Nrn1 in nTreg. However, the % of Foxp3+ among CD45.2 nTreg cells fell to 5-15% of CD4+ cells (Figure 2F). Since we do not know what is the % of Foxp3 among the injected cells, we do not know whether this very low % is due to very high Treg instability or to preferential expansion of contaminating Tconvs. It is possible that the % of Tconv contaminant is high since Treg was sorted using beads and not FACS in some experiments. As it is very likely that there are Tconv contaminants that would be Nrn1-/- in the group transferred with Nrn1-/- "nTreg", the higher tumor rejection could be due to an overactivation of Nrn1-/- Tconvs (rather than a defect in Nrn1-/- Treg function).

Reviewer #2 (Public Review):

Summary:

This manuscript explores the role of Nrn1 in T cell tolerance. A previous study has demonstrated that Nrn1 is up-regulated in the Tfr fraction of Foxp3+ T regulatory cells. These authors now confirm the expression of Nrn1 in Tregs as well as report here that Nrn1 is also greatly over-expressed in anergic CD4 T cells, and this is the stepping-off point for this investigation.

Most remarkably, experiments show that anergy induction is defective when T cells cannot express Nrn1. Furthermore, differentiation to a Foxp3+ Treg phenotype is inhibited in the absence of Nrn1, and the Tregs that do develop appear functionally defective. With such defects in the anergy induction and Treg differentiation and function, auto-reactive effector T cell activation is unrestrained, and Nrn1-/- mice are more susceptible to severe EAE development.

Strengths:

The characterizations of T cell Nrn1 expression both in vitro and in vivo are comprehensive and convincing. The in vivo functional studies of anergy development, Treg suppression, and EAE development are also well done to strengthen the notion that Nrn1 is an important regulator of CD4 responsiveness.

Weaknesses:

The major weakness of this study stems from a lack of a clear molecular mechanism involving Nrn1. Previous studies of Nrn1 have suggested its role as a soluble molecule involved in intracellular communication, perhaps influencing cellular ion channel function and/or triggering downstream NFAT and mTOR activation. However, a unique receptor for Nrn1 has not been discovered and it remains unclear whether it acts in a cell-intrinsic or cell-extrinsic fashion for any particular cell type.

Data shown here provide evidence of alterations in the electrical and metabolic state of T cells when the Nrn1 gene is deleted. Nrn1-/- Tregs and Teffector cells each express a unique pattern of genes associated with Neurotransmitter receptor, Metal ion transmembrane transport, Amino acid transport, and mTORC1 signaling activities, different than that seen in wild-type mice. Although the biochemical and informatics studies are well-performed, it is my opinion that these results are inconclusive in part due to the absence of key "naive" control groups. This limits my ability to understand the significance of these data.

Specifically, studies of the electrical and metabolic state of Nrn1-/- inducible Treg cells (iTregs) would benefit from similar data collected from wild-type and Nrn1-/- naive CD4 T cells. Even though naive T cells don't express Nrn1, they may be positively influenced by soluble Nrn1. Does deletion of Nrn1 lead to changes in metabolic and electrical state in naive T cells? Is that why Nrn1 deletion in mice blocks naive T cell activation?

Since the loss of Nrn1 inhibits the activation of T cells, are Nrn1-/- iTregs transcriptionally, electrically, and metabolically similar to naive T cells due to their suboptimal activation? Does this account for their persistent functional defects? Or is up-regulation of Nrn1 (and cell-intrinsic Nrn1 signaling) necessary to complete Treg differentiation and to promote T regulatory function (similar to how cell-intrinsic Nrn1 facilitates anergy induction)? The study of naive cells in parallel with iTregs would address these possibilities.

A comparison of Nrn1-/- naive cells to Teffector cells should also be undertaken to reveal how it is that Nrn1-/- Teffector cells regain the capacity to respond effectively to stimulation (e.g. increased mTOR activation) despite their early activation defects.

Author response:

To Reviewer #1:

Thank you for your thorough review and comments on our work, which you described as “the role of neuritin in T cell biology studied here is new and interesting.”.  We have summarized your comments into two categories: biology and investigation approach, experimental rigor, and data presentation.

Biology and Investigation approach comments:

(1) Questions regarding the T cell anergy model:

Major point “(4) Figure 1E-H. The authors assume that this immunization protocol induces anergic cells, but they provide no experimental evidence for this. It would be useful to show that T cells are indeed anergic in this model, especially those that are OVA-specific. The lack of IL-2 production by Cltr cells could be explained by the presence of fewer OVA-specific cells, rather than by an anergic status.”

T cell anergy is a well-established concept first described by Schwartz’s group. It refers to the hyporesponsive T cell functional state in antigen-experienced CD4 T cells (Chappert and Schwartz, 2010; Fathman and Lineberry, 2007; Jenkins and Schwartz, 1987; Quill and Schwartz, 1987).  Anergic T cells are characterized by their inability to expand and to produce IL2 upon subsequent antigen re-challenge. In this paper, we have borrowed the existing in vivo T cell anergy induction model used by Mueller’s group for T cell anergy induction (Vanasek et al., 2006).  Specifically, Thy1.1+ Ctrl or Nrn1-/- TCR transgenic OTII cells were co-transferred with the congenically marked Thy1.2+ WT polyclonal Treg cells into TCR-/- mice.  After anergy induction, the congenically marked TCR transgenic T cells were recovered by sorting based on Thy1.1+ congenic marker, and subsequently re-stimulation ex vivo with OVA323-339 peptide. We evaluated the T cell anergic state based on OTII cell expansion in vivo and IL2 production upon OVA323-339 restimulation ex vivo.  

“The authors assume that this immunization protocol induces anergic cells, but they provide no experimental evidence for this.”

Because the anergy model by Mueller's group is well established (Vanasek et al., 2006), we did not feel that additional effort was required to validate this model as the reviewer suggested. Moreover, the limited IL2 production among the control cells upon restimulation confirms the validity of this model.

“The lack of IL-2 production by Cltr cells could be explained by the presence of fewer OVAspecific cells, rather than by an anergic status”.

Cells from Ctrl and Nrn1-/- mice on a homogeneous TCR transgenic (OTII) background were used in these experiments. The possibility that substantial variability of TCR expression or different expression levels of the transgenic TCR could have impacted IL2 production rather than anergy induction is unlikely.

Overall, we used this in vivo anergy model to evaluate the Nrn1-/- T cell functional state in comparison to Ctrl cells under the anergy induction condition following the evaluation of Nrn1 expression, particularly in anergic T cells.  Through studies using this anergy model, we observed a significant change in Treg induction among OTII cells. We decided to pursue the role of Nrn1 in Treg cell development and function rather than the biology of T cell anergy as evidenced by subsequent experiments.

Minor points “(6) On which markers are anergic cells sorted for RNAseq analysis?”

Cells were sorted out based on their congenic marker marking Ctrl or Nrn1-/- OTII cells transferred into the host mice.  We did not specifically isolate anergic cells for sequencing.

(2) Question regarding the validity of iTreg differentiation model.

Major point: “(5) Figure 2A-C and Figure 3. The use of iTregs to try to understand what is happening in vivo is problematic. iTregs are cells that have probably no equivalent in vivo, and so may have no physiological relevance. In any case, they are different from pTreg cells generated in vivo. Working with pTreg may be challenging, that is why I would suggest generating data with purified nTreg. Moreover, it was shown in the article of Gonzalez-Figueroa 2021 that Nrn1-/- nTreg retained a normal suppressive function, which would not be what is concluded by the authors of this manuscript. Moreover, we do not even know what the % of Foxp3 cells is in the iTreg used (after differentiation and 20h of re-stimulation) and whether this % is the same between Ctlr and Nrn1 KO cells.”.

We thank Reviewer #1 for their feedback. While it is true that iTregs made in vitro and in vivo generated pTregs display several distinctions (e. g., differences in Foxp3 expression stability, for example), we strongly disagree with this statement by Revieweer#1 “The use of iTregs to try to understand what is happening in vivo is problematic. iTregs are cells that have probably no equivalent in vivo, and so may have no physiological relevance.” The induced Treg cell (iTreg) model was established over 20 years ago (Chen et al., 2003; Zheng et al., 2002), and the model is widely adopted with over 2000 citations. Further, it has been instrumental in understanding different aspects of regulatory T cell biology (Hurrell et al., 2022; John et al., 2022; Schmitt and Williams, 2013; Sugiura et al., 2022).   

Because we have observed reduced pTreg generation in vivo, we choose to use the in vitro iTreg model system to understand the mechanistic changes involved in Treg cell differentiation and function, specifically, neuritin’s role in this process. We have made no claim that iTreg cell biology is identical to pTreg generated in vivo or nTreg cells. However, the iTreg culture system has proved to be a good in vitro system for deciphering molecular events involved in complex processes. As such, it remains a commonly used approach by many research groups in the Treg cell field (Hurrell et al., 2022; John et al., 2022; Sugiura et al., 2022). Moreover, applying the iTreg in vitro culture system has been instrumental in helping us identify the cell electrical state change in Nrn1-/- CD4 cells and revealed the biological link between Nrn1 and the ionotropic AMPA receptor (AMPAR), which we will discuss in the subsequent discussion. It is technically challenging to use nTreg cells for T cell electrical state studies due to their heterogeneous nature from development in an in vivo environment and the effect of manipulation during the nTreg cell isolation process, which can both affect the T cell electrical state.   

“Moreover, it was shown in the article of Gonzalez-Figueroa 2021 that Nrn1-/- nTreg retained a normal suppressive function, which would not be what is concluded by the authors of this manuscript.” 

We have also carried out nTreg studies in vitro in addition to iTreg cells. Similar to Gonzalez-Figueroa et al.'s findings, we did not observe differences in suppression function between Nrn1-/- and WT nTreg using the in vitro suppression assay. However, Nrn1-/- nTreg cells revealed reduced suppression function in vivo (Fig. 2D-L). In fact, Gonzalez-Figueroa et al. observed reduced plasma cell formation after OVA immunization in Treg-specific Nrn1-/- mice, implicating reduced suppression from Nrn1-/- follicular regulatory T (Tfr) cells. Thus, our observation of the reduced suppression function of Nrn1-/- nTreg toward effector T cell expansion, as presented in Fig. 2D-L, does not contradict the results from Gonzalez-Figueroa et al. Rather, the conclusions of these two studies agree that Nrn1 can play important roles in immune suppression observable in vivo that are not captured readily by the in vitro suppression assay.

“Moreover, we do not even know what the % of Foxp3 cells is in the iTreg used (after differentiation and 20h of re-stimulation) and whether this % is the same between Ctlr and Nrn1 KO cells.”

We have stated in the manuscript on page 7 line 208 that “Similar proportions of Foxp3+ cells were observed in Nrn1-/- and Ctrl cells under the iTreg culture condition, suggesting that Nrn1 deficiency does not significantly impact Foxp3+ cell differentiation”. In the revised manuscript, we will include the data on the proportion of Foxp3+ cells before iTreg restimulation.

(3) Confirmation of transcriptomic data regarding amino acids or electrolytes transport change

Minor point“(3) Would not it be possible to perform experiments showing the ability of cells to transport amino acids or electrolytes across the plasma membrane? This would be a more interesting demonstration than transcriptomic data.”

We appreciate Review# 1’s suggestion regarding “perform experiments showing the ability of cells to transport amino acids or electrolytes across the plasma membrane”.  We have indeed already performed such experiments corroborating the transcriptomics data on differential amino acid and nutrient transporter expression. Specifically, we loaded either iTreg or Th0 cells with membrane potential (MP) dye and measured MP level change after adding the complete set of amino acids (complete AA).  Upon entry, the charge carried by AAs may transiently affect cell membrane potential. Different AA transporter expression patterns may show different MP change patterns upon AA entry, as we showed in Author response image 1. We observed reduced MP change in Nrn1-/- iTreg compared to the Ctrl, whereas in the context of Th0 cells, Nrn1-/- showed enhanced MP change than the Ctrl. We can certainly include these data in the revised manuscript.

Author response image 1.

Membrane potential change induced by amino acids entry. a. Nrn1-/- or WT iTreg cells loaded with MP dye and MP change was measured upon the addition of a complete set of AAs. b. Nrn1-/- or WT Th0 cells loaded with MP dye and MP change was measured upon the addition of a complete set of AAs.

(4) EAE experiment data assessment

Minor point ”(5) Figure 5F. How are cells re-stimulated? If polyclonal stimulation is used, the experiment is not interesting because the analysis is done with lymph node cells. This analysis should either be performed with cells from the CNS or with MOG restimulation with lymph node cells.”

In the EAE study, the Nrn1-/- mice exhibit similar disease onset but a protracted non-resolving disease phenotype compared to the WT control mice.  Several reasons may contribute to this phenotype: 1. Enhanced T effector cell infiltration/persistence in the central nervous system (CNS); 2. Reduced Treg cell-mediated suppression to the T effector cells in the CNS; 3. Protracted non-resolving inflammation at the immunization site has the potential to continue sending T effector cells into CNS, contributing to persistent inflammation. Based on this reasoning, we examined the infiltrating T effector cell number and Treg cell proportion in the CNS.  We also restimulated cells from draining lymph nodes close to the inflammation site, looking for evidence of persistent inflammation.  When mice were harvested around day 16 after immunization, the inflammation at the local draining lymph node should be at the contraction stage.  We stimulated cells with PMA and ionomycin intended to observe all potential T effector cells involved in the draining lymph node rather than only MOG antigen-specific cells.  We disagree with Reviewer #1’s assumption that “This analysis should either be performed with cells from the CNS or with MOG restimulation with lymph node cells.”. We think the experimental approach we have taken has been appropriately tailored to the biological questions we intended to answer.

Experimental rigor and data presentation.

(1) Data labeling and additional supporting data

Major points (2) The authors use Nrn1+/+ and Nrn1+/- cells indiscriminately as control cells on the basis of similar biology between Nrn1+/+ and Nrn1+/- cells at homeostasis. However, it is quite possible that the Nrn1+/- cells have a phenotype in situations of in vitro activation or in vivo inflammation (cancer, EAE). It would be important to discriminate Nrn1+/- and Nrn1+/+ cells in the data or to show that both cell types have the same phenotype in these conditions too.

(3) Figure 1A-D. Since the authors are using the Nrp1 KO mice, it would be important to confirm the specificity of the anti-Nrn1 mAb by FACS. Once verified, it would be important to add FACS results with this mAb in Figures 1A-C to have single-cell and quantitative data as well.

Minor points  

(1) Line 119, 120 of the text. It is said that one of the most up-regulated genes in anergic cells is Nrn1 but the data is not shown.

(2) For all figures showing %, the titles of the Y axes are written in an odd way. For example, it is written "Foxp3% CD4". It would be more conventional and clearer to write "% Foxp3+ / CD4+" or "% Foxp3+ among CD4+".

(4) For certain staining (Figure 3E, H) it would be important to show the raw data, in addition to MFI or % values.

We can adapt the labeling and provide additional data, including Nrn1 staining on Treg cells and flow graphs for pmTOR and pS6 staining (Fig. 3H), as requested by Reviewer #1.

(2) Experimental rigor:

General comments:

“However, it is disappointing that reading this manuscript leaves an impression of incomplete work done too quickly.”

We were discouraged to receive the comment, “this manuscript leaves an impression of incomplete work done too quickly.” Our study of this novel molecule began without any existing biological tools such as antibodies, knockout mice, etc.  Over the past several years, we have established our own antibodies for Nrn1 detection, obtained and characterized Nrn1 knockout mice, and utilized multiple approaches to identify the molecular mechanism of Nrn1 function. Through the use of the in vitro iTreg system described in this manuscript, we identified the association of Nrn1 deficiency with cell electrical state change, potentially connected to AMPAR function. We have further corroborated our findings by generating Nrn1 and AMPAR T cell specific double knockout mice and confirmed that T cell specific AMPAR deletion could abrogate the phenotype caused by the Nrn1 deficiency (see Author response image 2).  We did not include the double knockout data in the current manuscript because AMPAR function has not yet been studied thoroughly in T cell biology, and we feel this topic warrants examination in its own right.  However, the unpublished data support the finding that Nrn1 modulates the T cell electrical state and, consequently, metabolism, ultimately influencing tolerance and immunity.  In its current form, the manuscript represents the first characterization of the novel molecule Nrn1 in anergic cells, Tregs, and effector T cells. While this work has led to several exciting additional questions, we disagree that the novel characterization we have presented Is incomplete. We feel that our present data set, which squarely highlights Nrn1’s role as an important immune regulator while shedding unprecedented light on the molecular events involved, will be of considerable interest to a broad field of researchers.

“Multiple models have been used, but none has been studied thoroughly enough to provide really conclusive and unambiguous data. For example, 5 different models were used to study T cells in vivo. It would have been preferable to use fewer, but to go further in the study of mechanisms.”

We have indeed used multiple in vivo models to reveal Nrn1's function in Treg differentiation, Treg suppression function, T effector cell differentiation and function, and the overall impact on autoimmune disease. Because the impact of ion channel function is often context-dependent, we examined the biological outcome of Nrn1 deficiency in several in vivo contexts.  We would appreciate it if Reviewer#1 would provide a specific example, given the Nrn1 phenotype, of how to proceed deeper to investigate the electrical change in the in vivo models.

“Major points (1) A real weakness of this work is the fact that in most of the results shown, there are few biological replicates with differences that are often small between Ctrl and Nrn1 -/-. The systematic use of student's t-test may lead to thinking that the differences are significant, which is often misleading given the small number of samples, which makes it impossible to know whether the distributions are Gaussian and whether a parametric test can be used. RNAseq bulk data are based on biological duplicates, which is open to criticism.”

We respectfully disagree with Reviewer #1 on the question of statistical power and significance to our work. We have used 5-8 mice/group for each in vivo model and 3-4 technical replicates for the in vitro studies, with a minimum of 2-3 replicate experiments. These group sizes and replication numbers are in line with those seen in high-impact publications. While some differences between Ctrl and Nrn1-/- appear small, they have significant biological consequences, as evidenced by the various Nrn1-/- in vivo phenotypes. Furthermore, we believe we have subjected our data to the appropriate statistical tests to ensure rigorous analysis and representation of our findings.

To Reviewer #2.

We thank Reviewer #2 for the careful review of the manuscript. We especially appreciate the comments that “The characterizations of T cell Nrn1 expression both in vitro and in vivo are comprehensive and convincing. The in vivo functional studies of anergy development, Treg suppression, and EAE development are also well done to strengthen the notion that Nrn1 is an important regulator of CD4 responsiveness.”

“The major weakness of this study stems from a lack of a clear molecular mechanism involving Nrn1. “  

We fully understand this comment from Reviewer #2. The main mechanism we identified contributing to the functional defect of Nrn1-/- T cells involves novel effects on the electric and metabolic state of the cells. Although we referenced neuronal studies that indicate Nrn1 is the auxiliary protein for the ionotropic AMPA-type glutamate receptor (AMPAR) and may affect AMPAR function, we did not provide any evidence in this manuscript as the topic requires further in-depth study.   

For the benefit of this discussion, we include our preliminary Nrn1 and AMPAR double knockout data (Author response image 2), which indicates that abrogating AMPAR expression can compensate for the defect caused by Nrn1 deficiency in vitro and in vivo. This preliminary data supports the notion that Nrn1 modulates AMPAR function, which causes changes in T cell electric and metabolic state, influencing T cell differentiation and function.  

Author response image 2.

Deletion of AMPAR expression in T cells compensates for the defect caused by Nrn1 deficiency. Nrn1-/- mice were crossed with T cell-specific AMPAR knockout mice (AMPARfl/flCD4Cre+) mice. The following mice were generated and used in the experiment: T cell specific AMPAR-knockout and Nrn1 knockout mice (AKONKO), Nrn1 knockout mice (AWTNKO), Ctrl mice (AWTNWT). a. Deletion of AMPAR compensates for the iTreg cell defect observed in Nrn1-/- CD4 cells. iTreg live cell proportion, cell number, and Ki67 expression among Foxp3+ cells 3 days after aCD3 restimulation. b. Deletion of AMPAR in T cells abrogates the enhanced autoimmune response in Nrn1-/- Mouse in the EAE disease model. Mouse relative weight change and disease score progression after EAE disease induction.  

Ion channels can influence cell metabolism through multiple means (Vaeth and Feske, 2018; Wang et al., 2020). First, ion channels are involved in maintaining cell resting membrane potential. This electrical potential difference across the cell membrane is essential for various cellular processes, including metabolism (Abdul Kadir et al., 2018; Blackiston et al., 2009; Nagy et al., 2018; Yu et al., 2022). Second, ion channels facilitate the movement of ions across cell membranes. These ions are essential for various metabolic processes. For example, ions like calcium (Ca2+), potassium (K+), and sodium (Na+) play crucial roles in signaling pathways that regulate metabolism (Kahlfuss et al., 2020). Third, ion channel activity can influence cellular energy balance due to ATP consumption associated with ion transport to maintain ion balances (Erecińska and Dagani, 1990; Gerkau et al., 2019). This, in turn, can impact processes like ATP production, which is central to cellular metabolism. Thus, ion channel expression and function determine the cell’s bioelectric state and contribute to cell metabolism (Levin, 2021).

Because the AMPAR function has not been thoroughly studied using a genetic approach in T cells, we do not intend to include the double knockout data in this manuscript before fully characterizing the T cell-specific AMPAR knockout mice.  

“Although the biochemical and informatics studies are well-performed, it is my opinion that these results are inconclusive in part due to the absence of key "naive" control groups. This limits my ability to understand the significance of these data.

Specifically, studies of the electrical and metabolic state of Nrn1-/- inducible Treg cells (iTregs) would benefit from similar data collected from wild-type and Nrn1-/- naive CD4 T cells.”

We appreciate the reviewer’s comments. This comment reflects two concerns in data interpretation:

(1) Are Nrn1-/- naïve T cells fundamentally different from WT cells? Does this fundamental difference contribute to the observed electrical and metabolic phenotype in iTreg or Th0 cells? This is a very good question we will perform the experiments as the reviewer suggested. While Nrn1 is expressed at a basal (low) level in naïve T cells, deletion of Nrn1 may cause changes in naïve T cell phenotype.   

(2) Is the Nrn1-/- phenotype caused by Nrn1 functional deficiency or due to the secondary effect of Nrn1 deletion, such as non-physiological cell membrane structure changes?

We have done the following experiment to address this concern.  We have cultured WT T cells in the presence of Nrn1 antibody and compared the outcome with Nrn1-/- iTreg cells (Author response image 3). WT iTreg cells under antibody blockade exhibited similar changes as Nrn1-/- iTreg cells, confirming the physiological relevance of the Nrn1-/- phenotype.

Author response image 3.

Nrn1 antibody blockade in WT iTreg cell culture caused similar phenotypic change as in Nrn1-/- iTreg cells. Nrn1-/- and WT CD4 cells were differentiated under iTreg condition in the presence of anti-Nrn1 (aNrn1) antibody or isotype control for 3 days. Cells were restimulated with anti-CD3 and in the presence of aNrn1 or isotype. a. MP measured 18hr after anti-CD3 restimulation. b. live CD4 cell number and proportion of Ki67 expression among live cells three days after restimulation. c. The proportion of Foxp3+ cells among live cells three days after restimulation.  

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eLife assessment

This study presents a convincing analysis of the effects of covariates, such as age, sex, socio-economic status, or biomarker levels, on the predictive accuracy of polygenic scores for body mass index; The work is further supported by important approaches for improving prediction accuracy by accounting for such covariates across a variety of association studies. The authors did a commendable job addressing reviewer suggestions and comments. The work will be of interest to colleagues using and developing methods for phenotypic prediction based on polygenic scores.

Joint Public Review:

In this paper Hui and colleagues investigate how the predictive accuracy of a polygenic score (PGS) for body mass index (BMI) changes when individuals are stratified by 62 different covariates. After showing that the PGS has different predictive power across strata for 18 out of 62 covariates, they turn to understanding why these differences and seeing if predictive performance could be improved. First they investigated which types of covariates result in the largest differences in PGS predictive power, finding that covariates with with larger "main effects" on the trait and covariates with larger interaction effects (interacting with the PGS to affect the trait) tend to better stratify individuals by PGS performance. The authors then see if including interactions between the PGS and covariates improves predictive accuracy, finding that linear models only result in modest increases in performance but nonlinear models result in more substantial performance gains.

Overall, the results are interesting and well-supported. The results will be broadly interesting to people using and developing PGS methods, as well as the broader statistical genetics community.

A few of the important points of the paper are:

A major impediment to the clinical use of PGS is the interaction between the PGS and various other routinely measure covariates, and this work provides a very interesting empirical study along these lines. The problem is interesting, and the work presented here is a convincing empirical study of the problem.

The result that PGS accuracy differs across covariates, but in a way that is not well-captured by linear models with interactions is important for PGS method development.

The quantile regression analysis is an interesting approach to explore how and why PGS may differ in accuracy across different strata of individuals.

Author response:

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

We previously responded to reviewer comments in a previous iteration of this draft, edited the manuscript accordingly, and have no further comments on the majority of them. However, we performed additional analyses mainly in response to weaknesses Reviewer 1 highlighted related to “one shortcoming [being] the lack of a conceptual model explaining the results”, and the eLife assessment stating “the study falls short of providing a cogent interpretation of key findings, which could be of great interest and utility”. We provide a conceptual explanation that ties together many of our results, which we demonstrate using real data and further explore using simulated data – these analyses are in a new section titled “Increase in PGS effect for increasing percentiles of BMI itself, and its relation to R2 differences when stratifying by covariates”, with the Discussion also being updated accordingly.

Essentially, we demonstrate that the effect of PGSBMI increases as BMI itself increases (using quantile regression – newly created Figure 5). This finding helps explain the correlation between covariate main effects, interaction effects, and maximum R2 differences when stratifying on different covariates, and also why any one or combination of covariates did not seem to be of unusual interest. While this result readily explains why covariates with larger main effects have larger interaction effects, by itself it does not seem to explain the differences in R2 in covariate-stratified bins, but we show using portions of real data and simulated data that in the case of this study they are closely related.

Effectively, as the effect of PGSBMI increases, variance in the phenotype will also increase – so long as the residuals do not increase proportionately, this causes R2 to also increase as R2 directly depends on outcome variance. We demonstrate this using simulated data (newly created S Figure 2) and real data (newly created S Figure 3). So the largest R2 differences between certain covariate-stratified bins seems to be a direct consequence of those covariates also having the largest PGSBMI*covariate interaction effects. These results tie into our previous response to Reviewer 1, where essentially there is not only heteroskedasticity in the relationship between PGSBMI and BMI, but a cause of the heteroskedasticity is an increasing effect in PGSBMI as BMI itself increases.

In the Discussion, we highlight several broad implications of these findings. First, these results may, in part, provide a generalizable explanation for epistasis, as the effect of a PGS (or any individual SNP) seems to depend on phenotype, and as phenotype depends on many SNPs, the effect of PGS and individual SNPs depends on other SNPs. Second, these results may also provide a generalizable explanation for GxE, as, demonstrated in this paper, interaction effects for SNPs (or a PGS) may largely depend on the phenotypic value itself, rather than any specific environment(s) or combination of. Finally, related to our previous response to Reviewer 2, modeling effects of SNPs dependent on phenotype itself would almost certainly result in gains in PGS performance (and locus discovery), which should also be larger than e.g., just GxAge effects as we demonstrated in this manuscript.

Reviewer #2 (Public Review):

Summary

The authors developed new tools for isolating PI3K activity and for labeling newly made membrane proteins for monitoring membrane trafficking. They found that PI3K activity alone was able to explain the increased presence of TRPV1 on the membrane independent of other cascades induced by NGF signaling. They also showed an interesting feedback between PI3K and the insulin receptor trafficking to the membrane.

Strengths:

A major strength of the paper is the innovative combination of techniques. The first technique used the optogenetic PhyB/PIF system. They anchored PhyB to the membrane and fused PIF with the interSH2 domain from PI3K. This allowed them to use 650nm light to induce an interaction between the PhyB and PIF resulting in a recruitment of the endogenous PI3K to the membrane through the iSH2 domain without actual activation of an RTK. This allowed them to dissect out one function, just PI3K recruitment/activation from the vast number of RTK downstream cascades.

The second technique was the development of a new non-canonical amino acid that is cell-impermeant. The authors synthesized the sTSO-sulfa-Cy5 compound that will react with the Tet3 ncAA through click chemistry. They showed that the sulfa-Cy5 did not cross the membrane and would be used to track protein production over time, though the reaction rates were slow as noted by the authors. The comparison of the sulfa-Cy5 data with the standard GFP with TIRF showed a clear difference indicating the useful information that is gained with the ncAA.

Another strength comes from the discovery that an isolated PI3K is responsible for increasing TRPV1 and InR trafficking to the plasma membrane.

Weakness:

The discussion does not go into much detail regarding the importance of their discovery of TRPV1 and InR increases trafficking due to PI3K activation. It also jumps to the limitations of in vivo implementation prematurely. These weaknesses are minor however.

The authors achieved their goal of creating the tools needed to separate out one of the many RTK signals and give a strong proof of concept implementation of their tools. Their results support their conclusions and will help understand how TRPV1 is regulated by signals other than the traditional channel activators. The tools developed in the article will be of use to the broader cell biology and biophysics community, not just the channel community. The opto control of the PhyB/PIF system makes it more convenient than other systems since it does not take the typical wavelengths needed for fluorescence. The cell-impermeant ncAA will also be a great tool for those studying membrane proteins, protein trafficking and protein dynamics.

eLife assessment

This study develops a new and important method for dissecting out two overlapping cell signaling pathways, phosphoinositide signaling and membrane protein trafficking. The combination of two state-of-the-art spectroscopic techniques provides compelling evidence for a reciprocal influence between an enzyme and a channel. The work will be of interest to the broader cell biology, biophysics and biochemistry communities.

Reviewer #1 (Public Review):

Summary:

This work seeks to isolate the specific effects of phosphoinositide 3-kinase (PI3K) on the trafficking of the ion channel TRPV1, distinct from other receptor tyrosine kinase-activated effectors. It builds on earlier studies by the same group (Stein et al. 2006; Stratiievska et al. 2018), which described the regulatory relationship between PI3K, nerve growth factor (NGF), and TRPV1 trafficking. A central theme of this study is the development of methods that precisely measure the influence of PI3K on TRPV1 trafficking and vice versa. The authors employ a range of innovative methodologies to explore the dynamics between TRPV1 and PI3K trafficking.

Strengths:

A major strength of this study is the application of innovative methods to understand the interaction between PI3K and TRPV1 trafficking. The key techniques presented include:

(1) The optogenetic trafficking system based on phytochrome B, introduced in this research. Its interaction mechanism, dependent on reversible light activation, is comprehensively explained in Figures 1 and 2, with the system's efficacy demonstrated in Figure 3.

(2) An extracellular labeling method using click chemistry, which although not exclusive to this study, introduces specific reagents engineered for membrane impermeability.

The central biological insight presented here is the sufficiency of PI3K activation to guide TRPV1 trafficking to the plasma membrane. An additional notable discovery is the potential regulation of insulin receptors via this mechanism.

The paper's strengths are anchored in its innovative methodologies and the valuable collaboration between groups specializing in distinct areas of research.

Weaknesses:

The paper might benefit from a more streamlined structure and a clearer emphasis on its findings. A possible way to enhance its impact might be to focus more on its methodological aspects. The methodological facets stand out as both innovative and impactful. These experiments are well-executed and align with biological expectations. It's evident how these techniques could be tailored for many protein trafficking studies, a sentiment echoed in the manuscript (lines 287-288). When seen through a purely biological lens, some findings, like those concerning the PI3K-TRPV1 interaction, are very similar to previous work (Stratiievska et al. 2018). A biological focus demands further characterization of this interaction through mutagenesis. Also, the incorporation of insights on the insulin receptor feels somewhat tangential. A cohesive approach could be to reshape the manuscript with a primary focus on methodology, using TRPV1 and InsR as illustrative examples.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Koh, Stratiievska, and their colleagues investigate the mechanism by which TRPV1 channels are delivered to the plasma membrane following the activation of receptor tyrosine kinases, specifically focusing on the NGF receptor. They demonstrate that the activation of the NGF receptor's PI3K pathway alone is sufficient to increase the levels of TRPV1 at the plasma membrane.

Strengths:

The authors employ cutting-edge optogenetic, imaging, and chemical-biology techniques to achieve their research goals. They ingeniously use optogenetics to selectively activate the PI3K pathway without affecting other NGF pathways. Additionally, they develop a novel, membrane-impermeable fluorescent probe for labeling cell-surface proteins through click-chemistry.

Comment on revised version:

We commend the authors on the significant improvements made to the manuscript. They have adequately addressed our comments. Notably, the new control experiments shown in Figure 4E and Figure 5 Fig. Supp 1 convincingly demonstrate the specificity of the NGF and 650 nm light stimuli, respectively. The addition of quantitative analyses strengthens the findings significantly. Furthermore, the manuscript is now presented in a much linear manner, enhancing its clarity and impact.

Author response:

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

We thank the reviewers for their careful reading of our manuscript and their constructive comments. We have significantly improved the writing, consolidated figures, and include new experiments (see below). We now center the manuscript on the methods used and have updated the title to reflect this new emphasis. We have also added quantification with statistics, as described below. A detailed description of our improvements is provided below.

New data figures:   

• Fig 3 – fig supp 2 – new experiment with insulin-triggered endocytosis of InsR

• Fig 3 – fig supp 3 – new experiments, all using the same protein construct

• Fig 3 – movie–  new experiment with insulin-triggered endocytosis of InsR

• Fig 4 – added new vehicle-only negative control experiments

• Fig 5 – fig supp 1 – new negative control experiments with sequential exposures to 750 nm light

Added figure panels with quantification/statistics for:  Fig. 1F; , Figure 1- figure supp 2B, Figure 2B, D, Fig. 2 – fig supp 1B, D; Fig 2 – fig supp 2B;  Fig 2 – fig supp 3B;  

Reviewer #1:

(1) The paper might benefit from a more streamlined structure and a clearer emphasis on its findings. A possible way to enhance its impact might be to focus more on its methodological aspects. The methodological facets stand out as both innovative and impactful.

We thank the reviewer for this suggestion and have rewritten the manuscript to center the methods, with our applications to TRPV1 and the InsR serving as examples.

(2) Line 243: Please provide a reference for Tet3-Bu or clarify its origin in this study. A concise description would be helpful.

The Jang et al., 2020 and Jana et al., 2023 studies are cited and give the structure of Tet3-Bu in Figure 3A.

(3) Consider merging Figures 1 and 2 for clarity.  

Because the cell types and constructs expressed differ for the figures, we did not merge them. However, we moved Figure 1 to the supplement because it repeats previously published data.

(4) Lines 281 and 293 should refer to Figure 5C, not 5B.  

This is now corrected.

(5) Should the paper pivot towards methodology, combining Figures 6 and 7 might be more coherent. 

The experiments in Figures 6 and 7 are different, making it difficult to merge them. However, Figures 7 and 8 describe the same experimental approach applied to two different membrane proteins. To align with our new focus on the methods and deemphasis of the biological system, we have merged Figures 7 and 8.

(6) A brief discussion comparing the cell surface labeling techniques and the merits of the presented system would offer valuable context.

We agree that additional discussion here would be helpful but were also trying to satisfy Reviewer #3’s request to reduce review-like content that disrupts the flow of the primary results. We therefore did not add a discussion of cell-surface labeling techniques.

Reviewer #2:

(1) To monitor the phosphatidylinositol-3,4,5-trisphosphates, the pleckstrin homology (PH) domain from Akt was used. This PH domain is not specific for just PI(3,4,5)P3 as stated by the authors. The Akt PH domain also binds PI(3,4)P2. The observed PI3K localization increase will also increase PI(3,4)P2 concentrations so the observed responses may not be solely because of PI(3,4,5)P3…

…Repeating the PH domain experiments with a PH domain that is specific for just PI(3,4,5)P3, like GRP1 or Btk, would be useful to separate out any contributions from PI(3,4)P2.

We have repeated key experiments demonstrating optogenetic activation of PI3K with the Grp1-PH domain and included these data in Figure 1-figure supplement 2.

(2) The data in Figure 4 supplement was confusing to interpret since it is unclear whether a membrane protein with the Tet3 is being expressed at the same time as the ncAA for labeling or if the observed labeling is endogenous. If the observed labeling in Figure 4 supplement D is endogenous, then significant concerns come up regarding the background labeling of the sTCO-sulfo-Cy5 used in the rest of the experiments.

We have updated the data in this figure using the same protein (InsR-Tet3-Bu-GFP) for every sTCO-conjugated dye tested. The protein is also labelled with GFP, making it clear which cells in the field were transfected and which were not. The new panels showing the bright field images for each field further aid readers in identifying untransfected cells. We believe the new presentation addresses the reviewer’s concerns about distinguishing sTCO labeling of Tet3-Bu-incorporating protein from labeling of endogenous proteins.

(3) I recommend reorganizing the article to be more linear. For example, Figure 4 is not fully explained until after Figure 4 supplement and Figure 5. This non-linear organization required a lot of back and forth reading to fully understand the logic of the experiments as well as the conclusions. 

We have improved the presentation along the lines suggested by the reviewer.

(4) The InsR data is interesting as a proof of concept however the writing around the InsR looks like an afterthought. The explanation for why InsR is chosen, what is known and unknown about its trafficking is given secondary importance in the writing but not in the figures. This difference weakens the article.  

We have improved the presentation along the lines suggested by the reviewer.

(5) Line 244 should read Figure 4A.  

This is now corrected.

(6) Line 281 should read Figure 5C.  

This is now corrected.

(7) Line 645. Fig 4, says C and E were shown as inverted b&w images when they aren't.  

This is now corrected.

(8) Fig 8. Line 702. States that these are TRPV1 positive cells but the figure is about InsR.

This is now corrected.

Reviewer #3:

(1) The Results section is lengthy and disorganized. Consider revising it for better clarity and conciseness. For instance, moving lines 157 and 166-170 to the Discussion or Methods section can streamline the Results section.  

We have improved the presentation along the lines suggested by the reviewer.

(2) Provide more specificity in reporting: In lines 139-170, clarify why you chose to use PhyB and this particular technique. Eliminate extraneous details and maintain a more concise narrative.

We have improved the presentation along the lines suggested by the reviewer.

(3) Avoid excessive review-like content, and keep the Results section focused on presenting novel findings. Simplify lines 4 173-185 to provide a straightforward presentation of results rather than extensive references to previous work.

We have improved the presentation along the lines suggested by the reviewer.

(4) Reevaluate lines 196-204 to determine if they are best suited for the Results section or if they could be moved to the Discussion or Methods for improved focus.

We have improved the presentation along the lines suggested by the reviewer.

(5) 231-238, revise the content to be more concise and directly to the point.  

We have improved the presentation along the lines suggested by the reviewer.

(6) Limit the number of figures to a maximum of five and restructure them to enhance readability. Consider consolidating panels from Figures 1 (which replicates previouslypublished work), 2, and 3 into a single figure to improve organization and information flow. 

See response to Reviewer #1, Comment #3. Although we did not merge Figures 2 and 3, we have consolidated the writing to improve the flow of the writing.

(7) Move Fig 5, which depicts control experiments, to supplementary information to improve the overall flow of the paper. Also, Figure 5 comes in the text before Figure 4 C-F and before Figure 4- supp1, so placing it in supplementary information would fix this issue. 

We have moved this figure to the supplement as Figure 3 – figure supplement 1.

(8) Merge Figures 6, 7, and 8 (or at least 7 and 8) to facilitate the comparison of data obtained with different proteins or conditions.  

We have merged Figures 7 and 8.

(9) Line 303: when referring to the chemical structure of sTCO-sulfo-Cy5, refer to Figure 4 Supp 1 and not Figure 9. Alternatively, consider moving Fig 9 to supplementary information or placing it earlier in the figure list.  

We now refer to the earlier supplemental figure when describing the structure of sTCO-sulfo-Cy5.

(10) Ensure proper referencing of Figure 4E in the text, particularly since it's vital to understanding the selection of mutation sites for the Insulin receptor, as discussed in lines 392-400. 

We have made this correction.

(11) Maintain citation consistency by verifying that all references cited in the text, including those in the Introduction, Results, and Discussion sections, are included in the References list at the end of the paper.

We have reviewed all our citations for consistency.

The reviewer is also concerned by the lack of any statistical analyses, and of appropriate control experiments:

(1) The trapping of PI3K at the plasma membrane, shown in Figure 3 supplementary 1, is not very convincing. It is unclear whether PI3K is trapped at the membrane, as claimed by the authors, or whether PI3K slowly accumulates at the membrane independently of the light stimulation. Indeed, the baseline fluorescence isn't flat to start with (especially in F-11 cells), and the change in fluorescence under 650 nm light is very modest, much weaker, in fact, than in control experiments without TRPV1 (Figure 2C). Do the authors observe a similar drift in fluorescence in absence of photostimulation at 650 nm? Such control experiment needs to be performed and discussed. More importantly, authors need to provide quantitative (and not just qualitative) measures of the changes in fluorescence observed in the different conditions, and run adequate statistical analyses to compare the different conditions (for all the figures of the manuscript where this applies).  

We can see that the language of “trapped at the membrane” is more of an interpretation than a description. We now describe this result as a lack of dissociation of PIF-iSH2 from the membrane in response to 750 nm light. We more clearly explain our interpretation and label it as speculative.

(2) Consider moving Figure 3 Supplementary 1 from supplementary information to the main document due to its importance. It seems like an important finding to me, and I believe also to the authors, who wrote a whole paragraph on PI3K trapping in the discussion section (lines 361-380).  

We agree that the results from this figure are important. To better align with the request of all reviewers to shorten the manuscript and reduce the number of figures in the main text, however, we have left the figure in the supplement.

(3) Figure 3: why is the increase in IP3 levels not reversible as in Figure 2? Is this because IP3 is detected only at the membrane level (TIRF experiment) and not the entire cell? Authors should comment on this aspect. 

As described in response to Comment#2, we now better explain our interpretation. Briefly, we speculate that the PIF-iSH2 that encounters TRPV1 in the plasma membrane binds to the ankyrin repeat domain of TRPV1 and, therefore, does not readily dissociate from membrane in response to 750 nm light.

(4) Figure 4E: Verify the functionality of the Insulin receptor mutants, as was done for TRPV1.  

We have added new experiments to demonstrate that the insulin receptor incorporating Tet3-Bu is functional. Because the insulin receptor is not electrogenic, we could not use electrophysiology to validate its function. Instead, we measured the insulin-dependent endocytosis of the receptor. These data are now presented in Figure 3 – figure supplement 2 and Figure 3 –  supplemental movie.

(5) Figures 6 to 8: The authors quantify the change in plasma membrane expression of TRPV1 and insulin receptors after NGF treatment (or photoactivation), but an important control experiment is missing. They first label cells with sulfo-Cy5, then treat them with NGF (or photoactivate them with 650 nm light), and then label them again with sulfo-Cy5, supposedly to label only the TRPV1 receptors that newly arrived at the membrane. However, we have no evidence that the first sulfo-Cy5 labeling (1 uM, 5 min) was complete. In fact, labeling with sulfo-Cy5 (200 nM) in Figure 4 never reaches saturation, not even after 20 min. The authors need to control for this, by comparing the change in fluorescence with and without NGF treatment. The GFP control is simply not sufficient. Also, include Figure 8 in the text, as it is missing from the results section, and discuss the results in more detail. Indeed, the current data is appealing as it suggests that what was observed with TRPV1 is also true for the Insulin receptor, but without a proper control this could just be an artefact.  

We have performed several new control experiments to address the reviewer’s concerns. (1) For NGF-induced increase in TRPV1 at the plasma membrane, we repeated the experiment using a vehicle instead of NGF. These data, added to Figure 4E, demonstrate that the increase in plasma membrane TRPV1 depends on NGF. (2) For the light-activated increase in plasma membrane TRPV1, we repeated the experiment using a second exposure to the deactivating 750 nm light instead of the activating 650 nm light and added the data as Figure 5, figure supplement 1A-E. These new data demonstrate that the increase in plasma membrane TRPV1 occurred only in response to  the activating wavelength of light. (3) To address the same as the previous comment, but for the insulin receptor, we repeated the insulin receptor experiments also using a second exposure to the deactivating wavelength of light. These data are now shown in Figure 5, figure supplement 1F-I and demonstrate that the increase in the insulin receptor levels in the plasma membrane required the activating wavelength of light.

(6) Line 313: "Importantly, sTCO-sulfo-Cy5 did not appear to equilibrate across the cell membrane and did not label untransfected cells (i.e., those without GFP; Figure 4 - figure supplement 1)". I don't see where the absence of labeling of untransfected cells is shown. The authors should show fluorescence changes on the surface of both transfected and untransfected cells and, as discussed above, quantify the data and provide statistical analyses.

See response to Reviewer #2, Comment #2.

Minor Comments:

(1) Define « PM » and « RTK » in abstract  We have made the requested changes.

(2) Consider presenting the signaling pathways defined in the introduction in a scheme to improve readability.  

We have added the signaling pathways defined in the introduction to Figure 1A.

(3) In Figure 1A, include the CAAX lipidation signal in the schematic representation.  

We had already shown the lipidation itself, but we have added the lipidation signal as a magenta star, with its meaning explained in the figure legend. We hope the reviewer finds this useful.

(4) Terminology clarification: Given the broad readership of Elife, provide clearer explanations for terms and techniques used, such as the function of PIF (line 144).  

We define the acronym PIF in the text, but do not further elaborate on the biological function of PIF to align with other reviewers’ requests that we reduce the review-type material in the manuscript.

(5) Correct "m-1s-1" to "M-1s-1" in line 119.  

This is now corrected.

(6) Replace "activate" with "activation" in line 122.  

This is now corrected.

(7) Indicate 650 nm and 750 nm next to the arrows in Figure 2B for reader clarity.  

We have added the requested arrow labels.

(8) Correct Figure 5A to Figure 4A in line 244.  

This is now corrected.

(9) Correct Figure 5B to Figure 5C in line 293.  

This is now corrected.

(10) In lines 274, 293, 312 and 329, clearly specify which panels of the referenced figures are being discussed to avoid confusion. 

We have now clearly specified which panels are being referenced.

(11) Figure 1B: it is unclear how long after 650 nm light switching the image is taken. The red bar indicating 650 nm light makes it look like the image is taken right after light switching, which would suggest that PIF-YFP trafficking to the membrane takes milliseconds in response to 650 nm light. However, the legend says that photoactivation kinetics are in the range of 10 seconds. Please accurately position the red bar in Figure 1B to reflect the time between light switching and imaging, and specify the time between light switching and imaging in the figure legend.  

We have more accurately shown the timing of image acquisition in what is now Figure 1, figure supplement 1.

(12) Please add a merged image for all the immune data figure.

We are uncertain about which figures the reviewer is referring to. We do not have any immunohistochemistry in the manuscript.  

(13) Line 205: "we found that expression of TRPV1 trapped PIF-iSH2 at the PM upon stimulation with 650 nm light, so that it no longer translocated to the cytoplasm in response to 750 nm light (Figure 3B and Figure 3 - figure supplement 1A)." This is shown in the supplementary figure but not in Figure 3B. Same issue with the following sentence.  

We have corrected the figure references in the text.

(14) For Figures 7 and 8, the authors state ""We next asked whether click chemistry labeling could be executed in cells in which we also used the PhyB/PIF machinery for activating PI3K." Is this really the main motivation for conducting these experiments?

Good point. We have improved the writing around this issue.

Reviewer #2 (Public Review):

Summary:

The study tries to connect energy metabolism with immune tolerance during bacterial infection. The mechanism details the role of pyruvate transporter expression via ERRalpha-PGC1 axis, resulting in pro-inflammatory TNF alpha signalling responsible for acquired infection tolerance.

Strengths:

Overall, the study is an excellent addition to the role of energy metabolism during bacterial infection. The mechanism-based approach in dissecting the roles of metabolic coactivator, transcription factor, mitochondrial transporter, and pro-inflammatory cytokine during acquired tolerance towards infections indicates a detailed and well-written study. The in vivo studies in mice nicely corroborate with the cell line-based data, indicating the requirement for further studies in human infections with another bacterial model system.

Weaknesses:

The authors have involved various mechanisms to justify their findings. However, they have missed out on certain aspects which connect the mechanism throughout the paper. For example, they measured ATP and acetyl COA production linked with bacterial re-exposures and added various targets like MCP1, EER alpha, PGC1 alpha and TNF alpha. However, they skipped PGC1 alpha levels, ATP and acetyl COA in various parts of the paper. Including the details would make the work more comprehensive.

The use of public data sets to support their claim on immune tolerance is missing. Including various data sets of similar studies will strengthen the findings independently.

eLife assessment

This important study demonstrates that the Pseudomonas aeruginosa-derived quorum sensing signal, 2-aminoacetophenone, induces immune tolerization in macrophages by perturbing metabolism, particularly in the context of mitochondrial respiration and bioenergetics. The authors present convincing evidence for 2-aminoacetophenone-mediated reduction of pyruvate transport into mitochondria, with downstream effects that result in reduced ATP production in tolerized macrophages. The work will be of interest to those studying host-pathogen interactions.

Reviewer #1 (Public Review):

Summary:

Their findings elucidate the mechanisms underlying 2-AA-mediated reduction of pyruvate transport into mitochondria, which impairs the interaction between ERRα and PGC1α, consequently suppressing MPC1 expression and reducing ATP production in tolerized macrophages. While the data presented is intriguing and the paper is well-written, there are several points that warrant consideration. The authors should enhance the clarity, relevance, and impact of their study.

Strengths:

This paper presents a novel discovery regarding the mechanisms through which PA regulates the bioenergetics of tolerized macrophages.

Weaknesses:

The relevance of the in vivo model to support the conclusions is questionable. Further clarification is needed on this point.

Author response:

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

eLife assessment

This important study identifies differential Orsay virus infection of C. elegans when animals are fed on different bacteria. The evidence for this is however, incomplete, as experiments to control for feeding rate and bacterial pathogenicity are needed as well as direct quantification of viral load. 

We appreciate that the editors and reviewers felt that our manuscript addressed an important problem. We appreciate the constructive critiques provided by the reviewers and have worked to address all of the concerns, including a number of additional experiments as indicated below.

Public Reviews:

Reviewer #1 (Public Review):

Summary: 

This manuscript explores the importance of food type on virus infection dynamics using a nematode virus as a model system. The authors demonstrate that susceptibility to viral infection can change by several orders of magnitude based on the type of bacterial food that potential hosts consume. They go on to show that, for the bacterial food source that reduces susceptibility, the effect is modulated by quorum sensing molecules that the bacteria produce. 

Strengths: 

This manuscript shows convincingly that nematode susceptibility to viral infection changes by several orders of magnitude (i.e. doses must be increased by several orders of magnitude to infect the same fraction of the population) depending on the bacterial food source on which hosts are reared. The authors then focus on the bacteria that reduce host susceptibility to viral infection and demonstrate that certain bacterial quorum-sensing compounds are required to see this effect of reduced susceptibility. Overall, sample sizes are large, methods are generally rigorous, experiments are repeated, and patterns are clear. 

Weaknesses: 

Although the molecular correlate of reduced susceptibility is identified (i.e. quorum sensing compounds) the mechanisms underlying this effect are missing. For example, there are changes in susceptibility due to altered nutrition, host condition, the microbiome, feeding rate, mortality of infected hosts, etc. In addition, the authors focus almost entirely on the reduction in susceptibility even though I personally find the increased susceptibility generated when reared on Ochrobactrum to be much more exciting. 

I was a bit surprised that there was no data on basic factors that could have led to reductions in susceptibility. In particular, data on feeding rates and mortality rates seem really important. I would expect that feeding rates are reduced in the presence of Pseudomonas. Reduced feeding rates would translate to lower consumed doses, and so even though the same concentration of virus is on a plate, it doesn't mean that the same quantity of virus is consumed. Likewise, if Pseudomonas is causing mortality of virus-infected hosts, it could give the impression of lower infection rates. Perhaps mortality rates are too small in the experimental setup to explain this pattern, but that isn't clear in the current version of the manuscript. Is mortality greatly impacted by knocking out quorum-sensing genes? Also, the authors explored susceptibility to infection, but completely ignored variation in virus shedding. 

We have added data on feeding rates (Line numbers 141-148 and 176-182, Supplementary Figure 4). After six hours of exposure no differences in feeding rate were observed. After 24 hours minor differences emerged between O. vermis MYb71 and each Pseudomonas species, however feeding rate inversely correlated with susceptibility to Orsay virus in that O. vermis MYb71 displayed the lowest feeding rate while P. aeruginosa PA14 displayed the highest feeding rate.

We have also added data on mortality rates (Line numbers 183-200, Supplementary Figure 6). No significant mortality was observed within the 24-hour exposure period used for our Orsay infection and transmission assays. P. aeruginosa virulence is dependent upon temperature and as our assays are done at 20°C rather than 25°C this may account for reduced mortality compared to other published results. Regardless, we noted that O. vermis MYb71 killed C. elegans as quickly as P. aeruginosa PA14 under these conditions and these two bacteria led to the shortest lifespan compared to the other tested bacteria. Interestingly, P. lurida MYb11 was observed to be more virulent than P. aeruginosa PA01 under these conditions. These results suggest that there is no direct correlation between mortality and susceptibility to Orsay virus, although it does not rule out that virulence effects unique to each bacterium could contribute to alterations in host susceptibility.  

The reviewer is correct to assert that differences in viral shedding could exist. However, our susceptibility assays using exogenous Orsay virus remove this source of variation and yet we still observe the same trends such that O. vermis MYb71 promotes infection while P. lurida MYb11, P. aeruginosa PA01, and P. aeruginosa PA14 attenuate infection. Further we measured the amount of virus shed into the lawns in the presence of different bacteria and did not observe differences in shed virus that could account for the differences we observe in incidence proportion (Line numbers 241-254, Fig. 3 F). Viral stability could be an issue in both the transmission and susceptibility assays. We therefore tested viral stability in the presence of E. coli, P. lurida MYb11, P. aeruginosa PA01, and P. aeruginosa PA14 and successfully recovered virus from all lawns, suggesting virus is not rapidly degraded in the presence of any bacterium (Fig. 3D and 3E). However, we noted that the recovery of Orsay virus from lawns of E. coli OP50 and P. lurida MYb11 within 30 minutes was decreased compared to a spike-in control suggesting recovery from each lawn is not equivalent. This complicates a comparison of viral stability and shedding rates between different bacteria, but our ability to recover substantial amounts of virus in the shedding assay from the three Pseudomonas strains we examined precludes a substantial decrease in shedding rates as an explanation for the robust attenuation of Orsay virus observed in transmission assays.  

I was also curious why the authors did not further explore the mechanism behind the quorumsensing effect. Not sure whether this is possible, but would it be possible to add spent media to the infection plates where the spent media was from Pseudomonas that produce the quorum sensing compound but the plates contain OP50, Pseudomonas, or the quorum sensing knockout of Pseudomonas? That would reveal whether it is the compound itself vs. something that the compound does. 

We observed that quorum sensing mutants suppressed the attenuation of Orsay virus infection and we agree that this could be a consequence of the compounds themselves, or more likely an effect of the downstream consequences of quorum signaling. We added culture supernatant from each bacterium to lawns of E. coli OP50 to assess the effect on host susceptibility and did not observe any potent effect (Line numbers 311-318, Supplementary Figure 9). This supports an interpretation that it is not the compound itself that is responsible, however we cannot rule out that the compounds themselves may be responsible if provided at a higher concentration.

In addition, I was surprised by how much focus there was on the attenuation of infection and how little there was on the enhancement of infection. To me, enhancement seems like the more obvious thing to find a mechanism for -- is the bacteria suppressing immunity, preventing entry to gut cells, etc? 

We are also intrigued by the enhancement of infection by Ochrobactrum spp, however we chose to focus on attenuation given the availability of Pseudomonas aeruginosa genetic mutants for study. We have added data (Line numbers 371-402, Figure 7, and Supplemental Figure 12) that inform our current hypothesis regarding Ochrobactrum mediated enhancement of Orsay virus infection.

I was a bit concerned about the "arbitrary units", which were used without any effort to normalize them. David Wang and Hongbing Jiang have developed a method based on tissue culture infectious dose 50 (TCID50) that can be used to measure infectious doses in a somewhat repeatable way. Without some type of normalization, it is hard to imagine how this study could be repeated. The 24-hour time period between exposure and glowing suggests very high doses, but it is still unclear precisely how high. Also, it is clear that multiple batches of virus were used in this study, but it is entirely unclear how variable these batches were. 

We have clarified that we also measured the (TC)ID50 for every batch of virus used similar to the methods suggested by the Wang laboratory (Line numbers 107-119 and 499-506). We have added a figure showing the virus batch variability for all batches used in this study (Supp. Fig. 2). We have further clarified that the arbitrary units correspond to the actual microliters of viral filtrate used during infection and provided clear methods to replicate our viral batch production to assist with issues of reproducibility (Line numbers 107-119 and 499-506).

The authors in several places discuss high variability or low variability in incidence as though it is a feature of the virus or a feature of the host. It isn't. For infection data (or any type of binomial data) results are highly variable in the middle (close to 50% infection) and lowly variable at the ends (close to 0% or 100% infection). This is a result that is derived from a binomial distribution and it should not be taken as evidence that the bacteria or the host affect randomness. If you were to conduct dose-response experiments, on any of your bacterial food source treatments, you would find that variability is lowest at the extremely high and extremely low doses and it is most variable in the middle when you are at doses where about 50% of hosts are infected. 

Thank you for pointing this out, we have removed all reference to this throughout the manuscript.

Reviewer #2 (Public Review):

Summary and Major Findings/Strengths:

Across diverse hosts, microbiota can influence viral infection and transmission. C. elegans is naturally infected by the Orsay virus, which infects intestinal cells and is transmitted via the fecal-oral route. Previous work has demonstrated that host immune defense pathways, such as antiviral RNAi and the intracellular pathogen response (IPR), can influence host susceptibility to virus infection. However, little is known about how bacteria modulate viral transmission and host susceptibility. 

In this study, the authors investigate how diverse bacterial species influence Orsay virus transmission and host susceptibility in C. elegans. When C. elegans is grown in the presence of two Ochrobactrum species, the authors find that animals exhibit increased viral transmission, as measured by the increased proportion of newly infected worms (relative to growth on E. coli OP50). The presence of the two Ochrobactrum species also resulted in increased host susceptibility to the virus, which is reflected by the increased fraction of infected animals following exposure to the exogenous Orsay virus. In contrast, the presence of Pseudomonas lurida MYb11, as well as Pseudomonas PA01 or PA14, attenuates viral transmission and host susceptibility relative to E. coli OP50. For growth in the presence of P. aeruginosa PA01 and PA14, the attenuated transmission and susceptibility are suppressed by mutations in regulators of quorum sensing and the gacA two-component system. The authors also identify six virulence genes in P. aeruginosa PA14 that modulate host susceptibility to virus and viral transmission, albeit to a lesser extent. Based on the findings in P. aeruginosa, the authors further demonstrate that deletion of the gacA ortholog in P. lurida results in loss of the attenuation of viral transmission and host susceptibility. 

Taken together, these findings provide important insights into the species-specific effects that bacteria can have on viral infection in C. elegans. The authors also describe a role for Pseudomonas quorum sensing and virulence genes in influencing viral transmission and host susceptibility. 

Major weaknesses: 

The manuscript has several issues that need to be addressed, such as insufficient rigor of the experiments performed and questions about the reproducibility of the data presented in some places. In addition, confounding variables complicate the interpretations that can be made from the authors' findings and weaken some of the conclusions that are stated in the manuscript. 

(1) The authors sometimes use pals-5p::GFP expression to indicate infection, however, this is not necessarily an accurate measure of the infection rate. Specifically, in Figures 4-6, the authors should include measurements of viral RNA, either by FISH staining or qRT-PCR, to support the claims related to differences in infection rate. 

Following the reviewers comment we have corroborated our pals-5::GFP data using FISH staining (Line numbers 291-292 and 357-359, Figure 4D & 4E, and Figure 6C).  

(2) In several instances, the experimental setup and presentation of data lack sufficient rigor. For example, Fig 1D and Fig 2B only display data from one experimental replicate. The authors should include information from all 3 experimental replicates for more transparency. In Fig 3B, the authors should include a control that demonstrates how RNA1 levels change in the presence of E. coli OP50 for comparison with the results showing replication in the presence of PA14. In order to support the claim that "P. aeruginosa and P. lurida MYb11 do not eliminate Orsay virus infection", the authors should also measure RNA1 fold change in the presence of PA01 and P. lurida in the context of exogenous Orsay virus. Additionally, the authors should standardize the amount of bacteria added to the plate and specify how this was done in the Methods, as differing concentrations of bacteria could be the reason for species-specific effects on infection. 

All experimental replicates are now included within the supplementary information. 

We have also measured RNA1 fold change following infection in the presence of P. aeruginosa PA01 and P. lurida MYb11 (Line numbers Fig 3B and 3C) and found that these bacteria also do not eliminate Orsay virus replication. 

We thank the reviewer for their comment on controlling the amount of bacteria and have clarified our methods section to more clearly explain that we seed our plates with equivalent amounts (based on volume) of overnight bacterial culture before allowing the bacteria to grow on the plates for 48 hours.  

(3) The authors should be more careful about conclusions that are made from experiments involving PA14, which is a P. aeruginosa strain (isolated from humans), that can rapidly kill C. elegans. To eliminate confounding factors that are introduced by the pathogenicity of PA14, the authors should address how PA14 affects the health of the worms in their assays. For example, the authors should perform bead-feeding assays to demonstrate that feeding rates are unaffected when worms are grown in the presence of PA14. Because Orsay virus infection occurs through feeding, a decrease in C. elegans feeding rates can influence the outcome of viral infection. The authors should also address whether or not the presence of PA14 affects the stability of viral particles because that could be another trivial reason for the attenuation of viral infection that occurs in the presence of PA14. 

We have added data on feeding rates (Line numbers 141-148 and 176-182, Supplementary Figure 4). After six hours of exposure no differences in feeding rate were observed. After 24 hours minor differences emerged between O. vermis MYb71 and each Pseudomonas species, however feeding rate inversely correlated with susceptibility to Orsay virus in that O. vermis MYb71 displayed the lowest feeding rate while P. aeruginosa PA14 displayed the highest feeding rate.

We have also added data on mortality rates (Line numbers 183-200, Supplementary Figure 6). No significant mortality was observed within the 24-hour exposure period used for our Orsay infection and transmission assays. P. aeruginosa virulence is dependent upon temperature and as our assays are done at 20°C rather than 25°C this may account for reduced mortality compared to other published results. Regardless, we noted that O. vermis MYb71 killed C. elegans as quickly as P. aeruginosa PA14 under these conditions and these two bacteria led to the shortest lifespan compared to the other tested bacteria. Interestingly, P. lurida MYb11 was observed to be more virulent than P. aeruginosa PA01 under these conditions. These results suggest that there is no direct correlation between mortality and susceptibility to Orsay virus, although it does not rule out that virulence effects unique to each bacterium could contribute to alterations in host susceptibility.  

We tested viral stability in the presence of E. coli OP50 and Pseudomonas spp. and successfully recovered virus from all lawns, suggesting virus is not rapidly degraded in the presence of P. lurida MYb11, P. aeruginosa PA01, and P. aeruginosa PA14 (Line numbers 241-249, Fig 3D and Fig 3E). However, we noted that the recovery of Orsay virus from lawns of E. coli OP50 and P. lurida MYb11 within 30 minutes was decreased compared to a spike-in control suggesting recovery from each lawn is not equivalent. This complicates a comparison of viral stability and shedding rates between different bacteria, but our ability to recover substantial amounts of virus in the shedding assay from each Pseudomonas species precludes a substantial decrease in shedding rates as an explanation for the robust attenuation of Orsay virus observed in transmission assays.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Overall, I really liked this manuscript, I do think there are areas for improvement though. 

Some smaller things: 

Line 84: "can be observed spreading from a single animal" -- this isn't really great wording because the virus itself can't be observed (at least not very easily) -- even infection is hard to see. 

The wording in line 84-85 has now been adjusted to read “can spread from a single animal”.

Fig 1C: which groups are statistically significantly different from each other? 

Statistics have now been added to Figure 1C. 

Line 154: not necessary to do for this paper, but this sentence made me curious whether the effect would have been seen with mixtures of bacteria (i.e. what if 50% were OP50 and 50% were Pseudomonas?) 

This data has now been added in Line numbers 372-378, Figure 7A, and Supp. Fig. 12A and 12B.

Line 262-264: I don't find this interesting at all for the reasons mentioned earlier about binomial data being the most variable in the middle. 

These lines have been removed.

Figure 4 B: The labels for the first two tick marks on the x-axis are switched I suspect. Otherwise, the controls did not behave as expected. 

Figure 4B has been corrected.

Line 288, 297 and several other places: "Orsay Virus" should be "Orsay virus". 

We have corrected these instances.

Supplemental Figure 2: Labels in the figure legend are B and C instead of A and B. 

These labels have been adjusted for their placement within Figure 6.

Line 411: I suspect this was supposed to be 13,200 xg rather than 13.2 xg. 

This error has been corrected.

Line 416-417: This sentence is very hard to interpret. More details are needed. This is the ID50 in which host strain? Is this averaged over all batches of virus? How variable are the batches? 

This sentence (line number 114) has been amended to clarify that all ID50 values referred to here were calculated for ZD2611 populations in the presence of E. coli OP50. Further, Supplementary Figure 2 now shows all the ID50 values measured for each batch of virus used in this manuscript resulting in an average ID50 of 3.6.

Lines 467-469: Why exclude these instead of counting them as zeros in the analysis? How many plates fit this description -- were there lots or only a few over the course of all experiments? 

We have chosen to exclude these plates as these samples lost spreaders at some point during the course of the assay potentially skewing the eventual number of new infections counted depending on when the infected spreader animal crawled off the plate.  We have detailed the number of plates that fit this description in lines 559-562. 

Line 476: A critical detail that is missing here is what number of worms were counted to score infection. Please say here or in the figure legends. 

We have added the total number of worms counted and the minimum number counted per plate for each assay in the figure legends.

Line 546: Why was only a single representative experiment shown? I'm asking for a justification, not necessarily for you to show all the data. 

We chose to show a single representative experiment for two reasons:  We noted variability between susceptibility assays even when using the same batch of virus such that we could not combine experiments into a single plot as we did for transmission assays. Second, while we could normalize to a control within each experiment and expect to see similar relative differences across experiments, we believe this makes it more difficult to interpret the underlying data. For example, an increase in the infection rate of 80% compared to 10% within a population has only a single interpretation while a relative increase in the infection rate by 8x within a population could have several underlying meanings (e.g. 80% vs 10%, 64%vs 8%, 24% vs 3%). We have now included all experimental replicates in the supplementary material. 

Reviewer #2 (Recommendations For The Authors):

Minor concerns: 

(1) Lines 86-87: "utilized a collection of bacteria isolated from the environment with wild C. elegans". The authors should provide more context on the source of these bacterial strains. 

More references for the sources of these bacteria have been added to Supplementary Table 2.

(2) The presentation of data in Fig 1 could be improved. The authors should include the text "pals-5p::GFP" on the images shown in Fig 1B. The red dashed line in Fig. 1D should intersect the dose-response curve at y = 0.5. The column heading for Fig 1E states "ID50 +/- SD (a.u.)", but should read "ID50 ratio" and should not have units. It also might be more intuitive to normalize the ID50 value for O. vermis to E. coli OP50. This way, having an ID50 ratio >1 indicates decreased transmission relative to E. coli, and ID50 ratio <1 indicates increased transmission relative to E. coli. To increase the transparency and rigor of 1E, the authors should plot the ratios from all 3 experimental replicates. The authors should also briefly explain why different viral doses were used in Fig 1D and 1F. 

The text “pals-5p::GFP” has now been added to Figure 1B and throughout the text. The red dashed line in figure 1D has been corrected. Figure 1E has been adjusted to an actual figure as suggested and the y-axis label is “ID50 Ratio Compared to E. coli OP50”. The ID50 replicates have been plotted in Supplementary Figure 2. We have clarified that the doses used are the same. Briefly, the technical replicates of individual doses from Figure 1D and Supplementary Figure 3A and 3B were pooled and processed for FISH staining to provide each experimental replicate of Figure 1F. 

(3) Line 110: The claim is that Ochrobactrum and P. lurida MYb11 reduce the variability of infection levels. However, another possibility is that there's simply less dynamic range in the assay because the infection levels have been compressed to 100% and 0% under these conditions. 

This line has been removed.

(4) There are discrepancies between what is shown in Fig 2C and what is described in the text. Lines 163-164: "P. aeruginosa PA01 and P. lurida MYb11 attenuated average infection to 33% and 62% of the population respectively". In Fig 2C, the mean for PA01 is ~25% whereas the mean for P. lurida appears to be less than 62%. 

These values have been corrected.

(5) Line 196: Provide more context for why rde-1 mutants were tested. This is the first time rde-1 is mentioned in the text (i.e. why show results in rde-1 mutants when the results are in Fig 2). 

More context has been provided for why rde-1 mutants were tested (Line numbers 228-232). Briefly, using the rde-1 mutant, which has defective antiviral immunity and therefore supports higher viral replication levels than the wild-type (Félix et al. 2011), allows us to potentiate our infection assay in Figure 3B and 3C such that we maximize our chances of detecting viral replication in the presence of the Pseudomonas species, and especially P. aeruginiosa PA14, where fewer animals might be expected to get infected based upon Figure 2B and Supplementary Figure 5. 

(6) Lines 228-229: "Mutations of any the regulators of the las, rhl, or pqs quorum sensing systems suppressed the attenuation of Orsay virus infection caused by the presence of wild-type P. aeruginosa PA01". Based on this description, PA01 should have a lower fraction of GFP positive relative to the quorum sensing mutants in Fig 4B. It seems that the x-axis labels OP50 and PA01 are swapped. 

The x-axis labels of Figure 4B have been corrected. 

(7) To improve clarity, for any figures that have data showing the "fraction of individuals GFP positive", the authors should include "pals-5p::GFP" in the y-axis title and legend. 

The y-axis labels, legends, and text have been corrected throughout.  

(8) To improve overall clarity and flow, the order in which the data is presented could be reordered. In particular, Fig. 6 could be better positioned instead of being the last figure, as no further characterization is performed on the mutants, and the findings are not conserved in strains that are more relevant to the C. elegans microbiota, such as P. lurida. The overall story could be strengthened if the authors ended the manuscript with more details related to the mechanism by which regulators of quorum sensing modulate the outcome of viral infection. 

Figure 5 and Figure 6 have now been swapped.

(9) Fig 5A: Make arrow sizes consistent across diagrams (i.e. the diagram for gacA deletion). 

This figure (now Figure 6A) has been adjusted to make arrow sizes consistent across diagrams.  

(10) Lines 280-282: "These data suggest that gacA has a conserved role across distant Pseudomonas species..." Here, the authors can provide more context on how well-conserved gacA is across Pseudomonas species (i.e. phylogenetic analysis of gacA sequences across different Pseudomonas species/strains). Furthermore, the data in Fig 5 does not provide strong enough support for the conclusion that gacA has a conserved role broadly across Pseudomonas species, as the authors only assess the effects of a gacA deletion in two species, P. aeruginosa and P. lurida. 

We have adjusted lines 361-362 to “These data suggest that gacA has a conserved role between P. aeruginosa and P. lurida Myb11 in the attenuation of Orsay virus transmission and infection of C. elegans.” to reflect that we only assessed the effects of the gacA deletion in P. aeruginosa and P. lurida MYb11.

(11) The manuscript can be strengthened by performing additional experiments to elucidate the mechanism by which Pseudomonas modulates viral infection. Does the attenuation of viral transmission and host susceptibility by P. lurida and P. aeruginosa require C. elegans to be in the presence of live bacteria? For example, the authors could measure viral transmission and susceptibility of C. elegans grown on heat-killed Pseudomonas. Additionally, it would be interesting to determine if modulation of viral infection is dependent on a secreted molecule. To assess this, the authors could perform viral infections in the context of Pseudomonas culture supernatant. 

We added bacterial culture supernatant from each bacterium to lawns of E. coli OP50 to assess the effect on host susceptibility and did not observe any potent effect (Line numbers 311-318, Supplementary Figure 9). This supports an interpretation that attenuation is not mediated by a secreted molecule, however we cannot rule out that attenuation activity would become apparent if supernatant were provided at a higher concentration.

We have found substantial challenges appropriately controlling live vs. heat-killed experiments particularly with the specifics of our susceptibility experiments. With regards to the underlying question of mechanism we believe that the genetic mutants (e.g. rhlR/gacA) are equally informative and that further comparison of these mutants’ interaction with the C. elegans host as compared to wild-type may be informative. 

(12) The authors should include a discussion on the relative virulence potential of PA01, PA14, and P. lurida and the relationship between bacterial virulence potential and the outcome of viral infection. 

We have also added data on mortality rates (Line numbers 183-200, Supplementary Figure 6). No significant mortality was observed within the 24-hour exposure period used for our Orsay infection and transmission assays. P. aeruginosa virulence is dependent upon temperature and as our assays are done at 20°C rather than 25°C this may account for reduced mortality compared to other published results. Regardless, we noted that O. vermis MYb71 killed C. elegans as quickly as P. aeruginosa PA14 under these conditions and these two bacteria led to the shortest lifespan compared to the other tested bacteria. Interestingly, P. lurida MYb11 was observed to be more virulent than P. aeruginosa PA01 under these conditions. These results suggest that there is no direct correlation between mortality and susceptibility to Orsay virus, although it does not rule out that virulence effects unique to each bacterium could contribute to alterations in host susceptibility.  

(13) More information is needed on strains listed in Supplementary Table 2, particularly when there is no reference listed and the strain is "Gift of XXX lab". For example, the Troemel lab previously published about an Ochrobactrum strain in Troemel et al PLOS Biology 2008 PMID: 19071962 - is this the same strain? Please ensure that there is adequate information about each strain with as many published references as possible so that the work can be more easily reproduced. 

We have added additional information and references to the strain table in Supplementary Table 2. The strain listed as Ochrobactrum sp. has been amended to Ochrobactrum BH3 as it is the strain described in Troemel et al. 2008.

eLife assessment

This valuable work explores the utility of using analyte ratios for improved biological interpretation in a MALDI MSI workflow. The evidence supporting the conclusions is however incomplete, as relevant controls are missing and the novelty of the study has been exaggerated, omitting discussion of key relevant background. The work would be of interest to the mass spectrometry community.

Reviewer #1 (Public Review):

Cheng et al explore the utility of analyte ratios instead of relative abundance alone for biological interpretation of tissue in a MALDI MSI workflow. Utilizing the ratio of metabolites and lipids that have complimentary value in metabolic pathways, they show the ratio as a heat map which enhances the understanding of how multiple analytes relate to each other spatially. Normally, this is done by projecting each analyte as a unique color but using a ratio can help clarify visualization and add to biological interpretability. However, existing tools to perform this task are available in open-source repositories, and fundamental limitations inherent to MALDI MSI need to be made clear to the reader. The study lacks rigor and controls, i.e. without quantitative data from a variety of standards (internal isotopic or tissue mimetic models for example), the potential delta in ionization efficiencies of different species subtracts from the utility of pathway analysis using metabolite ratios.

Reviewer #2 (Public Review):

Summary:

In the article, "Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging" the authors describe their software package in R for visualizing metabolite ratio pairs. I think the novelty of this manuscript is overstated and there are several notable issues with the figures that prevent detailed assessment but the work would be of interest to the mass spectrometry community.

Strengths:

The authors describe a software that would be of use to those performing MALDI MSI. This software would certainly add to the understanding of metabolomics data and enhance the identification of critical metabolites.

Weaknesses:

The authors are missing several references and discussion points, particularly about SIMS MSI, where ratio imaging has been previously performed.

There are several misleading sentences about the novelty of the approach and the limitations of metabolite imaging.

Several sentences lack rigor and are not quantitative enough.

The figures are difficult to interpret/ analyze in their current state and lack some critical components, including labels and scale bars.

eLife assessment

This valuable study uses convincing state-of-the-art neuroimaging analyses to characterise whole-brain networks during reward-based motor learning. This work motivates future research to dissociate why the observed changes in neural connectivity occur and how they support reward-based motor learning. The study is highly relevant for researchers at the intersection of decision-making and sensorimotor learning.

Reviewer #1 (Public Review):

This important study uses a wide variety of convincing, state-of-the-art neuroimaging analyses to characterize whole-brain networks and relate them to reward-based motor learning. During early learning, the authors found increased covariance between the sensorimotor and dorsal attention networks, coupled with reduced covariance between the sensorimotor and default mode networks. During late learning, they observed the opposite pattern. It remains to be seen whether these changes reflect generic changes in task engagement during learning or are specific to reward-based motor learning. This study is highly relevant for researchers interested in reward-based motor learning and decision-making.

Reviewer #2 (Public Review):

This useful investigation of learning-driven dynamics of cortical and some subcortical structures combines a novel in-scanner learning paradigm with interesting analysis approaches. The new task for reward-based motor learning is compelling and goes beyond the current state of the art. The results are of interest to neuroscientists working on motor control and reward-based learning.

Comments on revised version:

The revision has produced a stronger manuscript. Thank you for your thorough responses to the comments and concerns.

Reviewer #3 (Public Review):

Summary:

The manuscript of Nick and colleagues addresses the intriguing question of how brain connectivity evolves during reward-based motor learning. The concept of quantifying connectivity through changes in extraction and contraction across lower-dimensional manifolds is both novel and interesting and the presented results are clear and well-presented. Overall, the manuscript is a valuable addition to the field.

Strengths:

This manuscript is written in a clear and comprehensible way. It introduces a rather novel technique of assessing connectivity across lower-dimensional manifold which has hitherto not been applied in this way to the question of reward-based motor learning. Thus, this presents a unique viewpoint on understanding how the brain changes with motor learning. I particularly enjoyed the combination of connectivity-based, followed by further scrutiny of seed-based connectivity analyses, thus providing a more comprehensive viewpoint. Now it also has added a more comprehensive report on the behavioural changes of learning, and the added statistical quantification, which is useful.

Weaknesses:

The main weakness of the manuscript is the lack of direct connection between the reported neural changes and behavioural learning. Namely, most of the results could also be explained by changes in attention allocation during the session, or changes in movement speed (independent of learning). The authors acknowledge some of these potential confounds and argue that factors like attention are important for learning. While this is true, it is nonetheless very limiting if one cannot ascertain whether the observed effects are due to attention (independent of learning) or attention allocated in the pursuit of learning. The only direct analysis linking behavioural changes to neural changes is based on individual differences in learning performance, where the DAN-A shows the opposite trend than group level effects, which they interpret as differences given the used higher-resolution parcellation. However, it could be that these learning effects are indeed much smaller and subtler compared to more dominant group-level attention effects during the task. The lack of a control condition in the task limits the interpretability of results as learning-related.

Author Response

We appreciate the thoughtful comments provided by the editor and reviewers. We were pleased to hear that they appreciated our work's contribution to the field of motor learning as well as our use of state-of-the-art analysis techniques.

We are currently preparing a comprehensive revision of our manuscript to address several of the recommendations of the reviewers. It is our belief that this revision will not only strengthen our paper but also help clarify several areas that were highlighted by the reviewers.

To address the concerns regarding potential confounds in our experimental design, we will be providing a more detailed justification and rationale for the experimental design and analysis choices made during our study. It appears that some reviewers’ comments may stem from misunderstandings concerning certain details of our task and we will carefully revise these sections to ensure that the design and purpose of the study are unambiguous. We will also be improving our characterizations of subjects’ learning behavior, which we believe will clarify some of the reviewers comments and enhance the overall rigor of our analyses. Lastly, we will be dealing with all concerns related to the statistical quantification of our results.

We appreciate the opportunity to improve our manuscript for eLife and are eager to provide a revision that satisfies the majority of the reviewers’ recommendations

eLife assessment

This study represents a valuable step toward understanding how brain connectivity changes during reward-based motor learning. However, the evidence presented is incomplete. On one hand, the study leverages state-of-the-art techniques to examine brain connectivity; on the other hand, there are potential confounds in the experimental design, some omissions in statistical quantification, and at times, a lack of clarity about the methods used and the motor learning mechanisms being isolated.

Reviewer #1 (Public Review):

The authors investigated how global brain activity varied during reward-based motor learning. During early learning, they found increased covariance between the sensorimotor and dorsal attention networks, coupled with reduced covariance between the sensorimotor and default mode networks; during late learning, they found the opposite pattern. Individual learning performance varied only with changes in the dorsal attention network. The authors certainly used a wide variety of valuable, state-of-the-art techniques to interrogate whole-brain networks and extract the key components of learning behavior. However, the findings are incomplete, tempered by potential confounds in the experimental design. As such, the underlying claim regarding how these networks jointly support reward-based motor learning is unclear.

Reviewer #2 (Public Review):

This useful investigation of learning-driven dynamics of cortical and some subcortical structures combines a novel in-scanner learning paradigm with interesting analysis approaches. The new task for reward-based motor learning is highly compelling and goes beyond the current state-of-the-art, but it is incomplete with respect to examining different signatures of learning, clarifying probed learning processes, and investigating changes in all relevant subcortical structures is incomplete and would benefit from more rigorous approaches. With the rationale and data presentation strengthened this paper would be of interest to neuroscientists working on motor control and reward-based learning.

Reviewer #3 (Public Review):

The manuscript of Nick and colleagues addresses the intriguing question of how brain connectivity evolves during reward-based motor learning. The concept of quantifying connectivity through changes in extraction and contraction across lower-dimensional manifolds is both novel and interesting and the presented results are clear and well-presented. Overall, the manuscript is a valuable addition to the field. The evidence supporting the presented findings is strong, though at times lacking rigorous statistical quantification. Nevertheless, there are several issues that require attention and clarification.

eLife assessment

This study provides important findings based on compelling evidence demonstrating that females and males have different strategies to regulate energy consumption in the brain in the context of low energy intake. While food deprivation reduces energy consumption and visual processing performance in the visual cortex of males, the female cortex is unaffected, likely at the expense of other functions. This study is relevant for scientists interested in body metabolism and neuroscience.

eLife assessment

This manuscript provides important information about the influence of TOR signaling pathway on development and aflatoxin production in the plant and human fungal pathogen Aspergillus flavus. Compared to an earlier version, the authors have addressed most of the concerns of the reviewers, including the convincing demonstration of the essential TOR pathway in this fungus by constructing a xylose promoter mutant strain.

Reviewer #1 (Public Review):

This paper reports the useful discovery of the roles and signaling components of the TOR pathway in vegetative growth, sexual development, stress response, and aflatoxin production in Aspergillus flavus.

While I acknowledge the authors' effort in conducting Southern blot analysis to address my prior concern regarding the presence of dual copies of torA and tapA, I find their current resolution inadequate. Specifically, the simple deletion of the respective result sections for torA and tapA significantly impacts the overall significance of this study. The repeated unsuccessful attempts to generate correct mutants only offer circumstantial evidence, as technical issues may have been a contributing factor. Therefore, instead of merely removing these sections, it is essential for the authors to present more compelling experimental data demonstrating that torA and tapA are indeed vital for the viability of A. flavus. Such data would enhance the overall significance of this study.

Reviewer #2 (Public Review):

In this study, authors identified TOR, HOG and CWI signaling network genes as modulators of the development, aflatoxin biosynthesis and pathogenicity of A. flavus by gene deletions combined with phenotypic observation. They also analyzed the specific regulatory process and proposed that the TOR signaling pathway interacts with other signaling pathways (MAPK, CWI, calcineurin-CrzA pathway) to regulate the responses to various environmental stresses. Notably, they found that FKBP3 is involved in sclerotia and aflatoxin biosynthesis and rapamycin resistance in A. flavus, especially that the conserved site K19 of FKBP3 plays a key role in regulating aflatoxin biosynthesis. In general, the study involved a heavy workload and the findings are potentially interesting and important for understanding or controlling the aflatoxin biosynthesis. However, the findings have not been deeply explored and the conclusions mostly are based on parallel phenotypic observations.

Author response:

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

Reviewer #1 (Public Review):

While I acknowledge the authors' effort in conducting Southern blot analysis to address my prior concern regarding the presence of dual copies of torA and tapA, I find their current resolution inadequate. Specifically, the simple deletion of the respective result sections for torA and tapA significantly impacts the overall significance of this study. The repeated unsuccessful attempts to generate correct mutants only offer circumstantial evidence, as technical issues may have been a contributing factor. Therefore, instead of merely removing these sections, it is essential for the authors to present more compelling experimental data demonstrating that torA and tapA are indeed vital for the viability of A. flavus. Such data would enhance the overall significance of this study.

We agree and appreciate reviewer's important comments on our manuscript. In this version, we address this issue by providing additional experimental data to further support the importance of torA and tapA in the viability of A. flavus. We conducted additional experiments to generate more compelling evidence regarding the essential role of torA and tapA in the growth and development of A. flavus. We constructed a mutant strain (xylPtorA) using an xylose-inducible promoter, which allows for conditional induction with the addition of xylose (Lines 204-238, page 10).

Due to the unsuccessful construction of TapA knockout strains and xylose promoter replacement strains, we used homologous recombination to replace the original promoter with the gpdA strong promoter for overexpression of tapA (OE::tapA). We thank reviewer for highlighting this important aspect, and we revise our manuscript accordingly to enhance its overall significance (Lines 277-297, page 13). We are grateful for the opportunity to enhance our manuscript and believe these revisions provide a more comprehensive understanding of the roles of torA and tapA in A. flavus.

Reviewer #1 (Recommendations For The Authors):

Minor comments

Lines 421-423 and 465-466: these sentences are grammatically awkward. Please rephrase them.

Thank you for your feedback on our manuscript. We conducted additional experiments, so we have removed the sentence from the manuscript to maintain coherence and avoid redundancy.

Reviewer #2 (Public Review):

In this study, authors identified TOR, HOG and CWI signaling network genes as modulators of the development, aflatoxin biosynthesis and pathogenicity of A. flavus by gene deletions combined with phenotypic observation. They also analyzed the specific regulatory process and proposed that the TOR signaling pathway interacts with other signaling pathways (MAPK, CWI, calcineurin-CrzA pathway) to regulate the responses to various environmental stresses. Notably, they found that FKBP3 is involved in sclerotia and aflatoxin biosynthesis and rapamycin resistance in A. flavus, especially that the conserved site K19 of FKBP3 plays a key role in regulating aflatoxin biosynthesis. In general, the study involved a heavy workload and the findings are potentially interesting and important for understanding or controlling the aflatoxin biosynthesis. However, the findings have not been deeply explored and the conclusions mostly are based on parallel phenotypic observations.

Thank you for your constructive comments on our manuscript. In response to your comments, we have conducted additional experiments, including the construction of a xylose promoter mutant strain and an overexpression strain. We have also expanded the discussion section to provide a more comprehensive analysis of our findings in the context of existing literature. Thank you again for your insightful feedback, which has been instrumental in improving the quality of our work. (Lines 464-469, page 22).

Reviewer #2 (Recommendations For The Authors):

Point 1: Our findings revealed that both the tor and tapA genes are present in double copies in our strains, which guided our decision to construct single-copy deletion strains using homologous recombination However, the tor gene in A. flavus exhibited varying copy numbers, as was confirmed by absolute quantification PCR at the genome level (Table S1). However, it is hard to understand for Table S1: Estimation of copy number of tor gene in A. flavus toro and sumoo stand for the initial copy number, and the data are graphed as the mean {plus minus} 95%confidence limit. CN is copy number. As indicated in the Methods, Using sumo gene as reference, the tor and tapA gene copy number was calculated by standard curve. In Table S1 of WT, for tor gene, CN value is1412537 compared to 1698243 in tor+/-, for the reference gene sumo,794328 compared to1584893, how these data could support copy gene numbers of tor?

Thank you for your insightful comments. We understand the confusion with the data presented in Table S1 regarding the copy number estimation of the torA gene in A. flavus. We apologize for not providing a clear explanation for the data in the table. Quantitative real-time PCR (qPCR) is widely used to determine the copy number of a specific gene. It involves amplifying the gene of interest and a reference gene simultaneously using specific primers and probes. By comparing the amplification curves of the gene of interest and the reference gene, we can estimate the relative copy number of the gene.

To address your concern and provide more accurate information, we have re-performed the copy number analysis using southern blot. Southern blot analysis allows for the direct estimation of gene copy number by hybridizing genomic DNA with a specific probe for the gene. This method provides more reliable and accurate results in determining gene copy numbers. We discovered that the A. flavus genome contains a single copy of the torA gene. Consequently, we conducted additional experiments to elucidate its function. Specifically, we generated strains with a xylose-inducible promoter system to modulate the expression of torA (Lines 204-238, page 10).

Point 2: In response: For the knockout of the FRB domain, we used the homologous recombination method, but because tor genes are double-copy genes, there are also double copies in the FRB domain. Despite our efforts, we encountered challenges in precisely determining the location of the other copy of the tor gene. I could not understand these consistent data, why not for using sequencing?

Thank you for your valuable feedback. We determined again and confirmed that the torA gene is a single copy. So we removed this part of the results to avoid any ambiguity or potential misinterpretation.

Point 3: Response in Due to the large number of genes involved, we did not perform a complementation experiment. If there were no complementation data, how to demonstrate data are solid?

Thank you for your important suggestion. We understand that complementation experiments are commonly used to validate gene deletions. Therefore, to ensure the reliability of our data, we have conducted supplementary experiments on specific gene deletions, such as Δ_sitA_-C and Δ_ppg1_-C. Thank you again for your positive comments and valuable suggestions, which have significantly contributed to enhancing the quality of our manuscript (Lines 320-322, page 15).

Point 4: Acknowledge the confusion? We acknowledge the confusion in our presentation and will ensure that accurate genetic nomenclature is used consistently

Thank you for your comments on our manuscript. We recognize the importance of precise and consistent use of genetic nomenclature, as it is critical for the clarity and integrity of our research findings. We have carefully reviewed the sections of our manuscript where genetic terms were used and have made the necessary corrections to ensure that all nomenclature is accurate and used consistently throughout the text.

Point 5: In the revised version of new manuscript, southern blotting was carried out and found only one copy was existed for tested genes at last. Thus, whole manuscript conclusions should be changed. In addition, Reviewer 1 suggestion for using Illumina-sequence strategy, their tor and tapA mutants could be verified whether they are aneuploid?

We would like to express our gratitude for your insightful comments and suggestions. Following the new experimental data obtained from Southern blotting, we have identified that only one copy of the tested genes exists, and we have revised our conclusions throughout the manuscript. This has led to a significant reinterpretation of our results and a reassessment of the implications for our study. Based on this result, we designed and constructed strains with the tor gene under the control of a xylose-inducible promoter. This approach allows for the conditional expression of the tor gene. Thank you once again for your meticulous review (Lines 204-238, page 10).

eLife assessment

This important study contributes to the understanding of how parafoveal words are neurally processed during naturalistic sentence reading. Convincing evidence is provided that the MEG response to a word can be modulated by the semantic congruency of a parafoveal target word. The study addresses a classic question in reading using a new Rapid Invisible Frequency Tagging (RIFT) technique, which can separately monitor the neural processing of multiple words during sentence reading.

Reviewer #1 (Public Review):

Summary:

The study investigates parafoveal processing during natural reading, combining eye-tracking and MEG techniques, building upon the RIFT paradigm previously introduced by Pan et al. (2021).

The manuscript is well-written with a clear structure, and the data analysis and experimental results are presented in a lucid manner.

Comments on revised version:

I am satisfied with the revisions made by the authors. I believe the study introduces a new research paradigm to the field.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The study investigates parafoveal processing during natural reading, combining eye-tracking and MEG techniques, building upon the RIFT paradigm previously introduced by Pan et al. (2021). Overall, the manuscript is well-written with a clear structure, and the data analysis and experimental results are presented in a lucid manner.

The authors have addressed the issues I raised in the previous round of review to my satisfaction. However, I still have two concerns that require the authors' consideration.

Firstly, the similarity between the RIFT analysis process in this study and traditional ERP analysis could lead readers to equate RIFT with components like N400, potentially influencing their interpretation of the results. Although the author's response has somewhat clarified my queries, I seek confirmation: does RIFT itself signify "visual attention" or the "allocation of attentional resources to the flickering target words" (line 208) in this study? While this may not be pivotal, as it primarily serves as an indicator to evaluate whether contextual congruity can indeed modulate the RIFT response rather than indicating early parafoveal semantic integration, I recommend that the authors explicitly address this point in the manuscript, maybe in the discussion section, to enhance reader comprehension of the article's rationale.

Secondly, regarding the study's conclusions, there appears to be an overemphasis in stating that "semantic information ... can also be integrated with the sentence context ..." (line 21-22). As raised by Reviewer 2 (Major Point 1) and acknowledged by the authors in the limitations of the revised manuscript (lines 403-412), the RIFT effect observed likely stems from local congruency. Therefore, adjusting the conclusion to "integrated with previous context" may offer a more precise reflection of the findings.

We appreciate the positive comments from the Reviewer.

In response to the first concern, we have rephrased the sentence (Line 207-209 in the revised manuscript) to clarify that RIFT measure visual attention : “Moreover, as RIFT directly measures visual attention, the left-skewed RIFT response curve suggests that more visual attention is allocated towards the flickering target words before fixating on them, aligning with the left-to-right order of reading English.”

Regarding the second concern, we have addressed the issue by modifying “sentence context” to “previous context” in both the Abstract (Line 18 and Line 22) and the Discussion section (Line 314 and Line 361) of the revised manuscript.

eLife assessment

The authors utilized scRNAseq profiling of NSCLC patient tumor samples to generate useful insights into the determinants of ICI responsiveness in NSCLC patients. While some of the findings add weight to the current literature, the analysis is incomplete due to the small cohort size and occasional departures from recognized subtype markers. This study would benefit from external cohorts to both validate the findings and to justify the statistical analysis undertaken.

Reviewer #1 (Public Review):

Summary:

The authors study the variability of patient response of NSCLC patients on immune checkpoint inhibitors using single-cell RNA sequencing in a cohort of 26 patients and 33 samples (primary and metastatic sites), mainly focusing on 11 patients and 14 samples for association analyses, to understand the variability of patient response based on immune cell fractions and tumor cell expression patterns. The authors find immune cell fraction, clonal expansion differences, and tumor expression differences between responders and non-responders. Integrating immune and tumor sources of signal the authors claim to improve prediction of response markedly, albeit in a small cohort.

Strengths:

- The problem of studying the tumor microenvironment, as well as the interplay between tumor and immune features is important and interesting and needed to explain the heterogeneity of patient response and be able to predict it.

- Extensive analysis of the scRNAseq data with respect to immune and tumor features on different axes of hypothesis relating to immune response and tumor immune evasion using state-of-the-art methods.

- The authors provide an interesting scRNAseq data set linked to outcomes data.

- Integration of TCRseq to confirm subtype of T-cell annotation and clonality analysis.

- Interesting analysis of cell programs/states of the (predicted) tumor cells and characterization thereof.

Weaknesses:

- Generally, a very heterogeneous and small cohort where adjustments for confounding are hard. Additionally, there are many tests for association with outcome, where necessary multiple testing adjustments would negate signal and confirmation bias likely, so biological takeaways have to be questioned.

- RNAseq is heavily influenced by the tissue of origin (both cell type and expression), so the association with the outcome can be confounded. The authors try to argue that lymph node T-cell and NK content are similar, but a quantitative test on that would be helpful.

- The authors claim a very high "accuracy" performance, however, given the small cohort and lack of information on the exact evaluation it is not clear if this just amounts to overfitting the data.

- Especially for tumor cell program/state analysis the specificity to the setting of ICIs is not clear and could be prognostic.

- Due to the small cohort with a lot of variability, more external validation is needed to be convincingly reproducible, especially when talking about AUC/accuracy of a predictor.

Reviewer #2 (Public Review):

Summary:

The authors have utilised deep profiling methods to generate deeper insights into the features of the TME that drive responsiveness to PD-1 therapy in NSCLC.

Strengths:

The main strengths of this work lie in the methodology of integrating single-cell sequencing, genetic data, and TCRseq data to generate hypotheses regarding determinants of IO responsiveness.

Some of the findings in this study are not surprising and well precedented eg. association of Treg, STAT3, and NFkB with ICI resistance and CD8+ activation in ICI responders and thus act as an additional dataset to add weight to this prior body of evidence. Whilst the role of Th17 in PD-1 resistance has been previously reported (eg. Cancer Immunol Immunother 2023 Apr;72(4):1047-1058, Cancer Immunol Immunother 2024 Feb 13;73(3):47, Nat Commun. 2021; 12: 2606 ) these studies have used non-clinical models or peripheral blood readouts. Here the authors have supplemented current knowledge by characterization of the TME of the tumor itself.

Weaknesses:

Unfortunately, the study is hampered by the small sample size and heterogeneous population and whilst the authors have attempted to bring in an additional dataset to demonstrate the robustness of their approach, the small sample size has limited their ability to draw statistically supported conclusions. There is also limited validation of signatures/methods in independent cohorts, no functional characterisation of the findings, and the discussion section does not include discussion around the relevance/interpretation of key findings that were highlighted in the abstract (eg. role of Th17, TRM, STAT3, and NFKb). Because of these factors, this work (as it stands) does have value to the field but will likely have a relatively low overall impact.

Related to the absence of discussion around prior TRM findings, the association between TRM involvement in response to IO therapy in this manuscript is counter to what has been previously demonstrated (Cell Rep Med. 2020;1(7):100127, Nat Immunol. 2017;18(8):940-950., J Immunol. 2015;194(7):3475-3486.). However, it should be noted that the authors in this manuscript chose to employ alternative markers of TRM characterisation when defining their clusters and this could indicate a potential rationale for differences in these findings. TRM population is generally characterised through the inclusion of the classical TRM markers CD69 (tissue retention marker) and CD103 (TCR experienced integrin that supports epithelial adhesion), which are both absent from the TRM definition in this study. Additional markers often used are CD44, CXCR6, and CD49a, of which only CXCR6 has been included by the authors. Conversely, the majority of markers used by the authors in the cell type clustering are not specific to TRM (eg. CD6, which is included in the TRM cluster but is expressed at its lowest in cluster 3 which the authors have highlighted as the CD8+ TRM population). Therefore, whilst there is an interesting finding of this particular cell cluster being associated with resistance to ICI, its annotation as a TRM cluster should be interpreted with caution.

Author response:

We appreciate the comprehensive reviews and would like to address the critiques and suggestions provided by both reviewers. We will make significant revisions to the manuscript to address these concerns. These include a more cautious interpretation of our results, an expanded discussion on key findings, additional analyses for TRM characterization, and a clearer outline of future validation efforts. We believe these changes will enhance the clarity and robustness of our study, and we hope they meet the reviewer’s expectations.

Reviewer 1:

Weaknesses:

(1) Heterogeneous and small cohort:

Increasing the cohort size is not feasible due to resource constraints. We acknowledge the challenges posed by the heterogeneous and small cohort, which complicate adjustments for confounding. We will apply multiple testing corrections to transparently assess and accurately report the robustness of our findings in the revision.

(2) Influence of tissue of origin on RNAseq:

We agree that RNAseq results can be heavily influenced by the tissue of origin. While immune cell composition in the normal lung tissues and lymph nodes is quite different, we found that in tumor tissues and metastatic lymph nodes, these differences diminish and common features dominate. Although we depicted this data in the supplementary figure 1, we did not provide a quantitative test in the original submission. In the revision, we will perform additional quantitative tests to compare immune cell composition across different tissue origins. These tests will provide a more precise understanding of the cellular composition and support our argument regarding the similarity of tumor-sculpted microenvironment. We will include these results and detailed methodologies in the revision.

(3) Accuracy performance and overfitting:

We acknowledge the concern regarding the high “accuracy” performance potentially indicating overfitting. We will clarify the evaluation methods used and moderate our claims regarding accuracy in the revision.

(4) Specificity of the tumor cell program/state analysis to the setting of ICIs:

The comment suggests that the tumor programs in our study may not be specific to the ICI group but rather prognostic in lung cancer. We acknowledge this possibility as we performed comparisons between responders and non-responders (with different cut-offs) to find common trends and interpreted them in terms of their association with ICI. In the revision, we will test the prognostic association of the tumor programs using public lung cancer data.

(5) More external validation needed:

We recognize the importance of external validation for reproducibility. While increasing the cohort size is not feasible, we will propose future directions for validation using larger, independent cohorts and potential experimental validations.

Reviewer 2:

Weaknesses:

(1) Small sample size and heterogeneous populations:

Increasing the cohort size is not feasible due to resource constraints. We acknowledge the challenges posed by the heterogeneous and small cohort, which complicate adjustments for confounding. We will apply multiple testing corrections to transparently assess and accurately report the robustness of our findings in the revision.

(2) Limited validation of signatures/ methods in independent cohorts:

We recognize the importance of external validation for reproducibility. While increasing the cohort size is not feasible, we will propose future directions for validation using larger, independent cohorts and potential experimental validations.

(3) Lack of functional characterization and discussion on key findings:

We appreciate the feedback regarding the need for functional characterization and a more thorough discussion of key findings on the roles of specific cell populations and genes. In the revised manuscript, we will expand the discussion section to include in-depth analysis of these findings and their relevance to the study. This includes a detailed interpretation of how these factors contribute to the immune response and potential implications for therapy.

(4) TRM findings and marker selection:

We understand the concern regarding the association between TRM involvement in response to IO therapy, which appears counter to previous demonstrations. It is indeed important to note that we employed alternative markers for TRM characterization. Our choice of markers was based on transcriptional references relevant to our study. However, we agree that classical TRM markers such as CD69 and CD103, which were absent in our definition, are critical for accurate TRM identification. To address this, we will include a detailed rationale for our marker selection and acknowledge the limitations of our TRM characterization. We will include additional analyses using classical TRM markers where possible and incorporate these findings into the revision. This will provide a clearer understanding of our TRM population and its role in the immune response to IO therapy.

eLife assessment

The current version of the study presents important findings on how the RelA/Stat3-dependent gene program in the liver influences intestinal homeostasis. The evidence supporting the conclusions is solid, with new data added compared to an earlier version of the study. The work will be of interest to scientists in gastrointestinal research fields.

Reviewer #2 (Public Review):

Summary:

Singh and colleagues employ a methodic approach to reveal the function of the transcription factors Rela and Stat3 in the regulation of the inflammatory response in the intestine.

Strengths of the manuscript include the focus on the function of these transcription factors in hepatocytes and the discovery of their role in the systemic response to experimental colitis. While the systemic response to induce colitis is appreciated, the cellular and molecular mechanisms that drive such systemic response, especially those involving other organs beyond the intestine are an active area of research. As such, this study contributes to this conceptual advance. Additional strengths are the complementary biochemical and metabolomics approaches to describe the activation of these transcription factors in the liver and their requirement - specifically in hepatocytes - for the production of bile acids in response to colitis.

In this revised version, the authors have addressed previously raised questions.

Reviewer #3 (Public Review):

Summary:

The authors try to elucidate the molecular mechanisms underlying the intra-organ crosstalks that perpetuate intestinal permeability and inflammation.

Strengths:

This study identifies a hepatocyte-specific rela/stat3 network as a potential therapeutic target for intestinal diseases via the gut liver axis using both murine models and human samples.

Weaknesses:

(1) The mechanism by which DSS administration induces the activation of the Rela and Stat3 pathways and subsequent modification of the bile acid pathway remains clear. As the authors state, intestinal bacteria are one candidate, and this needs to be clarified. I recommend the authors investigate whether gut sterilization by administration of antibiotics or germ free condition affects 1. the activation of the Rela and Stat3 pathway in the liver by DSS-treated WT mice and 2. the reduction of colitis in DSS-treated relaΔhepstat3Δhep mice.

(2) It has not been shown whether DSS administration causes an increase in primary bile acids, represented by CDCA, in the colon of WT mice following activation of the Rela and Stat3 pathways, as demonstrated in Figure 6.

(3) The implications of these results for IBD treatment, especially in what ways they may lead to therapeutic intervention, need to be discussed.

The above weakness points have been resolved by the revision and additional experiments.

Author response:

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

eLife assessment

This important study reveals the RelA/Stat3-dependent gene program in the liver influences intestinal homeostasis. The evidence supporting the conclusions is compelling, although some additional experiments will strengthen the study. The work will be of interest to scientists in gastrointestinal research fields.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, the authors showed that activation of RelA and Stat3 in hepatocytes of DSS-treated mice induced CYPs and thereby produced primary bile acids, particularly CDCA, which exacerbated intestinal inflammation.

Strengths:

This study reveals the RelA/Stat3-dependent gene program in the liver influences intestinal homeostasis.

Our reply: We thank the reviewer for the positive feedback and for appreciating the strength of our study.

Weaknesses:

Additional evidence will strengthen the conclusion.

(1) In Fig. 1C, photos show that phosphorylation of RelA and Stat3 was induced in only a few hepatocytes. The authors conclude that activation of both RelA and Stat3 induces inflammatory pathways. Therefore, the authors should show that phosphorylation of RelA and Stat3 is induced in the same hepatocytes during DSS treatment.

Our reply: The reviewers have raised a pertinent issue in Figure 1, as later on in our study we suggest that the combined activation of Rela and Stat3 is critical for aggravating the colitogenic phenotype in the murine model.

To address this issue, we have co-stained the fixed liver tissue of untreated and DSS-treated wild type mice with p-RelA (Ser536) and p-Stat3(Ser727) antibodies. Author response image 1 below shows the single staining for p-Rela (Ser536), pStat3 (Ser727), DAPI (to demarcate the nuclei) and merged image (p-Rela + pStat3).

Author response image 1.

Further, the signal intensity of p-RelA (Ser536) and p-Stat3(Ser727) per nuclei was calculated and plotted as a box plot. It is evident that the median of p-Rela and p-Stat3 signal intensity in DSS-treated samples is more than that of the control samples, suggesting that the majority of the treated hepatocytes have the presence of both p-Rela and p-Stat3 in the nuclei.

Author response image 2.

Further, we calculate the number of nuclei in the DSS-treated samples which are above the 90th percentile of the control samples (data has been provided in Author response table 1 below). We also calculate the percentage overlap of p-Rela to p-Stat3 and vice versa in Author response table 1 below.

Author response table 1.

Together our analysis concludes that indeed there is an activation of Rela and Stat3 in the same hepatocytes to generate the downstream effect that we observe in our study post-DSS treatment.

(2) In Fig. 5, the authors treated mice with CDCA intraperitoneally. In this experiment, the concentration of CDCA in the colon of CDCA-treated mice should be shown.

Our reply: We have experimentally examined if the CDCA supplemented intraperitoneally at the experimental dose used in our study, is reaching the colon or not. To quantify colonic CDCA we have performed targeted mass spectrometric studies and the data has been provided as a bar plot below.

Author response image 3.

It is evident from the plot that the CDCA levels are significantly higher in mice supplemented with CDCA as compared to their corresponding control (where only the vehicle was supplemented). The data has been added to the supplementary section S5b and the main text has been modified accordingly.

Reviewer #2 (Public Review):

Singh and colleagues employ a methodical approach to reveal the function of the transcription factors Rela and Stat3 in the regulation of the inflammatory response in the intestine.

Strengths of the manuscript include the focus on the function of these transcription factors in hepatocytes and the discovery of their role in the systemic response to experimental colitis. While the systemic response to induce colitis is appreciated, the cellular and molecular mechanisms that drive such systemic response, especially those involving other organs beyond the intestine are an active area of research. As such, this study contributes to this conceptual advance. Additional strengths are the complementary biochemical and metabolomics approaches to describe the activation of these transcription factors in the liver and their requirement - specifically in hepatocytes - for the production of bile acids in response to colitis.

Our reply: We express our gratitude to the reviewer for recognizing and appreciating the mechanistic insight provided by our work, and for considering it valuable in advancing conceptual understanding in the relevant field.

Some weaknesses are noted in the presentation of the data, including a comprehensive representation of findings in all conditions and genotypes tested.

Our reply: We thank the reviewer for the query and we have suitably modified the figures for a comprehensive representation of the findings, as described below:

● In Figure 2C, we have added the control alcian blue stained samples to clarify that there were no qualitative differences in the mucin levels observed in the relaΔhepstat3Δhep as compared to the wild type mice.

● We have also modified the figure 2D for a better presentation of the data.

● We have included histopathological analysis for the relaΔhepstat3Δhep mice in Figures S3a and S3b, following a format similar to the wild-type data previously provided as Figure S1a and S1b.

● For Figure 5C, the corresponding untreated samples with and without CDCA supplementation have been provided in the supplementary section Figure S5e.

● For Figure 2E, 3E, and 4C - the RT-qPCR data of the DSS-treated samples is plotted relative to their corresponding control samples, hence we only display two conditions in the bar plot. We have accordingly modified the figure legend for better clarity.

Reviewer #3 (Public Review):

Summary:

The authors try to elucidate the molecular mechanisms underlying the intra-organ crosstalks that perpetuate intestinal permeability and inflammation.

Strengths:

This study identifies a hepatocyte-specific rela/stat3 network as a potential therapeutic target for intestinal diseases via the gut-liver axis using both murine models and human samples.

Our reply: We thank the reviewer for appreciating the therapeutic potential of our work.

Weaknesses:

(1) The mechanism by which DSS administration induces the activation of the Rela and Stat3 pathways and subsequent modification of the bile acid pathway remains clear. As the authors state, intestinal bacteria are one candidate, and this needs to be clarified. I recommend the authors investigate whether gut sterilization by administration of antibiotics or germ-free condition affects 1. the activation of the Rela and Stat3 pathway in the liver by DSS-treated WT mice and 2. the reduction of colitis in DSS-treated relaΔhepstat3Δhep mice.

Our reply: We thank the reviewer for bringing up the aspect of gut microbiota in imparting colitis in our mice model. In accordance with reviewer's recommendation, we have sterilized the gut by administration of antibiotics, to evaluate if the intestinal bacteria are an important component leading to the activation of Rela and Stat3 pathway in the liver of DSS-treated WT mice or not.

(a) A brief schematic representation of the experimental design has been provided below and the detailed description of the methods has been described in supplementary methods.

Author response image 4.

Extract of liver tissues from mice treated with DSS for 6 days with/without prior antibiotic treatment were probed with p-Stat3 (Ser727) to examine the activation status of the hepatic Stat3 pathway. We observe that the signals for p-Stat3 (Ser727) are comparatively reduced post antibiotic treatment as evident from the blot below. p-Stat3 (Ser727) was a prominent activation signal at Day 6 DSS treatment that we have observed in Figure 1D,E.

Author response image 5.

These studies suggest that the activation status of Stat3 activation is hampered by antibiotic treatment and considering that Rela and Stat3 have to coordinate activity, presumably the downstream activation will be modulated upon gut sterilization. However, it should be appreciated that a sterilized gut is not likely to be physiologically relevant and intestinal bacteria along with bile acid levels would modulate Rela/Stat3 pathways.

b) It is likely that the hepatic deficiency of Rela and Stat3 may have modified the gut microbiome in relaΔhepstat3Δhep mice because of the altered bile composition. Moreover, the gut microbiota is a key component that guides the outcome of colitis. Hence, future studies are important to examine the role of the gut microbiome in imparting resistance in relaΔhepstat3Δhep mice, to colitogenic insults.

(2) It has not been shown whether DSS administration causes an increase in primary bile acids, represented by CDCA, in the colon of WT mice following activation of the Rela and Stat3 pathways, as demonstrated in Figure 6.

Our reply: In order to address the query, we would kindly like to request the reviewers to look at figure 4B where we show an increase in the CDCA levels of the colonic tissue, which is corresponding to our CDCA levels in the liver tissue (figure 4A) thus indicating that it may be driven by the hepatic Rela and Stat3 pathways.

(3) The implications of these results for IBD treatment, especially in what ways they may lead to therapeutic intervention, need to be discussed.

Our reply: We are grateful to the reviewer for bringing this topic for discussion.

Until now, only immunosuppressive agents and immunomodulators have been conventionally considered as therapeutic measures to manage IBD. However, with increasing research on the role of hepatic bile acid metabolism during experimental colitis, its potential cannot be undermined in the clinical setting. The potential of bile acids as a therapeutic target has been harnessed in the past; bile acid sequestrants have been utilized as a treatment for hyperlipidemia 46. Remedies like fecal microbial transplantation, which serve to normalize the bile acid ratios in the gut, are emerging as potential therapeutics in the last decade for IBD 47, 40. However, the potential of altering hepatic bile metabolism has remained unexplored for IBD, possibly due to a lack of mechanistic insight. Towards this, our work demonstrates the pro-inflammatory potential of CDCA during colitis following the activation of the Rela/Stat3 pathway. The suppression of Rela/Stat3-induced CDCA could provide beneficial effects in IBD patients while protecting the basal bile acid levels (through FXR signaling). Thus our studies identify a hepatocyte-specific rela/stat3 network as a potential therapeutic target for intestinal diseases. Another approach could be the use of bile acid sequestrants, which will temporarily decrease the levels of primary bile acids in the colon until the proinflammatory pathways are dampened as a combinatorial therapy alongside existing treatments.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor:

Fig. 4C should be Fig. 4D and vice versa.

Our reply: We have swapped Fig. 4C and Fig. 4D and corresponding changes have been incorporated in the main text.

Reviewer #2 (Recommendations For The Authors):

Please make note of the following specific comments

The immunostainings for phosphorylated p-Rela and STAT3 are unclear. Is there nuclear translocation of these phosphorylated transcription factors? Can the authors enumerate the percentage of cells in which nuclear translocation (presumably in hepatocytes) is detected?

Our reply: We apologize that immunostainings for phosphorylated p-Rela and STAT3 are unclear to the reviewers. Here we have tried our best to make the data clear by analyzing the stained section and plotting them.

To start with, we have co-stained the fixed liver tissue of untreated and DSS-treated wild type mice with p-RelA (Ser536) and p-Stat3(Ser727) antibodies, below we have provided a representative image used for analysis. To demarcate the nuclear boundary of the hepatocytes DAPI was used and the signal intensity for p-RelA (Ser536) and p-Stat3(Ser727) was quantified using ZenBlue software.

Author response image 6.

Below we have provided the box plot for the calculated nuclear intensities in the control (untreated) and DSS-treated samples for p-Rela and p-Stat3. We can clearly see that the median of p-Rela and p-Stat3 signal intensity in DSS-treated samples is more than that of the control samples, suggesting that the majority of the treated hepatocytes have the translocation of p-Rela and p-Stat3 in their nuclei.

Author response image 7.

The figure legends for Figures 2C and D are flipped. Please correct.

Our reply: Thank you for pointing it out, our apologies for the error and we have corrected the figure 2 accordingly.

For all H&E stainings, the authors should include histological scoring disease severity.

Our reply: Thank you for the query put forward, histological scoring to quantify the qualitative data obtained through microscopy is given below. Dot plot for the histological scoring of the H&E data for untreated and DSS-treated colon samples, we have referred to the scale described by Ren Y et al. 2019 (doi: 10.1038/s41598-019-53305-z) to score the sections.

Author response image 8.

We have added the dot plot to supplementary figure 2d, also the method applied for the above analysis has been described in the supplementary method section.

Please include Alcian Blue Staining in non-DSS treated WT and rel/stat3 double cKO mice.

Our reply: Thank you for pointing this out, we have added the Alcian Blue Staining of non-DSS treated WT and rel/stat3 double KO mice to figure 2C

For Figure 3C, can the authors indicate in the figure itself which bile acid is being represented (not only in the Figure legend)?

Our reply: Thank you for the suggestion we have indicated the respective bile acid in Figure 3C for better understanding.

As these data are from untargeted metabolomics, were other bile acids detected?

Our reply: This is a part of a separate study conducted by our collaborator, and will form a part of a new manuscript which will be focussed on human studies.

Can the authors validate the downregulation of key enzymes shown in Figure 3D, E at the protein level?

Our reply: We agree with the reviewer’s comment, that mRNA levels are not critical determinants of activation of any pathway, rather an indicator of probable activation. In that scenario, the estimation of protein levels is more determinative. But taking into consideration that we have the metabolomic data in subsequent figures (as in Figure 4 A, B) supporting our findings in Figure 3D, E, this makes RT-qPCR data a more robust indicator of an activated hepatic bile acid biosynthesis machinery.

The figure legends for Figures 4C and D are flipped. Please correct.

Our reply: Taking into consideration the suggestions by reviewer 1 we have swapped Fig. 4C and Fig. 4D and corrected the legend placement accordingly, thank you for pointing this out.

Also, please include representative images for the data represented in 4C.

Our reply: Thank you for the query, we have already added the representative images of confocal microscopy as figure S4.

Figure 5B should indicate that the data presented is from double cKO mice.

Our reply: We have indicated that the colon length data is from double KO animals in figure to make the visual representation clear for the readers, thank you for the concern.

Please correct typos: "entrocytic" and "Untread" in Figure Legend 5.

Our reply: Thank you for pointing out the error in the Legend, we apologize for the error in these errors we have corrected Figure 5.

Figure S4 includes a dataset (qPCR for Mmp3) that is not described. Neither Figure S4 nor S5 are described in the text.

Our reply: Thank you for the query, firstly we have already added Figure S4 and S5 to the text, our apologies that it has not been properly highlighted.

Secondly, the data for RT-qPCR for Mmp3 has been removed from supplementary figures as it may not be very relevant to the study.

Overall, the manuscript should be edited to ensure the correct use of English. Please also note that the last name of the first author seems to be missing in the main text.

Our reply: Thank you for the suggestion we have re-checked the manuscript for the probable errors and rectified them. The first author has a single name (with no surname) and we would like to correct that during the final print of the manuscript.

Reviewer #3 (Recommendations For The Authors):

(1) The authors need to show if DSS treatment affects the serological or histological changes in the liver of relaΔhepstat3Δhep mice.

Our reply: To address that, we have analyzed key serological markers of liver damage as well as looked into tissue histology.

The pathophysiological parameters of the liver of DSS treated relaΔhepstat3Δhep mice has been added to the revised manuscript as figure S3a and S3b. Here we show that the serological parameters are within the physiological range upon DSS treatment (Author response image 9a). Besides, the histological parameters remain unaltered as compared to the control tissue (Author response image 9b).

Cumulatively, both at the tissue level and functional level, there is not much effect of DSS

treatment on liver of relaΔhepstat3Δhep mice.

Author response image 9.

(2) It is recommended to use a second model to verify if this phenomenon is applicable to colitic status in general.

Our reply: We appreciate the query put forward, this is an ongoing study and we hope to examine further the role of hepatic RelA and Stat3 in TNBS-induced colitis model and in T cell transfer model of colitis.

eLife assessment

This work critically evaluates several widely-used assays of transcriptional responses to water limitation in Arabidopsis grown on defined agar-solidified media and, finding inconsistent responses in root transcriptome responses, introduces a new 'hard agar' assay with more consistent responses. The work is valuable as a simple and alternative experimental system that would enable high-throughput genetic screening (and GWAS) to assess the impacts of environmental perturbations on transcriptional responses in various genetic backgrounds. Within this scope, the work is solid, though the debate about whether field-level physiological inferences can be made from such assays remains.

Reviewer #2 (Public Review):

This manuscript describes new methodology to study low water potential (drought) stress responses in agar plates. They devote considerable effort in comparing transcriptome data among various previously published experimental systems, examining how different approaches of reducing water potential impact the Arabidopsis root and shoot transcriptome. Each method purported to reduce water potential in plate-grown seedlings has a different effect on Arabidopsis root transcriptome responses, which is problematic for the field. In this reviewer's view, differences in transcriptome are not as important, and often not as informative as measurement of physiological parameters, which they do very little of in their study.

The focus on transcriptome data to the almost complete exclusion of other types of data is a symptom of a broader over-emphasis on the transcriptome that is quite prevalent in plant science now. We measure transcriptomes because we can, not because it is inherently the most informative thing to do. The important thing is protein amount, and even more so protein activity/function, which we know has an imperfect, at best, correlation with transcript level. This reviewer acknowledges that using Arabidopsis transcriptomics is a commonly employed method, and as such, the outcomes of this study will hold value for a broad audience, even if largely as a cautionary tale. If transcriptomics is used to identify candidate genes for future investigations, an approach that has had some success, then appropriate cautions should be taken in translating expectations about gene, protein, and phenotypic responses in field conditions.

Reviewer #3 (Public Review):

This work compares transcriptional responses of shoots and roots harvested from four plate-based assays that aim to simulate drought and from plants subjected to water deficit in pots using the model plant Arabidopsis thaliana with the goal to select a plate-based assay that best recapitulates transcriptional changes that are observed during water-deficit in pots. For the plate-based assays polyethylene glycol (PEG), mannitol, and sodium chloride (salt) treatments were used as well as a 'hard agar' assay which was newly developed by the authors. In the 'hard agar' assay, less water was added to the solid components of the media leading to an increase in agar strength and nutrient concentration. Plants in pots were grown on vermiculite with the same nutrient mix as used in the plates and drought was induced by withholding watering for five days.

The authors observed a good directional agreement of differential expressed genes for shoots between the plate assays on the vermiculite drying experiment. However, less directional agreement was observed for differential expressed genes of roots, except for their newly developed 'hard agar' assay which had good directional agreement. Testing whether the increase in agar strength or more concentrated nutrients are attributed to this, they found that both factors contributed to the effect of the 'hard agar'. Arabidopsis ecotypes that showed a stronger reduction in shoot size when grown on the 'hard agar' tended to have a lower fitness according to an external study which may indicate that the 'hard agar' assay simulates physiological relevant conditions.

The work highlights that transcriptional responses for simulated drought on plates and drought caused by water deficit are highly variable and dependent on the tissues that are observed. The authors demonstrate that transcriptomics can be used to select a suitable plate assay that most closely recapitulates drought through water deficit for plants grown in pots. Interestingly their newly developed 'hard agar' assay provides an alternative to traditional plate-based assays with improved directional agreement of differential expressed genes in roots in comparison to plants experiencing water deficit in vermiculite. It is promising that the impact of 'hard agar' on the shoot size of 20 diverse Arabidopsis accessions shows some association with plant fitness under drought in the field. Their methodology could be powerful in identifying a better substitute for plate-based high-throughput drought assays that have an emphasis on gene expression changes.

Author response

The following is the authors’ response to the previous reviews

eLife assessment 

This work is an attempt to establish conditions that accurately and efficiently mimic a drought response in Arabidopsis grown on defined agar-solidified media - an admirable goal as a reliable experimental system is key to conducting successful low water potential experiments and would enable high-throughput genetic screening (and GWAS) to assess the impacts of environmental perturbations on various genetic backgrounds. The authors compare transcriptome patterns of plant subjected to water limitation imposed with different experimental systems. The work is valuable in that it lays out the challenges of such an endeavor and points out shortcomings of previous attempts. There was concern, however, that a purely gene expression-based approach may not provide sufficient physiologically relevant information about plant responses to drought, and therefore, despite improvements from a previous version, the new methodology championed by this work remains inadequate.   

Molecular biologists who study drought stress must make choices about which assays to use in their investigation. Serious resources and effort are put into their endeavor, and choice of assay matters. Our manuscript’s goal was largely practical: to guide molecular biologists employing transcriptomics in their choice of drought stress assay, and thus help ensure their work will discover transcriptional signatures of importance, and not those that may be an artifact from lowering water potential using chemical agents on agar plates.  

We examine how different approaches of reducing water potential impact the Arabidopsis root and shoot transcriptome. Our manuscript shows that each method of reducing water potential has a different effect on Arabidopsis root transcriptome responses. We acknowledge that drought stress induces a complex physiological response, and can vary depending on the method used. However, by comparing across assays, we find instances where a gene is downregulated by low water potential in one assay, and upregulated by low water potential in another assay. We feel it is only natural to question why this could be, and to hypothesize that it may be caused by secondary effects caused by the way low water potential is imposed.  We note that comparative transcriptomics has been a standard approach for decades. We take it as the reviewer’s opinion that it may not be insightful, but it does not factually impact our findings. 

Reviewer #2 (Public Review): 

This manuscript purports to develop a new system to study low water potential (drought) stress responses in agar plates. They make numerous problematic comparisons among transcriptome datasets, particularly to transcriptome data from a vermiculite drying experiment which they inappropriately present as representing an authentic "drought response" to the exclusion of all other data. For some reason, which the reviewer cannot fully understand, the authors seem intent on asserting the superiority of their experimental system to all others. They do not succeed in this and such an effort is ultimately a disservice to the field of drought research as a whole. 

While they devote considerable effort in comparing transcriptome data among various experimental systems, the potentially more informative experiment at the end of the manuscript of testing growth responses of a number of Arabidopsis accessions is only done for their "LW" system. The focus of this manuscript on transcriptome data to the almost complete exclusion of other types of data which is a symptom of a broader over-emphasis on transcriptome that unfortunately is quite prevalent in plant science now. It is worth reminding that for protein coding genes, which constitute the vast majority of genes, transcriptome data is a proxy measurement. The really important thing is protein amount, and even more so protein activity/function, which we know has an imperfect, at best, correlation with transcript level. We measure transcriptomes because we can, not because it is inherently the most informative thing to do. The author's quixotic quest to see if the transcriptomes of different stress treatments match is of limited value and further diminished by their misleading presentation of one particular transcriptome data set (from their vermiculite drying experiments) as somehow a special data set that everything else must be evaluated against. This study sheds no new light on how to do relevant drought (low water potential) experiments in the lab. 

Although the reviewer acknowledges that the authors have made some effort to respond to previous comments, the fundamental flaws remain and the present version of this study is little improved from the first submission. 

One challenge faced by the drought community is establishing consensus regarding the definition of drought itself. According to the criteria followed by the reviewer, any method leading to a reduction in water potential qualifies as drought stress. However, the findings presented in this manuscript demonstrate that transcriptional responses in roots vary considerably across five different methods of reducing water potential. This indicates that beyond responding to a change in water potential itself, root transcriptomes will also respond to the specific way low water potential is introduced. We believe this variability is of interest to the drought research community. 

Of the five methods we explore, we hold the view that the gene expression changes induced by vermiculite drying as the most analogous to the expression signatures Arabidopsis would exhibit in response to low water potential in the natural environment. In contrast, we posit that Arabidopsis grown on agar plates - where the root system is exposed to air and light, and where water potential is lowered using chemical agents - may contain gene expression signatures plant molecular biologists may not find particularly relevant. However, we acknowledge that this is our opinion, and will make this more explicit on our revised text. 

More broadly, we believe that the reviewer’s observation regarding the ‘over-emphasis’ on transcriptomics that is prevalent within the plant science community justifies, rather than diminishes, the work presented here. If transcriptomics is a commonly employed method, then we anticipate that the outcomes of this study will hold value for a broad audience. Such researchers are likely not only using transcriptomics as a proxy measure for protein abundance, as the reviewer suggests, but also because it is one of the more straightforward genomic techniques biologists can use to identify candidate genes that may be chosen for further scrutiny. 

Reviewer #3 (Public Review): 

Comments on revised version: 

Specific previous criticisms that were addressed are: 

(1) that gene expression changes were only compared between the highest dose of each stress assay. In the revised version, the authors changed their framework and are now using linear modelling to detect genes that display a dose response to each specific treatment. I agree that this might be a more robust approach to selecting genes that are specific to a certain treatment. 

(2) that concentrations of PEG, mannitol, NaCl, and the "low water" agar which were chosen are not comparable in regards to their specific osmotic component. I appreciate that the authors measured the osmotic potential of each treatment. It revealed that both PEG and NaCl at their highest concentration had a much more negative osmotic potential compared to the other treatment. The authors claim that using ANCOVA they did not detect any significant differences between the treatments (lines 113, 114). I do believe that ANCOVA is not the appropriate test in this case. ANCOVA has an assumption of linearity, while the dose response between concentration and osmotic potential is non-linear. This is particularly evident for PEG (Steuter AA. Water potential of aqueous polyethylene glycol. Plant Physiol. 1981 Jan;67(1):64-7. doi: 10.1104/pp.67.1.64.). Since the treatments are not the same at the highest level, I think this could have effects on the validity of comparisons by linear model. One approach could be to remove the treatment level with the highest concentration and compare the results or adjust the treatments to the same osmolarity. 

(3) that only two biological replicates were collected for RNA sequencing which makes it impossible to know how much variance exists between samples. The authors added a third replicate in the revised version for most treatments. However, some treatments still have only two replicates, which cannot be easily seen from the text or the figure. I would prefer that those differences are pointed out. 

(4) that the original manuscript did not explore what effect the increase of agar and nutrient concentration in the "low water" agar had on water potentials. The authors conducted additional experiments showing that changes in water potential were exclusively caused by changes in the nutrient concentration (Figure 2-figure supplement 5; lines 222-224). However, the increase in agar strength had also some effect on gene expression. While this is not further discussed in the text, I believe this effect of agar on gene expression could be similar to root responses to soil compaction. 

(5) That the lower volume of media in the "low water" agar could have an effect on plants. The authors compared these effects in Figure 2-figure supplement 7. They claim that "different volumes of LW agar media do not play a significant part in modulating gene expression". While I can see that they detected 313 overlapping DEGs, there were still 146 and 412 non-overlapping DEGs. The heatmap in subpanel E also shows that there were differences in particular in the up-regulated genes. My conclusion would be that the change in volume does play a role and this should be a consideration in the manuscript. 

We thank the reviewer for their suggestions. We plan to resubmit the manuscript reflecting the requested changes. Specifically, we will: 

-       We will detail more thoroughly the effects of agar volume on gene expression changes elicited by LW agar treatment. 

-       We will investigate whether the tensile stress introduced by hard agar is similar to soil compaction by an analysis with existing literature. 

-       Assess more rigorously the suitability of the ANCOVA model for assessing water potential changes of different media types.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

(1) The modeling process is outlined, but an explanation of why Maxent (Phillips & Dudík, 2008) was chosen for SDMs and why the specified predictor variables were used could provide additional context. This clarity would help readers understand the rationale behind the methodology.

In L.558-571 (Predictor variables subsection), we added the explanation about predictor variables as follows:

“Predictors encompass a range of environmental variables recognized to impact species distribution (Table 3): land use (Newbold et al., 2015), climate (bioclim variables (Booth et al., 2014)), vegetation (Abe, 2018), lithology (Ott, 2020) and elevational range (Udy et al., 2021). Additionally, categorical variables representing known biogeographic regions, reflecting geological history, were included. We applied  Blakiston's Line —Tsugaru straits dividing the northern and main islands of Japan (i.e., Hokkaido and Honshu islands)— reflecting a significant historical migration barrier for mammals and birds (Dobson, 1994; Saitoh et al., 2015). Due to the distinct fauna (Wepfer et al., 2016; Yamasaki, 2017), we also specified oceanic islands (i.e. Ogasawara and Daito isles) which have never been connected with the Asiatic continents. Continuous environmental variables were transformed into linear, quadratic and hinge feature classes to illustrate nonlinear associations between environments and species occurrence (Phillips et al., 2017). The regularisation multiplier was set at 2.5, falling within the established optimal range of 1.5 to 4 (Elith et al., 2010; MorenoAmat et al., 2015).”

In L.614-618 (Modelling subsection), we explain why we chose MaxEnt:

“To model species distributions from presence-only data, several algorithms have been utilised, including generalised additive models, random forest, and neural networks (Norberg et al., 2019; Valavi et al., 2022). In our study, we opted for MaxEnt (Phillips and Dudík, 2008) due to its high estimation accuracy and relatively low computational burden (Valavi et al., 2022).”

(2) While the study outlines a manual reidentification process by experts for wild individuals, it might be beneficial to elaborate on the criteria or expertise level of these experts. This transparency ensures the reliability of the reidentification process. Reply

In L.519-523, we added description about experts as follows:

“These experts have professional backgrounds, serving as a technician at a prefectural research institute (fish), highly-experienced field survey conductors (plants and insects, respectively), a post-doctoral researchers (amphibians and reptiles, and mammals, respectively), and a museum curator (mollusks) specialising in the focal taxa.”

(3) The analysis of the effects of data type (Biome+Traditional data or Traditional survey data) on BI is comprehensive. However, a brief discussion on the potential implications of these effects on the study's overall conclusions could add depth to the interpretation.

We enforced our discussion about the causes and consequences of improved modelling accuracy. 

In L.276-282, we argued about the causes: 

“Therefore, incorporating Biome data could significantly enhance modelling accuracy in urban and suburban landscapes, which are typically underrepresented in traditional survey data. As pseudo-absences are selected based on search effort, our models utilise numerous pseudoabsences from these areas. Consequently, this might lead to better estimation of species absence in such areas, not just presence, resulting in an overall increase in model accuracy across a wider range of species.”  

In L.370-387, we argued how improved modelling accuracy may help build naturepositive society as follows:

“By blending data from traditional surveys and communities, we improved the accuracy of species distribution estimates. This enhanced estimation lays the groundwork for more precise subsequent analyses. For instance, estimated distributions will be useful in selecting new protected areas or areas with OECMs (Other Effective area-based Conservation Measures: allowing a wider range of land use as long as biodiversity and ecosystem services are sustained/improved). Using estimated distributions of each species, hotspots of species or evolutionary diverse taxa can be inferred. Such sites will be good candidates for protected areas (Jones et al., 2016) or OECMs (Shiono et al., 2021). Further, estimated distributions can be used as input for spatial conservation prioritisation tools (e.g. Marxan (Ball et al., 2009)). 

In our experience, stakeholders—including corporate social responsibility managers and conservation practitioners—often seek the list of species potentially inhabiting their locations. Due to the uncertainty of SDMs and their thresholding into presence/absence, on-site surveys remain essential for assessing biodiversity status. SDMs can make such surveys costeffective by screening important locations for on-site assessment (e.g., Locate phase in TNFD framework) and narrowing down the target species for surveying. Improved estimation through SDMs can mitigate risks associated with their use in society and enable more informed decisionmaking for conservation efforts.”

Following the editorial policy, we have reorganised our supplementary materials as follows:

-        Formerly Supplementary File 1 - Remains unchanged.

-        Formerly Supplementary File 2 - Transferred into the main text, in the subsection "Filtering suspicious occurrence record in Biome data" in the Methods section, and Table 2. Citations remain as Supplementary File 2.

-        Formerly Supplementary File 3 - Remains unchanged.

-        Formerly Supplementary File 4 - Transferred into "Figure 3—figure supplement 1".

-        Formerly Supplementary File 5 - Transferred into Figure 4.

-        Formerly Supplementary File 6 - Transferred into the main text, in the subsection "Predictor variables" in the Methods section and Table 3.

-        Formerly Supplementary File 7 - Transferred into the main text, in the subsection "Pseudo-absence reflecting search effort" in the Methods section and Figure 5.

-        Formerly Supplementary File 8 - Transferred into the main text, in the subsection "Model evaluation" in the Methods section and Figure 6.

-        Formerly Supplementary File 9 - Renamed as Supplementary File 4.

eLife assessment

This important study presents findings of great practical value, offering fresh insights into natural species distributions across Japan. By combining multiple data sources (including those from non-academic sectors, aka citizen scientists), the manuscript also presents a compelling new tool that can be used to aid conservation agendas, detect species distribution changes, and testing of ecological theories.

Reviewer #1 (Public Review):

Summary:

The study presented by Atsumi et al. is about using smartphone-driven, community-sourced data to enhance biodiversity monitoring. The idea is to leverage the widespread use of smartphones to gather data from the community quickly, contributing to a more comprehensive understanding of biodiversity. The authors discuss the importance of ecosystem services linked to biodiversity and the threats posed by human activities. It emphasizes the need for comprehensive biodiversity data to implement the Kunming-Montreal Global Biodiversity Framework. The 'Biome' mobile app, launched in Japan, uses species identification algorithms and gamification to gather over 6 million observations since 2019. While community-sourced data may have biases, incorporating it into Species Distribution Models (SDMs) improves accuracy, especially for endangered species. The app covers urban-natural gradients uniformly, enhancing traditional survey data biased towards natural areas. Combining these sources provides valuable insights into species distributions for conservation, protected area designation, and ecosystem service assessment.

Strengths:

The use of a smartphone app ('Biome') for community-driven species occurrence data collection represents an innovative and inclusive approach to biodiversity monitoring, leveraging the widespread use of smartphones. The app has successfully accumulated a large volume of species occurrence data since its launch in 2019, showcasing its effectiveness in rapidly gathering information from diverse locations. Despite challenges with certain taxa, the study highlights high species identification accuracy, especially for birds, reptiles, mammals, and amphibians, making the 'Biome' app a reliable tool for species observation. The integration of community-sourced data into Species Distribution Models (SDMs) improves the accuracy of predicting species distributions. This has implications for conservation planning, including the designation of protected areas and assessment of ecosystem services. The rapid accumulation of data and advancements in machine learning methods open up opportunities for conducting time-series analyses, contributing to the understanding of ecosystem stability and interaction strength over time. The study emphasizes the collaborative nature of the platform, fostering collaboration among diverse stakeholders, including local communities, private companies, and government agencies. This inclusive approach is essential for effective biodiversity assessment and decision-making. The platform's engagement with various stakeholders, including local communities, supports biodiversity assessment, management planning, and informed decision-making. Additionally, the app's role in fostering nature-positive awareness in society is highlighted as a significant contribution to creating a sustainable society.

Weaknesses:

While the studies make significant contributions to biodiversity monitoring, they also have some weaknesses. Firstly, relying on smartphone-driven, community-sourced data may introduce spatial and taxonomic biases. The 'Biome' app, for example, showed lower accuracy for certain taxa like seed plants, molluscs, and fishes, potentially impacting the reliability of the gathered data. Furthermore, the effectiveness of Species Distribution Models (SDMs) relies on the assumption that biases in community-sourced data can be adequately accounted for. The unique distribution patterns of the 'Biome' data, covering urban-natural gradients uniformly, might not fully represent the diversity of certain ecosystems, potentially leading to inaccuracies in the models. Moreover, the divergence in data distribution patterns along environmental gradients between 'Biome' data and traditional survey data raises concerns. The app data shows a more uniform distribution across natural-urban gradients, while traditional data is biased towards natural areas. This discrepancy may impact the representation of certain ecosystems and influence the accuracy of Species Distribution Models (SDMs). While the integration of 'Biome' data into SDMs improves accuracy, the study notes that controlling the sampling efforts is crucial. Spatially-biased sampling efforts in community-sourced data need careful consideration, and efforts to control biases are essential for reliable predictions.

eLife assessment

The paper illustrates a valuable approach to generating TCR transgenic mice specific for known epitopes. There is some solid evidence for the efficacy of this approach, although only limited evidence is provided that the TCR clone in question successfully recapitulates the functional features of the endogenous response to the same antigen, and the claim that this method is superior to more traditional clone selection methods is incompletely substantiated by the data presented.

Reviewer #1 (Public Review):

Summary:

Debeuf et al. introduce a new, fast method for the selection of suitable T cell clones to generate TCR transgenic mice, a method claimed to outperform traditional hybridoma-based approaches. Clone selection is based on the assessment of the expansion and phenotype of cells specific for a known epitope following immune stimulation. The analysis is facilitated by a new software tool for TCR repertoire and function analysis termed DALI. This work also introduces a potentially invaluable TCR transgenic mouse line specific for SARS-CoV-2.

Strengths:

The newly introduced method proved successful in the quick generation of a TCR transgenic mouse line. Clone selection is based on more comprehensive phenotypical information than traditional methods, providing the opportunity for a more rational T cell clone selection.

The study provides a software tool for TCR repertoire analysis and its linkage with function.

The findings entail general practical implications in the preclinical study of a potentially very broad range of infectious diseases or vaccination.

A novel SARS-CoV-2 spike-specific TCR transgenic mouse line was generated.

Weaknesses:

The authors attempt to compare their novel method with a more conventional approach to developing TCR transgenic mice. In this reviewer's opinion, this comparison appears imperfect in several ways:

• Work presenting the "traditional" method was inadequate to justify the selection of a suitable clone. It is therefore not surprising that it yielded negative results. More evidence would have been necessary to select clone 47 for further development of the TCR transgenic line, especially considering the significant time and investment required to create such a line.

• The comparison is somewhat unfair, because the methods start at different points: while the traditional method was attempted using a pool of peptides whose immunogenicity does not appear to have been established, the new method starts by utilising tetramers to select T cells specific for a well-established epitope.

• Given the costs and time involved, only a single clone could be tested for either method, intrinsically making a proper comparison unfeasible. Even for their new method, the authors' ability to demonstrate that the selected clone is ideal is limited unless they made different clones with varying profiles to show that a particular profile was superior to others.

In my view, there was no absolute need to compare this method with existing ones, as the proposed method holds intrinsic value.

While having more data to decide on clone selection is certainly beneficial, given the additional cost, it remains unclear whether knowing the expression profiles of different proteins in Figure 2 aids in selecting a candidate. Is a cell expressing more CD69 preferable to a cell expressing less of this marker? Would either have been effective? Are there any transcriptional differences between clonotype 1 and 2 (red colour in Figure 2G) that justify selecting clone 1, or was the decision to select the latter merely based on their different frequency? If all major clones (i.e. by clonotype count) present similar expression profiles, would it have been necessary to know much more about their expression profiles? Would TCR sequencing and an enumeration of clones have sufficed, and been a more cost-effective approach?

Lastly, it appears that several of the experiments presented were conducted only once. This information should have been explicitly stated in the figure legends.

Reviewer #2 (Public Review):

Summary:

The authors seek to use single-cell sequencing approaches to identify TCRs specific for the SARS CoV2 spike protein, select a candidate TCR for cloning, and use it to construct a TCR transgenic mouse. The argument is that this process is less cumbersome than the classical approach, which involves the identification of antigen-reactive T cells in vitro and the construction of T cell hybridomas prior to TCR cloning. TCRs identified by single-cell sequencing that are already paired to transcriptomic data would more rapidly identify TCRs that are likely to contribute to a functional response. The authors successfully identify TCRs that have expanded in response to SARS CoV2 spike protein immunization, bind to MHC tetramers, and express genes associated with functional response. They then select a TCR for cloning and construction of a transgenic mouse in order to test the response of resulting T cells in vivo following immunization with spike protein of coronavirus infection.

Strengths:

(1) The study provides proof of principle for the identification and characterization of TCRs based on single-cell sequencing data.

(2) The authors employ a recently developed software tool (DALI) that assists in linking transcriptomic data to individual clones.

(3) The authors successfully generate a TCR transgenic animal derived from the most promising T cell clone (CORSET8) using the TCR sequencing approach.

(4) The authors provide initial evidence that CORSET8 T cells undergo activation and proliferation in vivo in response to immunization or infection.

(5) Procedures are well-described and readily reproducible.

Weaknesses:

(1) The purpose of presenting a failed attempt to generate TCR transgenic mice using a traditional TCR hybridoma method is unclear. The reasons for the failure are uncertain, and the inclusion of this data does not really provide information on the likely success rate of the hybridoma vs single cell approach for TCR identification, as only a single example is provided for either.

(2) There is little information provided regarding the functional differentiation of the CORSET8 T cells following challenge in vivo, including expression of molecules associated with effector function, cytokine production, killing activity, and formation of memory. The study would be strengthened by some evidence that CORSET8 T cells are successfully recapitulating the functional features of the endogenous immune response (beyond simply proliferating and expressing CD44). This information is important to evaluate whether the presented sequencing-based identification and selection of TCRs is likely to result in T-cell responses that replicate the criteria for selecting the TCR in the first place.

(3) While I find the argument reasonable that the approach presented here has a lot of likely advantages over traditional approaches for generating TCR transgenic animals, the use of TCR sequencing data to identify TCRs for study in a variety of areas, including cancer immunotherapy and autoimmunity, is in broad use. While much of this work opts for alternative methods of TCR expression in primary T cells (i.e. CRISPR or retroviral approaches), the process of generating a TCR transgenic mouse from a cloned TCR is not in itself novel. It would be helpful if the authors could provide a more extensive discussion explaining the novelty of their approach for TCR identification in comparison to other more modern approaches, rather than only hybridoma generation.

eLife assessment

This manuscript uses public datasets of myelodysplastic syndrome (MDS) patients to undertake a multi-omics analysis of clinical, genomic, and transcriptomic datasets. Useful findings are provided by way of interesting correlations of specific mutations with inflammation and differing clinical outcomes. While the evidence is extensive and interesting, it remains incomplete in the absence of pipeline validation and addressing the potential confounding factors present in the datasets used. When these issues are addressed, this will be of substantive value to hematologists and clinical immunologists.

Reviewer #1 (Public Review):

In their manuscript, Gerlevik et al. performed an integrative analysis of clinical, genetic and transcriptomic data to identify MDS subgroups with distinct outcomes. The study was based on the building of an "immunoscore" and then combined with genotype and clinical data to analyze patient outcomes using multi-omics factor analysis.

Strengths: Integrative analysis of RNA-seq, genotyping and clinical data

Weaknesses: Validation of the bioinformatic pipeline is incomplete

Major comments:

(1) This study considered two RNA-seq data sets publicly available and generated in two distinct laboratories. Are they comparable in terms of RNA-seq technique: polyA versus rRNA depletion, paired-end sequencing, fragment length?

(2) Data quality control (figure 1): the authors must show in a graph whether the features (dimensions) of factor 1 were available for each BMMNC and CD34+ samples.

(3) How to validate the importance of "immunoscore"? If GSEA of RNA-seq data was performed in the entire cohort, in the SF3B1-mutated samples or SRSF2-mutated samples (instead of patients having a high versus low level of factor 1 shown in Sup Fig. 4), what would be the ranking of Hallmarks or Reactome inflammatory terms among the others?

(4) To decipher cell-type composition of BMMNC and CD34+ samples, the authors used van Galen's data (2019; supplementary table 3). Cell composition is expressed as the proportion of each cell population among the others. Surprisingly, the authors found that the promonocyte-like score was increased in SF3B1-mutated samples and not in SRSF2-mutated samples, which are frequently co-mutated with TET2 and associated with a CMML-like phenotype. Is there a risk of bias if bone marrow subpopulations such as megakaryocytic-erythroid progenitors or early erythroid precursors are not considered?

(5) Figures 2a and 2b indicated that the nature of retrotransposons identified in BMMNC and CD34+ was different. ERVs were not detected in CD34+ cells. Are ERVs not reactivated in CD34+ cells? Is there a bias in the sequencing or bioinformatic method?

(6) What is the impact of factor 1 on survival? Is it different between BMMNC and CD34+ cells considering the distinct composition of factor 1 in CD34+ and BMMNC?

(7) In Figure 1e, genotype contributed to the variance of in the CD34+ cell analyses more importantly than in the BMMNC. Because the patients are different in the two cohorts, differences in the variance could be explained either by a greater variability of the type of mutations in CD34 or an increased frequency of poor prognosis mutations in CD34+ compared to BMMNC. The genotyping data must be shown.

(8) Fig. 2a-b: Features with high weight are shown for each factor. For factor 9, features seemed to have a low weight (Fig. 1b and 1c). However, factor 9 was predictive of EFS and OS in the BMMNC cohort. What are the features driving the prognostic value of factor 9?

(9) The authors also provided microarray analyses of CD34+ cell. It could be interesting to test more broadly the correlation between features identified by RNA-seq or microarrays.

(10) The authors should discuss the relevance of immunosenescence features in the context of SRSF2 mutation and extend the discussion to the interest of their pipeline for patient diagnosis and follow up under treatments.

Reviewer #2 (Public Review):

The authors performed a Multi-Omics Factor Analysis (MOFA) on analysis of two published MDS patient cohorts-1 from bone marrow mononuclear cells (BMMNCs) and CD34 cells (ref 17) and another from CD34+ cells (ref 15) --with three data modalities (clinical, genotype, and transcriptomics). Seven different views, including immune profile, inflammation/aging, Retrotransposon (RTE) expression, and cell-type composition, were derived from these modalities to attempt to identify the latent factors with significant impact on MDS prognosis.

SF3B1 was found to be the only mutation among 13 mutations in the BMMNC cohort that indicated a significant association with high inflammation. This trend was also observed to a lesser extent in the CD34+ cohort. The MOFA factor representing inflammation showed a good prognosis for MDS patients with high inflammation. In contrast, SRSF2 mutant cases showed a granulocyte-monocyte progenitor (GMP) pattern and high levels of senescence, immunosenescence, and malignant myeloid cells, consistent with their poor prognosis. Also, MOFA identified RTE expression as a risk factor for MDS. They proposed that this work showed the efficacy of their integrative approach to assess MDS prognostic risk that 'goes beyond all the scoring systems described thus far for MDS'.

Several issues need clarification and response:

(1) The authors do not provide adequate known clinical and molecular information which demonstrates prognostic risk of their sample cohorts in order to determine whether their data and approach 'goes 'beyond all the scoring systems described thus far for MDS'. For example, what data have the authors that their features provide prognostic data independent of the prior known factors related to prognosis (eg, marrow blasts, mutational, cytogenetic features, ring sideroblasts, IPSS-R, IPSS-M, MDA-SS)?

(2) A major issue in analyzing this paper relates to the specific patient composition from whom the samples and data were obtained. The cells from the Shiozawa paper (ref 17) is comprised of a substantial number of CMML patients. Thus, what evidence have the authors that much of the data from the BMMNCs from these patients and mutant SRSF2 related predominantly to their monocytic differentiation state?

(3) In addition, as the majority of patients in the Shiozawa paper have ring sideroblasts (n=59), thus potentially skewing the data toward consideration mainly of these patients, for whom better outcomes are well known.

(4) Further, regarding this patient subset, what evidence have the authors that the importance of the SF3B1 mutation was merely related to the preponderance of sideroblastic patients from whom the samples were analyzed?

(5) An Erratum was reported for the Shiozawa paper (Shiozawa Y, Malcovati L, Gallì A, et al. Gene expression and risk of leukemic transformation in myelodysplasia. Blood. 2018 Aug 23;132(8):869-875. doi: 10.1182/blood-2018-07-863134) that resulted from a coding error in the construction of the logistic regression model for subgroup prediction based on the gene expression profiles of BMMNCs. This coding error was identified after the publication of the article. The authors should indicate the effect this error may have had on the data they now report.

(6) What information have the authors as to whether the differing RTE findings were not predominantly related to the differentiation state of the cell population analyzed (ie higher in BM MNCs vs CD34, Fig 1)? What control data have the authors regarding these values from normal (non-malignant) cell populations?

(7) The statement in the Discussion regarding the effects of SRSF2 mutation is speculative and should be avoided. Many other somatic gene mutations have known stronger effects on prognosis for MDS.

Reviewer #1 (Public Review):

Summary:

Mao and colleagues re-analysed published spatial, bulk and single-cell transcriptomic datasets from primary colorectal cancers and colorectal-cancer-derived liver metastases. The analyses of paired cancer and non-cancer tissue samples showed that T cells are enriched in tumour tissue, accompanied by a reduction in the fraction of NK cells in the cancer tissue transcriptional datasets. Furthermore, authors claim that tumour tissue has a higher fraction of GZMK+ (resting) NK cells and suggest a correlation between the presence of these cells and poorer prognosis for cancer patients. In contrast, the increased frequency of KIR2DL4+ (activated) NK cells correlates with improved survival of cancer patients.

Strengths:

The authors performed a comprehensive analysis of published datasets, integrating spatial and single-cell transcriptomic data, which allowed them to discover the enrichment of GZMK+ NK cells in cancer tissues.

Weaknesses:

Despite their thorough analysis, the authors did not provide sufficient experimental evidence to support their claim that GZMK+ NK cells contribute to a worse prognosis for cancer patients or promote cancer progression. The terms resting and activated NK cells are used without properly defining the characteristics of these populations other than the gene expression of a handful of genes. Furthermore, the criteria used to quantify the NK cell population in spatial data is not entirely clear. While one can visually observe an increased fraction of GZMK+ NK cells compared to KIR2DL4+ NK cells in cancer tissues, no quantification is shown. They did not present any preclinical (animal model) or clinical data suggesting a causal relationship between NK cells and tumour growth. Thus, while a correlation may exist between the presence of GZMK+ NK cells and poorer tumour prognosis, causation cannot be claimed based on the available evidence. Furthermore, the in vitro data provided is limited to a single NK cell line derived from a lymphoma patient, which does not fully represent the diversity and functionality of human NK cells. Moreover, the in vitro experiments suffer from a lack of required controls and inadequate methodology.

Reviewer #2 (Public Review):

Summary:

This manuscript investigates the role of the abundant NK cells that are observed in colon cancer liver metastasis using sequencing and spatial approaches in an effort to clarify the pro and anti-tumourigenic properties of NK cells. This descriptive study characterises different categories of NK cells in tumour and tumour-adjacent tissues and some correlations. An attempt has been made using pseudotime trajectory analysis but no models around how these NK cells might be regulated are provided.

Strengths:

This study integrates multiomics data to attempt to resolve correlates of protection that might be useful in understanding NK cell diversity and activation.

Weaknesses:

While this work is interesting, the power of such studies is in taking the discovered information and applying this to other cohorts to determine the strength and predictive power of the genes identified. It is also clear that these 'snapshots' analysed poorly take account of the dynamic temporal changes that occur within a tumour. It would have been good to see a proposed model of NK cell regulation as it might occur in the tumour (accounting for turnover and recruitment) beyond the static data.

eLife assessment

This potentially useful study introduces an orthogonal approach for detecting RNA modification, without chemical modification of RNA, which often results in RNA degradation and therefore loss of RNA molecules. The approach might be of particular interest for sites where modifications are rare. However, the false positive and false negative rates are currently unclear, leaving the evidence for broad applicability of the method incomplete.

Reviewer #1 (Public Review):

The detection sensitivity and accuracy are unclear.

In this manuscript, Zhou et al describe a deaminase and reader protein-assisted RNA m5C sequencing method. The general strategy is similar to DART-seq for m6A sequencing, but the difference is that in DART-seq, m6A sites are always followed by C which can be deaminated by fused APOBEC1 to provide a high resolution of m6A sites, while in the case of m5C, no such obvious conserved motifs for m5C sites exist, therefore, the detection resolution is much lower. In addition, the authors used two known m5C binding proteins ALYREF and YBX1 to guide the fused deaminases, but it is not clear whether these two binding proteins can bind most m5C sites and compete with other m5C binding proteins.

It is well known that two highly modified m5C sites exist in 28S RNA and many m5C sites exist in tRNA, the authors should validate their methods first by detecting these known m5C sites and evaluate the possible false positives in rRNA and tRNA. In mRNA, it is not clear what is the overlap between the technical replicates. In Figures 4A and 4C, they detected more than 10K m5C sites, and most of them did not overlap with sites uncovered by other methods. These numbers are much larger than expected and possibly most of them are false positives. Besides, it is not clear what is the detection sensitivity and accuracy since the method is neither single base resolution nor quantitative. There are no experiments to show that the detected m5C sites are responsive to the writer proteins such as NSUN2 and NSUN6, and the determination of the motifs of these writer proteins.

Reviewer #2 (Public Review):

The fledgling field of epitranscriptomics has encountered various technical roadblocks with implications for the validity of early epitranscriptomics mapping data. As a prime example, the low specificity of (supposedly) modification-specific antibodies for the enrichment of modified RNAs, has been ignored for quite some time and is only now recognized for its dismal reproducibility (between different labs), which necessitates the development of alternative methods for modification detection. Furthermore, early attempts to map individual epitranscriptomes using sequencing-based techniques are largely characterized by the deliberate avoidance of orthogonal approaches aimed at confirming the existence of RNA modifications that have been originally identified.

Improved methodology, the inclusion of various controls, and better mapping algorithms as well as the application of robust statistics for the identification of false-positive RNA modification calls have allowed revisiting original (seminal) publications whose early mapping data allowed making hyperbolic claims about the number, localization and importance of RNA modifications, especially in mRNA. Besides the existence of m6A in mRNA, the detectable incidence of RNA modifications in mRNAs has drastically dropped.

As for m5C, the subject of the manuscript submitted by Zhou et al., its identification in mRNA goes back to Squires et al., 2012 reporting on >10.000 sites in mRNA of a human cancer cell line, followed by intermittent findings reporting on pretty much every number between 0 to > 100.000 m5C sites in different human cell-derived mRNA transcriptomes. The reason for such discrepancy is most likely of a technical nature. Importantly, all studies reporting on actual transcript numbers that were m5C-modified relied on RNA bisulfite sequencing, an NGS-based method, that can discriminate between methylated and non-methylated Cs after chemical deamination of C but not m5C. RNA bisulfite sequencing has a notoriously high background due to deamination artifacts, which occur largely due to incomplete denaturation of double-stranded regions (denaturing-resistant) of RNA molecules. Furthermore, m5C sites in mRNAs have now been mapped to regions that have not only sequence identity but also structural features of tRNAs. Various studies revealed that the highly conserved m5C RNA methyltransferases NSUN2 and NSUN6 do not only accept tRNAs but also other RNAs (including mRNAs) as methylation substrates, which in combination account for most of the RNA bisulfite-mapped m5C sites in human mRNA transcriptomes. Is m5C in mRNA only a result of the Star activity of tRNA or rRNA modification enzymes, or is their low stoichiometry biologically relevant?

In light of the short-comings of existing tools to robustly determine m5C in transcriptomes, other methods - like DRAM-seq, that allow the mapping of m5C independently of ex-situ RNA treatment with chemicals - are needed to arrive at a more solid "ground state", from which it will be possible to state and test various hypotheses as to the biological function of m5C, especially in lowly abundant RNAs such as mRNA.

Importantly, the identification of >10.000 sites containing m5C increases through DRAM-Seq, increases the number of potential m5C marks in human cancer cells from a couple of 100 (after rigorous post-hoc analysis of RNA bisulfite sequencing data) by orders of magnitude. This begs the question of whether or not the application of these editing tools results in editing artefacts overstating the number of actual m5C sites in the human cancer transcriptome.

Comments:

(1) The use of two m5C reader proteins is likely a reason for the high number of edits introduced by the DRAM-Seq method. Both ALYREF and YBX1 are ubiquitous proteins with multiple roles in RNA metabolism including splicing and mRNA export. It is reasonable to assume that both ALYREF and YBX1 bind to many mRNAs that do not contain m5C.

To substantiate the author's claim that ALYREF or YBX1 binds m5C-modified RNAs to an extent that would allow distinguishing its binding to non-modified RNAs from binding to m5C-modified RNAs, it would be recommended to provide data on the affinity of these, supposedly proven, m5C readers to non-modified versus m5C-modified RNAs. To do so, this reviewer suggests performing experiments as described in Slama et al., 2020 (doi: 10.1016/j.ymeth.2018.10.020). However, using dot blots like in so many published studies to show modification of a specific antibody or protein binding, is insufficient as an argument because no antibody, nor protein, encounters nanograms to micrograms of a specific RNA identity in a cell. This issue remains a major caveat in all studies using so-called RNA modification reader proteins as bait for detecting RNA modifications in epitranscriptomics research. It becomes a pertinent problem if used as a platform for base editing similar to the work presented in this manuscript.

(2) Since the authors use a system that results in transient overexpression of base editor fusion proteins, they might introduce advantageous binding of these proteins to RNAs. It is unclear, which promotor is driving construct expression but it stands to reason that part of the data is based on artifacts caused by overexpression. Could the authors attempt testing whether manipulating expression levels of these fusion proteins results in different editing levels at the same RNA substrate?

(3) Using sodium arsenite treatment of cells as a means to change the m5C status of transcripts through the downregulation of the two major m5C writer proteins NSUN2 and NSUN6 is problematic and the conclusions from these experiments are not warranted. Sodium arsenite is a chemical that poisons every protein containing thiol groups. Not only do NSUN proteins contain cysteines but also the base editor fusion proteins. Arsenite will inactivate these proteins, hence the editing frequency will drop, as observed in the experiments shown in Figure 5, which the authors explain with fewer m5C sites to be detected by the fusion proteins.

(4) The authors should move high-confidence editing site data contained in Supplementary Tables 2 and 3 into one of the main Figures to substantiate what is discussed in Figure 4A. However, the data needs to be visualized in another way than an Excel format. Furthermore, Supplementary Table 2 does not contain a description of the columns, while Supplementary Table 3 contains a single row with letters and numbers.

(5) The authors state that "plotting the distribution of DRAM-seq editing sites in mRNA segments (5'UTR, CDS, and 3'UTR) highlighted a significant enrichment near the initiation codon (Figure 3F).", which is not true when this reviewer looks at Figure 3F.

(6) The authors state that "In contrast, cells expressing the deaminase exhibited a distinct distribution pattern of editing sites, characterized by a prevalence throughout the 5'UTR.", which is not true when this reviewer looks at Figure 3F.

(7) The authors claim in the final conclusion: "In summary, we developed a novel deaminase and reader protein assisted RNA m5C methylation approach...", which is not what the method entails. The authors deaminate As or Us close to 5mC sites based on the binding of a deaminase-containing protein.

(8) The authors claim that "The data supporting the findings of this study are available within the article and its Supplementary Information." However, no single accession number for the deposited sequencing data can be found in the text or the supplementary data. Without the primary data, none of the claims can be verified.

eLife assessment

In this manuscript, the authors describe a new AlphaFold2 pipeline called PabFold that can represent a useful tool for identifying linear antibody epitopes (B-cell epitopes) for different antigens. This information can be used in the selection of different reagents in competitive ELISA assays which can save time and reduce costs. Several questions, however, remain and the study is currently incomplete.

Reviewer #1 (Public Review):

Summary:

In this manuscript, "PAbFold: Linear Antibody Epitope Prediction using AlphaFold2", the authors generate a python wrapper for the screening of antibody-peptide interactions using AlphaFold, and test the performance of AlphaFold on 3 antibody-peptide complexes. In line with previous observations regarding the ability of AlphaFold to predict antibody structures and antigen binding, the results are mixed. While the authors are able to use AlphaFold to identify and experimentally validate a previously characterized broad binding epitope with impressive precision, they are unable to consistently identify the proper binding registers for their control [Myc-tag, HA-tag] peptides. Further, it appears that the reproducibility and generality of these results are low, with new versions of AlphaFold negatively impacting the predictive power. However, if this reproducibility issue is solved, and the test set is greatly increased, this manuscript could contribute strongly towards our ability to predict antibody-antigen interactions.

Strengths:

Due to the high significance, but difficulty, of the prediction of antibody-antigen interactions, any attempts to break down these predictions into more tractable problems should be applauded. The authors' approach of focusing on linear epitopes (peptides) is clever, reducing some of the complexities inherent to antibody binding. Further, the ability of AlphaFold to narrow down a previously broadly identified experimental epitope is impressive. The subsequent experimental validation of this more precisely identified epitope makes for a nice data point in the assessment of AlphaFold's ability to predict antibody-antigen interactions.

Weaknesses:

Without a larger set of test antibody-peptide interactions, it is unclear whether or not AlphaFold can precisely identify the binding register of a given antibody to a given peptide antigen. Even within the small test set of 3 antibody-peptide complexes, performance is variable and depends upon the scFv scaffold used for unclear reasons. Lastly, the apparent poor reproducibility is concerning, and it is not clear why the results should rely so strongly on which multi-sequence alignment (MSA) version is used, when neither the antibody CDR loops nor the peptide are likely to strongly rely on these MSAs for contact prediction.

Major Point-by-Point Comments:

(1) The central concern for this manuscript is the apparent lack of reproducibility. The way the authors discuss the issue (lines 523-554) it sounds as though they are unable to reproduce their initial results (which are reported in the main text), even when previous versions of AlphaFold2 are used. If this is the case, it does not seem that AlphaFold can be a reliable tool for predicting antibody-peptide interactions.

(2) Aside from the fundamental issue of reproducibility, the number of validating tests is insufficient to assess the ability of AlphaFold to predict antibody-peptide interactions. Given the authors' use of AlphaFold to identify antibody binding to a linear epitope within a whole protein (in the mBG17:SARS-Cov-2 nucleocapsid protein interaction), they should expand their test set well beyond Myc- and HA-tags using antibody-antigen interactions from existing large structural databases.

(3) As discussed in lines 358-361, the authors are unsure if their primary control tests (antibody binding to Myc-tag and HA-tag) are included in the training data. Lines 324-330 suggest that even if the peptides are not included in the AlphaFold training data because they contain fewer than 10 amino acids, the antibody structures may very well be included, with an obvious "void" that would be best filled by a peptide. The authors must confirm that their tests are not included in the AlphaFold training data, or re-run the analysis with these templates removed.

(4) The ability of AlphaFold to refine the linear epitope of antibody mBG17 is quite impressive and robust to the reproducibility issues the authors have run into. However, Figure 4 seems to suggest that the target epitope adopts an alpha-helical structure. This may be why the score is so high and the prediction is so robust. It would be very useful to see along with the pLDDT by residue plots a structure prediction by residue plot. This would help to see if the high confidence pLDDT is coming more from confidence in the docking of the peptide or confidence in the structure of the peptide.

(5) Related to the above comment, pLDDT is insufficient as a metric for assessing antibody-antigen interactions. There is a chance (as is nicely shown in Figure S3C) that AlphaFold can be confident and wrong. Here we see two orange-yellow dots (fairly high confidence) that place the peptide COM far from the true binding region. While running the recommended larger validation above, the authors should also include a peptide RMSD or COM distance metric, to show that the peptide identity is confident, and the peptide placement is roughly correct. These predictions are not nearly as valuable if AlphaFold is getting the right answer for the wrong reasons (i.e. high pLDDT but peptide binding to a non-CDR loop region). Eventual users of the software will likely want to make point mutations or perturb the binding regions identified by the structural predictions (as the authors do in Figure 4).

Reviewer #2 (Public Review):

Summary:

The authors showed the applicability and usefulness of a new AlphaFold2 pipeline called PabFold, which can predict linear antibody epitopes (B-cell epitopes) that can be helpful for the selection of reagents to be applied in competitive ELISA assay.

Strengths:

The authors showed the accuracy of the pipeline to identify correctly the binding epitope for three different antibody-antigen systems (Myc, HA, and Sars-Cov2 nucleocapsid protein). The design of scFvs from Fab of the three antibodies to speed up the analysis time is extremely interesting.

Weaknesses:

The article justifies correctly the findings and no great weaknesses are present. However, it could be useful for a broader audience to show in detail how pLDDT was calculated for both Simple-Max approach (per residue-pLDDT) and Consensus analysis ( average pLDDT for each peptide), with associated equations.

Author response:

Reviewer #1 (Public Review):

How does the brain respond to the input of different complexity, and does this ability to respond change with age?

The study by Lalwani et al. tried to address this question by pulling together a number of neuroscientific methodologies (fMRI, MRS, drug challenge, perceptual psychophysics). A major strength of the paper is that it is backed up by robust sample sizes and careful choices in data analysis, translating into a more rigorous understanding of the sensory input as well as the neural metric. The authors apply a novel analysis method developed in human resting-state MRI data on task-based data in the visual cortex, specifically investigating the variability of neural response to stimuli of different levels of visual complexity. A subset of participants took part in a placebo-controlled drug challenge and functional neuroimaging. This experiment showed that increases in GABA have differential effects on participants with different baseline levels of GABA in the visual cortex, possibly modulating the perceptual performance in those with lower baseline GABA. A caveat is that no single cohort has taken part in all study elements, ie visual discrimination with drug challenge and neuroimaging. Hence the causal relationship is limited to the neural variability measure and does not extend to visual performance. Nevertheless, the consistent use of visual stimuli across approaches permits an exceptionally high level of comparability across (computational, behavioural, and fMRI are drawing from the same set of images) modalities. The conclusions that can be made on such a coherent data set are strong.

The community will benefit from the technical advances, esp. the calculation of BOLD variability, in the study when described appropriately, encouraging further linkage between complementary measures of brain activity, neurochemistry, and signal processing.

Thank you for your review. We agree that a future study with a single cohort would be an excellent follow-up.

Reviewer #2 (Public Review):

Lalwani et al. measured BOLD variability during the viewing of houses and faces in groups of young and old healthy adults and measured ventrovisual cortex GABA+ at rest using MR spectroscopy. The influence of the GABA-A agonist lorazepam on BOLD variability during task performance was also assessed, and baseline GABA+ levels were considered as a mediating variable. The relationship of local GABA to changes in variability in BOLD signal, and how both properties change with age, are important and interesting questions. The authors feature the following results: 1) younger adults exhibit greater task-dependent changes in BOLD variability and higher resting visual cortical GABA+ content than older adults, 2) greater BOLD variability scales with GABA+ levels across the combined age groups, 3) administration of a GABA-A agonist increased condition differences in BOLD variability in individuals with lower baseline GABA+ levels but decreased condition differences in BOLD variability in individuals with higher baseline GABA+ levels, and 4) resting GABA+ levels correlated with a measure of visual sensory ability derived from a set of discrimination tasks that incorporated a variety of stimulus categories.

Strengths of the study design include the pharmacological manipulation for gauging a possible causal relationship between GABA activity and task-related adjustments in BOLD variability. The consideration of baseline GABA+ levels for interpreting this relationship is particularly valuable. The assessment of feature-richness across multiple visual stimulus categories provided support for the use of a single visual sensory factor score to examine individual differences in behavioral performance relative to age, GABA, and BOLD measurements.

Weaknesses of the study include the absence of an interpretation of the physiological mechanisms that contribute to variability in BOLD signal, particularly for the chosen contrast that compared viewing houses with viewing faces.

Whether any of the observed effects can be explained by patterns in mean BOLD signal, independent of variability would be useful to know.

One of the first pre-processing steps of computing SDBOLD involves subtracting the block-mean from the fMRI signal for each task-condition. Therefore, patterns observed in BOLD signal variability are not driven by the mean-BOLD differences. Moreover, as noted above, to further confirm this, we performed additional mean-BOLD based analysis (See Supplementary Materials Pg 3). Results suggest that ∆⃗ MEANBOLD is actually larger in older adults vs. younger adults (∆⃗ SDBOLD exhibited the opposite pattern), but more importantly ∆⃗ MEANBOLD is not correlated with GABA or with visual performance. This is also consistent with prior research (Garrett et.al. 2011, 2013, 2015, 2020) that found MEANBOLD to be relatively insensitive to behavioral performance.

The positive correlation between resting GABA+ levels and the task-condition effect on BOLD variability reaches significance at the total group level, when the young and old groups are combined, but not separately within each group. This correlation may be explained by age-related differences since younger adults had higher values than older adults for both types of measurements. This is not to suggest that the relationship is not meaningful or interesting, but that it may be conceptualized differently than presented.

Thank you for this important point. The relationship between GABA and ∆⃗ SDBOLD shown in Figure 3 is also significant within each age-group separately (Line 386-388). The model used both age-group and GABA as predictors of ∆⃗ SDBOLD and found that both had a significant effect, while the Age-group x GABA interaction was not significant. The effect of age on ∆⃗ SDBOLD therefore does not completely explain the observed relationship between GABA and ∆⃗ SDBOLD because this latter effect is significant in both age-groups individually and in the whole sample even when variance explained by age is accounted for. The revision clarifies this important point (Ln 488-492). Thanks for raising it.

Two separate dosages of lorazepam were used across individuals, but the details of why and how this was done are not provided, and the possible effects of the dose are not considered.

Good point. We utilized two dosages to maximize our chances of finding a dosage that had a robust effect. The specific dosage was randomly assigned across participants and the dosage did not differ across age-groups or baseline GABA levels. We also controlled for the drug-dosage when examining the role of drug-related shift in ∆⃗ SDBOLD. We have clarified these points in the revision and highlighted the analysis that found no effect of dosage on drug-related shift in ∆⃗ SDBOLD (Line 407-418).

The observation of greater BOLD variability during the viewing of houses than faces may be specific to these two behavioral conditions, and lingering questions about whether these effects generalize to other types of visual stimuli, or other non-visual behaviors, in old and young adults, limit the generalizability of the immediate findings.

We agree that examining the factors that influence BOLD variability is an important topic for future research. In particular, although it is increasingly well known that variability modulation itself can occur in a host of different tasks and research contexts across the lifespan (see Garrett et al., 2013 Waschke et al., 2021), to address the question of whether variability modulation occurs directly in response to stimulus complexity in general, it will be important for future work to examine a range of stimulus categories beyond faces and houses. Doing so is indeed an active area of research in Dr. Garrett’s group, where visual stimuli from many different categories are examined (e.g., for a recent approach, see Waschke et.al.,2023 (biorxiv)). Regardless, only face and house stimuli were available in the current dataset. We therefore exploited the finding that BOLD variability tends to be larger for house stimuli than for face stimuli (in line with the HMAX model output) to demonstrate that the degree to which a given individual modulates BOLD variability in response to stimulus category is related to their age, to GABA levels, and to behavioral performance.

The observed age-related differences in patterns of BOLD activity and ventrovisual cortex GABA+ levels along with the investigation of GABA-agonist effects in the context of baseline GABA+ levels are particularly valuable to the field, and merit follow-up. Assessing background neurochemical levels is generally important for understanding individualized drug effects. Therefore, the data are particularly useful in the fields of aging, neuroimaging, and vision research.

Thank you, we agree!

Reviewer #3 (Public Review):

The role of neural variability in various cognitive functions is one of the focal contentions in systems and computational neuroscience. In this study, the authors used a largescale cohort dataset to investigate the relationship between neural variability measured by fMRI and several factors, including stimulus complexity, GABA levels, aging, and visual performance. Such investigations are valuable because neural variability, as an important topic, is by far mostly studied within animal neurophysiology. There is little evidence in humans. Also, the conclusions are built on a large-scale cohort dataset that includes multi-model data. Such a dataset per se is a big advantage. Pharmacological manipulations and MRS acquisitions are rare in this line of research. Overall, I think this study is well-designed, and the manuscript reads well. I listed my comments below and hope my suggestions can further improve the paper.

Strength:

1). The study design is astonishingly rich. The authors used task-based fMRI, MRS technique, population contrast (aging vs. control), and psychophysical testing. I appreciate the motivation and efforts for collecting such a rich dataset.

2) The MRS part is good. I am not an expert in MRS so cannot comment on MRS data acquisition and analyses. But I think linking neural variability to GABA in humans is in general a good idea. There has been a long interest in the cause of neural variability, and inhibition of local neural circuits has been hypothesized as one of the key factors. 3. The pharmacological manipulation is particularly interesting as it provides at least evidence for the causal effects of GABA and deltaSDBOLD. I think this is quite novel.

Weakness:

1) I am concerned about the definition of neural variability. In electrophysiological studies, neural variability can be defined as Poisson-like spike count variability. In the fMRI world, however, there is no consensus on what neural variability is. There are at least three definitions. One is the variability (e.g., std) of the voxel response time series as used here and in the resting fMRI world. The second is to regress out the stimulusevoked activation and only calculate the std of residuals (e.g., background variability). The third is to calculate variability of trial-by-trial variability of beta estimates of general linear modeling. It currently remains unclear the relations between these three types of variability with other factors. It also remains unclear the links between neuronal variability and voxel variability. I don't think the computational principles discovered in neuronal variability also apply to voxel responses. I hope the authors can acknowledge their differences and discuss their differences.

These are very important points, thank you for raising them. Although we agree that the majority of the single cell electrophysiology world indeed seems to prefer Poisson-like spiking variability as an easy and tractable estimate, it is certainly not the only variability approach in that field (e.g., entropy; see our most recent work in humans where spiking entropy outperforms simple spike counts to predict memory performance; Waschke et al., 2023, bioRxiv). In LFP, EEG/MEG and fMRI, there is indeed no singular consensus on what variability “is”, and in our opinion, that is a good thing. We have reported at length in past work about entire families of measures of signal variability, from simple variance, to power, to entropy, and beyond (see Table 1 in Waschke et al, 2021, Neuron). In principle, these measures are quite complementary, obviating the need to establish any single-measure consensus per se. Rather than viewing the three measures of neural variability that the reviewer mentioned as competing definitions, we prefer to view them as different sources of variance. For example, from each of the three sources of variance the reviewer suggests, any number of variability measures could be computed.

The current study focuses on using the standard deviation of concatenated blocked time series separately for face and house viewing conditions (this is the same estimation approach used in our very earliest studies on signal variability; Garrett et al., 2010, JNeurosci). In those early studies, and nearly every one thereafter (see Waschke et al., 2021, Neuron), there is no ostensible link between SDBOLD (as we normaly compute it) and average BOLD from either multivariate or GLM models; as such, we do not find any clear difference in SDBOLD results whether or not average “evoked” responses are removed or not in past work. This is perhaps also why removing ERPs from EEG time series rarely influences estimates of variability in our work (e.g., Kloosterman et al., 2020, eLife).

The third definition the reviewer notes refers to variability of beta estimates over trials. Our most recent work has done exactly this (e.g., Skowron et al., 2023, bioRxiv), calculating the SD even over single time point-wise beta estimates so that we may better control the extraction of time points prior to variability estimation. Although direct comparisons have not yet been published by us, variability over single TR beta estimates and variability over the time series without beta estimation are very highly correlated in our work (in the .80 range; e.g., Kloosterman et al., in prep).

Re: the reviewer’s point that “It also remains unclear the links between neuronal variability and voxel variability. I don’t think the computational principles discovered in neuronal variability also apply to voxel responses. I hope the authors can acknowledge their differences and discuss their differences.” If we understand correctly, the reviewer maybe asking about within-person links between single-cell neuronal variability (to allow Poisson-like spiking variability) and voxel variability in fMRI? No such study has been conducted to date to our knowledge (such data almost don’t exist). Or rather, perhaps the reviewer is noting a more general point regarding the “computational principles” of variability in these different domains? If that is true, then a few points are worth noting. First, there is absolutely no expectation of Poisson distributions in continuous brain imaging-based time series (LFP, E/MEG, fMRI). To our knowledge, such distributions (which have equivalent means and variances, allowing e.g., Fano factors to be estimated) are mathematically possible in spiking because of the binary nature of spikes; when mean rates rise, so too do variances given that activity pushes away from the floor (of no activity). In continuous time signals, there is no effective “zero”, so a mathematical floor does not exist outright. This is likely why means and variances are not well coupled in continuous time signals (see Garrett et al., 2013, NBR; Waschke et al., 2021, Neuron); anything can happen. Regardless, convergence is beginning to be revealed between the effects noted from spiking and continuous time estimates of variability. For example, we show that spiking variability can show a similar, behaviourally relevant coupling to the complexity of visual input (Waschke et al., 2023, bioRxiv) as seen in the current study and in past work (e.g., Garrett et al., 2020, NeuroImage). Whether such convergence reflects common computational principles of variability remains to be seen in future work, despite known associations between single cell recordings and BOLD overall (e.g., Logothetis and colleagues, 2001, 2002, 2004, 2008).

Given the intricacies of these arguments, we don’t currently include this discussion in the revised text. However, we would be happy to include aspects of this content in the main paper if the reviewer sees fit.

2) If I understand it correctly, the positive relationship between stimulus complexity and voxel variability has been found in the author's previous work. Thus, the claims in the abstract in lines 14-15, and section 1 in results are exaggerated. The results simply replicate the findings in the previous work. This should be clearly stated.

Good point. Since this finding was a replication and an extension, we reported these results mostly in the supplementary materials. The stimulus set used for the current study is different than Garrett et.al. 2020 and therefore a replication is important. Moreover, we have extended these findings across young and older adults (previous work was based on older adults alone). We have modified the text to clarify what is a replication and what part are extension/novel about the current study now (Line 14, 345 and 467). Thanks for the suggestion.

3) It is difficult for me to comprehend the U-shaped account of baseline GABA and shift in deltaSDBOLD. If deltaSDBOLD per se is good, as evidenced by the positive relationship between brainscore and visual sensitivity as shown in Fig. 5b and the discussion in lines 432-440, why the brain should decrease deltaSDBOLD ?? or did I miss something? I understand that "average is good, outliers are bad". But a more detailed theory is needed to account for such effects.

When GABA levels are increased beyond optimal levels, neuronal firing rates are reduced, effectively dampening neural activity and limiting dynamic range; in the present study, this resulted in reduced ∆⃗ SDBOLD. Thus, the observed drug-related decrease in ∆⃗ SDBOLD was most present in participants with already high levels of GABA. We have now added an explanation for the expected inverted-U (Line 523-546). The following figure tries to explain this with a hypothetical curve diagram and how different parts of Fig 4 might be linked to different points in such a curve.

Author response image 1.

Line 523-546 – “We found in humans that the drug-related shift in ∆⃗ SDBOLD could be either positive or negative, while being negatively related to baseline GABA. Thus, boosting GABA activity with drug during visual processing in participants with lower baseline GABA levels and low levels of ∆⃗ SDBOLD resulted in an increase in ∆⃗ SDBOLD (i.e., a positive change in ∆⃗ SDBOLD on drug compared to off drug). However, in participants with higher baseline GABA levels and higher ∆⃗ SDBOLD, when GABA was increased presumably beyond optimal levels, participants experienced no-change or even a decrease in∆⃗ SDBOLD on drug. These findings thus provide the first evidence in humans for an inverted-U account of how GABA may link to variability modulation.

Boosting low GABA levels in older adults helps increase ∆⃗ SDBOLD, but why does increasing GABA levels lead to reduced ∆⃗ SDBOLD in others? One explanation is that higher than optimal levels of inhibition in a neuronal system can lead to dampening of the entire network. The reduced neuronal firing decreases the number of states the network can visit and decreases the dynamic range of the network. Indeed, some anesthetics work by increasing GABA activity (for example propofol a general anesthetic modulates activity at GABAA receptors) and GABA is known for its sedative properties. Previous research showed that propofol leads to a steeper power spectral slope (a measure of the “construction” of signal variance) in monkey ECoG recordings (Gao et al., 2017). Networks function optimally only when dynamics are stabilized by sufficient inhibition. Thus, there is an inverted-U relationship between ∆⃗ SDBOLD and GABA that is similar to that observed with other neurotransmitters.”

4) Related to the 3rd question, can you show the relationship between the shift of deltaSDBOLD (i.e., the delta of deltaSDBOLD) and visual performance?

We did not have data on visual performance from the same participants that completed the drug-based part of the study (Subset1 vs 3; see Figure 1); therefore, we unfortunately cannot directly investigate the relationship between the drug-related shift of ∆⃗ SDBOLD and visual performance. We have now highlighted that this as a limitation of the current study (Line 589-592), where we state: One limitation of the current study is that participants who received the drug-manipulation did not complete the visual discrimination task, thus we could not directly assess how the drug-related change in ∆⃗ SDBOLD impacted visual performance.

5) Are the dataset openly available?? I didn't find the data availability statement.

An excel-sheet with all the processed data to reproduce figures and results has been included in source data submitted along with the manuscript along with a data dictionary key for various columns. The raw MRI, MRS and fMRI data used in the current manuscript was collected as a part of a larger (MIND) study and will eventually be made publicly available on completion of the study (around 2027). Before that time, the raw data can be obtained for research purposes upon reasonable request. Processing code will be made available on GitHub.

eLife assessment

This important study combines across multiple complementary neuroscientific methods to understand the neural response to visual stimulus complexity in the human brain across lifespan. Lalwani et al., provide solid evidence, drawing from appropriate and validated methodology. A weakness is that key information about methodological details and controls is still outstanding, as is a discussion on how generalizable the findings are. With these elements strengthened, the study would be of broad interest to neuroscientists and biologists interested in aging and sensory processing.

Reviewer #1 (Public Review):

How does the brain respond to the input of different complexity, and does this ability to respond change with age?

The study by Lalwani et al. tried to address this question by pulling together a number of neuroscientific methodologies (fMRI, MRS, drug challenge, perceptual psychophysics). A major strength of the paper is that it is backed up by robust sample sizes and careful choices in data analysis, translating into a more rigorous understanding of the sensory input as well as the neural metric. The authors apply a novel analysis method developed in human resting-state MRI data on task-based data in the visual cortex, specifically investigating the variability of neural response to stimuli of different levels of visual complexity. A subset of participants took part in a placebo-controlled drug challenge and functional neuroimaging. This experiment showed that increases in GABA have differential effects on participants with different baseline levels of GABA in the visual cortex, possibly modulating the perceptual performance in those with lower baseline GABA. A caveat is that no single cohort has taken part in all study elements, ie visual discrimination with drug challenge and neuroimaging. Hence the causal relationship is limited to the neural variability measure and does not extend to visual performance. Nevertheless, the consistent use of visual stimuli across approaches permits an exceptionally high level of comparability across (computational, behavioural, and fMRI are drawing from the same set of images) modalities. The conclusions that can be made on such a coherent data set are strong.

The community will benefit from the technical advances, esp. the calculation of BOLD variability, in the study when described appropriately, encouraging further linkage between complementary measures of brain activity, neurochemistry, and signal processing.

Reviewer #2 (Public Review):

Lalwani et al. measured BOLD variability during the viewing of houses and faces in groups of young and old healthy adults and measured ventrovisual cortex GABA+ at rest using MR spectroscopy. The influence of the GABA-A agonist lorazepam on BOLD variability during task performance was also assessed, and baseline GABA+ levels were considered as a mediating variable. The relationship of local GABA to changes in variability in BOLD signal, and how both properties change with age, are important and interesting questions. The authors feature the following results: 1) younger adults exhibit greater task-dependent changes in BOLD variability and higher resting visual cortical GABA+ content than older adults, 2) greater BOLD variability scales with GABA+ levels across the combined age groups, 3) administration of a GABA-A agonist increased condition differences in BOLD variability in individuals with lower baseline GABA+ levels but decreased condition differences in BOLD variability in individuals with higher baseline GABA+ levels, and 4) resting GABA+ levels correlated with a measure of visual sensory ability derived from a set of discrimination tasks that incorporated a variety of stimulus categories.

Strengths of the study design include the pharmacological manipulation for gauging a possible causal relationship between GABA activity and task-related adjustments in BOLD variability. The consideration of baseline GABA+ levels for interpreting this relationship is particularly valuable. The assessment of feature-richness across multiple visual stimulus categories provided support for the use of a single visual sensory factor score to examine individual differences in behavioral performance relative to age, GABA, and BOLD measurements. Weaknesses of the study include the absence of an interpretation of the physiological mechanisms that contribute to variability in BOLD signal, particularly for the chosen contrast that compared viewing houses with viewing faces. Whether any of the observed effects can be explained by patterns in mean BOLD signal, independent of variability would be useful to know. The positive correlation between resting GABA+ levels and the task-condition effect on BOLD variability reaches significance at the total group level, when the young and old groups are combined, but not separately within each group. This correlation may be explained by age-related differences since younger adults had higher values than older adults for both types of measurements. This is not to suggest that the relationship is not meaningful or interesting, but that it may be conceptualized differently than presented. Two separate dosages of lorazepam were used across individuals, but the details of why and how this was done are not provided, and the possible effects of the dose are not considered. The observation of greater BOLD variability during the viewing of houses than faces may be specific to these two behavioral conditions, and lingering questions about whether these effects generalize to other types of visual stimuli, or other non-visual behaviors, in old and young adults, limit the generalizability of the immediate findings.

The observed age-related differences in patterns of BOLD activity and ventrovisual cortex GABA+ levels along with the investigation of GABA-agonist effects in the context of baseline GABA+ levels are particularly valuable to the field, and merit follow-up. Assessing background neurochemical levels is generally important for understanding individualized drug effects. Therefore, the data are particularly useful in the fields of aging, neuroimaging, and vision research.

Reviewer #3 (Public Review):

The role of neural variability in various cognitive functions is one of the focal contentions in systems and computational neuroscience. In this study, the authors used a large-scale cohort dataset to investigate the relationship between neural variability measured by fMRI and several factors, including stimulus complexity, GABA levels, aging, and visual performance. Such investigations are valuable because neural variability, as an important topic, is by far mostly studied within animal neurophysiology. There is little evidence in humans. Also, the conclusions are built on a large-scale cohort dataset that includes multi-model data. Such a dataset per se is a big advantage. Pharmacological manipulations and MRS acquisitions are rare in this line of research. Overall, I think this study is well-designed, and the manuscript reads well. I listed my comments below and hope my suggestions can further improve the paper.

Strength:<br /> (1) The study design is astonishingly rich. The authors used task-based fMRI, MRS technique, population contrast (aging vs. control), and psychophysical testing. I appreciate the motivation and efforts for collecting such a rich dataset.<br /> (2) The MRS part is good. I am not an expert in MRS so cannot comment on MRS data acquisition and analyses. But I think linking neural variability to GABA in humans is in general a good idea. There has been a long interest in the cause of neural variability, and inhibition of local neural circuits has been hypothesized as one of the key factors.<br /> (3) The pharmacological manipulation is particularly interesting as it provides at least evidence for the causal effects of GABA and deltaSDBOLD. I think this is quite novel.

Weakness:<br /> (1) I am concerned about the definition of neural variability. In electrophysiological studies, neural variability can be defined as Poisson-like spike count variability. In the fMRI world, however, there is no consensus on what neural variability is. There are at least three definitions. One is the variability (e.g., std) of the voxel response time series as used here and in the resting fMRI world. The second is to regress out the stimulus-evoked activation and only calculate the std of residuals (e.g., background variability). The third is to calculate variability of trial-by-trial variability of beta estimates of general linear modeling. It currently remains unclear the relations between these three types of variability with other factors. It also remains unclear the links between neuronal variability and voxel variability. I don't think the computational principles discovered in neuronal variability also apply to voxel responses. I hope the authors can acknowledge their differences and discuss their differences.<br /> (2) If I understand it correctly, the positive relationship between stimulus complexity and voxel variability has been found in the author's previous work. Thus, the claims in the abstract in lines 14-15, and section 1 in results are exaggerated. The results simply replicate the findings in the previous work. This should be clearly stated.<br /> (3) It is difficult for me to comprehend the U-shaped account of baseline GABA and shift in deltaSDBOLD. If deltaSDBOLD per se is good, as evidenced by the positive relationship between brainscore and visual sensitivity as shown in Fig. 5b and the discussion in lines 432-440, why the brain should decrease deltaSDBOLD ?? or did I miss something? I understand that "average is good, outliers are bad". But a more detailed theory is needed to account for such effects.<br /> (4) Related to the 3rd question, can you should the relationship between the shift of deltaSDBOLD (i.e., the delta of deltaSDBOLD) and visual performance?<br /> (5) Are the dataset openly available ?? I didn't find the data availability statement.