Author response:

The following is the authors’ response to the original reviews

We thank the three reviewers for their insightful feedback. We look forward to addressing the raised concerns in a revised version of the manuscript. There were a few common themes among the reviews that we will briefly touch upon now, and we will provide more details in the revised manuscript. 

First, the reviewers asked for the reasoning behind the task ratios we implemented for the different attentional width conditions. The different ratios were selected to be as similar as possible given the size and spacing of our stimuli (aside from the narrowest cue width of one bin, the ratios for the others were 0.66, .6 and .66). As Figure 1b shows, while the ratios were similar, task difficulty is not constant across cue widths: spreading attention makes the task more difficult generally. But, while the modeled width of the spatial distribution of attention changes monotonically with cue width, task difficulty does not. Furthermore, prior work has indicated that there is a relationship between task difficulty and the overall magnitude of the BOLD response, however we don’t suspect that this will influence the width of the modulation. How task difficulty influences the BOLD response is an important topic, and we hope that future work will investigate this relationship more directly.   

Second, reviewers raised interest in the distribution of spatial attention in higher visual areas. In our study we focus only on early visual regions (V1-V3). This was primarily driven by pragmatic considerations, in that we only have retinotopic estimates for our participants in these early visual areas. Our modeling approach is dependent on having access to the population receptive field estimates for all voxels, and while the main experiment was scanned using whole brain coverage, retinotopy was measured in a separate session using a field of view only covering the occipital cortex.  

Lastly, we appreciate the opportunity to clarify the purpose of the temporal interval analysis. The reviewer is correct in assuming we set out to test how much data is needed to recover the cortical modulation and how dynamic a signal the method can capture. This analysis does show that more data provides more reliable estimates, though the model was still able to recover the location and width of the attentional cue at shorter timescales of as few as two TRs. This has implications for future studies that may involve more dynamic tracking of the attentional field.

Public Reviews

Reviewer #1 (Public review): 

The authors conducted an fMRI study to investigate the neural effects of sustaining attention to areas of different sizes. Participants were instructed to attend to alphanumeric characters arranged in a circular array. The size of attention field was manipulated in four levels, ranging from small (18 deg) to large (162 deg). They used a model-based method to visualize attentional modulation in early visual cortex V1 to V3, and found spatially congruent modulations of the BOLD response, i.e., as the attended area increased in size, the neural modulation also increased in size in the visual cortex. They suggest that this result is a neural manifestation of the zoomlens model of attention and that the model-based method can effectively reconstruct the neural modulation in the cortical space. 

The study is well-designed with sophisticated and comprehensive data analysis. The results are robust and show strong support for a well-known model of spatial attention, the zoom-lens model. Overall, I find the results interesting and useful for the field of visual attention research. I have questions about some aspects of the results and analysis as well as the bigger picture. 

(1) It appears that the modulation in V1 is weaker than V2 and V3 (Fig 2). In particular, the width modulation in V1 is not statistically significant (Fig 5). This result seems a bit unexpected. Given the known RF properties of neurons in these areas, in particular, smaller RF in V1, one might expect more spatially sensitive modulation in V1 than V2/V3. Some explanations and discussions would be helpful. Relatedly, one would also naturally wonder if this method can be applied to other extrastriate visual areas such as V4 and what the results look like. 

We agree with the reviewer. It’s very interesting how the spatial resolution within different visual regions contributes to the overall modulation of the attentional field, and how this in turn would influence perception. Our data showed that fits in V1 appeared to be less precise than in V2 and V3. This can be seen in the goodness of fit of the model as well as the gain and absolute angular error estimates. The goodness of fit and gain were lowest in V1 and the absolute angular error was largest in V1 (see Figure 5). We speculate that the finer spatial granularity of V1 RFs was countered by a lower amplitude and SNR of attention-related modulation in V1, resulting in overall lower sensitivity to variation in attentional field width. Prior findings concur that the magnitude of covert spatial attention increases when moving from striate to extrastriate cortex (Bressler & Silver (2010); Buracas & Boynton (2007)). Notably, in our perception condition, V1 showed more spatially sensitive modulation (see Figure 7), consistent with the known RF properties of V1 neurons.

Regarding the second point: unfortunately, our dataset did not allow us to explore higherorder cortical regions with the model-based approach. While the main experiment was scanned using a sequence with whole brain coverage, the pRF estimates came from a separate scanning session which only had limited occipital coverage. Our modeling approach is dependent on the polar angle estimates from this pRF session. We now explicitly state this limitation in the methods (lines 87-89):

“In this session, the field of view was restricted to the occipital cortex to maximize SNR, thereby limiting the brain regions for which we had pRF estimates to V1, V2, and V3.”

(2) I'm a bit confused about the angular error result. Fig 4 shows that the mean angular error is close to zero, but Fig 5 reports these values to be about 30-40 deg. Why the big discrepancy? Is it due to the latter reporting absolute errors? It seems reporting the overall bias is more useful than absolute value. 

The reviewer’s inference here is exactly right: Figure 4 shows signed error, whereas Figure 5 shows absolute error. We show the signed error for the example participant because, (1) by presenting the full distribution of model estimates for one participant, readers have access to a more direct representation of the data, and (2) at the individual level it is possible to examine potential directional biases in the location estimates (which do not appear to be present). As we don’t suspect a consistent directional bias across the group, we believe the absolute error in location estimates is more informative in depicting the precision in location estimates using the model-based approach. In the revised manuscript, we modified Figure 5 to make the example participant’s data visually distinct for easy comparison. We have clarified this reasoning in the text (results lines 59-64):

“The angular error distribution across blocks, separated by width condition, is shown in Figure 4 for one example participant to display block-to-block variation. The model reliably captured the location of the attentional field with low angular error and with no systematic directional bias. This result was observed across participants. We next examined the absolute angular error to assess the overall accuracy of our estimates.”

(3) A significant effect is reported for amplitude in V3 (line 78), but the graph in Fig 5 shows hardly any difference. Please confirm the finding and also explain the directionality of the effect if there is indeed one. 

We realize that the y-axis scale of Figure 5 was making it difficult to see that gain decreases with cue width in area V3. Instead of keeping the y-axis limits the same across visual regions, we now adapt the y-axis scale of each subplot to the range of data values:  

We now also add the direction of the effect in the text (results lines 83-86):

“We observed no significant relationship between gain and cue width in V1 and V2 (V1 t(7)=.54, p=.605; V2 t(7)=-2.19, p=.065), though we did find a significant effect in V3 illustrating that gain decreases with cue width (t(7)=-3.12, p=.017).”

(4) The purpose of the temporal interval analysis is rather unclear. I assume it has to do with how much data is needed to recover the cortical modulation and hence how dynamic a signal the method can capture. While the results make sense (i.e., more data is better), there is no obvious conclusion and/or interpretation of its meaning. 

We apologize for not making our reasoning clear. We now emphasize our reasoning in the revised manuscript (results lines 110-112). Our objective was to quantify how much data was needed to recover the dynamic signal. As expected, we found that including more data reduces noise (averaging helps), but importantly, we found that we still obtained meaningful model fits even with limited data. We believe this has important implications for future paradigms that explore more dynamic deployment of spatial attention, where one would not want to average over multiple repetitions of a condition.

The first paragraph of the Temporal Interval Analysis section in the results now reads: 

“In the previous analyses, we leveraged the fact that the attentional cue remained constant for 5-trial blocks (spatial profiles were computed by averaging BOLD measurements across a block of 10 TRs). We next examined the degree to which we were able to recover the attentional field on a moment-by-moment (TR-by-TR) basis. To do this, we systematically adjusted the number of TRs that contributed to the averaged spatial response profile. To maintain a constant number of observations across the temporal interval conditions, we randomly sampled a subset of TRs from each block. This allowed us to determine the amount of data needed to recover the attentional field, with a goal of examining the usability of our modeling approach in future paradigms involving more dynamic deployment of spatial attention.”

(5) I think it would be useful for the authors to make a more explicit connection to previous studies in this literature. In particular, two studies seem particularly relevant. First, how do the present results relate to those in Muller et al (2003, reference 37), which also found a zoom-lens type of neural effects. Second, how does the present method compare with spatial encoding model in Sprague & Serences (2013, reference 56), which also reconstructs the neural modulation of spatial attention. More discussions of these studies will help put the current study in the larger context.

We now make a more explicit connection to prior work in the discussion section (lines 34-54). 

“We introduced a novel modeling approach that recovered the location and the size of the attentional field. Our data show that the estimated spatial spread of attentional modulation (as indicated by the recovered FWHM) consistently broadened with the cue width, replicating prior work (Müller et al., 2003; Herrmann et al., 2010). Our results go beyond prior work by linking the spatial profiles to pRF estimates, allowing us to quantify the spread of both attentional and perceptual modulation in degrees of polar angle. Interestingly, the FWHM estimates for the attentional and perceptual spatial profiles were highly similar. Additionally, for area V3 we replicate that the population response magnitude decreased with cue width (Müller et al., 2003; Feldmann-Wüstefeld and Awh, 2020). One innovation of our method is that it directly reconstructs attention-driven modulations of responses in visual cortex, setting it apart from other methods, such as inverted encoding models (e.g. Sprague & Serences, 2013). Finally, we demonstrated that our method has potential to be used in more dynamic settings, in which changes in the attentional field need to be tracked on a shorter timescale.”

(6) Fig 4b, referenced on line 123, does not exist. 

We have corrected the text to reference the appropriate figure (Figure 5, results line 136).

Reviewer #2 (Public review):

Summary: 

The study in question utilizes functional magnetic resonance imaging (fMRI) to dynamically estimate the locus and extent of covert spatial attention from visuocortical activity. The authors aim to address an important gap in our understanding of how the size of the attentional field is represented within the visual cortex. They present a novel paradigm that allows for the estimation of the spatial tuning of the attentional field and demonstrate the ability to reliably recover both the location and width of the attentional field based on BOLD responses. 

Strengths: 

(1) Innovative Paradigm: The development of a new approach to estimate the spatial tuning of the attentional field is a significant strength of this study. It provides a fresh perspective on how spatial attention modulates visual perception. 

(2) Refined fMRI Analysis: The use of fMRI to track the spatial tuning of the attentional field across different visual regions is methodologically rigorous and provides valuable insights into the neural mechanisms underlying attentional modulation. 

(3) Clear Presentation: The manuscript is well-organized, and the results are presented clearly, which aids in the reader's comprehension of the complex data and analyses involved. 

We thank the reviewer for summarizing the strengths in our work. 

Weaknesses: 

(1) Lack of Neutral Cue Condition: The study does not include a neutral cue condition where the cue width spans 360°, which could serve as a valuable baseline for assessing the BOLD response enhancements and diminishments in both attended and non-attended areas. 

We do not think that the lack of a neutral cue condition substantially limits our ability to address the core questions of interest in the present work. We set out to estimate the locus and the spread of covert spatial attention. By definition, a neutral cue does not have a focus of attention as the whole annulus becomes task relevant. We agree with the reviewer that how spatial attention influences the magnitude of the BOLD response is still not well defined; i.e., does attending a location multiplicatively enhance responses at an attended location or does it instead act to suppress responses outside the focus of attention? A neutral cue condition would be necessary to be able to explore these types of questions. However, our findings don’t rest on any assumptions about this. Instead, we quantify the attentional modulation with a model-based approach and show that we can reliably recover its locus, and reveal a broadening in the attentional modulation with wider cues. 

We realize that throughout the original manuscript we often used the term ‘attentional enhancement,’ which might inadvertently specify an increase with respect to a neutral condition. To be more agnostic to the directionality of the effect, we have changed this to ‘attentional modulation’ and ‘attentional gain’ throughout the manuscript. Additionally, we have added results and visualizations for the baseline parameter to all results figures (Figures 4-7) to help readers further interpret our findings.  

(2) Clarity on Task Difficulty Ratios: The explicit reasoning for the chosen letter-to-number ratios for various cue widths is not detailed. Ensuring clarity on these ratios is crucial, as it affects the task difficulty and the comparability of behavioral performance across different cue widths. It is essential that observed differences in behavior and BOLD signals are attributable solely to changes in cue width and not confounded by variations in task difficulty.  

The ratios were selected to be as similar as possible given the size and spacing of our stimuli (aside from the narrowest cue width of one bin, the proportions for the others were 0.67, 0.60, and 0.67). We have updated the methods section to state this explicitly (methods lines 36-38): 

“The ratios were selected to be as similar as possible given the size and spacing of our stimuli (aside from the one-bin cue, the proportions for the other cues were 0.67, 0.60, 0.67).”

As Figure 1b shows, task accuracy showed small and non-monotonic changes across the three larger cue widths, dissociable from the monotonic pattern seen for the modelestimated width of the attentional field. Furthermore, as prior work has indicated that there is a relationship between task difficulty and the overall magnitude of the BOLD response (e.g., Ress, Backus & Heeger, 2000), we would primarily expect effects of task difficulty on the gain or baseline rather than the width. How exactly task difficulty influences the BOLD response and whether this would, in fact, interact with the width of the attentional field is an important topic, and we hope that future work will investigate this relationship more directly.  

We have clarified these points within the text, and now explicitly motivate future work looking at these important interactions (discussion lines 57-67):

“The observed effects of attentional field width were unlikely to be directly attributable to variation in task difficulty. Participants' task in our study was to discriminate whether more numbers or more letters were presented within a cued region of an iso-eccentric annulus of white noise. For our different cue widths, the ratios of numbers and letters were selected to be as similar as possible given the size and spacing of our stimuli. Changes in accuracy across the three larger cue widths were small and non-monotonic, implying task difficulty was dissociable from width per se. This dissociation bolsters the interpretability of our model fits; nevertheless, future work should further investigate how task difficulty interacts with the spread of the attentional field and the amplitude of attention-related BOLD effects (cf. Ress, Backus & Heeger, 2000).”

Reviewer #3 (Public review):

Summary: 

In this report, the authors tested how manipulating the contiguous set of stimuli on the screen that should be used to guide behavior - that is, the scope of visual spatial attention - impacts the magnitude and profile of well-established attentional enhancements in visual retinotopic cortex. During fMRI scanning, participants attended to a cued section of the screen for blocks of trials and performed a letter vs digit discrimination task at each attended location (and judged whether the majority of characters were letters/digits). Importantly, the visual stimulus was identical across attention conditions, so any observed response modulations are due to topdown task demands rather than visual input. The authors employ population receptive field (pRF) models, which are used to sort voxel activation with respect to the location and scope of spatial attention and fit a Gaussian-like function to the profile of attentional enhancement from each region and condition. The authors find that attending to a broader region of space expands the profile of attentional enhancement across the cortex (with a larger effect in higher visual areas), but does not strongly impact the magnitude of this enhancement, such that each attended stimulus is enhanced to a similar degree. Interestingly, these modulations, overall, mimic changes in response properties caused by changes to the stimulus itself (increase in contrast matching the attended location in the primary experiment). The finding that attentional enhancement primarily broadens, but does not substantially weaken in most regions, is an important addition to our understanding of the impact of distributed attention on neural responses, and will provide meaningful constraints to neural models of attentional enhancement. 

Strengths: 

(1) Well-designed manipulations (changing location and scope of spatial attention), and careful retinotopic/pRF mapping, allow for a robust assay of the spatial profile of attentional enhancement, which has not been carefully measured in previous studies.

(2) Results are overall clear, especially concerning width of the spatial region of attentional enhancement, and lack of clear and consistent evidence for reduction in the amplitude of enhancement profile.

(3) Model-fitting to characterize spatial scope of enhancement improves interpretability of findings.

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

Weaknesses: 

(1) Task difficulty seems to vary as a function of spatial scope of attention, with varying ratios of letters/digits across spatial scope conditions, which may complicate interpretations of neural modulation results  

The reviewer is correct in observing that task accuracy varied across cue widths. Though we selected the task ratios to be as similar as possible given the size and spacing of our stimuli (aside from the narrowest cue width of one bin, the proportions for the others were 0.67, 0.60, and 0.67), behavioral accuracy across the three larger cue widths was not identical. Prior research has shown that there is a relationship between task difficulty and the overall magnitude of the BOLD response (e.g., Ress, Backus & Heeger, 2000). Thus, we would primarily expect effects of task difficulty on gain rather than width. How task difficulty influences the BOLD response and whether this would, in fact, interact with the width of the attentional field is an important topic, and we hope that future work will investigate this relationship more directly.  

To clarify these points and highlight the potential for future work looking at these important interactions, we added the following text to the discussion section (discussion lines 57-67):

“The observed effects of attentional field width were unlikely to be directly attributable to variation in task difficulty. Participants' task in our study was to discriminate whether more numbers or more letters were presented within a cued region of an iso-eccentric annulus of white noise. For our different cue widths, the ratios of numbers and letters were selected to be as similar as possible given the size and spacing of our stimuli. Changes in accuracy across the three larger cue widths were small and non-monotonic, implying task difficulty was dissociable from width per se. This dissociation bolsters the interpretability of our model fits; nevertheless, future work should further investigate how task difficulty interacts with the spread of the attentional field and the amplitude of attention-related BOLD effects (cf. Ress, Backus and Heeger, 2000).”

(2) Some aspects of analysis/data sorting are unclear (e.g., how are voxels selected for analyses?) 

We apologize for not describing our voxel selection in sufficient detail. Some of the questions raised in the private comments are closely related to this point, we therefore aim to clarify all concerns below:

- Voxel selection: To select voxels that contribute to the 1D spatial profiles, we relied on the independent pRF dataset. We first defined some general requirements that needed to be met. Specifically, 1) the goodness of fit (R²) of the pRF fits needed to be greater than 10%; 2) the estimated eccentricity had to fall within [0.7 9.1] degree eccentricity (to exclude voxels in the fovea and voxels with estimated eccentricities larger than the pRF mapping stimulus); 3) the estimated size must be greater than 0.01 degree visual angle. 

Next, we included only voxels whose pRF overlapped with the white noise annulus. Estimated eccentricity was used to select all voxels whose eccentricity estimate fell within the annulus bounds. However, here it is also important to take the size of the pRF into account. Some voxels’ estimated eccentricity might fall just outside the annulus, but will still have substantial overlap due to the size of their pRF. Therefore, we further included all voxels whose estimated pRF size resulted in overlap with the annulus. 

This implies that some voxels with greater eccentricities and larger pRF sizes contribute to the 1D profile, which will influence the spatial specificity of the 1D profiles. However, we want to emphasize that in our view, the exact FWHM value is not so much of interest, as this will always be dependent on the voxel selection and many other data processing steps. Instead, we focus on the relative differences of the FWHM driven by the parametric attentional cue width manipulation. 

- Data sorting and binning. The reviewer raises an important point about how the FWHM value should be interpreted considering the data processing steps. To generate the 1D spatial profile, we binned voxels based on their estimated polar angle preference into 6degree bins and applied a moving average of 18 degrees to smooth the 1D profiles. Both of these processing steps will influence the spatial specificity of the profile. The binning step facilitates recentering based on cue center and combining across trials.

To explore the extent to which the moving average substantially impacted our results, we reran our analyses without that smoothing step. The vast majority of the results held. In V1, we found a significant effect of cue width on FWHM where the result was not significant previously (t(7)=2.52, p\=.040). Additionally, when looking at the minimum number of TRs needed to see a significant effect of cue width on FWHM, without the smoothing step in V1 it took 10 TRs (not significant at 10 TRs previously), in V2 it took 5 TRs (10 previously), and in V3 it took 3 TRs (2 previously). The other notable difference is that FWHM was generally a bit larger when the moving average smoothing was performed. We have visualized the group results for the FWHM estimates below to help with comparison. 

Author response image 1.

No moving average smoothing:

Voxel selection methods have been clarified in methods section lines 132-139:

“Within each ROI, pRF modeling results were used to constrain voxel selection used in the main experiment. We excluded voxels with a preferred eccentricity outside the bounds of the pRF stimulus (<0.7° and >9.1°), with a pRF size smaller than 0.01°, or with poor spatial selectivity as indicated by the pRF model fit (R2 < 10%). Following our 2D visualizations (see below), we further constrained voxel selection by only including voxels whose pRF overlapped with the white noise annulus. We included all voxels with an estimated eccentricity within the annulus bounds, as well as voxels with an estimated pRF size that would overlap the annulus.”

Data binning methods have been clarified in methods section lines 154-159: 

“Voxels with pRFs overlapping the white noise annulus were grouped into 60 bins according to their pRF polar angle estimate (6° polar angle bin width). We computed a median BOLD response within each bin. This facilitated the recentering of each profile to align all cue centers for subsequent combining across trials. To improve the signal-to-noise ratio, the resulting profile was smoothed with a moving average filter (width 18° polar angle; see Figure 2b).”

(3) While the focus of this report is on modulations of visual cortex responses due to attention, the lack of inclusion of results from other retinotopic areas (e.g. V3AB, hV4, IPS regions like IPS0/1) is a weakness 

We agree with the reviewer that using this approach in other retinotopic areas would be of significant interest. In this case, population receptive field mapping occurred in a separate session with a field of view only covering the occipital cortex (in contrast to the experimental session, which had whole-brain coverage). Because our modeling approach relies on these pRF estimates, we were unable to explore higher visual areas. However, we hope future work will follow up on this.

We have added the following text to the methods section describing the pRF mapping session (lines 87-89):

“In this session, the field of view was restricted to the occipital cortex to maximize SNR, thereby limiting the brain regions for which we had pRF estimates to V1, V2, and V3.”

(4) Additional analyses comparing model fits across amounts of data analyzed suggest the model fitting procedure is biased, with some parameters (e.g., FWHM, error, gain) scaling with noise. 

In this analysis, we sought to test how much data was needed to recover the attentional field, in view of the need for additional fMRI-based tools for use in tasks that involve more rapid dynamic adaptation of attention. Though we did find that more data reduced noise (and accordingly decreased absolute error and amplitude while increasing FWHM and R²), absolute angular error remained low across different temporal intervals (well below the chance level of 90°). With regard to FWHM, we believe that the more important finding is that the model-estimated FWHM was modulated by cue width at shorter timescales of as few as two TRs while maintaining relatively low angular error. We refrain from drawing conclusions here on the basis of the exact FWHM values, both because we don’t have a ground truth for the attentional field and because various processing pipeline steps can impact the values as well. Rather, we are looking at relative value and overall patterns in the estimates. The observed patterns imply that the model recovers meaningful modulation of the attentional field even at shorter time scales.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Additional data reporting and discussion of results are needed as outlined in the public review. 

Reviewer #2 (Recommendations for the authors):

(1) The current experimental design effectively captured the impact of varying cue widths on the BOLD response in the visual cortex. However, the inclusion of a neutral cue condition, where the cue width spans 360{degree sign} and all peripheral stimuli are attended, could serve as a valuable baseline. This would enable a quantitative assessment of how much the BOLD response is enhanced in specific spatial regions due to focused cues and, conversely, how much it is diminished in non-attended areas, along with the spatial extent of these effects. 

Please refer to our response in the public review. 

(2) While the study provides valuable insights into BOLD signal changes in visual areas corresponding to the focus of attention, it does not extend its analysis to the impact on regions outside the focus of attention. It would be beneficial to explore whether there is a corresponding decrease in BOLD signal in non-attended regions, and if identified, to describe the spatial extent and position of this effect relative to the attended area. Such an analysis could yield deeper insights into how attention influences activity across the visual cortex. 

We agree with the reviewer that it is very interesting to examine the spread of attention across the whole visual field. Our experiment was designed to focus on width modulations at a fixed eccentricity, but future work should explore how the attentional field changes with eccentricity and interacts with spatial variations across the visual field. This is highlighted in our discussion section (lines 76-81): 

“Future work can help provide a better understanding of the contribution of spatial attention by considering how the attentional field interacts with these well described spatial variations across the visual field. Measuring the full spatial distribution of the attentional field (across both eccentricity and polar angle) will shed light on how spatial attention guides perception by interacting with the non-uniformity of spatial representations.”

The addition of figure panels for the estimated baseline parameter in Figures 4-7 provides further information about BOLD effects in unattended regions of the annulus.  

(3) The rationale behind the selection of task difficulty ratios for different cue widths, specifically the letter-to-number ratios of 1:0, 1:2, 2:3, and 3:6 (or vice versa) for cue widths of 18{degree sign}, 54{degree sign}, 90{degree sign}, and 162{degree sign} respectively, was not explicitly discussed. It would be beneficial to clarify the basis for these ratios, as they may influence the perceived difficulty of the task and thus the comparability of behavioral performance across different cue widths. Ensuring that the task difficulty is consistent across conditions is crucial for attributing differences in behavior and BOLD signals solely to changes in cue width and not confounded by variations in task difficulty. 

Please refer to our response in the public review. We now clarify why we selected these ratios, and acknowledge more explicitly that behavioral performance differed across width conditions. See also our reply to private comment 1 from Reviewer 3 for some additional analyses examining task related influences.

Reviewer #3 (Recommendations for the authors):

(1) Task difficulty: the task seems exceptionally challenging. Stimuli are presented at a relativelyeccentric position for a very brief duration, and a large number of comparisons must be made across a broad region of space. This is reflected in the behavioral performance, which decreases rapidly as the scope of attention increases (Fig. 1). Because trials are blocked, does this change in task difficulty across conditions impact the degree to which neural responses are modulated? How should we consider differences in task difficulty in interpreting the conclusions (especially with respect to the amplitude parameter)? Also, note that the difficulty scales both with number of stimuli - as more need to be compared - but also with the ratio, which differs nonmonotonically across task conditions. One way to dissociate these might be RT: for 54/162, which both employ the same ratio of letter/digits and have similar accuracy, is RT longer for 162, which requires attending more stimuli? 

In addition to our comments in response to the public review, we emphasize that the reviewer makes an important point that there are differences in task difficulty, though the ratios are as close as they can be given the size and spacing of our stimuli. Behavioral performance varied non-monotonically with cue width, bolstering our confidence that our monotonically increasing model-estimated width is likely not entirely driven by task difficulty. There nevertheless remain open questions related to how task difficulty does impact BOLD attentional modulation, which we hope future work will more directly investigate.

The reviewer's comments identify two ways our data might preliminarily speak to questions about BOLD attentional modulation and task difficulty. First: how might the amplitude parameter reflect task difficulty? This is an apt question as we agree with the reviewer that it would be a likely candidate in which to observe effects of task difficulty. We do find a small effect of cue width on our amplitude estimates (amplitude decreases with width) in V3. Using the same analysis technique to look at the relationship between task difficulty and amplitude, we find no clear relationship in any of the visual areas (all p >= 0.165, testing whether the slopes differed from zero at the group level using a one-sample t-test). We believe future work using other experimental manipulations should look more systematically at the relationship between task difficulty and amplitude of the attentional BOLD enhancement.

Second: Does the same ratio at different widths elicit different behavioral responses (namely accuracy and RT)? We followed the reviewer’s suggestion to compare performance between cue widths of three and nine (identical ratios, different widths; see Author response image 2 and Figure 5). We found that, using a paired t-test, behavioral accuracy differed between the two cue widths (mean accuracy of 0.73 versus 0.69, p = 0.008), with better performance for cue width three. RT did not differ significantly between the two conditions (paired t-test, p = 0.729). This could be due to the fact that participants were not incentivized to respond as quickly as possible, they merely needed to respond before the end of the response window (1.25 s) following the stimulus presentation (0.5 s). The comparisons for accuracy and RT (calculated from time of stimulus appearance) are plotted below:

Author response image 2.

In summary, with matched stimulus ratios, the wider cue was associated with worse (though not slower) performance. This could be due to the fact that more elements are involved and/or that tasks become more difficult when attending to a broader swath of space. Given these results, we believe that future studies targeting difficulty effects should use direct and independent manipulations of task difficulty and attentional width. 

(2) Eye movements: while the authors do a good job addressing the average eccentricity of fixation, I'm not sure this fully addresses concerns with eye movements, especially for the character-discrimination task which surely benefits from foveation (and requires a great deal of control to minimize saccades!). Can the authors additionally provide data on, e.g., # of fixations within the attended stimulus annulus, or fixation heatmap, or # of saccades, or some other indicator of likelihood of fixating the letter stimuli for each condition? 

We agree with the reviewer that this task is surely much easier if one foveated the stimuli, and it did indeed require control to minimize saccades to the annulus. (We appreciate the effort and motivation of our participants!) We are happy to provide additional data to address these reasonable concerns about eye movements. Below, we have visualized the number of fixations to the annulus, separated by participant and width. Though there is variability across participants, there are at most 16 instances of fixations to the annulus for a given participant, combined across all width conditions. The median number of fixations to the annulus per width is zero (shown in red). Considering the amount of time participants engaged in the task (between 8 and 12 runs of the task, each run with 100 trials), this indicates participants were generally successful at maintaining central fixation while the stimuli were presented.

Author response image 3.

We added the results of this analysis to the methods section (lines 205-208):

“Additionally, we examined the number of fixations to the white noise annulus itself. No participant had more than 16 fixations (out of 800-1200 trials) to the annulus during the task, further suggesting that participants successfully maintained fixation.”

(3) pRF sorting and smoothing: Throughout, the authors are analyzing data binned based on pRF properties with respect to the attended location ("voxels with pRFs overlapping with the white noise annulus", line 243-244) First, what does this mean? Does the pRF center need to be within the annulus? Or is there a threshold based on the pRF size? If so, how is this implemented? Additionally, considering the methods text in lines 242-247, the authors mention that they bin across 6 deg-wide bins and smooth with a moving average (18 deg), which I think will lead to further expansion of the profile of attentional enhancement (see also below) 

We provide a detailed response in the public review. Furthermore, we have clarified the voxel selection procedure in the Methods (lines 132–139 & 154–159).

(4) FWHM values: The authors interpret the larger FWHMs estimated from their model-fitting than the actual size of the attended region as a meaningful result. However, depending on details of the sorting procedure above, this may just be due to the data processing itself. One way to identify how much expansion of FWHM occurs due to analysis is by simulating data given estimates of pRF properties for a 'known' shape of modulation (e.g., square wave exactly spanning the attended aperture) and compare the resulting FWHM to that observed for attention and perception conditions (e.g., Fig. 7c). 

We provide a detailed response in the public review. The essence of our response is to refrain from interpreting the precise recovered FWHM values, which will be influenced by multiple processing steps, and instead to focus on relative differences as a function of the attentional cue width. Accordingly, we did not add simulations to the revised manuscript, although we agree with the reviewer that such simulations could shed light on the underlying spatial resolution, and how binning and smoothing influences the estimated FWHM. We have clarified our interpretation of FWHM results in the manuscript as follows:

Results lines 137-141:

“One possibility is that the BOLD-derived FWHM might tend to overestimate the retinotopic extent of the modulation, perhaps driven by binning and smoothing processing steps to create the 1D spatial profiles. If this were the case, we would expect to obtain similar FWHM estimates when modeling the perceptual modulations as well.”

Results lines 169-175:

“Mirroring the results from the attentional manipulation, FWHM estimates systematically exceeded the nominal size of the perceptually modulated region of the visual field. Comparing the estimated FWHMs of the perceptual and attentional spatial profiles (Figure 7c) revealed that the estimated widths were highly comparable (Pearson correlation r=0.664 across width conditions and visual regions). Importantly, the relative differences in FWHM show meaningful effects of both cue and contrast width in a similar manner for both attentional and perceptual forms of modulation.”

Discussion lines 16-22:

“We also found that the estimated spatial spread of the attentional modulation (as indicated by the recovered FWHM) was consistently wider than the cued region itself. We therefore compared the spread of the attention field with the spatial profile of a perceptually induced width manipulation. The results were comparable in both the attentional and perceptual versions of the task, suggesting that cueing attention to a region results in a similar 1D spatial profile to when the stimulus contrast is simply increased in that region.”

(5) Baseline parameter: looking at the 'raw' response profiles shown in Fig. 2b, it looks, at first, like the wider attentional window shows substantially lower enhancement. However, this seems to be mitigated by the shift of the curve downwards. Can the authors analyze the baseline parameter in a similar manner as their amplitude analyses throughout? This is especially interesting in contrast to the perception results (Fig. 7), for which the baseline does not seem to scale in a similar way. 

We agree with the reviewer that the baseline parameter is worth examining, and have therefore added panels displaying the baseline parameter into all results figures (Figures 4-7). There was no significant association between cue width and baseline offset in any of the three visual regions.

(6) Outlier: Fig. 5, V2, Amplitude result seems to have a substantial outlier - is there any notable difference in e.g. retinotopy in this participant? 

One participant indeed has a notably larger median amplitude estimate in V2. Below, we plot the spatial coverage from the pRF data for this participant (022), as well as all other participants.

Author response image 4.

Each subplot represents a participant's 2D histogram of included voxels for the 1D spatial profiles; the colors indicate the proportion of voxels that fell within a specific x,y coordinate bin. Note that this visualization only shows x and y estimates and does not take into account size of the pRF. While there is variation across participants in the visual field coverage, the overall similarity of the maps indicates that retinotopy is unlikely to be the explanation. 

To further explore whether this participant might be an outlier, we additionally looked at behavioral performance, angular error and FWHM parameters as well as the goodness of fit of the model. On all these criteria this participant did not appear to be an outlier. We therefore see no reason to exclude this participant from the analyses.  

(7) Fig. 4 vs Fig. 5: I understand that Fig. 4 shows results from a single participant, showing variability across blocks, while Fig. 5 shows aggregate results across participants. However, the Angular Error figure shows complementary results - Fig. 4 shows the variability of best-fit angular error, while Fig. 5 shows the average deviation (approximately the width of the error distribution). This makes sense I think, but perhaps the abs(error) for the single participant shown in Fig. 4 should be included in the caption so we can easily compare between figures. 

That's right: the Figure 4 results show the signed error, whereas the Figure 5 results show the absolute error. We agree that reporting the absolute error values for the example participant would facilitate comparison. Rather than add the values to the text, we have made the example participant’s data visually distinct within Figure 5 for easy comparison.  

(8) Bias in model fits: the analysis shown in Fig. 6 compares the estimated parameters across amounts of data used to compute attentional modulation profiles for fitting those parameters. If the model-fitting procedure were unbiased, my sense is we would likely see no impact of the number of TRs on the parameters (R^2 should improve, abs(error) should improve, but FWHM, amplitude, baseline, etc should be approximately stable, if noisier). However, instead, it looks like more/less data leads to biased estimates, such that FWHM is biased to be smaller with more noise, and amplitude is biased to be larger. This suggests (to me) that the fit is landing on a spiky function that captures a noise wiggle in the profile. I don't think this is a problem for the primary results across the whole block of 10 TRs, which is the main point of the paper. Indeed, I'm not sure what this figure is really adding, since the single-TR result isn't pursued further (see below). 

Please refer to our response in the public review, comment 4. 

(9) 'Dynamics': The paper, starting in the title, claims to get at the 'dynamics' of attention fields. At least to me, that word implies something that changes over time (rather than across trials). Maybe I'm misinterpreting the intent of the authors, but at present, I'm not sure the use of the word is justified. That said, if the authors could analyze the temporal evolution of the attention field through each block of trials at 1- or 2-TR resolution, I think that could be a neat addition to the paper and would support the claim that the study assays dynamic attention fields. 

We thank the reviewer for giving us a chance to speak more directly to the dynamic aspect of our approach. Here, we specifically use the word “dynamic” to refer to trial-to-trial dynamics.  Importantly, our temporal interval analysis suggests that we can recover information about the attentional field at a relatively fine-grained temporal resolution (a few seconds, or 2 TRs). Following this methodological proof-of-concept to dynamically track the attentional field, we are excited about future work that can more directly investigate the manner in which the attentional field evolves through time, especially in comparison to other methods that first require training on large amounts of data.

(10) Correction for multiple comparisons across ROIs: it seems that it may be necessary to correct statistical tests for multiple comparisons across each ROI (e.g., Fig. 5 regression tests). If this isn't necessary, the authors should include some justification. I'm not sure this changes any conclusions, but is worth considering. 

We appreciate the opportunity to explain our reasoning regarding multiple comparisons. We thought it appropriate not to correct as we are not comparing across regions and are not treating tests of V1, V2, and V3 as multiple opportunities to support a common hypothesis. Rather, the presence or absence of an effect in each visual region is a separate question. We would typically perform correction for multiple comparisons to control the familywise error rate when conducting a family of tests addressing a common hypothesis. We have added this to the Methods section (lines 192-195): 

“No multiple comparison correction was applied, as the different tests for each region are treated as separate questions. However, using a threshold of 0.017 for p-values would correct for comparisons across the three brain regions.”

However, we are happy to provide corrected results. If we use Bonferroni correction across ROIs (i.e. multiply p-values by three), there are some small changes from significant to only trending towards significance, but these changes don’t affect any core results. The changes that go from significant to trending are:

Associated with Figure 5 – In V3, the relationship of cue width to amplitude goes from a p-value of 0.017 to 0.051.

Associated with Figure 6 –

V1: the effect of cue width on FWHM goes from p = 0.043 to 0.128.

V2: the effect of TR on both FWHM and R2 goes from p = ~0.02 to ~0.06. 

V3: the effect of cue width on amplitude goes from p = 0.024 to 0.073.

Author Response:

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

Reviewer #1 (Public review):

Expressed concern that FOOOF may not be sensitive to peaks located at the edges of the spectrum and suggested using rhythmicity as an alternative measure of oscillatory activity.

To address this concern, we first conducted a simulation in which we generated power spectra with a single periodic component while varying its parameters. The results confirmed that FOOOF may indeed have reduced sensitivity to low-frequency periodic components. In such cases, periodic activity can be conflated with aperiodic activity, leading to inflated estimates of the aperiodic component. These simulation results are presented in detail at the end of the Supplement.

To further investigate whether the low-frequency activity in our datasets may be oscillatory, we employed the phase-autocorrelation function (pACF), a measure of rhythmicity developed by Myrov et al. (2024). We compared pACF and FOOOF-derived parameters using linear mixed models at each channel–frequency– time point (see Methods for details). Our analyses showed that pACF activity closely resembles periodic activity across all three datasets, and is dissimilar to aperiodic parameters (see Figures 5, S4, S5, S21, S22, S34, S35). This supports the interpretation that, in our data, aperiodic activity is not conflated with periodic activity.

I was concerned that “there were no dedicated analyses in the paper to show that the aperiodic changes account for the theta changes.”

To address this concern, we used linear mixed models to estimate the association between FOOOF parameters and baseline-corrected time-frequency activity. These models were fitted at each channel-frequency-time point. Our results indicate that aperiodic activity is correlated with low-frequency (theta) baseline-corrected activity, while periodic activity is correlated primarily with activity in the alpha/beta range, but not with theta (see Figures 4, S3, S20, S33). Additionally, the exponent parameter exhibited a negative correlation in the gamma frequency range.

These findings support the reviewer's hypothesis: “I would also like to note that if the theta effect is only the aperiodic shift in disguise, we should see a concomitant increase in delta activity too – maybe even a decrease at high frequencies.” Overall, the results are consistent with our interpretation that low-frequency baseline-corrected activity reflects changes in aperiodic, rather than periodic, activity.

“On page 7 it is noted that baseline correction might subtract a significant amount of ongoing periodic activity. I would replace the word "subtract" with "remove" as not all baseline correction procedures are subtractive. Furthermore, while this sentence makes it sound like a problem, this is, to my mind, a feature, not a bug - baseline correction is meant to take away whatever is ongoing, be it oscillatory or not, and emphasise changes compared to that, in response to some event.”

We thank the reviewer for this helpful clarification. We have revised the sentence accordingly to read: “Our results show that classical baseline correction can remove continuous oscillatory activity that is present both during baseline and after stimulus onset, because it treats all baseline signals as 'background' to be removed without distinguishing between transient and continuous oscillations. While this is consistent with the intended purpose of baseline correction---to highlight changes relative to ongoing activity---it may also lead to unintended consequences, such as misinterpreting aperiodic activity as an increase in poststimulus theta oscillations.”

In addition, we have made several broader revisions throughout the manuscript to improve clarity and accuracy in response to the reviewer’s feedback:

(1) We have softened our interpretation of changes in the theta range. We no longer claim that these effects are solely due to aperiodic activity; rather, we now state that our findings suggest a potential contribution of aperiodic activity to signals typically interpreted as theta oscillations.

(2) We have revised our language to avoid suggesting a direct “interplay” between periodic and aperiodic components. Instead, we emphasize the concurrent presence of both components, using more precise and cautious formulations.

(3) We have clarified our discussion of baseline normalization approaches, explicitly noting that our findings hold regardless of whether a subtractive or divisive baseline correction was applied.

(4) Finally, we have restructured the introduction to improve readability and address points of potential confusion. Specifically, we have clarified the definition and role of 1/f activity, refined the discussion linking baseline correction to aperiodic activity, and improved transitions between key concepts.

Reviewer suggested that “it might be good to show that the findings were not driven by the cognitive-complaint subgroup (although the internal replications suggest they were not).”

We agree that it is important to demonstrate that our findings are not driven solely by the cognitive-complaint subgroup. While we did not include additional figures in the manuscript due to their limited relevance to the primary research question, we have attached figures that explicitly show the comparison between the clinical and control groups here in the response to reviewers. These figures include non-significant effects.

Author response image 1.

Results of the linear mixed model analysis of periodic activity for comparison between conditions, including non-significant effect (see also Figure 7 in the paper)

Author response image 2.

Results of the linear mixed model analysis of aperiodic exponent for comparison between conditions, including nonsignificant effects (see also Figure 9 in the paper)

Author response image 3.

Results of the linear mixed model analysis of aperiodic offset for comparison between conditions, including non-significant effects (see also Figure S11 in the paper)

“Were lure trials discarded completely, or were they included in the non-target group?”

Thank you for the question. As described in the Methods section (EEG data preprocessing), lure trials were discarded entirely from further analysis and were not included in the non-target group.

“Also, just as a side note, while this time-resolved approach is definitely new, it is not novel to this paper, at least two other groups have tried similar approaches, e.g., Wilson, da Silva Castanheira, & Baillet, 2022; Ameen, Jacobs, et al., 2024.”

Thank you for drawing our attention to these relevant studies. We have now cited both Wilson et al. (2022) and Ameen et al. (2024) in our manuscript. While these papers did indeed use time-resolved approaches, to our knowledge our study is the first to use such an approach within a task-based paradigm.

noted that it was unclear how the periodic component was reconstructed: “I understand that a Gaussian was recreated based on these parameters, but were frequencies between and around the Gaussians just zeroed out? Or rather, given a value of 1, so that it would be 0 after taking its log10.”

The periodic component was reconstructed by summing the Gaussians derived from the FOOOF model parameters. Since the Gaussians asymptotically approach, but never reach, zero, there were no explicit zeros between them. We have included this explanation in the manuscript.

“If my understanding is correct, the periodic and aperiodic analyses were not run on the singletrial level, but on trial-averaged TF representations. Is that correct? In that case, there was only a single observation per participant for each within-subject cell at each TF point. This means that model (4) on p. 15 just simplifies to a repeated-measures ANOVA, does it not? As hinted at later in this section, the model was run at each time point for aperiodic analyses, and at each TF point for periodic analyses, resulting in a series of p-values or a map of p-values, respectively, is that correct?”

We thank the reviewer for this careful reading and helpful interpretation. The reviewer is correct that analyses were conducted on trial-averaged time-frequency representations. Model presented in equation 7 (as referred to in the current version of the manuscript) is indeed conceptually similar to a repeated-measures ANOVA in that it tests within-subject effects across conditions. However, due to some missing data (i.e., excluded conditions within subjects), we employed linear mixed-effects models (LMER), which can handle unbalanced data without resorting to listwise deletion. This provides more flexibility and preserves statistical power.

The reviewer is also correct that the models were run at each channel-time point for the aperiodic analyses, and at each channel-time-frequency point for the periodic analyses, resulting in a series or map of p-values, respectively.

suggested marking the mean response time and contrasting scalp topographies of response-related ERPs with those of aperiodic components.

We thank the reviewer for this helpful suggestion. In response, we have now marked the mean response time and associated confidence intervals on the relevant figures (Figures 8 and S8). Additionally, we have included a new figure (Figure S13) presenting both stimulus- and response-locked ERP scalp topographies for comparison with aperiodic activity.

In the previous version of the manuscript, we assessed the relationship between ERPs and aperiodic parameters by computing correlations between their topographies at each time point. However, to maintain consistency with our other analyses and to provide a more fine-grained view, we revised this approach and now compute correlations at each channel–time point. This updated analysis is presented in Figure S14. The results confirm that the correlation between ERPs and aperiodic activity remains low, and we discuss these findings in the manuscript.

Regardless of the low correlation, we have added the following statement to the manuscript to clarify our conceptual stance: “While contrasting response-related ERPs with aperiodic components can help address potential confounds, we believe that ERPs are not inherently separate from aperiodic or periodic activity. Instead, ERPs may reflect underlying changes in aperiodic and periodic activity. Therefore, different approaches to studying EEG activity should be seen as providing complementary rather than competing perspectives.”

“On page 3, it is noted that distinct theta peaks were only observed in 2 participants. Was this through visual inspection?”

Yes, this observation was based on visual inspection of the individual power spectra. We have included this explanation in the text.

suggested improving the plots by reducing the number of conditions (e.g., averaging across conditions), increasing the size of the colorbars, and using different color scales for different frequency bands, given their differing value ranges. Additionally, the reviewer noted that the theta and alpha results appeared surprising and lacked their expected topographical patterns, possibly due to the color scale.

We appreciate these thoughtful suggestions and have implemented all of them to improve the clarity and interpretability of the figures. Specifically, we reduced the number of conditions by averaging across them where appropriate, enlarged the colorbars for better readability, and applied separate color scales for different frequency bands to account for variability in dynamic range.

In the process, we also identified and corrected an error in the code that had affected the topographies of periodic activity in the previous version of the manuscript. With this correction, the resulting topographical patterns are now more consistent with canonical findings and are easier to interpret. For example, activity in the beta range now shows a clear central distribution (see Figure 6B and Figure S5B), and frontal activity in the theta range is more apparent.

This correction also directly addresses the reviewer’s concern that the “theta and alpha results (where visible) look surprising – the characteristic mid-frontal and posterior topographies, respectively, are not really present.” These unexpected patterns were primarily due to the aforementioned error.

“Relatedly, why is the mu parameter used here for correlations? Why not simply the RT mean/median, or one of the other ex-Gaussian parameters? Was this an a priori decision?”

We appreciate the reviewer's thoughtful question. While mean and median RTs are indeed commonly used as summary measures, we chose the mu parameter because it provides a more principled estimate of central tendency that explicitly accounts for the positive skew typically observed in RT distributions. Although we did not directly compare mu, mean and median in this dataset, our experience with similar datasets suggests that differences between them are typically small. We chose not to include other ex-Gaussian parameters (e.g., sigma, tau) to avoid unnecessary model complexity and potential overfitting, especially since our primary interest was not in modelling the full distribution of response variability. This decision was made a priori, although we note that the study was not pre-registered. We have now added a clarification in the manuscript to reflect this rationale.

“Relatedly, were (some) analyses of the study preregistered?”

The analyses were not preregistered. Our initial aim was to investigate differences in phaseamplitude coupling (PAC) between the clinical and control groups. However, we did not observe clear PAC in either group—an outcome consistent with recent concerns about the validity of PAC measures in scalp EEG data (see: https://doi.org/10.3390/a16120540). This unexpected finding prompted us to shift our focus toward examining the presence of theta activity and assessing its periodicity.

The reviewer suggested examining whether there might be differences between trials preceded by a target versus trials preceded by a non-target, potentially reflecting a CNV-like mechanism.

We appreciate the reviewer’s insightful suggestion. The idea of investigating differences between trials preceded by a target versus a non-target, possibly reflecting a CNV-like mechanism, is indeed compelling. However, this question falls outside the scope of the current study and was not addressed in our analyses. We agree that this represents an interesting direction for future research.

Reviewer #2 (Public review):

“For the spectral parameterization, it is recommended to report goodness-of-fit measures, to demonstrate that the models are well fit and the resulting parameters can be interpreted.”

We thank the reviewer for this suggestion. We have added reports of goodness-of-fit measures in the supplementary material (Fig. S9, S25, S41). However, we would like to note that our simulation results suggest that high goodness-of-fit values are not always indicative of accurate parameter estimation. For example, in our simulations, the R² values remained high even when the periodic component was not detectable or when it was conflated with the aperiodic component (e.g., compare Fig. S48 with Fig. S47). We now mention this limitation in the revised manuscript to clarify the interpretation of the goodness-of-fit metrics.

“Relatedly, it is typically recommended to set a maximum number of peaks for spectral parameterization (based on the expected number in the analyzed frequency range). Without doing so, the algorithm can potentially overfit an excessive number of peaks. What is the average number of peaks fit in the parameterized spectra? Does anything change significantly in setting a maximum number of peaks? This is worth evaluating and reporting.”

We report the average number of peaks, which was 1.9—2 (Figure S10). The results were virtually identical when setting number of peaks to 3.

“In the main text, I think the analyses of 'periodic power' (e.g. section ‘Periodic activity...’ and Figures 4 & 5 could be a little clearer / more explicit on the measure being analyzed. ‘Periodic’ power could in theory refer to the total power across different frequency bands, the parameterized peaks in the spectral models, the aperiodic-removed power across frequencies, etc. Based on the methods, I believe it is either the aperiodic power or an estimate of the total power in the periodic-only model fit. The methods should be clearer on this point, and the results should specify the measure being used.”

We thank the reviewer for highlighting this point. In our analyses, “periodic power” (or “periodic activity”) refers specifically to the periodic-only model fit. We have added clarifications under Figure 3 and in the Methods section to make this explicit in the revised manuscript.

“The aperiodic component was further separated into the slope (exponent) and offset components". These two parameters describe the aperiodic component but are not a further decomposition per se - could be rephrased.”

We thank the reviewer for alerting us to this potential misunderstanding. We have now rephrased the sentence to read: “The aperiodic component was characterised by the aperiodic slope (the negative counterpart of the exponent parameter) and the offset, which together describe the underlying broadband spectral shape.”

“In the figures (e.g. Figure 5), the channel positions do not appear to be aligned with the head layout (for example - there are channels that extend out in front of the eyes).”

Corrected.

“Page 2: aperiodic activity 'can be described by a linear slope when plotted in semi-logarithmic space'. This is incorrect. A 1/f distributed power spectrum has a linear slope in log-log space, not semi-log.”

Corrected.

Page 7: "Our results clearly indicate that the classical baseline correction can subtract a significant amount of continuous periodic activity". I am unclear on what this means - it could be rephrased.

We thank the reviewer to pointing out that the statement is not clear. We have now rephrased is to read: “Our results show that classical baseline correction can remove continuous oscillatory activity that is present both during baseline and after stimulus onset, because it treats all baseline signals as 'background' to be removed without distinguishing between transient and continuous oscillations.”

”Page 14: 'the FOOOF algorithm estimates the frequency spectrum in a semi-log space'. This is not quite correct - the algorithm parameterizes the spectrum in semi-log but does not itself estimate the spectrum.”

Again, we thank the reviewer for alerting us to imprecise description. We have now changed the sentence to: “The FOOOF algorithm parameterises the frequency spectrum in a semi-logarithmic space”.

We have made refinements to improve clarity, consistency, and flow of the main text. First, we streamlined the introduction by removing redundancies and ensuring a more concise presentation of key concepts. We also clarified our use of terminology, consistently referring to the ‘aperiodic slope’ throughout the manuscript, except where methodological descriptions necessitate the term ‘exponent.’ Additionally, we revised the final section of the introduction to better integrate the discussion of generalisability, ensuring that the inclusion of additional datasets feels more seamlessly connected to the study’s main objectives rather than appearing as an addendum. Finally, we carefully reviewed the entire manuscript to enhance coherence, particularly ensuring that discussions of periodic and aperiodic activity remain precise and do not imply an assumed interplay between the two components. We believe these revisions align with the reviewer’s suggestions and improve the overall readability and logical structure of the manuscript.

Reviewer #3 (Public review):

Raised concerns regarding the task's effectiveness in evoking theta power and the ability of our spectral parameterization method (specparam) to adequately quantify background activity around theta bursts.

We thank Reviewer #3 for their constructive feedback. To address the concerns regarding the task’s effectiveness in evoking theta power and the adequacy of our spectral parameterization method, we have added additional visualizations using a log-y axis ****(Figures S1, S19, S32). These figures demonstrate that, in baseline-corrected data, low-frequency activity during working memory tasks appears as both theta and delta activity. Additionally, we have marked the borders between frequency ranges with dotted lines to facilitate clearer visual differentiation between these bands. We believe these additions help clarify the results and address the reviewer’s concerns.

The reviewer noted that “aperiodic activity seems specifically ~1–2 Hz.”

In our data baseline-corrected low-frequency post-stimulus increase in EEG activity spans from approximately 3 to 7 Hz, with no prominent peak observed in the canonical theta band (4–7 Hz). While we did not analyze frequencies below 3 Hz, we agree with the reviewer that some of this activity could potentially fall within the delta range.

Nonetheless, we would like to emphasize that similar patterns of activity have often been interpreted as theta in the literature,  even  in  the  absence  of a distinct spectral  peak (see: https://doi.org/10.1016/j.neulet.2012.03.076;    https://doi.org/10.1016/j.brainres.2006.12.076; https://doi.org/10.1111/psyp.12500; https://doi.org/10.1038/s42003-023-05448-z — particularly, see the interpretation of State 1 as a “theta prefrontal state”).

To accommodate both interpretations, we have opted to use the more neutral term “low-frequency activity” where appropriate. However, we also clarify that such activity is frequently referred to as “theta” in prior studies, even in the absence of a clear oscillatory peak.

“Figure 4 [now Figure 6]: there is no representation of periodic theta.”

Yes, this is one of the main findings of our study - periodic theta is absent in the vast majority of participants. A similar finding was found in a recent preprint on a working memory task (https://doi.org/10.1101/2024.12.16.628786), which further supports our results.

“Figure 5 [now Figure 7]: there is some theta here, but it isn't clear that this is different from baseline corrected status-quo activity.”

This figure shows comparisons of periodic activity between conditions. Although there are differences between conditions in the theta band, this does not indicate the presence of theta oscillations. Instead, the differences between the conditions in the theta band are most likely due to alpha components extending into the theta band (see Figure S6). This is further supported by the large overlap of significant channels between theta and alpha in Figure 7.

“Figure 8: On the item-recognition task, there appears to be a short-lived burst in the high delta / low theta band, for about 500 ms. This is a short phenomenon, and there is no evidence that specparam techniques can resolve such time-limited activity.”

We thank the reviewer for their comment. As we noted in our preliminary response, specparam, in the form we used, does not incorporate temporal information; it can be applied to any power spectral density (PSD), regardless of how the PSD is derived. Therefore, the ability of specparam to resolve temporal activity depends on the time-frequency decomposition method used. In particular, the performance of specparam is limited by the underlying time-frequency decomposition method and the data available for it. In fact, Wilson et al. (2022, https://doi.org/10.7554/eLife.77348), who have developed an approach for timeresolved estimation of aperiodic parameters, actually compare two approaches that differ only in their underlying time-frequency estimation method, while the specparam algorithm is the same in both cases. For the time-frequency decomposition we used superlets (https://doi.org/10.1038/s41467-020-20539-9), which have been shown to resolve short bursts of activity more effectively than other methods. To our knowledge, superlets provide the highest resolution in both time and frequency compared to wavelets or STFT.

To improve the stability of the estimates, we performed spectral parameterisation on trial-averaged power rather than on individual trials (unlike the approach in Wilson et al., 2022). In contrast, Gyurkovics et al. (2022) who also investigated task-related changes in aperiodic activity, estimated power spectra at the single-trial level, but stabilised their estimates by averaging over 1-second time windows; however, this approach reduced their temporal resolution. We have now clarified this point in the manuscript.

“The authors note in the introduction that ‘We hypothesised that the aperiodic slope would be modulated by the processing demands of the n-back task, and that this modulation would vary according to differences in load and stimulus type.’. This type of parametric variation would be a compelling test of the hypothesis, but these analyses only included alpha and beta power (Main text & Figure 4)”

We appreciate the reviewer's comment, but would like to clarify that the comparison between conditions was performed separately for both periodic power and aperiodic parameters. The periodic power analyses included all frequencies from 3 to 50 Hz (or 35 Hz in the case of the second dataset). All factors were included in the linear model (see LMM formula in equation 7 - subsection Methods / Comparisons between experimental conditions), but the figures only include fixed effects that were statistically significant. For example, Figure 7 shows the periodic activity and Figure 9 shows the exponent, with further details provided in other supplementary figures.

“Figure 5 does show some plots with some theta activity, but it is unclear how this representation of periodic activity has anything to do with the major hypothesis that aperiodic slope accounts for taskevoked theta.” /…/ In particular, specparam is a multi-step model fitting procedure and it isn't impressively reliable even in ideal conditions (PMID: 38100367, 36094163, 39017780). To achieve the aim stated in the title, abstract, and discussion, the authors would have to first demonstrate the robustness of this technique applied to these data.

We acknowledge these concerns and have taken several steps to clarify the relationship between the aperiodic slope and low-frequency activity, and to assess the robustness of the specparam (FOOOF) approach in our data.

First, we directly compared baseline-corrected activity with periodic and aperiodic components in all three data sets. These analyses showed that low-frequency increases in baseline-corrected signals consistently tracked aperiodic parameters - in particular the aperiodic exponent - rather than periodic theta activity (see Figs 4, S3, S20, S33). Periodic components, on the other hand, were primarily associated with baseline corrected activity in the alpha and beta bands. The aperiodic exponent also showed negative correlations with high beta/gamma baseline-corrected activity, which is exactly what would be expected in the case of a shift in the aperiodic slope (rather than delta/theta oscillations). See also examples at https://doi.org/10.1038/s41593-020-00744-x (Figures 1c-iv) or https://doi.org/10.1111/ejn.15361 (Figures 3c,d).

Next, because reviewer #1 was concerned that FOOOF might be insensitive to peaks at the edges of the spectrum, we ran a simulation that confirmed this concern. We then applied an alternative phase-based measure of oscillatory activity: the phase-autocorrelation function (pACF; Myrov et al., 2024). This method does not rely on spectral fitting and is sensitive to phase rather than amplitude. Across all datasets, pACF results were in close agreement with periodic estimates from FOOOF and were not correlated with aperiodic parameter estimates (Figs 5, S4, S5, S21, S22, S34, S35).

Taken together, these complementary analyses suggest that the apparent low-frequency (delta, theta) activity observed in the baseline-corrected data is better explained by changes in the aperiodic slope than by true low-frequency oscillations. While we acknowledge the limitations of any single method, the convergence between the techniques increases our confidence in this interpretation.

“How did the authors derive time-varying changes in aperiodic slope and exponent in Figure 6 [now Figure 8]?”

We thank the reviewer for this question. As explained in the Methods section, we first performed a time-frequency decomposition, averaged across trials, and then applied a spectral decomposition to each time point.

“While these methodological details may seem trivial and surmountable, even if successfully addressed the findings would have to be very strong in order to support the rather profound conclusions that the authors made from these analyses, which I consider unsupported at this time:

(a) ‘In particular, the similarities observed in the modulation of theta-like activity attributed to aperiodic shifts provide a crucial validation of our conclusions regarding the nature of theta activity and the aperiodic component.’

(b) ‘where traditional baseline subtraction can obscure significant neural dynamics by misrepresenting aperiodic activity as theta band oscillatory activity’

(d) ‘our findings suggest that theta dynamics, as measured with scalp EEG, are predominantly a result of aperiodic shifts.’

(e)  ‘a considerable proportion of the theta activity commonly observed in scalp EEG may actually be due to shifts in the aperiodic slope’.

(f) ‘It is therefore essential to independently verify whether the observed theta activity is genuinely oscillatory or primarily aperiodic’

[this would be great, but first we need to know that specparam is capable of reliably doing this].”

We believe that our claims are now supported by the aforementioned analyses, namely associations between baseline-corrected time-frequency activity and FOOOF parameters and associations between FOOOF parameters and PACF.

The reviewer found it unclear what low-frequency phase has to do with 1/f spectral changes: ‘Finally, our findings challenge the established methodologies and interpretations of EEG-measured crossfrequency coupling, particularly phase-amplitude coupling’

We thank the reviewer for their comment. To address this concern, we have added further clarification in the Discussion section. Our results are particularly relevant for phase-amplitude coupling (PAC) based on theta, such as theta-gamma coupling. PAC relies on the assumption that there are distinct oscillations at both frequencies. However, if no clear oscillations are present at these frequencies— specifically, if theta oscillations are absent—then the computation of PAC becomes problematic.

Author response:

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

Weaknesses to be addressed: 

(1) More detail is required to understand the effects of genetic and drug manipulations on heart rate as these are important experiments. At the very least, a discussion on the limitations of these manipulations is needed. 

- For example, how does one separate the pulsatile versus nutritive effects of blood flow/heartrate reduction? 

- The conclusion that arterial SMC differentiation is driven by pulsatile blood flow needs to be toned down. Indeed, this conclusion is mainly supported by in vitro cell co-cultures exposed to laminar versus pulsatile flow. In vivo, reducing Tnnt2a expression affects cardiac contractility and blood flow does not selectively affect pulsatility. To make this conclusion, the authors would need an experimental means to selectively dampen the pulsatility of blood flow.

We understand this concern and we toned down the statements related to the pulsatile flow of our conclusion by using 'flow' instead of 'pulsatile flow' in all text except for the in vitro co-cultures part. We also added a paragraph to discuss the limited capability of qualitatively reduce blood flow in vivo, and acknowledge that the effects of nutrients and flow reduction could not be uncoupled in live zebrafish embryos. We proposed that in the future, in vitro 3D vascular culture models may be combined with microfluidics to precisely calibrate nutrient composition in culture media, flow velocity and pulse; these methods would help address these questions more thoroughly. See page 11-12 line 312-322.

(2) Since mural cells are sensitive to transmural pressure, could the authors elaborate on the potential role of raised intravascular pressure in SMC differentiation? This would better parallel rodents and humans. 

We thank you for this suggestion. We added a paragraph to discuss the potential role of raised intravascular pressure in VSMC differentiation in the discussion section (see page 11 line 296-311).

(3) The authors use nifedipine to reduce blood flow. Nifedipine is a specific and potent inhibitor of voltage-dependent calcium channels (VDCC) which are expressed in SMCs. Prior studies (PMID: 35588738) showed that VDCC blockers increased rather than inhibited SMC differentiation. Nifedipine is also likely to act upon VSMC calcium handling in the circle of Willis, which may in turn affect cell maturation. Could the authors comment on this seeming discrepancy?

It is possible that off-target or indirect effects of Nifedipine decrease smooth muscle cell proliferation, or that altered cardiac contractility fundamentally alters aspects of vascular development other than blood flow. 

- Additionally, it would be helpful to report the quantitative heart rate reduction achieved with Nifedipine. This would clear up concerns that the heart rate reduction is too large for normal vascular development to occur, and thus decrease proliferation rate independent of changes in blood flow pulsatility. 

We concur with these comments, which is why our experimentation with Nifedipine is reinforced by employing an alternative, non-pharmacological strategy to inhibit blood flow: the use of morpholino against tnnt2a gene. The results with either Nifedipine or tnnt2a support the lack of VSMCs maturation. In addition, we provided the quantitative heart rate reduction achieved with Nifedipine shown in new Figure S2A-S2C, suggesting that the drug is not completely halting the heart rate but decreasing it. Nevertheless, we report that Zebrafish embryos can survive and develop a normal blood vascular system without any heartbeat. Hence, we exclude that the effect on VSMCs maturation is linked non-specifical effects caused by the loss of heartbeat. Nevertheless, we now acknowledged in our discussion the limitation of nifedipine, as it may affect VSMC through VDCCs (page 12, line 323-334).

We also added a paragraph in the discussion section to compare nifedipine, an L-type VDCC blocker, and ML218, a T-type VDCC selective inhibitor from the previous study (Ando et al., 2022). We noted that in this previous study, the increase in VSMC differentiation only occur on anterior metencephalic central arteries (AMCtAs) that are more than 40 mm away from the BCA; these AMCtAs are much smaller than CoW arteries and have different geometry hence possible different kinetics of VSMC maturation (Ando et al., 2022) as our manuscript discovery would suggest.

(4) The authors should provide more information on how blood flow velocity and wall shear stress are calculated from the Circle of Willis vascular structure. It is presumed that these values are dependent upon the 3-D morphology of the vessel network, as labeled by intravenous dextran dye, but this is not clear. (a second reviewer similarly comments: I was unclear how flow velocity values were obtained in Fig. 3E. Are they based on computational simulation, or are they experimentally calculated following the dextran injection?) Small local differences in vessel diameter and shape will influence blood flow velocity, but these morphological changes are not clearly articulated. Further, it is unclear how flow input levels to the CaDI and basilar arteries are decided across time points. For instance, is it possible to measure the blood flow speed empirically with line-scanning or high-speed tracking of labeled blood cells or particles? This would provide validation of the modeling results. 

The computational fluid dynamic simulation was performed according to previous study from our lab (Barak et al., 2021). Blood flow velocity and wall shear stress are dependent upon the 3D morphology of the vessel network labeled by intravascular dextran. Details on how the computational fluid dynamic simulation was performed are added in method section page 17 line 433-449.

Moreover, to address this reviewer concern we have now provided new experimental measurement of blood flow using the red blood cell (RBC) velocity via axial line scanning microscopy in Tg(kdrl:gfp;gata1:DsRed)zn1/sd2 zebrafish embryos at 54 hpf, 3 dpf, and 4 dpf. By using the experimental RBC velocity, we re-simulated the computational fluid dynamic. The new findings align with our conclusion and are further elaborated upon in response to this reviewer comment listed as point 6. Details on how RBC velocity calculated is added in method section page 16 line 414-431.

(5) Does the cardiac injection of dextran itself affect the diameter of the arteries, given the invasiveness of the procedure? This could be examined in fish with a transgenic endothelial label with and without dextran. 

Here, we performed an experiment on wildtype zebrafish at 5 days post-fertilization (dpf) with and without Dextran injection, examining the effects of Dextran injection on vessel diameters. As shown in the representative image below, the XZ panel clearly illustrates a Dextran-filled PCS vessel with no alteration in vessel size. Dextran microangiography, a technique employed to obtain vessel geometry with fluorescent microsphere, has been well established in zebrafish (Kamei et al., 2010). Our findings, demonstrating that Dextran does not affect vessel size, are consistent with previous studies utilizing Dextran microangiography.

Author response image 1.

(6) The data from the microangiography experiment in Figure 3 does not fully support the stated results. The authors report that the CaDI had the highest blood flow speed starting from 54 hpf, but it does not appear to be higher than the other arteries at this time point. Additionally, there is not sufficient evidence that wall shear stress coincides with smooth muscle cell differentiation in the CaDI. Wall shear stress appears to be similar between 54 hpf and 3 dpf in the CaDI, only increasing between 3 dpf and 4 dpf, while differentiation is shown to begin at 3 dpf. The authors need to address this and/or soften conclusions. 

First, In response to this specific reviewer concern, we measured red blood cell (RBC) velocity by used axial line scanning microscopy to analyze Tg(kdrl:gfp;gata1:DsRed)zn1/sd2 zebrafish embryos (the detailed method was added in Method section in the manuscript). We replaced the computational simulated blood flow velocity by RBC velocity in new Figure 3E-3G, and re-run the computational simulated wall shear stress (WSS) using the RBC velocity in new Figure 3I-3K. We compared RBC velocity and WSS among different vessels at each time point. We confirmed that CaDI has the highest RBC velocity starting from 54 hpf to 4 dpf (new Figure 3A-3C, and 3E-3G) and found an overall increase in average WSS from 54 hpf to 4 dpf (new Figure 3A-3C, and 3H). Further, WSS in CaDI was significantly higher than BCA and PCS at 54 hpf, 3 dpf, and 4 dpf (new Figure 3A-3C, 3I-3K). Altogether, the CFD simulation suggests that CoW arteries experience different hemodynamic WSS that is associated with spatiotemporal pattern of VSMC differentiation on CoW arteries.”.  (Page 6, line 153-162)

Second, to identify the correlation of WSS and VSMC differentiation in CaDI, we performed Pearson correlation analysis. In the image provided here, we plotted a linear regression with normalized # of acta2+ cells in CaDI and WSS with developmental stages (54 hpf, 3 and 4 dpf), and performed Pearson correlation coefficient analysis by using GraphPad Prism 10.0.3. The correlation coefficient r = 0.595, suggesting that the two variables (acta2+ cells and WSS) tend to increase together with developmental stages (54 hpf, 3 and 4 dpf).

Author response image 2.

Third, we softened our conclusion as the RBC velocity across CoW arteries was differentially distributed while VSMC differentiation occurred in these vessels.

(7) It is unclear if acta2 expression is conferring vascular tone, as would be expected if the cells are behaving as mature VSMCs. Does arterial diameter decrease with an increase in acta2 expression? Are acta2-positive mural cells associated with more dynamic changes in arteriole diameter under basal or stimulated conditions? 

Thanks for this interesting question. VSMC maturation and its vasoactivity could be further investigated in the future. Our study focused on early stage of VSMC differentiation, in which pdgfrb+ progenitors started to express VSMC marker acta2. We discussed the onset of transgelin expression and loss of abcc9 expression as markers of VSMC maturation. In addition, a previous study found that VSMC covered vessels in zebrafish brain dilate as early as 4 dpf and constrict at 6 dpf (Bahrami & Childs, 2020). Future study may focus on the association between expression of different VSMC markers and VSMC functional maturation. (page 10, line 272-279)

(8) The authors argue that CoW vessels transition from venous to arterial identity (Fig. 1). However, kdrl is not an ideal arterial marker for this experiment as it is expressed in both arteries and veins. While it is true that many arterial beds have stronger kdrl expression than the veins, its expression in both arteries and veins changes with developmental stage, and its expression level may vary depending on the type of vessel. Therefore, showing that kdrl increases from 32 hpf - 4 dpf in CoW vessels is not convincing because its expression may increase in both venous or arterial vasculature as the vessels mature. In addition, flt4 expression is not exclusively venous; for example, it has noticeable expression in the dorsal aorta at 24-32 hpf stages. It would be helpful to confirm this transition by analyzing additional arterial and venous markers. 

We acknowledge this and we added a paragraph to discuss the limitation. We combined loss of flt4 and increase in kdrl to establish the temporal sequence of circle of Willis morphogenesis, arterial specification, and VSMC differentiation. We acknowledge that additional arterial and venous markers need to be analyzed for a more thorough characterization of arterial specification in vertebrate brain vascular development. See page 12 line 335-341.

(9) The authors show that acta2+ VSMCs are absent in tnnt2a MO embryos, concluding that blood flow is required for their differentiation from pericytes. However, there is no data showing that pericytes are still present in tnnt2a MO embryos. Although this has been previously shown by Ando et al 2016, it would be beneficial to confirm in the current study as this is a critical piece of evidence needed for this conclusion. 

To determine if blood flow is dispensable for pdgfrb+ progenitor recruitment, we performed tnnt2a MO (0.35 ng/embryo) injection in Tg(pdgrb:egfp, kdrl:ras-mcherry) ncv22/s896. Loss of blood flow did not affect pdgfrb+ progenitor emergence around the CoW (new Figure S2G-S2H) at 3 days post fertilization (dpf). This is consistent with previous observation in Ando et al 2016 Figure S2C (Ando et al., 2016).

(10) The authors show that klf2a MO injected embryos have a reduced number of VSMCs at 3 dpf but a normal number at 4 dpf (Fig. 6), concluding that klf2a is only important to initiate CaDI muscularization. If this is true, it would raise important questions about how VSMCs differentiate at a later stage in the absence of klf2a. For instance, is blood flow not required to differentiate at a later stage, or is there another factor that compensates in the absence of klf2a? The alternative explanation/ caveat is that klf2a MO loses efficacy with development, leading to the recovery of VSMCs at this stage. Therefore, it would be important to confirm this result using a genetic klf2a mutant. 

Thank you for pointing this out.  We note that based on the klf2a reporter line, klf2a activity in CoW arterial endothelial cells is highly correlated with the number of acta2+ VSMCs in CaDI, BCA and PCS at 3 dpf (r = 0.974, new Figure S5J). Interestingly however, klf2a activity remained stable from 3 dpf to 4 dpf, well beyond initiation of VSMC differentiation. Thus, we speculate sustained klf2a expression may support further maturation of VSMCs, as acta2+ VSMCs showed distinct morphology at 4 dpf compared with 3 dpf. (Page 10, line 268-272). As for the observation that klf2a morphants have normal number of VSMCs at 4 dpf, we think that in addition to the temporary effect of morpholino, a proximal explanation is compensation by paralogous klf2b in zebrafish. We acknowledge that further characterization of CoW VSMC development in klf2a and klf2b double genetic mutants (Rasouli et al., 2018; Steed et al., 2016) may help determine whether klf2b compensates klf2a in CoW VSMC differentiation beyond 4 dpf. See page 10-11 line 292-295.

(11) A large part of the discussion focuses on Notch and Wnt signaling, as downstream Klf2 effectors. While these are reasonable hypotheses to propose, there is no data on the involvement of these pathways in the current study. It seems excessive to speculate on detailed mechanisms of how Klf2 activates Notch and Wnt signaling in the absence of data showing that these pathways are affected in CoW vessels. Therefore, the discussion could be shortened here unless additional data can be obtained to demonstrate the involvement of these pathways in VSMCs in CoW.

We concur and have condensed the discussion on Notch and Wnt signaling as downstream klf2 effectors.

Minor comments: 

(1) Line 138 "CaDI is the only vessels in the CoW receiving pulsatile arterial blood low ... ". Adding a reference to support this statement would be useful. 

We agree and revised this sentence into ‘CaDI receive proximal arterial feed through lateral dorsal aorta from cardiac outflow tract (Isogai et al., 2001)’. It was also based on our general observation of zebrafish vascular anatomy and blood flow under a confocal microscope.

(2) The image insets in Figs. 1A, 2A, 4E-L, 5A, 6A are quite small. Please make them larger to help the reader interpret the findings. 

We agree. We maximized the image size to help the reader interpret the finding, and to visualize confocal images and schematics side-by-side.

(3) The schematics in Figs. 1-2, and 4-6 are helpful, but the different cell types are difficult to see because they are small and their colors/shapes are not very distinct. 

We agree. We increased the size and color contrast to provide better visualization of the schematics in new schematic Figures. 1-2 and 4-6.

(4) It is stated that there are no diameter differences between different arteries, but statistics are not reported. 

The statistics in Figure 3D were performed by ordinary two-way ANOVA followed by Tukey’s multiple comparisons test, with a single pooled variance. Here we added pairwise comparisons among vessels in the CoW. Hence when non indicated the difference are non-significant.

(5) Figure 3F would be better visualized on a log scale, as it is difficult to see the differences between each post-fertilization timepoint. 

We agree. In the new Figure 3H, the average wall shear stress (WSS) in CoW arteries is presented on log scale in y axis to see the differences between each post-fertilization timepoint.

(6) Please provide more background and validation on the pericyte cell line, and their use for the questions in this study. 

Thank you for the question, TgBAC(pdgfrb:egfp)ncv22 was generated and described by Ando et al 2016 to clarify mural cell coverage of vascular endothelium in zebrafish (Ando et al., 2016). We added a describe in the method section to provide background and validation on this pericyte line (see page 13 line 368-372).

(7) Flow velocity and WSS changes are shown in each vessel in Figs. 3E,G. However, the comparison should be made between different types of vessels to see if there is a statistical difference and PCS, for example, which would explain differences in VSMC coverage. 

We agreed. We compared the difference among arteries in the CoW at each developmental timepoint and performed ordinary one-way ANOVA with Tukey’s multiple comparisons test. Figure. 3E is replaced by new Figure. 3E-G and Figure. 3G is replaced by new Figure. 3I-K.

(8) Similarly, between CaDI, the number of klf2a cells in Fig. 5B should be compared between different vessels, not between different stages of the same vessel. 

We agree. In new Figure 5B-E, the number of klf2a+ cells per 100 μm vessel length are compared among different vessels at each developmental stage and analyzed by ordinary one-way ANOVA with Tukey’s multiple comparisons test.

(9) When quantifying klf2+ cells in Fig. 5, it would be helpful to quantify klf2 expression level between cells in different vessels. This could be done by quantifying GFP expression in existing images. The difference in expression level may explain the variation between CaDI and PCS more accurately than just the difference in cell number. 

The GFP expression reflect the stability of GFP protein expression and labels discrete nuclei with active klf2a expression. Hence the quantification of GFP level might not give an accurate readout of klf2a expression per se but rather of its activity. For this reason we don’t think that this experiment will add accurate measurement of klf2a expression.

(10) Do data points in Figure 4D correspond to different cells in the same chamber experiment? If so, they cannot be treated as independent replicates. Each data point should correspond to an independent replicate experiment. 

We agree. Now in the figure legend, we report the number of cells analyzed.

(11) Graph placement is confusing in Figs. 4I, M. An adjacent Fig. 4G shows Nifedipine treated embryos, while the graph next to (Fig. 4I) shows acta+ cell number from tnnt2a 4 dpf experiment. Similarly, the bottom Fig. 4K tnn2a 4 dpf MO experiment has an adjacent graph Fig. 4M, which shows nifedipine treatment quantification, which makes it very confusing. 

We agreed. We rearranged Figure 4E (representative images of control embryos at 3 dpf and 4 dpf), Figure 4F (tnnt2a MO embryos at 3 dpf and 4 dpf), Figure 4G (nifedipine treated embryos at 3 dpf and 4 dpf).

Reference:

Ando, K., Fukuhara, S., Izumi, N., Nakajima, H., Fukui, H., Kelsh, R. N., & Mochizuki, N. (2016). Clarification of mural cell coverage of vascular endothelial cells by live imaging of zebrafish. Development, 143(8), 1328-1339. https://doi.org/10.1242/dev.132654

Ando, K., Tong, L., Peng, D., Vazquez-Liebanas, E., Chiyoda, H., He, L., Liu, J., Kawakami, K., Mochizuki, N., Fukuhara, S., Grutzendler, J., & Betsholtz, C. (2022). KCNJ8/ABCC9-containing K-ATP channel modulates brain vascular smooth muscle development and neurovascular coupling. Dev Cell, 57(11), 1383-1399 e1387. https://doi.org/10.1016/j.devcel.2022.04.019

Bahrami, N., & Childs, S. J. (2020). Development of vascular regulation in the zebrafish embryo. Development, 147(10). https://doi.org/10.1242/dev.183061

Barak, T., Ristori, E., Ercan-Sencicek, A. G., Miyagishima, D. F., Nelson-Williams, C., Dong, W., Jin, S. C., Prendergast, A., Armero, W., Henegariu, O., Erson-Omay, E. Z., Harmanci, A. S., Guy, M., Gultekin, B., Kilic, D., Rai, D. K., Goc, N., Aguilera, S. M., Gulez, B., . . . Gunel, M. (2021). PPIL4 is essential for brain angiogenesis and implicated in intracranial aneurysms in humans. Nat Med, 27(12), 2165-2175. https://doi.org/10.1038/s41591-021-01572-7

Isogai, S., Horiguchi, M., & Weinstein, B. M. (2001). The vascular anatomy of the developing zebrafish: an atlas of embryonic and early larval development. Dev Biol, 230(2), 278-301. https://doi.org/10.1006/dbio.2000.9995

Kamei, M., Isogai, S., Pan, W., & Weinstein, B. M. (2010). Imaging blood vessels in the zebrafish. In Methods in cell biology (Vol. 100, pp. 27-54). Elsevier.

Rasouli, S. J., El-Brolosy, M., Tsedeke, A. T., Bensimon-Brito, A., Ghanbari, P., Maischein, H. M., Kuenne, C., & Stainier, D. Y. (2018). The flow responsive transcription factor Klf2 is required for myocardial wall integrity by modulating Fgf signaling. Elife, 7. https://doi.org/10.7554/eLife.38889

Steed, E., Faggianelli, N., Roth, S., Ramspacher, C., Concordet, J. P., & Vermot, J. (2016). klf2a couples mechanotransduction and zebrafish valve morphogenesis through fibronectin synthesis. Nat Commun, 7, 11646. https://doi.org/10.1038/ncomms11646

Author Response

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

We sincerely thank the editor and reviewers for their constructive feedback on our manuscript. Based on their recommendations, we've conducted additional experiments, made revisions to the text and figures, and provide a point-by-point response below.

Reviewer #1 (Recommendations for the authors):

1) The lack of behavioral/physiological measures of the depth of anesthesia (ventilation, heart rate, blood pressure, temperature, O2, pain reflexes, etc...) combined with the lack of dose-response and the use of different routes of administration makes the data difficult to interpret. Sure, there is a clear difference in network activation between KET and ISO, but are those effects due to the depth of the anesthesia, the route of administration, and the dose used? The lack of behavioral/physiological measures prevents the identification of brain regions responsible for some of the physiological effects and different effects of anesthetics.

We greatly appreciate the insightful feedback you have provided.

In response to the concerns about anesthesia depth:

a. We recorded EEG and EMG data both before and after drug administration. Supplementary Figure 1 showcases the changes in EEG and EMG power observed 30 minutes post-drug administration, normalized to a 5-minute baseline taken prior to the drug's administration. Notably, no significant differences were detected in the normalized EEG and EMG power between the ISO and KET groups. Given the marked statistical differences observed between the EEG power in the KET and saline groups, and the EMG power in the home cage and ISO groups, we infer that both anesthetics effectively induced a loss of consciousness.

b. We used standard methods and doses for inducing c-Fos expression with anesthetics, as documented in prior studies (Hua, T, et al., Nat Neurosci, 2020; 23(7): 854-868; Jiang-Xie, L F, et al., Neuron, 2019; 102(5): 1053-1065.e4; Lu, J, et al., J Comp Neurol, 2008; 508(4): 648-62). In future research, it might be more optimal to adopt continuous intraperitoneal or intravenous administration of ketamine.

c. Within the scope of our study, while disparities in anesthesia duration might potentially influence the direct statistical comparison of ISO and KET, such disparities wouldn't compromise the identification of brain regions activated by KET or ISO when assessed as distinct stimuli (ISO vs. home cage; KET vs. saline) or in relation to their individual functional network hub node results.

We hope these additions and clarifications adequately address your concerns and enhance the comprehensibility of our data.

2) Under anesthesia there should be an overall reduction of activity, is that the case? There is no mention of significantly downregulated regions. The authors use multiple transformations of the data to interpret the results (%, PC1 values, logarithm) without much explanation or showing the full raw data in Fig 1. It would be helpful to interpret the data to compare the average fos+ neurons in each region between treatment and control for each drug.

Absence of Significantly Downregulated Regions Under Anesthesia: There are two primary reasons for this observation:

a. Our study's sampling time for the home cage, ISO, saline, and KET groups was during Zeitgeber Time (ZT) 6-7.5. During this period, mice in both the home cage and saline groups typically showed reduced spontaneous activity or were in a sleep state. Our Supplementary Figure 1 EEG and EMG data corroborate this, revealing no significant statistical variations in EEG power between the home cage and ISO groups, nor in EMG power between the saline and KET groups.

b. Our immunohistochemical data showed that the total number of c-Fos positive cells in the two control groups was notably lower than in the experimental groups (Saline group vs KET group: 11808±2386 versus 308705±106131, P = 0.006; Home cage vs ISO group: 3371±840 vs 12326±1879, P = 0.001). This is in line with previous studies, like the one by Cirelli C and team, which found minimal c-Fos expression throughout the mouse brain during physiological sleep (Cirelli, C, and G Tononi, Sleep, 2000; 23(4): 453-69). Thus, in our analysis, we did not detect regions with significant downregulation when comparing anesthetized mice with controls.

Interpreting Raw Data from Figure 1: Regarding the average Fos+ neurons:

In Figures 4 and 5, we utilized raw data (c-Fos cell count) to assess cell expression differences across 201 brain regions within each group. Only brain regions that had significant statistical differences after multiple comparison corrections are shown in the figures.

3) I do not understand their interpretation of the PCA analyses. For instance, in Fig 2 they claim that KET is associated with PC1 while ISO is associated with PC2. Looking at the distribution of points it's clear that the KET animals are all grouped at around +2.5 on PC1 and -2.0 on PC2, this means that KET is associated with both PC1 and PC2 to a similar degree (2 to 2.5). Moreover, I'm confused about why they use PCA to represent the animals/group. PCA is a powerful technique to reduce dimensionality and identify groups of variables that may represent the same underlying construct; however, it is not the best way to identify clusters of individuals or groups.

Clarification on PCA Analyses in Figure 2: Thank you for pointing out the ambiguities in our initial presentation of the PCA analyses. We are grateful for the opportunity to address these concerns.

KET and ISO Associations with PC1 and PC2: You rightly observed that KET samples manifest both a positive value on PC1 (around +2.5) and a negative one on PC2 (around -2.0), suggesting that KET has a substantial influence on both principal components. In PCA, a positive score implies a positive association with that component, whereas a negative score suggests a negative association. Contrarily, ISO samples predominantly exhibit values around +2.5 on PC2, with nearly neutral values for PC1, underlining its stronger association with PC2 and lack of significant correlation with PC1. To ensure transparency and clarity, we've adjusted the corresponding descriptions in our manuscript, which can be found on Line 100.

Rationale Behind Using PCA to Represent Animals/Groups: Our initial step was to conduct PCA clustering analysis on the 201 brain regions within both the ISO and KET groups. In the accompanying chart, varying colors denote different brain regions, while distinct shapes represent separate clusters. There wasn't a pronounced distribution pattern within the ISO and KET groups, which led us to adopt the current computational method presented in the paper. This approach was chosen to directly contrast the relative differential expressions between ISO and KET.

We deeply value your feedback, which has steered us toward a clearer and more accurate presentation of our data. We genuinely appreciate your meticulous review.

Author response image 1.

4) The actual metric used for the first PCA is unclear, is it the FOS density in each of the regions (some of those regions are large and consist of many subregions, how does that affect the analysis) is it the %-fos, or normalized cells? The wording describing this is variable causing some confusion. How would looking at these different metrics influence the analysis?

Thank you for raising concerns about the metrics used in our PCA analysis. We recognize the need for clearer exposition and appreciate the opportunity to clarify.

PCA Metrics: The metric for our PCA is calculated by obtaining the ratio of the Fos density within a specific brain region to the global Fos density across the brain. Briefly, this entails dividing the number of Fos-positive cells in a given region by its volume, and then comparing this to the Fos density of the whole brain. The logarithm of this ratio provides our PCA metric. We've elaborated on this in the Materials and Methods section (Lines 401) and enhanced clarity in our revised manuscript, particularly at Line 96.

In Figure 2A, we employed 53 larger, mutually exclusive brain regions based on the reference from the study by Do et al. (eLife, 2016;5:e13214). However, in Figure 3A, we used a more detailed segmentation, incorporating 201 distinct brain areas that are more granular than those in Figure 2A. Notably, the PCA results from both representations were consistent. The rationale behind selecting either the 53 or 201 brain regions can be found in our response to Question 10.

Rationale for Metric Choice: The log ratio of regional c-Fos densities relative to the global brain density was chosen due to:

a. Notable disparities in c-Fos cell expression across the groups.

b. A significant non-normal distribution of density values across animals within the group. Employing the log ratio effectively mitigates the impact of extreme values and outliers, achieving a more standardized data distribution.

We've added PCA plots based on c-Fos densities, depicted in Author response image 2. However, the data dispersion has resulted in a significantly spread-out horizontal scale for these visuals.

Author response image 2.

5) Based on Fig 3 the authors concludes that ISO activates the hypothalamic regions and inhibits the cortex, however, Fig 1 shows neither an activation of the hypothalamus in the ISO nor an inhibition of the cortex when compared to home cage control. If anything it suggests the opposite.

Thank you for your insightful observations regarding the discrepancies between Figures 2 and 3. We believe that when you refer to Figure 1, you are actually referencing Figure 2C.

ISO activation in Hypothalamus: In Figure 2C, we regret the oversight where we inadvertently interchanged the positions of ISO and Saline. When accurately represented, Figure 2C indeed shows that ISO notably activates the periventricular zone (PVZ) and the lateral zone (LZ) of the hypothalamus compared to the home cage group. Moreover, there's a discernible difference in the hypothalamic response between ISO and KET.

ISO's Effect on the Cortex: The main aim of Figure 3 was to highlight the differing responses between ISO and KET in the cortex. Notably, KET demonstrates a positive correlation with PC1 (+7 on PC1), whereas ISO shows a negative association (-3 on PC1). Given that the coefficient of PC1 for the cortical region is positive, it suggests that the cortical areas activated by KET are inhibited by ISO (with KET's distribution around 0 on PC2). However, the divergence between ISO and the home cage is most apparent in PC2, with ISO clusters at +4 and the home cage approximately at -2, suggesting that ISO activates a different set of cortical nuclei. In alignment with this, Figure 2C also illustrates that ISO activates specific cortical areas, such as ILA and PIR, in contrast to the home cage.

Thus, Figure 3 primarily employs PCA to delineate the contrasts between ISO and KET, whereas Figure 2C emphasizes the comparison of each against their respective controls.

6) Control for isoflurane should be air in the induction chamber rather than home cage. It is possible that Fos activation reflects handling/stress pre-anesthesia in the animals, which would increase Fos expression in the stress-related regions such as the BST, striatum (CeA), hypothalamus (PVH) and potentially the LC.

Thank you for emphasizing the importance of an appropriate control for Isoflurane.

In our efforts to minimize the potential impact of stress-induced c-Fos expression, we implemented several precautionary measures. Prior to the experiment, both groups of mice were subjected to handling and acclimatization within the induction chamber over four days. By the day of the experiment, for the mice in the experimental group, we ensured they were comfortable and exhibited no signs of distress or fear—such as cowering or evading. With care, we slowly relocated them to the nearby anesthesia induction chamber. Using 5% ISO, anesthesia was induced promptly, following a meticulously devised protocol to reduce stress impacts on c-Fos expression.

Moreover, existing studies have shown Isoflurane's activation of BST/CeA (Hua, T, et al., Nat Neurosci, 2020, 23: 854-868), PVH (Xu, Z, et al., British Journal of Anaesthesia, 2023, 130: 446-458), and LC (Lu, J, et al., J Comp Neurol, 2008, 508: 648-62), even when using oxygen controls. Such literature supports our findings, indicating that the activation we observed was indeed due to Isoflurane and not purely stress-related.

7) In the Ket network there are a few anticorrelated regions, most of which are amongst the list of the most activated regions, does this mean that the strong correlation results from an overall decreased activation? And if so, is it possible that the ketamine anesthesia was stronger than the isoflurane, causing a more general reduction in activity?

The pronounced correlations observed within the ketamine (KET) network do not signify a generalized decrease in activation. Instead, these correlations reflect significantly enhanced activity in specific regions under KET anesthesia. This amplified correlation is an indication of a more widespread increase in activity, rather than a decrease. These findings are consistent with previous research, which showed that anesthetic doses of ketamine produce patterns of Fos expression in the CNS similar to wakefulness (Lu, J, et al., J Comp Neurol, 2008; 508(4): 648-62).

Regarding the comparative strength of KET versus ISO anesthesia, our electroencephalographic evidence confirms that both agents induce a loss of consciousness. No significant differences were observed in EEG and EMG readings within the first 30 minutes post-administration. In future research, a continuous intravenous or intraperitoneal administration of KET might be a preferable method.

8) Since they have established networks it would be easy and useful to look at how the different regions identified (sleep, pain, neuroendocrine, motor-related, ...) work together to maintain analgesia, are they within the same module? Do they become functionally connected and is this core network of functional connections similar for KET and ISO?

Thank you for your suggestion. In response to your inquiry, we undertook analysis of the core functional networks for KET and ISO, using a set threshold at r>0.82 and P<0.05. For evaluating the modularity of each network, we utilized Newman's spectral community detection algorithm.

(A) The ISO’s core functional network (56 nodes, 372 edges) predominantly divides into two modules with a modularity quotient of 0.345. ISO-active regions include arousal-associated regions (PL, ILA, PVT), analgesia-related (CeA, LC, PB), neuroendocrine function nuclei (TU, PVi, ARH, PVH, SON) as detailed in Figure 5. Notably, ARH and SON weren't incorporated into the core network. Analgesia-associated regions, such as CeA, LC, and PB, reside within module 1, while neuroendocrine nuclei are spread between modules 1 and 2.

(B) In contrast, KET's core functional network (61 nodes, 1820 edges) splits into three distinct modules, but its low modularity quotient (0.06) indicates a lack of clear functional modularization, suggesting denser interconnections among brain regions. Furthermore, functionally-related regions such as arousal (PL, ILA, PVT, DR), analgesia-related (ACA, APN, PAG, LC), and neuroendocrine regulation (PVH, SON),etc., as seen in Figure 4, are distributed across different modules. This distribution may implies that functions like analgesia and neuroendocrine regulation are not governed by simple, linear processes, but arise from complex, overlapping pathways spanning various modules and functional zones.

In summary, the core functional networks of ISO and KET differ, with functionally-related regions spanning multiple modules, reflecting their diverse roles in varied physiological regulations.

Author response image 3.

9) The naming of the function of some of the regions is very much debatable. For instance, PL/ILA are named "sleep-wakefulness regulation" regions in the paper. I can think of many more important functions of the PL/IL including executive functions, behavioral flexibility, and emotional control. It is unclear how the functions of all the regions were attributed. I am not sure that this biased labeling of structure-function is useful to the reports, it may instead suggest wrong conclusions.

Thank you for your thoughtful feedback regarding our classification of the functions of the PL/ILA regions in our manuscript.

We recognize the challenge in accurately defining the functions of brain regions. While there is evidence highlighting the role of PL/ILA in arousal pathways, we also acknowledge their documented roles in executive functions, behavioral flexibility, and emotional control. In response to your comments, we have refined our description, changing "sleep-wakefulness regulation" to "wake-promoting pathways" (see Line: 159, 164).

It's worth noting that many brain regions, including the PL/ILA, have multiple functions. We agree that a single label might not capture the entirety of their roles. To provide a broader perspective, we will add a section in our manuscript that sheds light on the varied functions of these regions (Line: 181).

10) A point of concern and confusion is the number of brain regions analyzed. In the introduction, it is mentioned that 987 brain regions are considered, but this is reduced to 53 selected brain regions in Figure 2, then 201 brain regions in Figure 3, and reduced again to 63 for the network analysis. The rationale for selecting different brain regions is not clear.

For the 987 brain regions: Using the standard mouse atlas available at http://atlas.brain-map.org/, the mouse brain is organized into nine levels. The broadest category is the grey matter, which then progresses to more specific subdivisions, totaling 987 unique regions.

For the 53 brain regions: To effectively understand the activation patterns of ISO and KET, we started with a broad approach, looking at larger brain areas like the thalamus and hypothalamus. This broad view, presented in Figure 2, focuses on the 5th-level brain regions, encompassing 53 primary areas. This methodology is also employed in the study by Do et al. (Elife, 2016; 5: e13214). We have added the rationale for selecting these brain regions in the main text (Line: 92).

Regarding the 201 brain regions in Figures 3, 4, and 5: We delved deeper, examining the 6th-level brain regions, a common granularity in neuroscience research. This detailed view allowed us to highlight specific areas, like the CeA and PVH (Line:129).

Finally, for Figures 6 and 7, we selected 63 regions that were activated by both ISO and KET, as well as regions previously reported to be related to the mechanism of general anesthesia(Leung, L, et al., Progress in neurobiology, 2014; 122: 24-44) (Line: 220). Using these regions, we analyzed the correlation of c-Fos expression, aiming to construct a functional brain network with strong positive connections.

We hope this clarifies our approach and the rationale behind our region selection at each stage of the study. Thank you for your attention to this detail.

11) The statistical analysis does not seem appropriate considering the high number of comparisons. They use simple t-tests without correction for multiple comparisons.

Thank you for pointing out the concern regarding our statistical analysis. In the revised manuscript, we addressed the issue of multiple comparisons correction in our t-tests. We adopted the statistical methods detailed in the papers by Renier, N, et al., Cell, 2016; and Benjamini, Y, and Y Hochberg, 1995. P-values were adjusted for multiple comparisons using the two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli, with a false discovery rate (FDR) threshold (Q) of 0.05. This approach is now explained in the Materials and Methods section (Line: 434). After this adjustment, the brain regions we initially identified remained statistically significant. Furthermore, we revisited the original immunohistochemical images to confirm the differences in c-Fos cell expression between the experimental and control groups, reinforcing our conclusions.

12) There is no statistical analysis in Fig 2C。

Thank you for bringing to our attention the lack of statistical analysis in Fig 2C. We have now added the relevant statistical data in Supplementary Table 1 and provided annotations in Fig 2C to reflect this.

Reviewer #2

1) The authors report 987 brain regions in the introduction, but I cannot find any analysis that incorporates these or even which regions they are. Very little rationale is provided for the regions included in any of the analyses and numbers range from 53 in Figure 1, to 201 in Figure 3, to 63 in Figure 6. It would help if the authors could first survey Fos+ counts across all regions to identify a subset that is of interest (significantly changed by either condition compared to control) for follow up analysis.

Thank you for your insightful comments on the number of brain regions analyzed in our study.

987 Brain Regions: The reference to 987 brain regions from the standard mouse atlas (http://atlas.brain-map.org/) represents the entire categorization of the mouse brain across nine levels. We recognize that a comprehensive analysis of all these regions would be valuable, but to ensure clarity and depth, we took a focused approach.

Region Selection Rationale:

Figure 2: Concentrated on 5th-level brain regions (53 areas), inspired by methods from Do et al. (eLife, 2016;5:e13214). This provided a broad overview of c-Fos expression differences. Figures 4 and 5: Delved into 6th-level brain regions (201 areas), a common practice in neuroscience for more detailed study. Figure 6: We focused on 63 regions, which encompass not only the regions activated by both ISO and KET but also those previously reported to be associated with the mechanisms of general anesthesia. Methodological Approach: Our region selection was rooted in identifying areas with significant changes under anesthetic conditions compared to controls. This staged approach allowed a targeted analysis of the most affected regions, ensuring robust conclusions.

Enhancements: We've incorporated comparative analyses of activated brain regions at different hierarchical levels in Figures 4 and 5. For clearer comprehension, we’ve added clarifications in the manuscript at Lines: 92, 130, and 220.

2) Different data transformations are used for each analysis. One that is especially confusing is the 'normalization' of brain regions by % of total brain activation for each animal prior to PCA analysis in Figures 2 and 3. This would obscure any global differences in activation and make it unlikely to observe decreases in activation (which I think is likely here) that could be identified using the Fos+ counts after normalizing for region size (ie. Fos+ count / mm3) which is standard practice in such Fos-based activity mapping studies. While PCA can be powerful approach to identify global patterns, the purpose of the analysis in its current form is unclear. It would be more meaningful to show that regional activation patterns (measured as counts/mm3) are on separate PCs by group.

Thank you for your thoughtful comments. We regret any confusion caused by our initial presentation. For the PCA analysis in Figures 2A and 3A, we calculated the ratio of cell density in each brain region to the overall brain density, and then applied a logarithmic transformation to this ratio. Our approach in Figure 2C was to use the proportion of c-Fos cell counts in individual brain regions to the total cell counts throughout the brain. This methodology considers variations in overall c-Fos cell counts across animals, effectively mitigating potential biases due to differential global activation levels across subjects.

Furthermore, our direct comparison of differences in c-Fos cell counts between ISO, KET, and their respective control groups in Figures 4 and 5 addresses your concerns about potential decreases in activation. Notably, we did not identify any brain regions with significant suppression in these figures, which is consistent with the trends observed post-normalization in Figure 2C.

Given your feedback, we conducted another PCA using cell densities for each region (counts/mm3). However, we found significant variability and non-normal distribution of c-Fos density across the groups, leading to extensive data dispersion. Consequently, normalizing the cell counts across regions and then applying a logarithmic transformation before PCA might be more appropriate.

Author response image 4.

Additionally, our exploration of regional activation patterns using PCA analysis for ISO and KET separately, based on the logarithm ratio of the c-Fos density, revealed that there was no distinct clustering feature among the different brain regions (as illustrated in Author response image 5: colors represented distinct brain regions, while the shapes were indicative of different clusters). This observation further suggests that our original statistical approach might be more suitable.

Author response image 5.

3) Critical problem: The authors include a control group for each anesthetic (ketamine vs. saline, isofluorane vs. homecage) but most analyses do not make use of the control groups or directly compare Fos+ counts across the groups. Strictly speaking, they should have compared relative levels of induction by ketamine versus induction by isoflurane using ANOVAs. Instead, each type of induction was separate from the other. This does not account for increased variability in the ketamine versus isoflurane groups. There is no mention in the Statistics section or in Results section that any multiple comparison corrections were used. It appears that the authors only used Students t-test for each region and did not perform any corrections.

We appreciate the reviewer's insights and have addressed your concerns:

Given the pronounced difference in c-Fos cell count expression between the KET and ISO groups, a direct comparison of Fos+ counts may not effectively capture their inherent disparities. To better highlight these distinctions, we used the logarithm ratio of c-Fos density in our PCA analysis (Figure 3), mitigating potential disparities in overall cell counts between samples and emphasizing relative variations. However, in response to your feedback, we've included additional analyses. Author response image 6 depicts the c-Fos density (cells/mm^3) across different brain regions for the home cage, ISO, saline, and KET groups, with regions like the cerebral cortex, cerebral nuclei, thalamus, and others differentiated by shaded backgrounds. Data are represented as mean ± SEM. We performed a one-way ANOVA followed by Tukey’s post hoc test, marking significant differences between ISO and KET with asterisks: P < 0.001, P < 0.01, P < 0.05.

Regarding multiple comparison corrections, we've conducted thorough analyses on the data in Figure 2C and Figures 4, 5, and 6, implementing multiple comparison corrections. The detailed methodology is provided in the “Statistical analysis” section.

Author response image 6.

4) Figures 4 and 5 show brain regions 'significantly activated' following KET or ISO respectively, but again a subset of regions are shown and the stats seem to be t-tests with no multiple comparisons correction. It would help to show these two figures side by side, include the same regions, and keep the y axis ranges similar so the reader can easily compare the 'activation patterns' across the two treatments. Indeed, it looks like KET/Saline induced activation is an order or magnitude or two higher than ISO/Homecage. I would also recommend that this be the first data figure before any other analyses and maybe further analysis could be restricted to regions that are significantly changed in following KET or ISO here.

Thank you for your constructive feedback regarding Figures 4 and 5.

Comparison and Presentation of Figures 4 and 5: We acknowledge your suggestion to present these figures side by side for easier comparison. In the supplementary figure provided in the previous question, we've placed Figures 4 and 5 adjacent to each other, with consistent y-axis ranges, ensuring that readers can make direct comparisons between the activation patterns elicited by KET and ISO.

Statistical Concerns and Region Selection: As mentioned in our previous response, we have conducted multiple comparison corrections on the data presented in Figures 4 and 5. Detailed procedures are elaborated in the “Statistical analysis” section. We believe this approach addresses your concerns regarding the use of t-tests without corrections for multiple comparisons.

Difference in Activation Levels: We observed that the c-Fos activation due to KET is significantly higher than that from ISO. When presented side-by-side using the same scale, ISO activations appear less prominent, potentially mask subtle differences in the activation patterns of ISO, particularly if both KET and ISO showed changes in the same direction in certain brain regions but differed in magnitude. To address this, we used the proportion of c-Fos cell counts in Figure 2C, the logarithm ratio of c-Fos density in Figure 2A and Figure 3. This method emphasizes the relative changes, rather than absolute values, giving a more balanced view of the effects of each treatment.

5) Analyses in Figure 6 and 7 are interesting but again the choice of regions to include is unclear and makes interpreting the results impossible. For example, in Figure 7 it is unclear why the list of regions in bar graphs showing Degree and Betweenness Centrality are not the same even within a single row?

Thank you for your pertinent observation. The choice of brain regions in Figures 6 and 7 was carefully determined based on two main criteria: regions that were significantly activated by ISO or KET within the scope of our study, and those previously reported to be associated with anesthesia mechanisms and sleep-wake regulation.

Regarding your second concern on Figure 7, the discrepancies observed in the x-axes of the bar graphs arise from our methodological approach. We prioritized presenting the top 20% of regions based on their Degree or Betweenness Centrality values. By separately ranking these regions from highest to lowest, the regions presented for each metric inherently differ. This approach was taken to elucidate nodes that consistently emerge as significant across both metrics, thereby highlighting core nodes in the functional network. Were we to use a consistent x-axis without this ranking, it would not only necessitate a more extensive presentation but might also dilute the emphasis on key information. To clarify this methodology and its rationale for our readers, we have expanded upon this in the manuscript at Line 243.

We hope these clarifications address your concerns and facilitate a clearer understanding of our findings.

Reviewer #1 (Recommendations For The Authors):

Minor points

1) In Table 1: the separation of which substructures belong to which brain structure is not clear

2) Line 132 on page 3 seems to repeat the sentence earlier in the paragraph "KET predominantly affects brain regions within the cerebral cortex (CTX), while significantly inhibiting the hypothalamus, midbrain, and hindbrain."

3) Typos

a) Line 99/100 and 130 Central nucleus (CNU) should be cerebral nucleus

b) Comma on line 166

c) Fig. 4D: KET instead of Keta

d) Line 263 "ep"

e) Line 332: 35" "ml (add space)

4) Will data and code be made available?

Thank you for your detailed feedback.

1. We have revised Table 1 to clarify which substructures belong to which brain structures.

2. We acknowledge the redundancy and have now edited line 139 on page 3 to remove the repeated sentence regarding the effects of KET on brain regions.

3. We have addressed the typos you pointed out:

a. The terms "Central nucleus (CNU)" have been corrected to "cerebral nucleus."

b. The comma issue on line 166 has been rectified.

c. In Fig. 4D, we have corrected "Keta" to "KET."

d. We have corrected the typo "ep" on line 263.

e. A space has been added between "35" and "ml" on line 332 as you indicated.

1. Regarding the availability of data and code, we are currently conducting additional analyses related to this study. Once these analyses are completed, we will be more than happy to make the data and code available.

Thank you for assisting us in improving our manuscript.

Reviewer #2 (Recommendations For The Authors):

Minor comments:

6) The term 'whole-brain mapping' in the title suggests that the mapping was performed on 'intact brains' where in fact serial sections were used here. Maybe the authors could change to 'brain-wide mapping' to align better with the study.

Thank you for your insightful comments.

We have revised the title as suggested, changing "whole-brain mapping" to "brain-wide mapping".

7) It is unclear if the mice were kept under anesthesia for the 90-min duration and how the authors monitored the level of sedation. Additionally, if the KET mice were already sedated why were they further sedated with ISO before perfusions and tissue extraction? The methods should be clarified and any potential confounds discussed.

To maintain consistency in the experimental protocol and to reduce stress reactions in the mice, ISO was used before perfusion in all cases. However, this does not affect c-Fos expression as the expression of c-Fos protein starts 20-30 minutes after stimulation (Lara Aparicio, S Y, et al., NeuroSci, 2022; 3(4): 687-702).

We appreciate your guidance in enhancing the clarity of our manuscript.

Reviewer #3 (Recommendations For The Authors):

Recommendation: Minor corrections.

1) The authors should delve deeper into the molecular mechanisms underlying the observed effects, particularly the changes associated with NMDA and GABA receptors. Exploring these mechanisms would provide a more comprehensive understanding of how Ketamine and Isoflurane modulate neural activity and induce anesthesia.

2) The clinical relevance of these findings has not been sufficiently addressed. It would be valuable to elaborate on how the current research outcomes could potentially lead to changes in current anesthesia practices. For instance, identifying the distinct pathways of action for Ketamine and Isoflurane could aid anesthesiologists in selecting the most appropriate anesthetic based on the specific needs of individual patients or surgical procedures.

3) Both Ketamine and Isoflurane have been associated with neurotoxicity. It is important to discuss how the c-Fos activation induced by these anesthetics could contribute, at least partially, to anesthesia-related neurotoxicity. Examining the potential neurotoxic effects would provide a more comprehensive understanding of the risks associated with these anesthetics and aid in the development of safer anesthesia protocols.

Thank you for your valuable suggestions.

Regarding the three points (1, 2, and 3) you've raised, we fully recognize their significance. In the current study, our primary focus was on the differential impacts of Isoflurane and Ketamine on widespread c-Fos expression in the brain. However, we indeed acknowledge the importance of delving deeper into these mechanisms and their clinical relevance. Therefore, we intend to explore these critical issues in greater detail in our future research endeavors.

We appreciate your feedback, which provides constructive guidance for our subsequent research directions.

Author Response

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

We thank the reviewers and the editors for their careful reading of our manuscript and for the detailed and constructive feedback on our work. Please find attached the revised version of the manuscript. We performed an extensive revision of the manuscript to address the issues raised by the referees. We provide new analyses (regarding the response consistency and the neural complexity), added supplementary figures and edits to figures and texts. Based on the reviewers’ comments, we introduced several major changes to the manuscript.

Most notably, we

• added a limitation statement to emphasize the speculative nature of our interpretation of the timing of word processing/associative binding

• emphasized the limitations of the control condition

• added analyses on the interaction between memory retrieval after 12h versus 36h

• clarified our definition of episodic memory

• added detailed analyses of the “Feeling of having heard” responses and the confidence ratings

We hope that the revised manuscript addresses the reviewers' comments to their satisfaction. We believe that the revised manuscript has been significantly improved owing to the feedback provided. Below you can find a point-by-point response to each reviewer comment in blue. We are looking forward that the revision will be published in the Journal eLife.

Reviewer #1 (Public Review):

The authors show that concurrently presenting foreign words and their translations during sleep leads to the ability to semantically categorize the foreign words above chance. Specifically, this procedure was successful when stimuli were delivered during slow oscillation troughs as opposed to peaks, which has been the focus of many recent investigations into the learning & memory functions of sleep. Finally, further analyses showed that larger and more prototypical slow oscillation troughs led to better categorization performance, which offers hints to others on how to improve or predict the efficacy of this intervention. The strength here is the novel behavioral finding and supporting physiological analyses, whereas the biggest weakness is the interpretation of the peak vs. trough effect.

R1.1. Major importance:

I believe the authors could attempt to address this question: What do the authors believe is the largest implication of this studies? How far can this technique be pushed, and how can it practically augment real-world learning?

We revised the discussion to put more emphasis on possible practical applications of this study (lines 645-656).

In our opinion, the strength of this paper is its contribution to the basic understanding of information processing during deep sleep, rather than its insights on how to augment realworld learning. Given the currently limited data on learning during sleep, we believe it would be premature to make strong claims about potential practical applications of sleep-learning. In addition, as pointed out in the discussion section, we do not know what adverse effects sleep-learning has on other sleep-related mechanisms such as memory consolidation.

R1.2. Lines 155-7: How do the authors argue that the words fit well within the half-waves when the sounds lasted 540 ms and didn't necessarily start right at the beginning of each half-wave? This is a major point that should be discussed, as part of the down-state sound continues into the up-state. Looking at Figure 3A, it is clear that stimulus presented in the slow oscillation trough ends at a time that is solidly into the upstate, and would not neurolinguists argue that a lot of sound processing occurs after the end of the sound? It's not a problem for their findings, which is about when is the best time to start such a stimulus, but it's a problem for the interpretation. Additionally, the authors could include some discussion on whether possibly presenting shorter sounds would help to resolve the ambiguities here.

The word pairs’ presentations lasted on average ~540 ms. Importantly, the word pairs’ onset was timed to occur 100 ms before the maximal amplitude of the targeted peaks/troughs.

Therefore, most of a word’s sound pattern appeared during the negative going half-wave (about 350ms of 540ms). Importantly, Brodbeck and colleagues (2022) have shown that phonemes are continuously analyzed and interpreted with delays of about 50-200 ms, peaking at 100ms delay. These results suggest that word processing started just following the negative maximum of a trough and finished during the next peak. Our interpretation (e.g. line 520+) suggests that low-level auditory processing reaches the auditory cortex before the positive going half-wave. During the positive going half-wave the higher-level semantic networks appear the extract the presented word's meaning and associate the two simultaneously presented words. We clarified the time course regarding slow-wave phases and sound presentation in the manuscript (lines 158-164). Moreover, we added the limitation that we cannot know for sure when and in which slow-wave phase words were processed (lines 645-656). Future studies might want to look at shorter lasting stimuli to narrow down the timing of the word processing steps in relation to the sleep slow waves.

R1.3. Medium importance:

Throughout the paper, another concern relates to the term 'closed-loop'. It appears this term has been largely misused in the literature, and I believe the more appropriate term here is 'real-time' (Bergmann, 2018, Frontiers in Psychology; Antony et al., 2022, Journal of Sleep Research). For instance, if there were some sort of algorithm that assessed whether each individual word was successfully processed by the brain during sleep and then the delivery of words was subsequently changed, that could be more accurately labelled as 'closed-loop'.

We acknowledge that the meaning of “closed-loop” in its narrowest sense is not fulfilled here. We believe that “slow oscillation phase-targeted, brain-state-dependent stimulation” is the most appropriate term to describe the applied procedure (BSDBS, Bergmann, 2018). We changed the wording in the manuscript to brain-state-dependent stimulation algorithm. Nevertheless, we would like to point out that the algorithm we developed and used (TOPOSO) is very similar to the algorithms often termed closed-loop algorithm in memory and sleep (e.g. Esfahani et al., 2023; Garcia-Molina et al., 2018; Ngo et al., 2013, for a comparison of TOPOSO to these techniques see Wunderlin et al., 2022 and for more information about TOPOSO see Ruch et al., 2022).

R1.4. Figure 5 and corresponding analyses: Note that the two conditions end up with different sounds with likely different auditory complexities. That is, one word vs. two words simultaneously likely differ on some low-level acoustic characteristics, which could explain the physiological differences. Either the authors should address this via auditory analyses or it should be added as a limitation.

This is correct, the two conditions differ on auditory complexities. Accordingly, we added this issue as another limitation of the study (line 651-653). We had decided for a single word control condition to ensure that no associative learning (between pseudowords) could take place in the control condition because this was the critical learning process in the experimental condition. We would like to point out that we observed significant differences in brain responses to the presentation of word-pairs (experimental condition) vs single pseudowords (control condition) in the Trough condition, but not the Peak condition. If indeed low-level acoustic characteristics explained the EEG differences occurring between the two conditions then one would expect these differences occurring in both the trough and the peak condition because earlier studies showed that low-level acoustic processing proceeds in both phases of slow waves (Andrillon et al., 2016; Batterink et al., 2016; Daltrozzo et al., 2012).

R1.5. Line 562-7 (and elsewhere in the paper): "episodic" learning is referenced here and many times throughout the paper. But episodic learning is not what was enhanced here. Please be mindful of this wording, as it can be confusing otherwise.

The reported unconscious learning of novel verbal associations during sleep may not match textbook definitions of episodic memory. However, the traditional definitions of episodic memory have long been criticised (e.g., Dew & Cabeza, 2011; Hannula et al., 2023; Henke, 2010; Reder et al., 2009; Shohamy & Turk-Browne, 2013).

We stand by our claim that sleep-learning was of episodic nature. Here we use a computational definition of episodic memory (Cohen & Eichenbaum, 1993; Henke, 2010; O’Reilly et al., 2014; O’Reilly & Rudy, 2000) and not the traditional definition of episodic memory that ties episodic memory to wakefulness and conscious awareness (Gabrieli, 1998; Moscovitch, 2008; Schacter, 1998; Squire & Dede, 2015; Tulving, 2002). We revised the manuscript to clarify that and how our definition differs from traditional definitions. Please see reviewer comment R3.1 for a more extensive answer.

Reviewer #2 (Public Review):

In this project, Schmidig, Ruch and Henke examined whether word pairs that were presented during slow-wave sleep would leave a detectable memory trace 12 and 36 hours later. Such an effect was found, as participants showed a bias to categorize pseudowords according to a familiar word that they were paired with during slow-wave sleep. This behavior was not accompanied by any sign of conscious understanding of why the judgment was made, and so demonstrates that long-term memory can be formed even without conscious access to the presented content. Unconscious learning occurred when pairs were presented during troughs but not during peaks of slow-wave oscillations. Differences in brain responses to the two types of presentation schemes, and between word pairs that were later correctly- vs. incorrectly-judged, suggest a potential mechanism for how such deep-sleep learning can occur.

The results are very interesting, and they are based on solid methods and analyses. Results largely support the authors' conclusions, but I felt that there were a few points in which conclusions were not entirely convincing:

R2.1. As a control for the critical stimuli in this study, authors used a single pseudoword simultaneously played to both ears. This control condition (CC) differs from the experimental condition (EC) in a few dimensions, among them: amount of information provided, binaural coherence and word familiarity. These differences make it hard to conclude that the higher theta and spindle power observed for EC over CC trials indicate associative binding, as claimed in the paper. Alternative explanations can be made, for instance, that they reflect word recognition, as only EC contains familiar words.

We agree. In the revised version of the manuscript, we emphasise this as a limitation of our study (line 653-656). Moreover, we understand that the differences between stimuli of the control and the experimental condition must not rely only on the associative binding of two words. We cautioned our interpretation of the findings.

Interestingly, EC vs CC exhibits differences following trough- but not peak targeting (see R1.4). If indeed all the EC vs CC differences were unrelated to associative binding, we would expect the same EC vs CC differences when peaks were targeted. Hence, the selective EC vs CC differences in the trough condition suggest that the brain is more responsive to sound, information, word familiarity and word semantics during troughs, where we found successful learning, compared to peaks, where no learning occurred. Troughtargeted word pairs (EC) versus foreign words (CC) enhanced the theta power 336 at 500 ms following word onset and this theta enhancement correlated significantly with interindividual retrieval performance indicating that theta probably promoted associative learning during sleep. This correlation was insignificant for spindle power.

R2.2. The entire set of EC pairs were tested both following 12 hours and following 36 hours. Exposure to the pairs during test #1 can be expected to have an effect over memory one day later, during test #2, and so differences between the tests could be at least partially driven by the additional activation and rehearsal of the material during test #1. Therefore, it is hard to draw conclusions regarding automatic memory reorganization between 12 and 36 hours after unconscious learning. Specifically, a claim is made regarding a third wave of plasticity, but we cannot be certain that the improvement found in the 36 hour test would have happened without test #1.

We understand that the retrieval test at 12h may have had an impact on performance on the retrieval test at 36h. Practicing retrieval of newly formed memories is known to facilitate future retrieval of the same memories (e.g. Karpicke & Roediger, 2008). Hence, practicing the retrieval of sleep-formed memories during the retrieval test at 12h may have boosted performance at 36h.

However, recent literature suggests that retrieval practice is only beneficial when corrective feedback is provided (Belardi et al., 2021; Metcalfe, 2017). In our study, we only presented the sleep-played pseudowords at test and participants received no feedback regarding the accuracy of their responses. Thus, a proper conscious re-encoding could not take place. Nevertheless, the retrieval at 12h may have altered performance at 36h in other ways. For example, it could have tagged the reactivated sleep-formed memories for enhanced consolidation during the next night (Rabinovich Orlandi et al., 2020; Wilhelm et al., 2011).

We included a paragraph on the potential carry-over effects from retrieval at 12h on retrieval at 36h in the discussion section (line 489-496; line 657-659). Furthermore, we removed the arguments about the “third wave of plasticity”.

R2.3. Authors claim that perceptual and conceptual processing during sleep led to increased neural complexity in troughs. However, neural complexity was not found to differ between EC and CC, nor between remembered and forgotten pairs. It is therefore not clear to me why the increased complexity that was found in troughs should be attributed to perceptual and conceptual word processing, as CC contains meaningless vowels. Moreover, from the evidence presented in this work at least, I am not sure there is room to infer causation - that the increase in HFD is driven by the stimuli - as there is no control analysis looking at HFD during troughs that did not contain stimulation.

With the analysis of the HFD we would like to provide an additional perspective to the oscillation-based analysis. We checked whether the boundary condition of Peak and Trough targeting changes the overall complexity or information content in the EEG. Our goal was to assess the change in neural complexity (relative to a pre-stimulus baseline) following the successful vs unsuccessful encoding of word pairs during sleep.

We acknowledge that a causal interpretation about HFD is not warranted, and we revised the manuscript accordingly. It was unexpected that we could not find the same results in the contrast of EC vs CC or correct vs incorrect word pairs. We suggest that our signal-to noise ratio might have been too weak.

One could argue that the phase targeting alone (without stimulation) induces peak/trough differences in complexity. We cannot completely rule out this concern. But we tried to use the EEG that was not influenced by the ongoing slow-wave: the EEG 2000-500ms before the stimulus onset and 500-2000ms after the stimulus onset. Therefore, we excluded the 1s of the targeted slow-wave, hoping that most of the phase inherent complexity should have faded out (see Figure 2). We could not further extend the time window of analysis due to the minimal stimulus onset interval of 2s. Of course we cannot exclude that the targeted Trough impacted the following HFD. We clarified this in the manuscript (line 384-425).

Furthermore, we did find a difference of neural complexity between the pre-stimulus baseline and the post-stimulus complexity in the Peak condition but not in the Trough condition (we now added this contrast to the manuscript, line 416-419). Hence, the change in neural complexity is a reaction to the interaction of the specific slow-wave phase with the processing of the word pairs. Even though these results cannot provide unambiguous, causal links, we think they can figure as an important start for other studies to decipher neural complexity during slow wave sleep.

Reviewer #3 (Public Review):

The study aims at creating novel episodic memories during slow wave sleep, that can be transferred in the awake state. To do so, participants were simultaneously presented during sleep both foreign words and their arbitrary translations in their language (one word in each ear), or as a control condition only the foreign word alone, binaurally. Stimuli were presented either at the trough or the peak of the slow oscillation using a closed-loop stimulation algorithm. To test for the creation of a flexible association during sleep, participant were then presented at wake with the foreign words alone and had (1) to decide whether they had the feeling of having heard that word before, (2) to attribute this word to one out of three possible conceptual categories (to which translations word actually belong), and (3) to rate their confidence about their decision.

R3.1. The paper is well written, the protocol ingenious and the methods are robust. However, the results do not really add conceptually to a prior publication of this group showing the possibility to associate in slow wave sleep pairs of words denoting large or small object and non words, and then asking during ensuing wakefulness participant to categorise these non words to a "large" or "small" category. In both cases, the main finding is that this type of association can be formed during slow wave sleep if presented at the trough (versus the peak) of the slow oscillation. Crucially, whether these associations truly represent episodic memory formation during sleep, as claimed by the authors, is highly disputable as there is no control condition allowing to exclude the alternative, simpler hypothesis that mere perceptual associations between two elements (foreign word and translation) have been created and stored during sleep (which is already in itself an interesting finding). In this latter case, it would be only during the awake state when the foreign word is presented that its presentation would implicitly recall the associated translation, which in turn would "ignite" the associative/semantic association process eventually leading to the observed categorisation bias (i.e., foreign words tending to be put in the same conceptual category than their associated translation). In the absence of a dis-confirmation of this alternative and more economical hypothesis, and if we follow Ocam's razor assumption, the claim that there is episodic memory formation during sleep is speculative and unsupported, which is a serious limitation irrespective of the merits of the study. The title and interpretations should be toned down in this respect

Our study conceptually adds to and extends the findings by Züst et al. (a) by highlighting the precise time-window or brain state during which sleep-learning is possible (e.g. slow-wave trough targeting), (b) by demonstrating the feasibility of associative learning during night sleep, and (c) by uncovering the longevity of sleep-formed memories.

We acknowledge that the reported unconscious learning of novel verbal associations during sleep may not match textbook definitions of episodic memory. However, the traditional definitions of episodic memory have long been criticised (e.g, (Dew & Cabeza, 2011; Hannula et al., 2023; Henke, 2010; Reder et al., 2009; Shohamy & Turk-Browne, 2013). We stand by our claim that sleep-learning was of episodic nature. We use a computational definition of episodic memory (Cohen & Eichenbaum, 1993; Henke, 2010; O’Reilly et al., 2014; O’Reilly & Rudy, 2000), and not the traditional definition of episodic memory that ties episodic memory to wakefulness and conscious awareness (Gabrieli, 1998; Moscovitch, 2008; Schacter, 1998; Squire & Dede, 2015; Tulving, 2002). The core computational features of episodic memory are 1) rapid learning, 2) association formation, and 3) a compositional and flexible representation of the associations in long-term memory.

Therefore, we revised the manuscript to emphasize how our definition differs from traditional definitions (line 64).

For the current study, we designed a retrieval task that calls on the core computational features of episodic memory by assessing flexible retrieval of sleep-formed compositional word-word associations. Reviewer 3 suggests an alternative interpretation for the learning observed here: mere perceptual associations between foreign words and translations words are stored during sleep, and semantic associations are only inferred at retrieval testing during ensuing wakefulness. First, these processing steps would require the rapid soundsound associative encoding, long-term storage, and the flexible sound retrieval, which would still require hippocampal processing and computations in the episodic memory system. Second, this mechanism seems highly laborious and inefficient. The sound pattern of a word at 12 hours after learning triggers the reactivation of an associated sound pattern of another word. This sound pattern then elicits the activation of the translation words’ semantics leading to the selection of the correct superordinate semantic category at test.

Overall, we believe that our pairwise-associative learning paradigm triggered a rapid conceptual-associative encoding process mediated by the hippocampus that provided for flexible representations of foreign and translation words in episodic memory. This study adds to the existing literature by examining specific boundary conditions of sleep-learning and demonstrates the longevity (at least 36 hours) of sleep-learned associations.

Other remarks:

R3.2. Lines 43-45 : the assumption that the sleeping brain decides whether external events can be disregarded, requires awakening or should be stored for further consideration in the waking state is dubious, and the supporting references date from a time (the 60') during which hypnopedia was investigated in badly controlled sleep conditions (leaving open the doubt about the possibility that it occurred during micro awakenings)

We revised the manuscript to add timelier and better controlled studies that bolster the 60ties-born claim (line 40-51). Recently, it has been shown that the sleeping brain preferentially processes relevant information. For example the information conveyed by unfamiliar voices (Ameen et al., 2022), emotional content (Holeckova et al., 2006; Moyne et al., 2022), our own compared to others’ names (Blume et al., 2018).

R3.3. 1st paragraph, lines 48-53 , the authors should be more specific about what kind of new associations and at which level they can be stored during sleep according to recent reports, as a wide variety of associations (mostly elementary levels) are shown in the cited references. Limitations in information processing during sleep should also be acknowledged.

In the lines to which R3 refers, we cite an article (Ruch & Henke, 2020) in which two of the three authors of the current manuscript elaborate in detail what kind of associations can be stored during sleep. We revised these lines to more clearly present the current understanding of the potential and the limitations of sleep-learning (line 40-51). Although information processing during sleep is generally reduced (Andrillon et al., 2016), a variety of different kinds of associations can be stored, ranging from tone-odour to word-word association (Arzi et al., 2012, 2014; Koroma et al., 2022; Züst et al., 2019).

R3.4. The authors ran their main behavioural analyses on delayed retrieval at 36h rather than 12h with the argument that retrieval performance was numerically larger at 36 than 12h but the difference was non-significant (line 181-183), and that effects were essentially similar. Looking at Figure 2, is the trough effect really significant at 12h ? In any case, the fact that it is (numerically) higher at 36 than 12h might suggest that the association created at the first 12h retrieval (considering the alternative hypothesis proposed above) has been reinforced by subsequent sleep.

The Trough effect at 12h is not significant, as stated on line 185 (“Planned contrasts against chance level revealed that retrieval performance significantly exceeded chance at 36 hours only (P36hours = 0.036, P12hours = 0.094).”). It seems that our wording was not clear. Therefore, we refined the description of the behavioural analysis in the manuscript (lines 188-193).

In brief, we report an omnibus ANOVA with a significant main effect of targeting type (Trough vs Peak, main effect Peak versus Trough: F(1,28) = 5.237, p = 0.030, d = 0.865). Because Trough-targeting led to significantly better memory retention than Peak-targeting, we computed a second ANOVA, solely including participants with through-targeted word-pair encoding. The memory retention in the Trough condition is above chance (MTrough = 39.11%, SD = 10.76; FIntercept (1,14) = 5.660, p = 0.032) and does not significantly differ between the 12h and 36h retrieval (FEncoding-Test Delay (1,14) = 1.308, p = 0.272). However, the retrieval performance at 36h numerically exceeds the performance at 12h and the direct comparison against chance reveals that the 36h but not the 12h retrieval was significant (P36hours = 0.036, P12hours = 0.094). Hence, we found no evidence for above chance performance at the 12h retrieval and focused on the retrieval after 36h in the EEG analysis.

We agree with the reviewer that the subsequent sleep seems to have improved consolidation and subsequent retrieval. We assume that the reviewer suggests that participants merely formed perceptual associations during sleep and encoded episodic-like associations during testing at 12h (as pointed out in R 3.1). However, we believe that it is unlikely that the awake encoding of semantic associations during the 12h retrieval led to improved performance after 36h. We changed the discussion regarding the interaction between retrieval at 12h and 36h (line 505-512, also see R 2.2)

R3.5> In the discussion section lines 419-427, the argument is somehow circular in claiming episodic memory mechanisms based on functional neuroanatomical elements that are not tested here, and the supporting studies conducted during sleep were in a different setting (e.g. TMR)

Indeed, the TMR and animal studies are a different setting compared to the present study. We re-wrote this part and only focused on the findings of Züst and colleagues (2019), who examined hippocampal activity during the awake retrieval of sleep-formed memories (lines 472-482). Additionally, we would like to emphasise that our main reasoning is that the task requirements called upon the episodic memory system.

R3.6. Supplementary Material: in the EEG data the differentiation between correct and incorrect ulterior classifications when presented at the peak of the slow oscillation is only significant in association with 36h delayed retrieval but not at 12h, how do the authors explain this lack of effect at 12 hour ?

We assume that the reviewer refers to the TROUGH condition (word-pairs targeted at a slow-wave trough) and not as written to the peak condition. We argue that the retention performance at 12h is not significantly above chance (M12hours = 37.4%, P12hours = 0.094).

Hence, the distinction between “correctly” and “incorrectly” categorised word pairs was not informative for the EEG analysis during sleep. For whatever reason the 12h retrieval was not significantly above chance, the less successful memory recall and thus a less balanced trial count makes recall accuracy a worse delineator for separating EEG trials then the recall performance after 36 hours.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor importance:

Abstract: The opening framing is confusing here and in the introduction. Why frame the paper in the broadest terms about awakenings and threats from the environment when this is a paper about intersections between learning & memory and sleep? I do understand that there is an interesting point to be made about the counterintuitive behavioral findings with respect to sleep generally being perceived as a time when stimuli are blocked out, but this does not seem to me to be the broadest points or the way to start the paper. The authors should consider this but of course push back if they disagree.

We understand the reviewer’s criticism but believe that this has more to do with personal preferences than with the scientific value or validity of our work. We believe that it is our duty as researchers to present our study in a broader context because this may help readers from various fields to understand why the work is relevant. To some readers, evidence for learning during sleep may seem trivial, to others, it may seem impossible or a weird but useless conundrum. By pointing out potential evolutionary benefits of the ability to acquire new information during sleep, we help the broad readership of eLife understand the relevance of this work.

Lines 31-32: "Neural complexity" -> "neural measures of complexity" because it isn't clear what "neural complexity" means at this point in the abstract. Though, note my other point that I believe this analysis should be removed.

To our understanding, “neural complexity” is a frequently used term in the field and yields more than 4000 entries on google scholar. Whereas ‘neural measures of complexity’ only finds 3 hits on google scholar [September 2023]. In order to link our study with other studies on neural complexity, we would like to keep this terminology. As an example, two recent publications using “neural complexity” are Lee et al. (2020) and Frohlich et al. (2022).

Lines 42-43: The line of work on 'sentinel' modes would be good to cite here (e.g., Blume et al., 2017, Brain & Language).

We added the suggested citation to the manuscript (lines 52).

Lines 84-90: While I appreciate the authors desire to dig deep and try to piece this all together, this is far too speculative in my opinion. Please see my other points on the same topic.

In this paragraph, we point out why both peaks and troughs are worth exploring for their contributions to sensory processing and learning during sleep. Peaks and troughs are contributing mutually to sleep-learning. Our speculations should inspire further work aimed at pinning down the benefits of peaks and troughs for sleep-learning. We clarified the purpose and speculative nature of our arguments in the revised version of the manuscript.

Line 109: "outlasting" -> "lasting over" or "lasting >"

We changed the wording accordingly.

Line 111: I believe 'nonsense' is not the correct term here, and 'foreign' (again) would be preferred. Some may be offended to hear their foreign word regarded as 'nonsense'. However, please let me know if I have misunderstood.

We would like to use the linguistic term “pseudoword” (aligned with reviewer 2’s comment) and we revised the manuscript accordingly.

Figure 1A: "Enconding" -> "Encoding"

Thank you for pointing this out.

Lines 201-2: Were there interactions between confidence and correctness on the semantic categorization task? Were correct responses given with more confidence than incorrect ones? This would not necessarily be a problem for the authors' account, as there can of course be implicit influences on confidence (i.e., fluency).

As is stated in the results section, confidence ratings did not differ significantly between correct and incorrect assignments (Trough condition: F(1,14) = 2.36, p = 0.15); Peak condition: F(1,14) = 0.48, p = 0.50).

Line 236: "Nicknazar" -> "Niknazar"

Thank you for pointing this out.

Line 266: "profited" -> "benefited"

We changed the wording accordingly.

Lines 280-4: There seems some relevance here with Malerba et al. (2018) and her other papers to categorize slow oscillations.

Diving into the details on how to best categorise slow oscillations is beyond the scope of this manuscript. Here, we build on work from the field of microstate analyses and use two measures to describe and quantify the targeted brain states: the topography of the electric field (i.e., the correlation of the electric field with an established template or “microstate”), and the field strength (global field power, GFP). While the topography of a quasi-stable electric field reflects activity in a specific neural network, the strength (GFP) of a field most likely mirrors the degree of activation (or inactivity) in the specific network. Here, we find that consistent targeting of a specific network state yielding a strong frontal negativity benefitted learning during sleep. For a more detailed explanation of the slow-wave phase targeting see (Ruch et al., 2022).

Lines 343-6: Was it intentional to have 0.5 s (0.2-0.7 s) surrounding the analysis around 500 ms but only 0.4 s (0.8-1.2 s) surrounding the analysis around 1 s? Could the authors use the same size interval or justify having them be different?

We apologise for the misleading phrasing and we clarified this in the revised manuscript. We applied the same procedure for the comparison of later correctly vs incorrectly classified pseudowords as we did for the comparison between EC and CC. Hence, we analysed the entire window from 0s to 2.5s with a cluster-based permutation approach. Contrary to the EC vs CC contrast, no cluster remained significant for the comparison of the subsequent memory effect. By mistake we reported the wrong time window. In the revised manuscript, the paragraph is corrected (lines 364-369).

Line 356-entire HFD section: it is unclear what's gained by this analysis, as it could simply be another reflection of the state of the brain at the time of word presentation. In my opinion, the authors should remove this analysis and section, as it does not add clarity to other aspects of the paper.

(If the authors keep the section) Line 361-2 - "Moreover, high HFD values have been associated with cognitive processing (Lau et al., 2021; Parbat & Chakraborty, 2021)." This statement is vague. Could the authors elaborate?

Please see our answer to Reviewer 2 (2.3) for a more detailed explanation. In brief, we would like to keep the analysis with the broad time window of -2 to -0.5 and from 0.5 to 2 s.

Lines 403-4: How was it determined that these neural networks mediated both conscious/unconscious processes? Perhaps the authors meant to make a different point, but the way it reads to me is that there is evidence that some neural networks are conscious and others are not and both forms engage in similar functions.

We revised the manuscript to be more precise and clear: “The conscious and unconscious rapid encoding and flexible retrieval of novel relational memories was found to recruit the same or similar networks including the hippocampus(Henke et al., 2003; Schneider et al., 2021). This suggests that conscious and unconscious relational memories are processed by the same memory system.” (p. 22, top).

Lines 433-41: Performance didn't actually significantly increase from 12 to 36 hours, so this is all too speculative in my opinion.

We removed the speculative claim that performance may have increased from the retrieval at 12 hours to the retrieval at 36 hours.

Line 534: "assisted by enhanced" -> "coincident with". It's unclear whether theta reflects successful processing as having occurred or whether it directly affects or assists with it.

We have adjusted the wording to be more cautious, as suggested (line 588).

Line 572-4: Rothschild et al. (2016) is relevant here.

Unfortunately, we do not see the relevance of this article within the context of our work.

Line 577 paragraph: The authors may consider adding a note on the importance of ethical considerations surrounding this form of 'inception'.

We extended this part by adding ethical considerations to the discussion section (Stickgold et al., 2021, line 657).

Line 1366: It would be better if the authors could eventually make their data publicly available. This is obviously not required, but I encourage the authors to consider it if they have not considered it already.

In my opinion, the discussion is too long. I really appreciate the authors trying to figure out the set of precise times in which each level of neural processing might occur and how this intersects with their slow oscillation phase results. However, I found a lot of this too speculative, especially given that the sounds may bleed into parts of other phases of the slow oscillation. I do not believe this is a problem unique to these authors, as many investigators attempting to target certain phases in the target memory reactivation literature have faced the same problem, but I do believe the authors get ahead of the data here. In particular, there seems to be one paragraph in the discussion that is multiple pages long (p. 22-24). This paragraph I believe has too much detail and should be broken up regardless, as it is difficult for the reader to follow.

Considering the recent literature, we believe this interpretation best explains the data. As argued earlier, we believe that a speculative interpretation of the reported phenomena can provide substantial added value because it inspires future experimental work. We have improved the manuscript by clearly distinguishing between data and interpretation. We do declare the speculative nature of some offered interpretations. We hope that these speculations, which are testable hypotheses (!), will eventually be confirmed or refuted experimentally.

Reviewer #2 (Recommendations For The Authors):

I very much enjoyed the paper and think it describes important findings. I have a few suggestions for improvement, and minor comments that caught my eye during reading:

(1) I was missing an analysis of CC ERP, and its comparison to EC ERP.

We added this analysis to the manuscript (line 299-301). The comparison of CC ERP with EC ERP did not yield any significant cluster for either the peak (cluster-level Monte Carlo p=0.54) or the trough (cluster-level Monte Carlo p>0.37). We assume that the noise level was too high for the identification of differences between CC and EC ERP.

(2) Regarding my public review comment #2, some light can be shed on between-test effects, I believe, using an item-based analysis - looking at correlations between items' classifications in test #1 and test #2. The assumption seems to be that items that were correct in test #1 remained correct in test #2 while other new correct classifications were added, owing to the additional consolidation happening between the two tests. But that is an empirical question that can be easily tested. If no consistency in item classification is found, on the other hand, or if only consistency in correct classification is found, that would be interesting in itself. This item-based analysis can help tease away real memory from random correct classification. For instance, the subset of items that are consistently classified correctly could be regarded as non-fluke at higher confidence and used as the focus of subsequent-memory analysis instead of the ones that were correct only in test #2.

Thanks, we re-analysed the data accordingly. Participants were consistent at choosing a specific object category for an item at 12 hours and 36 hours (consistency rate = 47% same category, chance level is 1/3). Moreover, the consistency rate did not differ between the Trough and the Peak condition (MTrough = 47.2%, MPeak = 47.0%, P = 0.98). The better retrieval performance in the Trough compared to the Peak condition after 36 hours is due to: A) if participants were correct at 12h, they chose again the correct answer at 36h (Trough: 20% & Peak: 14%). B) Following an incorrect answer at 12h, participants switched to another object category at 36h (Trough: 72%, Peak: 67%). C) If participants switched the object category following an incorrect answer at 12h, they switched more often to the correct category at 36h in the trough versus the peak condition (Trough: in 56% & Peak: 53%). Hence, the data support the reviewer’s assumption: items that were correct after 12 hours remained correct after 36 hours, while other new correct classifications were generated at 36h owing to the additional consolidation happening between the two tests. We added this finding to the manuscript (line 191-200, Figure S6):

Author response image 1.

As suggested, we re-analysed the ERP with respect to the subsequent memory effect. This time we computed four conditions according to the reviewer’s argument about consistently correctly classified pseudowords, presented in the figure below: ERP of trials that were correctly classified at 36h (blue), ERP of trials that were incorrectly classified at 36h (light blue), ERP of trials that were correctly classified twice (brown) and ERP of trials that were not correctly classified twice (orange, all trials that are not in brown). Please note that the two blue lines are reported in the manuscript and include all trials. The brown and the orange line take the consistency into account and together include as well all trials.

Author response image 2.

By excluding even more trials from the group of correct retrieval responses, the noise level gets high. Therefore, the difference between the twice-correct and the not-twice-correct trials is not significant (cluster-level Monte Carlo p > 0.27). Because the ERP of twice-correct trials seems very similar to the ERP of the trials correctly classified at 36h at frontal electrodes, we assume that our ERP effect is not driven by a few extreme subjects. Similarly, not-twicecorrect trials (orange) have a stronger frontal trough than the trials incorrectly classified at 36h (light blue).

(3) In a similar vein, a subject-based analysis would be highly interesting. First and foremost, readers would benefit from seeing the lines that connect individual dots across the two tests in figures 2B and 2C. It is reasonable to expect that only a subset of participants were successful learners in this experiment. Finding them and analyzing their results separately could be revealing.

We added a Figure S1 to the supplementary material, providing the pairing between performance of the 12h and the 36h retrieval.

It is an interesting idea to look at successful learners alone. We computed the ERP of the subsequent memory effect for those participants, who had an above change retrieval accuracy at 36h. The result shows a similar effect as reported for all participants (frontal cluster ~0-0.3s). The p-value is only 0.08 because only 9 of 15 participants exhibited an above chance retrieval performance at 36 hours.

Author response image 3.

ERP effect of correct (blue) vs incorrect (light blue) pseudoword category assignment of participants with a retrieval performance above chance at 36h (SD as shades):

We prefer to not include this data in the manuscript, but are happy to provide it here.

(4) I wondered why the authors informed subjects of the task in advance (that they will be presented associations when they slept)? I imagine this may boost learning as compared to completely naïve subjects. Whether this is the reason or not, I think an explanation of why this was done is warranted, and a statement whether authors believe the manipulation would work otherwise. Also, the reader is left wondering why subjects were informed only about test #1 and not about test #2 (and when were they told about test #2).

Subjects were informed of all the tests upfront. We apologize for the inconsistency in the manuscript and revised the method part. The explanation of why participants were informed is twofold: a) Participants had to sleep with in-ear headphones. We wanted to explain to participants why these are necessary and why they should not remove them. b) We hoped that participants would be expecting unconsciously sounds played during sleep, would process these sounds efficiently and would remain deeply asleep (no arousals).

(5) FoHH is a binary yes/no question, and so may not have been sensitive enough to demonstrate small differences in familiarity. For comparison, the Perceptual Awareness Scale (Ramsøy & Overgaard, 2004) that is typically used in studies of unconscious processing is of a 4-point scale, and this allows to capture more nuanced effects such as partial consciousness and larger response biases. Regardless, it would be informative to have the FoHH numbers obtained in this study, and not just their comparison between conditions. Also, was familiarity of EC and CC pseudowords compared? One may wonder whether hearing the pseudowords clearly vs. in one ear alongside a familiar word would make the word slightly more familiar.

We apologize for having simplified this part too much in the manuscript. Indeed, the FoHH is comparable to the PAS. We used a 4-point scale, where participants rated their feeling of whether they have heard the pseudoword during previous sleep. In the revised manuscript, we report the complete results (line 203-223). The FoHH did not differ between any of the suggested contrasts. Thus, for both the peak and the trough condition, the FoHH did not differ between sleep-played vs new; correct EC trials vs new; correct vs incorrect EC trials; EC vs CC trials. To illustrate the results, a figure of the FoHH has been added to the supplement (Figure S4).

(6) Similarly, it would be good to report the numbers of the confidence ratings in the paper as well.

In the revised manuscript, we extended the description of the confidence rating results. We added the descriptive statistics (line 224-236) and included a corresponding figure in the supplement (Figure S5).

Minor/aesthetic comments:

We implemented all the following suggestions.

(1) I suggest using "pseudoword" or "nonsense word" instead of "foreign word", because "foreign word" typically means a real word from a different language. It is quite confusing when starting to read the paper.

After reconsidering, we think that pseudoword is the appropriate linguistic term and have revised the manuscript accordingly.

(2) Lines 1000-1001: "The required sample size of N = 30 was determined based on a previous sleep-learning study". I was missing a description of what study you are referring to.

(3) I am not sure I understood the claim nor the rationale made in lines 414-417. Is the claim that pairs did not form one integrated engram? How do we know that? And why would having one engram not enable extracting the meaning from a visual-auditory presentation of the cue? The sentence needs some rewording and/or unpacking.

(4) Were categories counterbalanced (i.e., did each subjects' EC contain 9 animal words, 9 tool words and 9 place words)?

(5) Asterisks indicating significant effects are missing from Figure 4 and S2.

(6) Fig1 legend: "Participants were played with pairs" is ungrammatical.

(7) Line 1093: no need for a comma.

(8) Line 1336: missing opening parenthesis

(9) Line 430: "observe" instead of "observed".

(10) Line 466: two dots instead of one..

Reviewer #3 (Recommendations For The Authors):

Methods: 2 separate ANOVAs are performed (lines 160-185), but would not it make more sense to combine both in one ? If kept separated then a correction for multiple comparisons might be needed (p/2 = 0.025)

We computed an omnibus ANOVA. In a next step, we examined the effect in the significant targeting condition by computing another ANOVA. For further explanations, see reviewer comment 3.4.

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Author Response

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

Reviewer #1

As written in my public review I consider the science of this work to be high quality. I have some suggestions for the write-up though. As a general comment, I think that too much has been put into the appendices. In particular, the main text could contain more details about the model.

We are pleased that this Reviewer feels that our work to be of “high quality”. We value the reviewer’s insightful suggestions and comments. Following this Reviewer’s suggestion we have moved certain sections to the main text.

In what follows, we provide responses to each of the reviewer’s inquiry, and indicate the appropriate changes in the revised version.

P2 -

ϕ is introduce as packing fraction - on p3 it’s called cell density. Also it is not clear whether it is an area fraction or a cell number density. Please define properly and I would suggest sticking to one notion.

ϕ is the cell packing fraction. In two dimensions (as is the case in our simulations) it is the area fraction. However, in order to stick to one general notation (independent of dimension) we use “packing fraction” to represent how densely the cells are packed. We changed it the revised manuscript to ensure uniformity.

P3 -

“which should and should slow down the overall dynamics” Typo?

Corrected it in the revised manuscript.

“One would intuitively expect that the ϕfree should decrease with increasing cell density” Please, define ϕfree

ϕfree is defined in Eqn. 4. We ought to have defined it in the introduction.

“When ϕ exceeds ϕS, the free area ϕfree saturates because the soft cells interpenetrate each other,” I suggest clearly distinguishing between biological cells and the agents (disks) used in the simulation. Please, also clarify What interpenetration of agents corresponds to in tissues?

We have rewritten the sentence as, ”The simulations show that when..” Soft disks used in the simulations seem to be not an unrealistic model for biological cells. The small deformations noted in our model is not that different from the cells in the tissues. For visual reference, please see Author response image 1. In the left panel of the figure, a 2D snapshot of the experimental zebrafish tissue, displays the deformation of cells labeled as 1 and 2. Likewise, the right panel illustrates the extent to which such deformations are replicated in the simulation by allowing two cells to overlap (the white area in the right panel of Author response image 1 represents the interpenetration). In the revised manuscript, we have made the necessary change from “soft cells” to “soft disks.”

Author response image 1.

Snapshots of zebrafish tissue (left panel) (Ref. [14] main text) and model two dimensional tissue (right). In the right panel the white area represents the overlap and the black vertical line represents the intersection.

“The facilitation mechanism, invoked in glassy systems [22] allows large cells to move with low mobility.” What is the facilitation mechanism?

Facilitation, which is an intuitive idea, that refers to a mechanism by which cells in a in highly jammed environment can only move if the neighboring cells get out of the way. In our case (as shown in the text (Fig.3 (A) and Fig. 13 (A) & (B)) the smaller cells move faster almost independent of ϕ. When a small cell moves, it creates a void which could facilitate neighboring cells (including big ones) to move.

“η (or relaxation time)” I suggest explaining the link between η and the relaxation time.

First, in making this point on aging we only showed that the relaxation time is independent of the waiting time. In the revised manuscript we deleted η.

Although not germane to this study, in the literature on glass transition, it is not uncommon to use relaxation time τα (as a proxy of viscosity η) to describe the dynamics. The relation between τα and η is given by

where G∞ is the “infinite frequency” shear modulus, which holds in unjammed or in liquids. This relation suggests that τα is proportional to η, which is almost never satisfied in glass forming systems.

P5 - “In addition, the elastic forces characterizing cell-cell interactions are soft, which implies that the cells can penetrate with rij − (Ri + Rj) < 0 when they are jammed.” Is this about the model or the biological tissue? Presumably the former, because real cells do not penetrate each other, right? What are rij, Ri and Rj?

This is about the model. The cells are sufficiently soft that they can be deformed, which allows for modest interpenetration. Real cells exhibit similar behavior (see Fig. 1). In inset of Fig. 4 (b) rij is the center to center distance between cells with radii Ri and Rj. It is better to use the word overlap instead of penetrate, which is what we have done in the revised version.

“we simulated a highly polydisperse system (PDs) in which the cell sizes vary by a factor of ∼ 8” Is it important to have a factor 8 - the zebra fish tissue presents a factor 5 − 6?

This is an important question, which is difficult to answer using analytic theory. It does require simulations unfortunately. We do not know a priori the polysipersity value needed to observe saturation in η at high value of ϕ. However, we have shown that the a system with one type of cell (monodisperse) crystallizes. Furthermore, mixtures of two cell types do not show any saturation in η over the parameter range that we explored. A systematic simulation study is needed to explore a range of parameter values to determine the minimum PD, which would match the experimental findings.

We performed 3D simulations to figure out if much less PD would yield saturation in η. Preliminary simulations in three dimensions with a lower value of PD (11.5% with a size variations by a factor of ≈ 2 ) exhibits saturation in the relaxation time. For comparison, the value of PD in the current work is ≈ 24% with a size variation by a factor of 8.

P6 -

“which is related to the Doolittle equation [26] for fluidity ( )” what is the Doolittle equation? Is it important here? Also: “VFT equation for cells”? Is it the same as given on p.2 - so nothing special for cells - or a different one?

Historically, the Doolittle equation was proposed to describe the change in η in terms of free volume in the context polymer systems over 60 years ago. The physics in the polymers is very different from the soft models for cells considered here. Nevertheless, the equations has meaning in the context as well. The Doolittle (other names associated with similar equations are Ferry, Flory... ) equation is given by

, where A and B are constants, V is the total volume and Vhc is the hardcore volume. Essentially, is the relative free volume. It can be shown that one can arrive at the VFT equation starting from the Doolittle equation.

The VFT equation for cells is same as given in page 2, which we restate for completeness. Here, we introduce the apparent activation energy.

“The stress-stress tensor” Why not simply stress tensor?

We have corrected it.

“shows qualitatively the same behavior as the estimate of viscosity (using dimensional arguments) made in experiments.” Where is this shown?

The dependence of viscosity as a function ϕ is shown in Figure 1 (c).

P7 -

Fig 2A caption “dashed line” Maybe full line?

This should be full line. It is fixed in in the revised manuscript.

P8 -

“a puzzling finding that is also reflected” Why is it puzzling?

In figure 2 (C), it shows that the increase in the duration in the plateau of Fs(q,t) ceases when ϕ exceeds ≈ 0.90. This to us is puzzling (always a matter of perspective) because we expected that the duration of Fs(q,t) plateau to increase as a function of ϕ based on the VFT behavior for ϕ ≤ ϕS. As a result, we imagined that the relaxation time τα would continue to increase beyond ϕS. However, the simulations show that the relaxation time is essentially a constant for ϕ > 0.90, which implies that the soft disk system (our model for the tissue) is an unusual with behavior that has no counter part in the material world.

“If the VFT relation continues” –“If the VFT relation continued”

We have fixed it.

First paragraph does not seem to be coherent

What is RS (or Rs)?

RS is the radius of the small cell. In the revised manuscript we have made this clear.

P10 -

Please, define the waiting time.

The waiting time refers to the period between sample preparation and data collection either in experiments or in simulations. In an ergodic system, the properties should not depend on the waiting time provided provided it is large. In other words, after the system reaches thermal equilibrium, the waiting time tω should not have an impact on the properties of the system.

“fully jammed” Please, define.

The term “fully jammed” refers to a state in which the constituent particles in a system do not move. For example, it a hard sphere system at a packing fraction of approximately 0.84 is fully jammed, which implies there is wiggle room for a particle move without violating the excluded volume restriction. At this specific packing fraction, the hard sphere system undergoes a jamming transition, resulting in the particles becoming completely immobile. The nonconfluent tissue modeled here is not fully jammed.

P11 -

Fig.4 it is hard to see that the width of P(hij) increases with ϕ.

Please see Author response image 2 with a less number of curves for a better visualization. We have replaced this figure in the revised version.

Author response image 2.

Probability of overlap (hij) between two cells, P(hij), for various ϕ values.

“Thus, even if the cells are highly jammed at ϕ ≈ ϕS, free area is available because of an increase in the overlap between cells.” This conclusion seems premature at this point.

The Referee is correct. This is shown in Fig. 5. We amended the ends of the sentence to reflect this observation.

P12 -

“as is the case when the extent of compression increases” extent of compression = density?

This is correct. Extent of compression corresponds to the packing fraction or the density.

“This effect is expected to occur with high probability at ϕS and beyond,” Why? What is special about ϕS.

To achieve high packing fractions beyond a certain value of ϕ soft cells have, which would occur at a certain value ϕS. In the system studied here, ϕ ≈ 0.90 = ϕS. Note that ϕS could be altered by changing the system parameters.

P15 -

“local equilibrium” In a thermodynamic sense? There is also cell migration, so thermodynamic equilibrium does not seem to be appropriate.

This is an important point. The observation that equilibrium concepts hold in what is manifestly a non-equilibrium system is a surprise. It is referred in a thermodynamic sense. We agree with the reviewer because of cell division (in Ref. [14] main text), cell death, thermodynamic equilibrium does not seems to be appropriate. This is exactly the point we raise in the introduction. However, considering the timescale of cell division and death it appears that there may be a local steady state, which we we call a “local equilibrium”. As a consequence phase transition ideas and Green-Kubo relations are applicable. Indeed, a surprise in the conclusion in Ref. [14] is that in the zebrafish morphogenesis equilibrium description seems adequate.

“number of near neighbor cells that is in contact with the ith cell. The jth cell is the nearest neighbor of the ith cell, if hij > 0” A neighbour cell or the nearest neihbor?

A neighbour cell is accurate.

P16 -

“In our model there is no dynamics with only systematic forces because the temperature is zero.” What is a systematic force? I do not understand the sentence.

Systematic force between two cells is defined in Eqn. 5 in the main text. Because temperature is not a relevant variable in our model, we want to emphasize that in the absence of self propulsion, the cells would not move at all.

Reviewer #2

Major comments:

A/ Role of size polydispersity

In the text, and also in the methods (Appendix A), the authors mention that they need large polydispersity of particle sizes to explain the viscous plateau, as the dynamics of small vs large cells are ”dramatically different” (Appendix G). They simulate a system where cell sizes vary by a factor 8, mentioning this is typical in tissues, but I found this quite surprising - this would be heterogeneities in cell volume of 500, many orders of magnitude above what has been measured in tissues. As far as I’m aware, divisions are quite symmetric and synchronous in early vertebrate embryogenesis, so volume variations are expected to be very small (similarly in epithelial tissues, where jamming has been looked at extensively, I’m not aware of examples with ratio of 8 between cell diameters). One question I had is that when the authors look at ”small polydispersity”, there are 50 − 50 mixtures. Would small polydispersity with continuous distributions change this picture? Could they take their current simulations but smoothly change the ratio of polydispersity from 8 to 0 to see exactly how much they need to explain viscosity plateauing, and at which point is the transition?

We thank the reviewer for raising this important question, which was also a concern for Reviewer #1. The value of polydispersity (PD) required to observe such behavior is not known a priori even within the simple model used. We selected a PD value, with a size variation of a factor of 8, guided in part by the experiment (projection onto 2D) shown in Figure 1(B) and Figure 6(D). We also showed that the monodisperse system crystallizes, and the binary system do not show signs of saturation within the explored range of parameter space and ϕ. This suggests that a certain degree of size dispersity is necessary to obtain saturation in η.

As discussed in Appendix B, the binary system is characterized by the variables , where RB and RS represent the radii of the big and small cells, respectively, and the packing fraction ϕ. By more fully exploring the parameter space encompassing λ and ϕ than we did, it maybe possible, as the Referee suggests, that a system with two different cell sizes would yield the experimentally observed dependence of η on ϕ.

As part of an answer to the Reviewer #1 on a the same issue, we mentioned results of preliminary simulations in three dimensions with reduced levels of polydispersity, and discovered that at lower levels of polydispersity (variation in size by a factor of ≈ 2 and polydispersity value 11.50%), the relaxation time does saturate beyond a certain packing fraction (see Fig. 3). We have not established if η, the key quantity of interest, would exhibit a similar behavior in 3D.

Author response image 3.

(A) τα as a function of ϕ for 11% polydispersity with size variation by a factor of ∼ 2 in the three dimensional system. (B) Same as (A) except polydispersity value is 24% and a size variation by a factor of ∼ 8.

B/ Role of fluctuations/self-propulsion in this system, and relationship to recent findings

“A priori it is unclear why equilibrium concepts should hold in zebrafish morphogenesis, which one would expect is controlled by non-equilibrium processes such as self-propulsion, growth and cell division. ”

This is raised as a key paradox, but is not very clear to me in the context raised by the authors. In particular, they use self-propulsion as a source of activity and explain the evolution of viscosity but a facilitation process involving re-arrangements/motility. But I don’t think self-propulsion has been argued to play a role in zebrafish blastoderm - Ref 14 argues that this is effectively a zerotemperature phenomenon and that cell motility/rearrangements do not show any correlation with viscosity. So this part of the model assumption was not clear to me in relationship with the proposed experimental system. Active noise has been proposed to play key roles in other systems, including motility-driven and tension fluctuation-driven unjamming (among many others Bi et al, PRX, 2016, Mitchel et al, Nat Comm, 2020, Pinheiro et al, Nat Phys, 2022 as well as Kim & Campas, Nat Physics, 2021) - maybe this is somewhere where the author model could fit? In Kim & Campas, Nat Phys, 2021 in particular, the authors develop simulations of non-confluent tissues with noise, that seems to bear some resemblance to the model developed here, so it would be important to discuss the similarities and distinctions (usually I think polydispersity is not considered indeed). In general, the authors look here at a particle based model, but cells have adhesions with well-defined contact angles, so there is a question of the cross-over between their findings and the large body of recent literature on active foams/vertex models (which are not really discussed there).

We appreciate the lengthy comment here, and there is a lot to unpack. We also thank the referee for the references, some of which we did not know about earlier.

The primary objective of our study is to determine the simplest minimal model that would explain the experimentally observed dependence of viscosity in zebrafish blastoderm tissue as ϕ is increased beyond a certain packing fraction during morphogenesis. In Reference 14, the authors analyzed the data using the framework of rigidity percolation theory and presented evidence of a genuine equilibrium phase transition. Consequently, one would that expect zebrafish blastoderm tissue to be in equilibrium, which is surprising from many perspectives. However, since the tissue is a growing system involving numerous cell divisions and cell death, it is not immediately evident whether the assumption of equilibrium is valid. Indeed, the same problem arises when considering the glass transition where rapid cooling drives the system out of equilibrium. Nevertheless, heat capacity and η are often analyzed using the notion of equilibrium. Hence, considering this issue within the context of our research appears to be reasonable.

To the best of our knowledge, the authors in Ref. 14 did not provide an explanation for the η behavior. The focus was, which was excellent and is the basis on which we initiated this study, was on the use of rigidity percolation theory to explain the results. Indeed, they performed an experiment by mildly reducing myosin II activity, which apparently affects cell motility. The quantitative effect was not reported.

We did not impose any requirement of cell rearrangements etc in the model. There is essentially one variable, free area available, that explains the η dependence on ϕ. It is possible that one can come up with other zero temperature models that could also explain the data. To the best of our knowledge, it has not been proposed.

It would be interesting to set our model in the context of other models that the referee points out. This would be an interesting research topic to explore. The only comment we would like to make is that it is unclear how vertex model for confluent tissues could explain the viscosity data.

C/ Calculation of the effective shear viscosity

The authors calculate viscosity from a Green-Kubo relation, although it would be good to clarify at which time scale (and maybe even shear amplitude) they expect this to be valid. These kinds of model would be expected to show plastic rearrangements for large deformations for instance, could the authors simulate realistic rheological deformations (e.g. Kim & Campas, 2021 applying external shear on the simulations) to see how much this matches both their expectation and the data?

Once it is established that there is local equilibrium (as implied by the use of phase transition ideas to analyse the experimental data in Ref. 14), it is natural to use the Green-Kubo relation to calculate transport properties. Hence, for our purposes, it is valid for all time scales and amplitude. The Reviewer also wonders if the model could be used to simulate response to shear in order to probe rheological properties. There is no conceptual issue here and indeed this is an excellent suggestion that we intend to pursue in the future.

D/ Role of cell adhesion

The authors consider soft elastic disks of different sizes but unless I missed it, there is no adhesion being considered. This is expected to play a key role in jamming and multicellular mechanics, so I think the authors should either look at what this changes in their simulations, or at least discuss why they are neglecting it. One reason I’m asking is that it’s not totally clear to me that the ”free space” picture, coming from the fact that cells can interpenetrate in their model would hold in a model of deformable cells adhering to each other with constant volume (leading to more equilibration of deformations it would seem?).

The referee raises another question regarding the lack of adhesion in the simulations. As pointed out before, we were trying to create a minimal model to account for the experimental observations for η upon changing the packing fraction. Thus, we a coarse-grained model where we considered poly-disperse cells with elastic interactions which recapitulates the experimental observations. The referee is correct that adhesion plays a role in jammed systems, and examination of how it would affect is an aspect that would be interesting to consider in the future. We hasten to add that even systems without attractive adhesion-type interaction become jammed. In principle, in many-body systems, the parameter space is large and one needs to carefully determine which parameter is important for the problem at hand. Therefore, in the first pass we did not find the need to consider the role of adhesion.

Minor comments:

The writing could be condensed in some places, with some details being moved to SI (for instance, section E on ageing is very short and seem more suited for supplements, or at least not as an independent section, note that the figure numbering also jumps to Fig. 9 there, although it’s Fig. 3 just before and Fig. 9 just after - re-ordering into main and supporting figures would be clearer.

We thank the Reviewer for this recommendation. The ageing section, although is short, it does provide a line of evidence that equilibrium approaches could be valid. We have modestly expanded the section by moving Appendix D to the main text, a general suggestion made by Referee 1. We have tried to be consistent in the numbering of figures in the revision.

Reviewer #3

I am very much in favor of the manuscript in its present form - I only suggest commenting (in the manuscript) on the issue described below.

Motivated by the fact that the experimental system consists of living, motile cells the authors use an active particle model (eq. 6) with stochastic selfpropulsion as the only source for noise (zero-temperature). It would be useful to elaborate briefly how important this stochastic self-propulsion is for the emergent rheological properties of the system (as summarized above): would these properties also be present in the “passive” version of the same model at “non-vanishing” temperature, and if not, why? Or analogously in a “passive” version which is “shaken”, reminiscent of shaken granular matter? To clarify these issues would relate this study to (or discriminate it from) passive, but complex, liquids or granular matter.

We appreciate the reviewer’s positive feedback on our work. The reviewer has raised an important question concerning our model in which self-propulsion serves as the source of noise. Without self-propulsion, the system would come to a stationary state after reaching mechanical equilibrium. As mentioned in Eqn. (6) (in the main text), we can define a characteristic time . It is possible that scaling the time t by τ would not alter the results.

The second question raised by the reviewer is also important. A passive version of the model would be to consider Eq. 6 in our article, and instead of using activity use the standard stochastic force. The resulting force would be at a finite temperature,. The coefficient of noise (a diffusion term) would be related to γi through the Fluctuation dissipation theorem(FDT)). Such a system of equations cannot ne mapped to Eq. 6 in which µ and γi are independently varied. It is unlikely that such a model, incorporating a “non-vanishing” temperature, would not result in the observed dependence of η on ϕ for the following reason. The passive model represents a polydisperse system, which would form a glass with η increasing with volume fraction, following the VFT law, as has been demonstrated in the glass transition literature for harmonic glasses. The other proposal whether the shaken version version would explain the experiments is also interesting. These are worth pursuing in future studies.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Zanetti et al use biophysical and cellular assays to investigate the interaction of the birnavirus VP3 protein with the early endosome lipid PI3P. The major novel finding is that association of the VP3 protein with an anionic lipid (PI3P) appears to be important for viral replication, as evidenced through a cellular assay on FFUs.

Strengths:

Support previously published claims that VP3 associates with early endosome membrane, potentially through binding to PI3P. The finding that mutating a single residue (R200) critically affects early endosome binding and that the same mutation also inhibits viral replication suggests a very important role for this binding in the viral life cycle.

Weaknesses:

The manuscript is relatively narrowly focused: the specifics of the bi-molecular interaction between the VP3 of an unusual avian virus and a host cell lipid (PIP3). Further, the affinity of this interaction is low and its specificity relative to other PIPs is not tested, leading to questions about whether VP3-PI3P binding is relevant.

Regarding the manuscript’s focus, we challenge the notion that studying a single bi-molecular interaction makes the scope of the paper overly narrow. This interaction—between VP3 and PI3P—plays a critical role in the replication of the birnavirus, which is the central theme of our work. Moreover, identifying and understanding such distinct interactions is a fundamental aspect of molecular virology, as they shed light on the precise mechanisms that viruses exploit to hijack the host cell machinery. Consequently, far from being narrowly focused, we believe our work contributes to the broader understanding of host-pathogen interactions.

As for the low affinity of the VP3-PI3P interaction, we argue that this is not a limitation but rather a biologically relevant feature. As discussed in the manuscript, the moderate strength of this interaction is likely critical for regulating the turnover rate of VP3/endosomal PI3P complexes, which in turn could optimize viral replication efficiency. A stronger affinity might trap VP3 on the endosomal membrane, whereas weaker interactions might reduce its ability to efficiently target PI3P. Thus, the observed affinity may reflect a fine-tuned balance that supports the viral life cycle.

With regard to specificity, we emphasize that in the context of the paper, we refer to biological specificity, which is not necessarily the same as chemical specificity. The binding of PI3P to early endosomes is “biologically” preconditioned by the distribution of PI3P within the cell. PI3P is predominantly localized in endosomal membranes, which “biologically precludes” interference from other PIPs due to their distinct cellular distributions. Moreover, while early endosomes also contain other anionic lipids, our work demonstrates that among these, PI3P plays a distinctive role in VP3 binding. This highlights its functional relevance in the context of early endosome dynamics.

Reviewer #3 (Public review):

Summary:

Infectious bursal disease virus (IBDV) is a birnavirus and an important avian pathogen. Interestingly, IBDV appears to be a unique dsRNA virus that uses early endosomes for RNA replication that is more common for +ssRNA viruses such as for example SARS-CoV-2. This work builds on previous studies showing that IBDV VP3 interacts with PIP3 during virus replication. The authors provide further biophysical evidence for the interaction and map the interacting domain on VP3.

Strengths:

Detailed characterization of the interaction between VP3 and PIP3 identified R200D mutation as critical for the interaction. Cryo-EM data show that VP3 leads to membrane deformation.

We thank the reviewer for the feedback.

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zanetti et al. use biophysical and cellular assays to investigate the interaction of the birnavirus VP3 protein with the early endosome lipid PI3P. The major novel finding is that the association of the VP3 protein with an anionic lipid (PI3P) appears to be important for viral replication, as evidenced through a cellular assay on FFUs.

Strengths:

Supports previously published claims that VP3 may associate with early endosomes and bind to PI3P-containing membranes. The claim that mutating a single residue (R₂₀₀) critically affects early endosome binding and that the same mutation also inhibits viral replication suggests a very important role for this binding in the viral life cycle.

Weaknesses:

The manuscript is relatively narrowly focused: one bimolecular interaction between a host cell lipid and one protein of an unusual avian virus (VP3-PI3P). Aspects of this interaction have been described previously. Additional data would strengthen claims about the specificity and some technical issues should be addressed. Many of the core claims would benefit from additional experimental support to improve consistency.

Indeed, our group has previously described aspects of the VP3-PI3P interaction, as indicated in lines 100-105 from the manuscript. In this manuscript, however, we present biochemical and biophysical details that have not been reported before about how VP3 connects with early endosomes, showing that it interacts directly with the PI3P. Additionally, we have now identified a critical residue in VP3—the R₂₀₀—for binding to PI3P and its key role in the viral life cycle. Furthermore, the molecular dynamics simulations helped us come up with a mechanism for VP3 to connect with PI3P in early endosomes. This constitutes a big step forward in our understanding of how these "non-canonical" viruses replicate.

We have now incorporated new experimental and simulation data; and have carefully revised the manuscript in accordance with the reviewers’ recommendations. We are confident that these improvements have further strengthened the manuscript.

Reviewer #2 (Public Review):

Summary:

Birnavirus replication factories form alongside early endosomes (EEs) in the host cell cytoplasm. Previous work from the Delgui lab has shown that the VP3 protein of the birnavirus strain infectious bursal disease virus (IBDV) interacts with phosphatidylinositol-3-phosphate (PI3P) within the EE membrane (Gimenez et al., 2018, 2020). Here, Zanetti et al. extend this previous work by biochemically mapping the specific determinants within IBDV VP3 that are required for PI3P binding in vitro, and they employ in silico simulations to propose a biophysical model for VP3-PI3P interactions.

Strengths:

The manuscript is generally well-written, and much of the data is rigorous and solid. The results provide deep knowledge into how birnaviruses might nucleate factories in association with EEs. The combination of approaches (biochemical, imaging, and computational) employed to investigate VP3-PI3P interactions is deemed a strength.

Weaknesses:

(1) Concerns about the sources, sizes, and amounts of recombinant proteins used for co-flotation: Figures 1A, 1B, 1G, and 4A show the results of co-flotation experiments in which recombinant proteins (control His-FYVE v. either full length or mutant His VP3) were either found to be associated with membranes (top) or non-associated (bottom). However, in some experiments, the total amounts of protein in the top + bottom fractions do not appear to be consistent in control v. experimental conditions. For instance, the Figure 4A western blot of His-2xFYVE following co-flotation with PI3P+ membranes shows almost no detectable protein in either top or bottom fractions.

Liposome-based methods, such as the co-flotation assay, are well-established and widely regarded as the preferred approach for studying protein-phosphoinositide interactions. However, this approach is rather qualitative, as density gradient separation reveals whether the protein is located in the top fractions (bound to liposomes) or the bottom fractions (unbound). Our quantifications aim to demonstrate differences in the bound fraction between liposome populations with and without PI3P. Given the setting of the co-flotation assays, each protein-liposome system [2xFYVE-PI3P(-), 2xFYVE-PI3P(+), VP3-PI3P(-), or VP3-PI3P(+)] is assessed separately, and even if the experimental conditions are homogeneous, it is not surprising to observe differences in the protein level between different experiments. Indeed, the revised version of the manuscript includes membranes with more similar band intensities, as depicted in the new versions of Figures 1 and 4.

Reading the paper, it was difficult to understand which source of protein was used for each experiment (i.e., E. coli or baculovirus-expressed), and this information is contradicted in several places (see lines 358-359 v. 383-384). Also, both the control protein and the His-VP3-FL proteins show up as several bands in the western blots, but they don't appear to be consistent with the sizes of the proteins stated on lines 383-384. For example, line 383 states that His-VP3-FL is ~43 kDa, but the blots show triplet bands that are all below the 35 kDa marker (Figures 1B and 1G). Mass spectrometry information is shown in the supplemental data (describing the different bands for His-VP3-FL) but this is not mentioned in the actual manuscript, causing confusion. Finally, the results appear to differ throughout the paper (see Figures 1B v. 1G and 1A v. 4A).

Thank you for pointing out these potentially confusing points in the previous version of the manuscript. Indeed, we were able to produce recombinant VP3 from the two sources: Baculovirus and Escherichia coli. Initially, we opted for the baculovirus system, based on evidence from previous studies showing that it was suitable for ectopic expression of VP3. Subsequently, we successfully produced VP3 using Escherichia coli. On the other side, the fusion proteins His-2xFYVE and GST-2xFYVE were only produced in the prokaryotic system, also following previous reported evidence. We confirmed that VP3, produced in either system, exhibited similar behavior in our co-flotation and bio-layer interferometry (BLI) assays. However, the results of co-flotation and BLI assays shown in Figs. 1 and 4 were performed using the His-VP3 FL, His-VP3 FL R₂₀₀D and His-VP3 FL DCt fusion proteins produced from the corresponding baculoviruses. We have clarified this in the revised version of our manuscript. Please, see lines 430-432.

Additionally, we have made clear that the His-VP3 FL protein purification yielded four distinct bands, and we confirmed their VP3 identity through mass spectrometry in the revised version of the manuscript. Please, see lines 123-124.

Finally, we replaced membranes for Figs. 4A and 1G (left panel) with those with more similar band intensities. Please, see the new version of Figures 1 and 4.

(2) Possible "other" effects of the R₂₀₀D mutation on the VP3 protein. The authors performed mutagenesis to identify which residues within patch 2 on VP3 are important for association with PI3P. They found that a VP3 mutant with an engineered R₂₀₀D change (i) did not associate with PI3P membranes in co-floatation assays, and (ii) did not co-localize with EE markers in transfected cells. Moreover, this mutation resulted in the loss of IBDV viability in reverse genetics studies. The authors interpret these results to indicate that this residue is important for "mediating VP3-PI3P interaction" (line 211) and that this interaction is essential for viral replication. However, it seems possible that this mutation abrogated other aspects of VP3 function (e.g., dimerization or other protein/RNA interactions) aside from or in addition to PI3P binding. Such possibilities are not mentioned by the authors.

The arginine amino acid at position 200 of VP3 is not located in any of the protein regions associated with its other known functions: VP3 has a dimerization domain located in the second helical domain, where different amino acids across the three helices form a total of 81 interprotomeric close contacts; however, R₂₀₀ is not involved in these contacts (Structure. 2008 Jan;16(1):29-37, doi:10.1016/j.str.2007.10.023); VP3 has an oligomerization domain mapped within the 42 C-terminal residues of the polypeptide, i.e., the segment of the protein composed by the residues at positions 216-257 (J Virol. 2003 Jun;77(11):6438–6449, doi: 10.1128/jvi.77.11.6438-6449.2003); VP3’s ability to bind RNA is facilitated by a region of positively-charged amino acids, identified as P1, which includes K₉₉, R₁₀₂, K₁₀₅, and K₁₀₆ (PLoS One. 2012;7(9):e45957, doi: 10.1371/journal.pone.0045957). Furthermore, our findings indicate that the R₂₀₀D mutant retains a folding pattern similar to the wild-type protein, as shown in Figure 4B. All these lead us to conclude that the loss of replication capacity of R₂₀₀D viruses results from impaired, or even loss of, VP3-PI3P interaction.

We agree with the reviewer that this is an important point and have accordingly addressed it in the Discussion section of the revised manuscript. Please, see lines 333-346.

(3) Interpretations from computational simulations. The authors performed computational simulations on the VP3 structure to infer how the protein might interact with membranes. Such computational approaches are powerful hypothesis-generating tools. However, additional biochemical evidence beyond what is presented would be required to support the authors' claims that they "unveiled a two-stage modular mechanism" for VP3-PI3P interactions (see lines 55-59). Moreover, given the biochemical data presented for R₂₀₀D VP3, it was surprising that the authors did not perform computational simulations on this mutant. The inclusion of such an experiment would help tie together the in vitro and in silico data and strengthen the manuscript.

We acknowledge that the wording used in the previous version of the manuscript may have overstated the "unveiling" of the two-stage binding mechanism of VP3. Our intention was to propose a potential mechanism, that is consistent both with the biophysical experiments and the molecular simulations. In the revised version of the manuscript, we have tempered these claims and framed them more appropriately.

Regarding the simulations for the R₂₀₀D VP3 mutant, these simulations were indeed performed and included in the original manuscript as part of Figure S14 in the Supplementary Information. However, we realize that this was not sufficiently emphasized in the main text, an oversight on our part. We have now revised the manuscript to highlight these results more clearly.

Additionally, to further strengthen the connection between experimental and simulation trends, we have now included a new figure in the Supplementary Information (Figure S15). This figure depicts the binding energy of VP3 ΔNt and two of its mutants, VP3 ΔNt R₂₀₀D and VP3 ΔNt P2 Mut, as a function of salt concentration. The results show that as the number of positively charged residues in VP3 is systematically reduced, the binding of the protein to the membrane becomes weaker. The effect is more pronounced at lower salt concentrations, which highlights the weight of electrostatic forces on the adsorption of VP3 on negatively charged membranes. Please, see Supplementary Information (Figure S15).

Reviewer #3 (Public Review):

Summary:

Infectious bursal disease virus (IBDV) is a birnavirus and an important avian pathogen. Interestingly, IBDV appears to be a unique dsRNA virus that uses early endosomes for RNA replication that is more common for +ssRNA viruses such as for example SARS-CoV-2.

This work builds on previous studies showing that IBDV VP3 interacts with PIP3 during virus replication. The authors provide further biophysical evidence for the interaction and map the interacting domain on VP3.

Strengths:

Detailed characterization of the interaction between VP3 and PIP3 identified R₂₀₀D mutation as critical for the interaction. Cryo-EM data show that VP3 leads to membrane deformation.

Weaknesses:

The work does not directly show that the identified R₂₀₀ residues are directly involved in VP3-early endosome recruitment during infection. The majority of work is done with transfected VP3 protein (or in vitro) and not in virus-infected cells. Additional controls such as the use of PIP3 antagonizing drugs in infected cells together with a colocalization study of VP3 with early endosomes would strengthen the study.

In addition, it would be advisable to include a control for cryo-EM using liposomes that do not contain PIP3 but are incubated with HIS-VP3-FL. This would allow ruling out any unspecific binding that might not be detected on WB.

The authors also do not propose how their findings could be translated into drug development that could be applied to protect poultry during an outbreak. The title of the manuscript is broad and would improve with rewording so that it captures what the authors achieved.

In previous works from our group, we demonstrated the crucial role of the VP3 P2 region in targeting the early endosomal membranes and for viral replication, including the use of PI3K inhibitors to deplete PI3P, showing that both the control RFP-2xFYVE and VP3 lost their ability to associate with the early endosomal membranes and reduces the production of an infective viral progeny (J Virol. 2018 May 14;92(11):e01964-17, doi: 10.1128/jvi.01964-17; J Virol. 2021 Feb 24;95(6):e02313-20, doi: 10.1128/jvi.02313-20). In the present work, to further characterize the role of R₂₀₀ in binding to early endosomes and for viral replication, we show that: i) the transfected VP3 R₂₀₀D protein loses the ability to bind to early endosomes in immunofluorescence assays (Figure 2E and Figure 3); ii) the recombinant His-VP3 FL R₂₀₀D protein loses the ability to bind to liposomes PI3P(+) in co-flotation assays (Figure 4A); and, iii) the mutant virus R₂₀₀D loses replication capacity (Figure 4C).

Regarding the cryo-electron microscopy observation, we verified that there is no binding of gold particles to liposomes PI3P(-) when they are incubated solely with the gold-particle reagent, or when they are pre-incubated with the gold-particle reagent with either His-2xFYVE or His-VP3 FL. We have incorporated a new panel in Figure 1C showing a representative image of these results. Please, see lines 143-144 in the revised version of our manuscript and our revised version of Figure 1C.

We have replaced the title of the manuscript by a more specific one. Thus, our current is " On the Role of VP3-PI3P Interaction in Birnavirus Endosomal Membrane Targeting".

Regarding the question of how our findings could be translated into drug development, indeed, VP3-PI3P binding constitutes a good potential target for drugs that counteract infectious bursal disease. However, we did not mention this idea in the manuscript, first because it is somewhat speculative and second because infected farms do not implement any specific treatment. The control is based on vaccination.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Critical issues to address:

(1) The citations in the important paragraph on lines 101-5 are not identifiable. These references are described as showing that VP3 is associated with EEs via P2 and PI3P, which is basically what this paper also shows. The significant advance here is unclear.

We apologize for this mistake. These citations are identifiable in the revised version of the manuscript (lines 100-105). As mentioned before, in this manuscript we present biochemical and biophysical details that have not been reported before about how VP3 connects with early endosomes, showing that it interacts directly with the PI3P. Additionally, we have now identified a critical residue in VP3 P2—the R₂₀₀—for binding to PI3P and its key role in the viral life cycle. Furthermore, the molecular dynamics simulations helped us come up with a mechanism for VP3 to connect with PI3P in early endosomes. This constitutes a big step forward in our understanding of how these "non-canonical" viruses replicate.

(2) Even if all the claims were to be clearly supported through major revamping, authors should make the significance of knowing that this protein binds to early endosomes through PI3P more clear?

Thank you for the recommendation, which aligns with a similar suggestion from Reviewer #2. In response, we have revised the significance paragraph to emphasize the mechanistic aspects of our findings. Please refer to lines 62–67 in the revised manuscript.

(3) Flotation assay shows binding, but this is not quantitative. An estimate of a Kd would be useful. BLI experiments suggest that half of the binding disappears at 0.5 mM, implying a very low binding affinity.

We agree with the reviewer that our biophysical and molecular simulation results suggest a specific but weak interaction of VP3 with PI3P bearing membranes. Indeed, our previous version of the manuscript already contained a paragraph in this regard. Please, see lines 323-332 in the revised version of the manuscript.

From a biological point of view, a low binding affinity of VP3 for the endosomes may constitute an advantage for the virus, in the sense that its traffic through the endosomes may be short lived during its infectious cycle. Indeed, VP3 has been demonstrated to be a "multifunctional" protein involved in several processes of the viral cycle (detailed in lines 84-90), and in our laboratory we have shown that the Golgi complex and the endoplasmic reticulum are organelles where further viral maturation occurs. Taking all of this into account, a high binding affinity of VP3 for endosomes could result in the protein becoming trapped on the endosomal membrane, potentially hindering the progression of the viral infection within the host cell.

(4) There are some major internal inconsistencies in the data: Figure 1B quantifies VP3-FL T/B ratio ~4 (which appears inconsistent with the image shown, as the T lanes are much lighter than the B) whereas apparently the same experiment in Figure 1G shows it to be ~0.6. With the error bars shown, these results would appear dramatically different from each other, despite supposedly measuring the same thing. The same issue with the FYVE domain between Figures 1A and 4A.

We appreciate the reviewer’s comment, as it made us aware of an error in Figure 1B. There, the mean value for the VP3-FL Ts/B ratio is 3.0786 for liposomes PI3P(+) and 0.4553 for liposomes PI3P(-) (Please, see the new bar graph on Figure 1B). This may have occurred because, due to the significance of these experiments, we performed multiple rounds of quantification in search of the most suitable procedure for our observations, leading to a mix-up of data sets. Anyway, it’s possible that these corrected values still seem inconsistent given that T lanes are much lighter than the B for VP3-FL in the image shown. Flotation assays are quite labor-intensive and, at least in our experience, yield fairly variable results in terms of quantification. To illustrate this point, the following image shows the three experiments conducted for Figure 1B, where it is clear that, despite producing visually distinct images, all three yielded the same qualitative observation. For Figure 1B, we chose to present the results from experiment #2. However, all three experiments contributed to a Ts/B ratio of 3.0786 for His-VP3 FL, which may account for the apparent inconsistency when focusing solely on the image in Figure 1B.

Author response image 1.

We acknowledge that, at first glance, some inconsistencies may appear in the results, and we have thoroughly discussed the best approach for quantification. However, we believe the observations are robust in terms of reproducibility and reliable, as the VP3-PI3P interaction was consistently validated by comparison with liposomes lacking PI3P, where no binding was observed.

(5) Comparison of PA (or PI) to PI3P at the same molar concentration is inappropriate because PI3P has at least double charge. The more interesting question about specificity would be whether PI45P2 (or even better PI35P2) binds or not. Without this comparison, no claim to specificity can be made.

For us, "specificity" refers to the requirement of a phosphoinositide in the endosomal membrane for VP3 binding. Phosphoinositides have a conspicuous distribution among cellular compartments, and knowing that VP3 associates with early endosomes, our specificity assays aimed to demonstrate that PI3P is strictly required for the binding of VP3. To validate this, we used PI (lacking the phosphate group) and PA (lacking the inositol group) despite their similar charges. In spite of the potential chemical interactions between VP3 and various phosphoinositides, our experimental results suggest that the virus specifically targets endosomal membranes by binding to PI3P, a phosphoinositide present only in early endosomes.

That said, we agree with the reviewer’s point and consider adequate to smooth our specificity claim in the manuscript as follows: “We observed that His-VP3 FL bound to liposomes PI3P(+), but not to liposomes PA or PI, reinforcing the notion that a phosphoinositide is required since neither a single negative charge nor an inositol ring are sufficient to promote VP3 binding to liposomes (SI Appendix, Fig. S2)” (Lines 136-139).

(6) In the EM images, many of the gold beads are inside the vesicles. How do they cross the membranes?

They do not cross the membrane. Our EM images are two-dimensional projections, meaning that the gold particles located on top or beneath the plane appear to be inside the liposome.

(7) Images in Figure 2D are very low quality and do not show the claimed difference between any of the mutants. All red signal looks basically cytosolic in all images. It is not clear what criteria were used for the quantification in Figure 2E. The same issue is in Figure 2E, where no red WT puncta are observable at all. Consistently, there is minimal colocalization in the quantification in Figure S3, which appears to show no significant differences between any of the mutants, in direct contradiction to the claim in the manuscript.

We apologize for the poor quality of panels in Figures 2D and 2E. Unfortunately, this was due to the PDF conversion of the original files. Please, check the high-quality version of Figure 2. As suggested by reviewers #2 and #3, we have incorporated zoomed panels, which help the reader to better see the differences in distribution.

As mentioned in the legend to Figure 2, the quantification in Figure 2D was performed by calculating the percentage of cells with punctuated fluorescent red signal (showing VP3 distribution) for each protein. The data were then normalized to the P2 WT protein, which is the VP3 wild type.

Figure S3 certainly shows a tendency which positively correlates with the results shown in Figure 3, where we used FYVE to detect PI3P on endosomes and observed significantly less co-localization when VP3 bears its P2 region all reversed or lacks the R₂₀₀

(8) The only significant differences in colocalization are in Figure 3B, whose images look rather dramatically different from the rest of the manuscript, leading to some concern about repeatability. Also, it is unclear how colocalization is quantified, but this number typically cannot be above 1. Finally, it is unclear what is being colocalized here: with three fluorescent components, there are 3 possible binary colocalizations and an additional ternary colocalization.

We thank the reviewer for pointing out those aspects related to Figure 3. The experiments performed for Figure 3B were conducted by a collaborator abroad handling the purified GST-2xFYVE, which recognizes endogenous PI3P, while the rest of the cell biology experiments were conducted in our laboratory in Argentina. This is why they are aesthetically different. We have made an effort in homogenizing the way they look for the revised version of the manuscript. Please, see the new version of Figure 3.

For quantification of the co-localization of VP3 and EGFP-2xFYVE (Figure 3A), the Manders M2 coefficient was calculated out of approximately 30 cells per construct and experiment. The M2 coefficient, which reflects co-localization of signals, is defined as the ratio of the total intensities of magenta image pixels for which the intensity in the blue channel is above zero to the total intensity in the magenta channel. JACoP plugin was utilized to determine M2. For VP3 puncta co-distributing with EEA1 and GST-FYVE (Figure 3B), the number of puncta co-distributing for the three signals was manually determined out of approximately 40 cells per construct and experiment per 200 µm². We understand that Manders or Pearson coefficients, typically ranging between 0 and 1, is the most commonly used method to quantify co-localizing immunofluorescent signals; however, this “manual” method has been used and validated in previous published manuscripts [Figures 3 and 7 from (Morel et al., 2013); Figure 7 in (Khaldoun et al., 2014); and Figure 4 in (Boukhalfa et al., 2021)].

(9) SegA/B plasmids are not introduced, and it is not clear what these are or how this assay is meant to work. Where are the foci forming units in the images of Figure 4C? How does this inform on replication? Again, this assay is not quantitative, which is essential here: does the R₂₀₀ mutant completely kill activity (whatever that is here)? Or reduce it somewhat?

We apologize for the missing information. Segments A and B are basically the components of the IBDV reverse genetics system. For their construction, we used a modification of the system described by Qi and coworkers (Qi et al., 2007), in which the full length sequences of the IBDV RNA segments A and B, flanked by a hammerhead ribozyme at the 5’-end and the hepatitis delta ribozyme at the 3’-end, were expressed under the control of an RNA polymerase II promoter within the plasmids pCAGEN.Hmz.SegA.Hdz (SegA) and pCAGEN.Hmz.SegB.Hdz (SegB). For this specific experiment we generated a third plasmid, pCAGEN.Hmz.SegA.R₂₀₀D.Hdz (SegA.R₂₀₀D), harboring a mutant version of segment A cDNA containing the R₂₀₀D substitution. Then, QM7 cells were transfected with the plasmids SegA, SegB or Seg.R₂₀₀D alone (as controls) or with a mixture of plasmids SegA+SegB (wild type situation) or SegA.R₂₀₀D+SegB (mutant situation). At 8 h post transfection (p.t.), when the new viruses have been able to assemble starting from the two segments of RNA, the cells were recovered and re-plated onto fresh non-transfected cells for revealing the presence (or not) of infective viruses. At 72 h post-plating, the generation of foci forming units (FFUs) was revealed by Coomassie staining. As expected, single-transfections of SegA, SegB or Seg.R₂₀₀D did not produce FFUs and, as shown in Figure 4C, the transfection of SegA+SegB produced detectable FFUs (the three circles in the upper panel) while no FFUs (the three circles in the lower panel) were detected after the transfection of SegA.R₂₀₀D+SegB (Figure 4C). This system is quantitative, since the FFUs detected 72 h post-plating are quantifiable by simply counting the FFUs. However, since no FFUs were detected after the transfection of SegA.R₂₀₀D+SegB, evidenced by a complete monolayer of cells stained blue, we did not find any sense in quantifying. In turn, this drastic observation indicates that viruses bearing the VP3 R₂₀₀D mutation lose their replication ability (is “dead”), demonstrating its crucial role in the infectious cycle.

We agree with the reviewer that a better explanation was needed in the manuscript, so we have incorporated a paragraph in the results section of our revised version of the manuscript (lines 209-219).

(10) Why pH 8 for simulation?

The Molecular Theory calculations were performed at pH 8 for consistency with the experimental conditions used in our biophysical assays. These biophysical experiments were also performed at pH 8, following the conditions established in the original study where VP3 was first purified for crystallization (DOI: 10.1016/j.str.2007.10.023).

(11) There is minimal evidence for the sequential binding model described in the abstract. The simulations do not resolve this model, nor is truly specific PI3P binding shown.

In response to your concerns, we would like to emphasize that our simulations provide robust evidence supporting the two more important aspects of the sequential binding model: 1) Membrane Approach: In all simulations, VP3 consistently approaches the membrane via its positively charged C-terminal (Ct) region. 2) PI3P Recruitment: Once the protein is positioned flat on the membrane surface, PI3P is unequivocally recruited to the positively charged P2 region. The enrichment of PI3P in the proximity to the protein is clearly observed and has been quantified via radial distribution functions, as detailed in the manuscript and supplementary material.

While we understand that opinions may vary on the sufficiency of the data to fully validate the model, we believe the results offer meaningful insights into the proposed binding mechanism. That said, we acknowledge that the specificity of VP3 binding may not be restricted solely to PI3P but could extend to phosphoinositides in general. To address this, we performed the new set of co-flotation experiments which are discussed in detail in our response to point 5.

Reviewer #2 (Recommendations For The Authors):

(1) Line 1: Consider changing the title to better reflect the mostly biochemical and computational data presented in the paper: "Mechanism of Birnavirus VP3 Interactions with PI3P-Containing Membranes". There are no data to show hijacking by a virus presented.

We appreciate this recommendation, which was also expressed by reviewer #3. Additionally, we thank for the suggested title. We have replaced the title of the manuscript by a more specific one. Thus, our current is

"On the Role of VP3-PI3P Interaction in Birnavirus Endosomal Membrane Targeting".

(2) Lines 53-54 and throughout: Consider rephrasing "demonstrate" to "validate" to give credit to Gimenez et al., 2018, 2022 for discovery.

Thanks for the suggestion. We have followed it accordingly. Please see line 52 from our revised version of the manuscript.

(3) Line 56-59 and throughout: Consider tempering and rephrasing these conclusions that are based mostly on computational data. For example, change "unveil" to "suggest" or another term.

We have now modified the wording throughout the manuscript.

(4) The abstract could also emphasize that this study sought to map the resides within VP3 that are important for P13P interaction.

Thanks for the suggestion. We have followed it accordingly. Please, see lines 53-55 from our revised version of the manuscript.

(5) Lines 63-69: This Significance paragraph seems tangential. The findings in this paper aren't at all related to the evolutionary link between birnaviruses and positive-strand RNA viruses. The significance of the work for me lies in the deep biochemical/biophysical insights into how a viral protein interacts with membranes to nucleate its replication factory.

We have re-written the significance paragraph highlighting the mechanistic aspect of our findings. Please, see lines 62-67 in our revised version of the manuscript.

(6) Line 74: Please define "IDBV" abbreviation.

We apologize for the missing information. We have defined the IBDV abbreviation in our revised version of the manuscript (please, see line 73).

(7) Line 88: Please define "pVP2" abbreviation.

We apologize for the missing information. We have defined the pVP2 abbreviation in our revised version of the manuscript (please, see line 87).

(8) Lines 101-105: Please change references (8, 9, 10) to be consistent with the rest of the manuscript (names, year).

We apologize for this mistake. These citations are identifiable and consistent in the revised version of the manuscript (lines 100-105).

(9) Line 125: For a broad audience, consider explaining that recombinant His-2xFYVE domain is known to exhibit PI3P-binding specificity and was used as a positive control.

Thanks for the recommendation. We have incorporated a brief explanation supporting the use of His-2xFYVE as a positive control in our revised version of the manuscript. Please, see lines 127-129.

(10) Lines 167-171: The quantitative data in Figure S3 shows that there was a non-significant co-localization coefficient of the R₂₀₀D mutant. For transparency, this should be stated in the Results section when referenced.

We agree with this recommendation. We have clearly mentioned it in the revised version of the manuscript. Please, see lines 177-179. Also, we have referred this fact when introducing the assays performed using the purified GST-2xFYVE, shown in Figure 3. Please, see lines 182-184.

(11) Lines 156 and 173: These Results section titles have nearly identical wording. Consider rephrasing to make it distinct.

We agree with the reviewer’s observation. In fact, we sought to do it on purpose as for them to be a “wordplay”, but we understand that could result in a awkwarded redundancy. So, in the revised version of the manuscript, both titles are:

Role of VP3 P2 in the association of VP3 with the EE membrane (line 163).

VP3 P2 mediates VP3-PI3P association to EE membranes (line 182).

(12) Line 194: Is it alternatively possible that the R₂₀₀D mutant lost its capacity to dimerize, and that in turn impacted PI3P interaction?

Thanks for the relevant question. VP3 was crystallized and its structure reported in (Casañas et al., 2008) (DOI: 10.1016/j.str.2007.10.023). In that report, the authors showed that the two VP3 subunits associate in a symmetrical manner by using the crystallographic two-fold axes. Each subunit contributes with its 30% of the total surface to form the dimer, with 81 interprotomeric close contacts, including polar bonds and van der Waals contacts. The authors identified the group of residues involved in these interactions, among which the R₂₀₀ is not included. Addittionally, the authors determined that the interface of the VP3 dimer in crystals is biologically meaningful (not due to the crystal packing).

To confirm that the lack of binding was not due to misfolding of the mutant, we compared the circular dichroism spectra of mutant and wild type proteins, without detecting significant differences (shown in Figure 4B). These observations do not exclude the possibility mentioned by the reviewer, but constitute solid evidences, we believe, to validate our observations.

(13) Lines 231-243: Consider changing verbs to past tense (i.e., change "is" to "was") for the purposes of consistency and tempering.

Thanks for the recommendation, we have proceeded as suggested. Please, see lines 249-262 in our revised version of the manuscript.

(14) Lines 306-308: Is there any information about whether it is free VP3 (v. VP3 complexed in RNP) that binds to membrane? I am just trying to wrap my head around how these factories form during infection.

Thanks for pointing this out. We first observed that in infected cell, all the components of the RNPs [VP3, VP1 (the viral polymerase) and the dsRNA] were associated to the endosomes. Since by this moment it had been already elucidated that VP3 "wrapped" de dsRNA within the RNPs (Luque et al., 2009) (DOI: 10.1016/j.jmb.2008.11.029), we sought that VP3 was most probably leading this association. We answered yes after studying its distribution, also endosome-associated, when ectopically expressed. These results were published in (Delgui et al., 2013) (DOI: 10.1128/jvi.03152-12).

Thus, in our subsequent studies, we have worked with both, the infection-derived or the ectopically expressed VP3, to advance in elucidating the mechanism by which VP3 hijacks the endosomal membranes and its relevancy for viral replication, reported in this current manuscript.

(15) Lines 320-334: This last paragraph discussing evolutionary links between birnaviruses and positive-strand RNA viruses seems tangential and distracting. Consider reducing or removing.

Thanks for highlighting this aspect of our work. Maybe difficult to follow, but in the context of other evidences reported for the Birnaviridae family of viruses, we strongly believe that there is an evolutionary aspect in having observed that these dsRNA viruses replicate associated to membranous organelles, a hallmark of +RNA viruses. However, we agree with the reviewer that this might not be the main point of our manuscript, so we reduced this paragraph accordingly. Please, see lines 358-367 in our revised version of the manuscript.

(16) Lines 322-324: Change "RdRd" to "RdRp" if keeping paragraph.

Thanks. We have corrected this mistake in lines 360 and 361.

(17) Figures 1A, 1B, and throughout: Again, please check and explain protein sizes and amounts. This would improve the clarity of the manuscript.

All our flotation assays were performed using 1 mM concentration of purified protein in a final volume of 100 mL (mentioned in M&M section). The complete fusion protein His-2xFYVE (shown in Figs. 1A and 4A left panel) is 954 base pairs-long and contains 317 residues (~35 kDa). The complete fusion protein His-VP3 FL (shown in Figs. 1B and 1G left panel) is 861 base pairs-long and contains 286 residues (~32 kDa). The complete fusion protein His-VP3 DCt (shown in Fig. 1G, right panel) is 753 bp-long and contains 250 residues (~28 kDa). The complete fusion protein His-VP3 FL R₂₀₀D (shown in Fig. 4A right panel) is 861 bp-long and contains 286 residues (~32 kDa). This latter information was incorporated in our revised version of the manuscript. Please, see lines 381-382, 396-397 and 399-400 from the M&M section, and lines in the corresponding figure legends.

(18) Figures 1B and 1G show different results for PI3P(+) membranes. I see protein associated with the top fraction in 1B, but I don't see any such result in 1G.

As already mentioned, liposome-based methods, such as the co-flotation assay, are well-established and widely regarded as the preferred approach for studying protein-phosphoinositide interactions. However, this approach is rather qualitative, as density gradient separation reveals whether the protein is located in the top fractions (bound to liposomes) or the bottom fractions (unbound). Our quantifications aim to demonstrate differences in the bound fraction between liposome populations with and without PI3P. Given the setting of the co-flotation assays, each protein-liposome system [2xFYVE-PI3P(-), 2xFYVE-PI3P(+), VP3-PI3P(-), or VP3-PI3P(+)] is assessed separately, and even if the conditions are homogeneous, it’s not surprising to observe differences in the protein level between each one. Indeed, the revised version of the manuscript include a membrane for Figure 1G, were His-VP3 FL associated with the top fraction is more clear. Please, see the new version of Figure 1G.

(19) Figure 1C: Please include cryo-EM images of the liposome PI3P(-) variables to assess the visual differences of the liposomal membranes under these conditions.

Thanks for the recommendation. it has been verified that there is no binding of gold particles to liposomes PI3P(-) when they are incubated solely with the gold-particle reagent, or when they are pre-incubated with the gold-particle reagent with either His-2xFYVE or His-VP3 FL. We have incorporated a new panel in Figure 1C showing a representative image of these results. Please, see lines 143-144 in the revised version of our manuscript and our revised version of Figure 1C.

(20) Figures 2D, 2E, and 3A: The puncta are not obvious in these images. Consider adding Zoomed panels.

We apologize for this aspect of Figures 2 and 3, also highlighted by reviewer #1. We believe that this was due to the low quality resulting from the PDF conversion of the original files. For Figure 3A, we have homogenized its aspect with those from 3B. Regarding Figure 2, we have incorporated zoomed panels, as suggested. Please, see the revised versions of both Figures.

(21) Figure 4A: There is almost no protein in the control PI3P(+) blot. Why? Also, the quantification shows no significant membrane association for this control. This result is different from Figure 1A and very confusing (and concerning).

We apologize for the confusion. We replaced membranes for Figure 4A (left panel) with more similar band intensities to that shown in Figure 1A. Please, visit our new version of Figure 4. The quantification shows no significant difference in the association to liposomes PI3P(+) compared to liposomes PI3P(+); it’s true and this is due to, once more, the intrinsically lack of homogeneity of co-flotation assays. However, this one shown in Figure 4A is a redundant control (has been shown in Figure 1A) and we believe that the new membrane is qualitative eloquent.

Reviewer #3 (Recommendations For The Authors):

(1) Overall, the title is general and does not summarize the study. I recommend making the title more specific. The current title is better suited for a review as opposed to a research article. This study provides further biophysical details on the interaction. This should be reflected in the title.

We appreciate this recommendation, which was also expressed by reviewer #2. We have chosen a new title for the manuscript: “On the Role of VP3-PI3P Interaction in Birnavirus Endosomal Membrane Targeting”.

(2) References 8,9,10 are important but they were not correctly cited in the work, this should be corrected.

We apologize for this mistake. These citations are identifiable in our revised version of the manuscript. See lines 100-105.

(3) Flotation experiments and cryo-EM convincingly show that VP3 binds to membranes in a PIP3-dependent manner. However, it would be advisable to include a control for cryo-EM using liposomes that do not contain PIP3 but are incubated with HIS-VP3-FL. This would allow us to rule out any unspecific binding that might not be detected on WB.

Thanks for the advice, also given by reviewer #2. We confirmed that no gold particles were bound on liposomes PI3P(-) even when incubated with the Ni-NTA reagent alone or pre-incubated with His-2xFYVE of His-VP3 FL. We have incorporated a new panel to Figure 1C showing a representative image of these results. Please, see lines 143-144 in the revised version of the manuscript and see the revised version of Figure 1C.

(4) It is not clear what is the difference between WB in B and WB in G. Figure 1G seems to show the same experiment as shown in B, is this a repetition? In both cases, plots next to WBs show quantification with bars, do they represent STD or SEM? Legend A mentions significance p>0.01 (**) but the plot shows ***. This should be corrected.

The Western blot membrane in Figure 1B shows the result of co-flotation assay using His-VP3 FL protein, while the Western blot membrane in Figure 1G (left panel) shows a co-flotation assay using His-VP3 FL protein as a positive control. In another words, in 1B the His-VP3 FL protein is the question while in 1G (left panel) it’s the co-flotation positive control for His-VP3 DCt. The bar plots next to Western blots show quantification, the mean and the STD. Thanks for highlighting this inconsistency. We have now corrected it on the revised version of the manuscript.

(5) It would be useful to indicate positively charged residues and P2 on the AF2 predicted structure in Fig 1.

These are indicated in panels A and B of Figure 2.

(6) Figure 1 legend: Change cryo-fixated liposomes to cryo-fixation or better to "liposomes were vitrified". There is a missing "o" in the cry-fixation in the methods section.

Thanks for the recommendation. We have modified Figure 1. legend to "liposomes were vitrified" (line 758), and fixed the word cryo-fixation in the methods section (line 512).

(7) Figure 2B. It is not clear how the punctated phenotype was unbiasedly characterized (Figure 2D). I see no difference in the representative images. Magnified images should be shown. This should be measured as colocalization (Pearson's and Mander's coefficient) with an early endosomal marker Rab5. Perhaps this figure could be consolidated with Figure 3.

Unfortunately, the lack of clarity in Figure 2D was due to the PDF conversion of the original files. Please, observe the high-quality original image above in response to reviewer #1, where we have additionally included zoomed panels, as also suggested by the other reviewers. For quantification of the co-localization of VP3 and either EGFP-Rab5 orEGFP-2xFYVE, the Manders M2 coefficient was calculated out of approximately 30 cells per construct and experiment and were shown in Figure S3 and Figure 3A, respectively, in our previous version of the manuscript.

(8) PIP3 antagonist drugs should be used to further substantiate the results. If PIP3 specifically recruits VP3, this interaction should be abolished in the presence of PIP3 drug and VP3 should show a diffused signal.

We certainly agree with this point. These experiments were performed and the results were reported in (Gimenez et al., 2020). Briefly, in that work, we blocked the synthesis of PI3P in QM7 cells in a stable cell line overexpressing VP3, QM7-VP3, with either the pan-PI3Kinase (PI3K) inhibitor LY294002, or the specific class III PI3K Vps34 inhibitor Vps34-IN1. In Figure 4, we showed that 98% of the cells treated with these inhibitors had the biosensor GFP-2FYVE dissociated from EEs, evidencing the depletion of PI3P in EEs (Figure 4A). In QM7-VP3 cells, we showed that the depletion of PI3P by either inhibitor caused the dissociation of VP3 from EEs and the disaggregation of VP3 puncta toward a cytosolic distribution (Figure 4B). Moreover, since this observation was crucial for our hipothesis, these results were further confirmed with an alternative strategy to deplete PI3P in EEs. We employed a system to inducibly hydrolyze endosomal PI3P through rapamycin-induced recruitment of the PI3P-myotubularin 1 (MTM1) to endosomes in cells expressing MTM1 fused to the FK506 binding protein (FKBP) and the rapamycin-binding domain fused to Rab5, using the fluorescent proteins mCherry-FKBP-MTM1 and iRFP-FRB-Rab5, as described in (Hammond et al., 2014). These results, shown in Figures 5, 6 and 7 in the same manuscript, further reinforced the notion that PI3P mediates and is necessary for the association of VP3 protein with EEs.

(9) The authors should show the localization of VP3 in IBDV-infected cells and treat cells with PI3P antagonists. The fact that R₂₀₀ is not rescued does not necessarily mean that this is because of the failed interaction with PI3P. As the authors wrote in the discussion: VP3 bears multiple essential roles during the viral life cycle (line 305).

Indeed, after having confirmed that the VP3 lost its localization associated to the endosomes after the treatment of the cells with PI3P antagonists, we demonstrated that depletion of PI3P significantly reduced the production of IBDV progeny. For this aim, we used two approaches, the inhibitor Vps34-IN1 and an siRNA against VPs34. In both cases, we observed a significantly reduced production of IBDV progeny (Figures 9 and 10). Specifically related to the reviewer’s question, the localization of VP3 in IBDV-infected cells and treated with PI3P antagonists was shown and quantified in Figure 9a.

(10) Could you provide adsorption-free energy profiles and MD simulations also for the R₂₀₀ mutant?

Following the reviewer’s suggestion, we have added a new figure to the supplementary information (Figure S15). Instead of presenting a full free-energy profile for each protein, we focused on the adsorption free energy (i.e., the minimum of the adsorption free-energy profile) for VP3 ΔNt and its mutants, VP3 ΔNt R₂₀₀D and VP3 ΔNt P2 Mut, as a function of salt concentration. The aim was to compare the adsorption free energy of the three proteins and evaluate the effect of electrostatic forces on it, which become increasingly screened at higher salt concentrations. As shown in the referenced figure, reducing the number of positively charged residues from VP3 ΔNt to VP3 ΔNt P2 Mut systematically weakens the protein’s binding to the membrane. This effect is particularly pronounced at lower salt concentrations, underscoring the importance of electrostatic interactions in the adsorption of the negatively charged VP3 onto the anionic membrane.

(11) Liposome deformations in the presence of VP3 are interesting (Figure 6G), were these also observed in Figure 1C?

Good question. The liposome deformations in the presence of VP3 shown in Figure 6G were a robust observation since, as mentioned, it was detectable in 36% of the liposomes PI3P(+), while they were completely absent in PI3P(-) liposomes. However, and unfortunately, the same deformations were not detectable in experiments performed using gold particles shown in Figure 1C. In this regard, we think that it might be possible that the procedure of gold particles incubation itself, or even the presence of the gold particles in the images, would somehow “mask” the deformations effect.

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Author response:

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

We thank the reviewers for their time and the thoughtful reviews on our manuscript. The reviewers brought good points regarding the sample size, and the low exposure in the South Asian cohort owing to their unique cultural and social practices. We recognize these as limitations of the paper and discussed these in the revised version. In the revised manuscript, we have taken the key suggestions by reviewers to 1) better illustrate the analytical flow and statistical methods, in particular, to show which datasets had been used in discovery, validation, and testing of the score – as a main figure in the manuscript and in the graphical abstract; 2) demonstrate there is no possibility of overfitting in our approach using statistical metrics of performance; 3) emphasize the goal was not for discovery (e.g. our own EWAS was not used for deriving the score), but to compare with existing EWASs and contrast the results from the white European and SA populations; 4) and supplement the analysis with previously derived maternal smoking, smoking and air pollution methylation score and to explore additional health outcomes in relation to lung health in newborns. Finally, we would also like to take this opportunity to re-iterate that it was not our objective to derive the most powerful methylation score of smoking nor to demonstrate the causal role of maternal smoking on birth weight via DNAm. We have restructure the manuscript as well as the discussion to clarify this. Please find below a point-by-point response to the comments below.

Reviewer #1:

The manuscript could benefit from a more detailed description of methods, especially those used to derive MRS for maternal smoking, which appears to involve overfitting. In particular, the addition of a flow chart would be very helpful to guide the reader through the data and analyses. The FDR correction in the EWAS corresponds to a fairly liberal p-value threshold. 

We thank the reviewer for these good suggestions. In the revised manuscript, we have provided a flow chart as the new Figure 1, more detailed description of the method (added a subsection “Statistical analysis” under Materials and Methods) as well as metrics including measures of fit indices such as AUC and adjusted R2 for each validation and testing dataset to illustrate there is no danger of overfitting (in new Supplementary Table 5).

The choice of use FDR was indeed arbitrary as there has been no consensus on what significance threshold, if any, should be used in the context of EWAS. Here we simply followed the convention in previous studies to contrast the top associated signals for their effects between different populations and with reported effect sizes. Throughout the manuscript, we have removed the notion of significant associations and used the phrase “top associated signals” or “top associations” when discussion EWAS results for individual CpGs.

Reviewer #2:

(1) The number of mothers who self-reported any smoking was very low, much lower than in the general population and practically non-existent in the South Asian population. As a result, all analyses appeared to have been underpowered. It is possibly for this reason that the authors chose to generate their DNA methylation model using previously published summary statistics. The resulting score is not of great value in itself due to the low-powered dataset used to estimate covariance between CpG sites. In fact, a score was generated for a much larger, better-powered dataset several years ago (Reese, EHP, 2017, PMID 27323799). 

We thank the reviewer for pointing out the low exposure in the South Asian population, which we believe is complementary to the literature on maternal smoking that almost exclusively focused on white Europeans. However, the score was validating in the white European cohort (CHILD; current smoking 3.1%), which was reasonably similar to the trend that maternal cigarettes smoking is on the decline from 2016 to 2021, from 7.2% to 4.6% (Martin, Osterman, & Driscoll, 2023). This is also consistent with the fact that CHILD participants were recruited from major metropolitans of Canada with relatively high SES and education as compared to FAMILY.

We do agree with the reviewers that a higher prevalence of maternal smoking in the validating sample could potential improve the power of the score. Our original analytical pipeline focused on CHILD as the validation dataset; FAMILY (see the new Figure 1) was used as the testing data. We alternatively provided an analytical scheme using FAMILY as the validation dataset, as it had a higher proportion of current smokers, however, this is limited by the number of CpGs available (128 in FAMILY vs. 2,619 in CHILD out of the 2,620 CpGs from (Joubert et al., 2016)). The results of all possible combinations of validation vs. testing and restriction of targeted array vs. HM450 are summarized in the new new Supplementary Table 5 and Supplementary Figure 5.

To clarify, our choice to construct DNAm score using published summary statistics was not an ad-hoc decision due to the observed low power from CHILD EWAS. We agree with the reviewer that our study was indeed underpowered and was not originally intended for EWAS discovery. Thus, we specifically proposed to adopt a multivariate strategy from the literature of polygenic risk scores. This approach enabled us to leverage well-powered association signals without individual-level access to data with a sample size of n > 5,000 (Joubert et al., 2016). In comparison, the Reese maternal smoking score (Reese et al., 2017) had a discovery sample size of only n = 1,057. Our score was not out-performed, in fact, the AUC in both FAMILY (external validating dataset; n=411) and CHILD (external testing dataset; n=352) and was larger than that based on the Reese score as tabulated below (part of the new Supplementary Table 5).

Author response table 1.

Further, regarding the comment on the covariance matrix. Indeed, lassosum via elastic-net and summary data requires a reference covariance matrix that is consistent between the discovery data and external validation data. In fact, for moderately sized correlation/covariance values (r2 > 0.1), a sample size of >100 is sufficiently powered to detect it being different from 0 and thus used for estimation. Similar to the linkage disequilibrium of genotype data, the CpGs also exhibit a block-wise correlation structure and thus the theoretical framework of lassosum extends naturally to MRS.

In the revised manuscript, we included the Reese score, as well as a few additional scores to compare their predictiveness of smoking phenotypes in white European cohorts. We note that the applicability was limited in the FAMILY cohort that was profiled using a targeted array and only 7 out of 28 of the CpGs in the Reese score were available. As a result, though the Reese score had similar performance than our derived score in CHILD (0.94 vs. 0.95), its performance in FAMILY was compromised (0.72 vs. 0.89).

(2) The conclusion that "even minimal smoking exposure in South Asian mothers who were not active smokers showed a DNAm signature of small body size and low birthweight in newborns" is not warranted because no analyses were performed to show that the association between DNA methylation and birth size/weight was driven by maternal smoking. 

We thank the reviewer for this subtle point – it was not our intention to suggest there was a causal relationship between DNA methylation and birth size that was mediated by maternal smoking. We meant to suggest that the maternal smoking methylation score was consistently associated with negative outcomes in newborns of both white European and South Asian mothers despite no maternal smoking was present in South Asian mothers. It is possible that maternal smoking MRS was capturing a lot more than just smoking and second-hand smoking, such as other environmental exposures that also lead to oxidative stress. These together are associated with reduced birth size/weight.

In the revised manuscript, we have modified the conclusion above to:

“Notably, these results indicate a consistent association between the DNAm signature of maternal smoking and a small body size and low birthweight in newborns, in both white European mothers who exhibited some amount of smoking and in South Asian mothers who themselves were not active smokers.”

(3) Although it was likely that some mothers were exposed to second-hand smoke and/or pollution, data on this was either non-existent or not included in this study. Including this would have allowed a more novel investigation of the effects of smoke exposure on the pregnancies of non-smoking mothers.

We agree with this comment – second-hand smoking was captured by self-reported weekly smoking exposure by the mothers. We reported the association with smoking exposure and found that it was not consistently associated with our methylation scores across the cohorts (cohort specific association p-values of 5.4×10-5, 3.4×10-5, and 0.58, for CHILD, FAMILY, and START; original Table 3), possibly due to the low exposure in South Asian population (max weekly exposure was 42 hrs in contrast to 168 hrs in FAMILY and 98 hrs in CHILD). Meanwhile, air pollution data are currently not available. Here we additionally performed the association between maternal smoking and air pollution methylation score, using key CpGs from the largest air pollution EWAS to-date (Gondalia et al., 2021). However, there was no association between the air pollution score and any maternal smoking phenotypes (ps > 0.4).

(4) One of the European cohorts and half of the South Asian cohort had DNA methylation measured on only 2500 CpG sites. This set of sites included only 125 sites previously linked to prenatal smoking. The resulting model of prenatal smoking was small (only 11 CpG sites). It is possible that a large model may have been more powerful.

That is correct – also see our response to R2 comment #1. In our previous analysis, we validated two scores (one based on CpGs on the < 3,000 CpGs array and the other one for the full HM450K). The score with more CpGs indeed had slightly better performance. We included this as one of the limitations of the paper. Nevertheless, it does not impact the conclusion that the scores (based on a larger or smaller model) are transferrable to diverse populations and can be used to comparatively study the DNAm influence of maternal smoking in newborns.

The following was added in the discussion:

“First, the customized array with a limited number of CpGs (<3,000) was designed in 2016 and many large EWASs on smoking and maternal smoking conducted more recently had not been included.”

(5) The health outcomes investigated are potentially interesting but there are other possibly more important outcomes of interest such as birth complications, asthma, and intellectual impairment which are known to be associated with prenatal smoking.

We thank the reviewer for bring up this point. One of the key health outcomes in the CHILD study was asthma, and data at later time points are available. However, we do not have similar outcomes collected in the other two studies (FAMILY and START), which focused on cardiometabolic health in young children. Thus, we did not initially include outcomes that were not available across all cohorts as the intention was to contrast the effects between populations.

We recognize that this is an important question and decided to provide the association results for asthma and allergy at available time points in CHILD, FAMILY, and START. We also included mode of delivery via emergency C-section as an additional proxy outcome of birth complications. However, none of these were marginally (p < 0.05) associated with the DNAm smoking score. These are now included in the updated Supplementary Table 8.

Reviewer #1 (Recommendations For The Authors):

(1) The number of samples in the South Asian birth cohort given in the abstract (n = 887) does not match the sample size of the START cohort from the results section (results, page 7, line 139, n = 880). It is also different from the final analytical dataset size from the methods section (page 17, line 386, n = 890). Please clarify. 

We thank the reviewer for pointing this out. In the abstract, it was the final sample sized used for EWAS (no missingness in smoking history). The 880 in result was a typo for 890, which contains three individuals with missing smoking data. These have been updated with the correct sample size for START cohort that had full epigenome-wide methylation data (n = 504, and 503 with non-missing smoking history).

(2) Page 3, line 54: "consistent signal from the GFI1 gene (ps < 5×10-5)". Is ps a typo? If not then it might be clearer to state how many sites this included. 

No, these summarized the six CpG sites in the GFI1 gene as outlined in Table 2. We have clarified in the abstract to show the number of CpG sites included.

(3) Please report effect sizes together with information about the statistical significance (p values). 

We have updated the manuscript with (standardized) effect sizes whenever possible along with p-values.

(4) Page 4, line 80. This paragraph could be improved by adding a sentence explaining DNA methylation. 

We thank the reviewer for this suggestion. A sentence was included to introduce DNAm at the beginning of the second paragraph:

“DNA methylation is one of the most commonly studied epigenetic mechanisms by which cells regulate gene expression, and is increasingly recognized for its potential as a biomarker (13).”

(5) Page 4, line 84. Sentence difficult to understand, please rephrase: "Our recent systematic review of 17 cord blood epigenome-wide association studies (EWAS) demonstrated that out of the 290 CpG sites reported, 19 sites were identified in more than one study; all of them associated with maternal smoking". 

We have revised to clarify the review was on cord blood EWAS with five outcomes: maternal diabetes, pre-pregnancy body mass index, diet during pregnancy, smoking, and gestational age.

“Our recent systematic review of 17 cord blood epigenome-wide association studies (EWAS) found that out of the 290 CpG sites reported to be associated with at least one of the following: maternal diabetes, pre-pregnancy body mass index (BMI), diet during pregnancy, smoking, and gestational age, 19 sites were identified in more than one study and all of them associated with maternal smoking.”

(6) Page 5, line 93. The second part of the sentence is not necessary: "The majority of cohort studies have focused on participants of European ancestry, but few were designed to assess the influence of maternal exposures on DNA methylation changes in non-Europeans". 

We have revised accordingly to:

“Only a handful of cohort studies were designed to assess the influence of maternal exposures on DNA methylation changes in non-Europeans.”

(7) Page 5, line 95. "It has been suggested that ancestral background could influence both systematic patterns of methylation (27), such as cell composition and smoking behaviours (28)". The sentence is slightly unclear. Could it be rephrased to say that cell composition differences may be present by ancestry, which can lead to differential DNAm patterns? 

We have revised accordingly to:

“It has been suggested that systematic patterns of methylation (Elliott et al., 2022), such as cell composition, could differ between individuals of different ancestral backgrounds, which could in turn confound the association between differential DNAm and smoking behaviours (Choquet et al., 2021).”

(8) Page 5, line 108. How does reducing the number of predictors lead to more interpretable effect sizes? 

This was meant as a general comment in the context of variable selection, whereby the fewer predictors there are, the effect size of each predictor becomes more interpretable. However, we recognize this comment might be irrelevant to the specific approaches we adopted. We have revised it to motivate methylation score as a powerful instrument for analysis:

“Reducing the number of predictors and measurement noise in the data can lead to better statistical power and a more parsimonious instrument for subsequent analyses.”

(9) Page 5, line 112. Health consequences seem a bit strong, given that the analysis describes correlations/associations. 

We have revised it to “association with”:

“In this paper, we investigated the epigenetic signature of maternal smoking on cord blood DNA methylation in newborns, as well as its influence on newborn and later life outcomes in one South Asian which refers to people who originate from the Indian subcontinent, and two predominantly European-origin birth cohorts.”

Results

(10) It would be very helpful to have a flow diagram to detail all of your analyses.

We thank the reviewer for this suggestion. In the revised manuscript, we have provided a flow chart as the new Figure 1, updated the summary of analysis in . Table 3, and added a new Supplementary Table 5 for the DNAm score derivation, as well as more detailed description of the statistical analysis in the Materials and Methods under the subsection “Statistical analysis”.

(11) Page 7, line 138. Please add a reference to the CHILD study. 

We have added a reference of the CHILD study.

(12) Tables in results and in supplemental data a) contain a mixture of fields describing the newborn and its mother (this is not true for Supplementary Table 2), b) lack column descriptions, c) lack descriptions of abbreviations and formatting used in tables, d) use different font types, e) lack descriptions of statistical tests that were used to obtain p-values, f) use inconsistent rounding. Please correct and add the missing information.

We have consolidated the notation and nomenclature in all Tables and text. All numerical results are now rounded to 2 decimal places. The tests used were included in the Table headers as well as described in the Materials and Methods:

“For continuous phenotypes, an analysis of variance (ANOVA) using the F-statistics or a two-sample t-test was used to compare the mean difference across the three cohorts or two groups, respectively. For categorical phenotypes, a chi-square test of independence was used to compare the difference in frequencies of observed categories. Note that three of the categories under smoking history in the START cohort had expected cell counts less than 5, and was thus excluded from the comparison, the reported p-value was for CHILD and FAMILY.”

(13) Table 1. Sample sizes given in column descriptions do not add up to 1,650 (legend text).

We thank the reviewer for pointing this out. The updated sample size is 1,267, based on the 352 CHILD samples, 411 FAMILY samples, and 352 START samples. Notice that we did not remove those without full smoking history data as Table 1 was intended for the epigenetic subsamples.

(14) Page 7, line 156. Supplementary Tables are incorrectly numbered. In the text, Supplementary Table 4 comes after Supplementary Table 2.

We thank the reviewer for catching this and have corrected the ordering of the Supplementary Tables and Figures. 

(15) Page 7, line 158. "cell compositions" - do you mean estimated white cell proportions? 

We have revised it to “estimated cord blood cell proportions” in the text throughout.

(16) Smoking EWAS - do you see any overlap/directional consistency with the top findings from adult EWASs of smoking such as AHRR? 

We annotated the top EWAS signals from the literature in the meta-analysis (new Figure 2; Supplementary Figures 1 and 3), but was only able to confirm associations in the GFI1 gene. The AHRR signals were also annotated, but below the FDR correction threshold as seen in new Figure 2 at the start of chromosome 5. We further added a new Supplementary Figure 3 to show the directional consistency with top findings (2,620 CpGs reported and 128 CpGs overlapped with our meta-analysis) from Joubert et al., 2016. The Pearson’s correlation coefficient with meta-analyzed effect for maternal smoking was 0.72 and for smoking exposure was 0.60.

We added the following to Results:

“Further, we observed consistency in the direction of association for the 128 CpGs that overlapped between our meta-analysis and the 2,620 CpGs with evidence of association for maternal smoking (19) (Supplementary Figure 3). Specifically, the Pearson’s correlation coefficient for maternal smoking and weekly smoking exposure was 0.72 and 0.60, respectively.”

(17) Page 8, line 169. "also coincided with the GFI1 gene" this is a bit imprecise. Please report the correlation with the CpG from the maternal smoking analysis. 

The CpG was inside the GFI1 gene, we have included the Pearson’s correlation with the top hit in the text below:

“There were no CpGs associated with the ever-smoker status at an FDR of 0.05, though the top signal (cg09935388) was also mapped to the GFI1 gene (Pearson’s r2 correlation with cg12876356 = 0.75 and 0.68 in CHILD and FAMILY, respectively; Supplementary Figure 1).”

(18) Page 8, line 171. Typo "ccg": "ccg01798813". 

It has been corrected to “cpg01798813”.

(19) Page 8, line 176. Please be clear about the phenotype used in these analyses. 

The EWAS of weekly smoking exposure in START was removed in this version of the manuscript, in reflection of the results and the reviewer’s comments, as a result of this phenotyping being skewed and possibly leading to only spurious results (also see response to comment #20).

We have clarified the phenotypes for these results under “Epigenetic Association of Maternal Smoking in White Europeans” below:

“The maternal smoking and smoking exposure EWASs in CHILD did not yield any CpGs after FDR correction (Supplementary Figure 3).”

(20) What was the genomic inflation for the EWASs? 474 loci in the South Asian EWAS seems like a lot of findings. Perhaps a more robust method (e.g., OSCA MOMENT) might help to control the false positive rate. 

The genomic inflation factor was moderately across the cohorts for smoking exposure: 1.02 in CHILD, 0.94 in FAMILY, and 1.00 in START. However, there was more inflation in the tail of the distribution in START than the European cohorts. The empirical type I error rates at 0.01, 0.001, 0.00001, were high in START (x1.7, x5.7, and x165 times at each respective threshold), in contrast to CHILD (x1.06, x1.05, and x0.6) or FAMILY (x1.6, x1.9, and 0). The smoking exposure EWAS based on START was thus removed as these are likely false positives and there was very low smoking exposure to start with (11 reported weekly exposure between 2–42 hrs/week out of 462 with non-missing data). We have added the QQ-plots as well as the genomic inflation factor for the reported meta-analysis in the new Supplementary Figure 2. The following was added to the Results:

“There was no noticeable inflation of empirical type I error in the association p-values from the meta-analysis, with the median of the observed association test statistic roughly equal to the expected median (Supplementary Figure 2).”

(21) What is the targeted array? I don't think it has been introduced prior to this point. 

We introduced it in the Materials and Methods under subsection “Methylation data processing and quality controls”. Considering this comment and previous comments on the ordering of Tables and Figures, we have decided to place Materials and Methods after Introduction and before Results.

(22) The MRS section is described poorly in the results section. It is not clear where the 11 or 114 CpGs come from.

We now include an analytical summary of all scores (derived or external from literature) in the new Supplementary Table 5. Further, we updated the description of scores in Materials and Methods under the subsection “Using DNA Methylation to Construct Predictive Models for Maternal Smoking” to clarify the source and types of MRSs derived:

“To evaluate whether the targeted GMEL-EPIC array design has comparable performance as the epigenome-wide array to evaluate the epigenetic signature of maternal smoking, a total of three MRSs were constructed, two using the 128 CpGs available in all cohorts – across the HM450K and targeted GMEL-EPIC arrays – and with either CHILD (n = 347 with non-missing smoking history) or FAMILY (n = 397) as the validation cohort, and another using 2,107 CpGs that were only available in CHILD and START samples with CHILD as the validation cohort. Henceforth, we referred to these derived maternal smoking scores as the FAMILY targeted MRS, CHILD targeted MRS, and the HM450K MRS, respectively.”

(23) Page 9, line 187. "There was no statistically significant difference between the two scores in all samples (p = 1.00) or among non-smokers (p = 0.24).". How was the significance assessed? Please describe the models (outcome, covariates, model type) used for comparing the two models. It would also be good to report the correlation between the scores.

We have added a subsection “Statistical analysis” under Materials and Methods that described the tests. The correlation between scores is now summarized as a heatmap across all cohorts in the new Supplementary Figure 6.

“For each cohort, we contrasted the three versions of the derived scores using an analysis of variance analysis (ANOVA) along with pairwise comparisons using a two-sample t-test to examine how much information might be lost due to the exclusion of more than 10-fold CpGs at the validation stage. We also examined the correlation structure between all derived and external MRSs using a heatmap summarizing their pairwise Pearson’s correlation coefficient.”

(24) Please include the number of samples in the training/validation and in the test set in the methods and in the results.

We thank the reviewer for this suggestion. In the revised manuscript, we have provided a flow chart as the new Figure 1 and more detailed description of the method in the Materials and Methods. Please also see response to comment #22. The training sample size is based on Joubert et al., (2016), which is 5,647. For our main analyses, the validation sample with non-missing phenotypes remained the CHILD cohort (n=347), while the FAMILY (n=397) and START (n=503) samples were the independent testing data. We alternatively provided another scenario, in which the FAMILY sample was the validation cohort, while CHILD and START were the testing cohorts. The exact sample size and performance metrics for each scenario and score are clearly summarized in the new Supplementary Table 5.

(25) Table 3. Please clarify the type of information contained in the four last columns (p-value?).

Yes – these are the individual cohort p-values. We have taken the suggestion from comment #12 to fully describe all columns and fields.

(26) Page 10, line 215: "The meta-analysis revealed no heterogeneity in the direction nor the effect size of associations between populations". Please quote/refer to the results. 

In the revision, the heterogeneity p-values were quoted and the relevant tables (Supplementary Table 8) were added to this sentence.

(27) Figure 2 has issues with x labels. Due to the low number of ever smokers in START, the boxplot may not be the best visualisation method. It would also benefit from listing n's per group.

We appreciate this comment to improve the figure presentation. We increased the font size for the X-labels. The sample size for each group in START was also labeled in the new Figure 3 (previously Figure 2).

Discussion

(28) Studying the association between maternal smoking and cord blood DNAm is interesting from a biological perspective as it allows for assessing the immediate and long-term effects of maternal smoking on newborn health. However, in terms of calculating the MRS, what are the benefits of using cord blood over the mother's blood? We know that blood-based DNAm smoking score is a powerful predictor of long-term smoking status. 

The reviewer raises an interesting point – abundant literature supports that DNAm changes are tissue-specific. While mother’s blood DNAm smoking score reflect the long-term exposure to smoking in mothers, the cord blood DNAm captures the consequence of such long-term exposure for newborn health. One of the key results of our study is showing that established DNAm signatures of maternal smoking, which is known to mediate birth size and weight in white Europeans (these references were cited in the original manuscript), carries the same effect of reducing birth weight and size in the South Asian population. This is a critical finding from a DoHaD and public health perspective, as DNAm signatures of maternal smoking, irrespective of the smoking status of the mother, can influence the health trajectory of the newborns.

We have expanded our discussion based on this suggestion to highlight the unique features of studying maternal smoking via different tissues and their implications. The following was added to the discussion:

“There are several advantages of using a cord blood based biomarker from the DoHaD perspective. Firstly, cord blood provides a direct reflection of the in utero environment and fetal exposure to maternal smoking. Additionally, since cord blood is collected at birth, it eliminates potential confounding factors such as postnatal exposures that may affect maternal blood samples. Furthermore, studying cord blood DNAm allows for the assessment of epigenetic changes specifically relevant to the newborn, offering valuable information on the potential long-term health implications.”

(29) Page 13, line 285: "Fourth" without "third".

It has been revised accordingly.

Methods 

(30) The methods section does not contain all the details required to replicate the analysis. Whenever statistical analysis is conducted, this section should clearly describe the type of the analysis (linear regression, t-test, etc.) and name the dependent and independent variables. Sample sizes should also be given. 

We added further details of test used and sample size for each analysis. We have also included a new “Statistical analysis” subsection under Materials and Methods.

(31) Please describe MRS testing in the methods.

We tested MRS with respect to binary and continuous smoking phenotypes using a logistic and linear regression, respectively. The predictive value was assessed using area under the roc curve for the binary outcome and an adjusted R2 for the continuous outcome. These were added to the new “Statistical analysis” subsection under Materials and Methods. See response to comments #22-24, and #30.

(32) Please describe the methods used to compare the two versions of MRS for maternal

smoking.

It was a two-sample t-test, which was described in the Figure legends. We have now added this to the new “Statistical analysis” subsection under Materials and Methods.

(33) Please describe testing the associations between MRS and Offspring Anthropometrics in more detail.

We added further details on the regression model and the test for association in the methods. We have now added this to the new “Statistical analysis” subsection under Materials and Methods.

(34) Meta analysing the 450k and GMEL arrays is going to substantially reduce the number of CpGs under investigation.

We agree with the reviewer that this is not optimal for signal discovery. However, this is the only way we could synthesize evidence across the cohorts as FAMILY samples were only processed using the customized array. We added the following as a limitation of the study in the discussion.

“First, the customized array with a limited number of CpGs (<3,000) was designed in 2016 and many large EWASs on smoking and maternal smoking conducted more recently had not been included.”

(35) Page 16, line 364: GDM abbreviation was used in the results section (line 145), yet it is introduced in line 364. 

Thank you for catching this, we have removed the duplicate.

(36) Page 17, line 381: Given the stated importance of ancestry, why not restrict the sample to genetically confirmed groups?

The reviewer has a valid point that ancestry, either perceived or genetic, can introduce additional heterogeneity due to potential differences in genetics, cultural and social practices, and lifestyles. Genetic data are indeed available for a subset of the individuals. In the original version of the manuscript, we used a stringent ancestry calling method by mapping all individuals with the 1000 Genomes samples from continental populations. The final definition was based on a combination of self-reported and genetically confirmed ancestry. However, if we restricted only to genetically confirmed groups, the sample size would be reduced to 312 (vs. 411), 268 (vs. 352), and 488 (vs. 504) in FAMILY, CHILD, and START, respectively.

We compared the mean difference in the beta-values of the top associated CpGs and the derived MRS between those genetically confirmed vs. self-reported ancestral groups, and observed no material difference. These results are now included in the Supplementary Materials as part of the sensitivity analysis. Thus, given these considerations, we decided to use this complementary approach to retain the maximum number of samples while ensuring some aspect of ancestral homogeneity.

“To maximize sample size in FAMILY and CHILD, we retained either self-identified or genetically confirmed Europeans based on available genetic data (Supplementary Table 1).”

(37) Page 18, line 397: sensitivity analysis not sensitive analysis.

Thank you for catching this, we have revised accordingly.

(38) Page 18, line 409: smoking was rank transformed however, it would be good to see regression diagnostics for the lead loci in the EWAS to check that assumptions were met. 

We thank the reviewer for this suggestion. Smoking exposure is indeed skewed and in fact very much zero-inflated across the cohorts. The raw phenotype violated several model assumptions in terms of variance heteroskedasticity, outlying values (influential points), and linearity. The diagnostics suggested improved deviation from model assumption, yet some aspects of the violation remained at a lesser degree. We included a comparison of results before and after transformation and model diagnostics for the lead CpG using CHILD and FAMILY data in the Supplementary Materials. The following was added to the results:

“As a sensitivity analysis, we repeated the analysis for the continuous smoking exposure under rank transformation vs. raw phenotype for the associated CpG in GFI1 and examined the regression diagnostics (Supplementary Material), and found that the model under rank-transformation deviated less from assumptions.”

(39) Page 19, line 418: FDR seems quite a lenient threshold, especially when genome-wide significance thresholds exist. I would be inclined to view the EWAS findings as null.

The choice of use FDR to was indeed arbitrary as there has been no consensus on what significance threshold, if any, should be used in the context of EWAS. The significance threshold for GWAS (Pe’er et al., 2008) probably does not apply directly to EWAS as the number of effective tests will likely differ between genome-wide genetic variants and CpGs. The Bonferroni corrected p-value threshold in this context would be 0.05/200,050=2.5´10-7, which is still less stringent than the GWAS significance threshold. We originally decided to follow the convention of previous studies and use FDR to filter out a subset of plausible associations to contrast the top association signals for their effects between different populations and with reported effect sizes.

We have revised the manuscript throughout by removing the notion of significant associations, and instead used the phrase “top associated signals” or “top associations” when discussion EWAS results for individual CpGs. The following was added to Materials and Methods to clarify the choice of our threshold:

“For each EWAS or meta-analysis, the false discovery rate (FDR) adjustment was used to control multiple testing and we considered CpGs that passed an FDR-adjusted p-value < 0.05 to be relevant for maternal smoking.”

(40) I do not understand Supplementary Figure 6 - how have the data been standardised? Why not plot the CpGs on the beta-value scale?

The standardized values were plotted as the reported p-values for the mean and variance equality tests (i.e. ANOVA F-test, Levene’s test, Anderson-Darling test) were based on these transformed values to reduce inflation due to non-normality. We have since removed this comparison and kept only the comparison of the overall score as the number of CpGs in the HM450k score (143 CpGs) for comparison is too high to be visually interpretable.

(41) It is my understanding, that the MRS for maternal smoking was constructed using external weights projected and regularised using elastic net (effectively trained) in CHILD cohort. The results section discusses associations between maternal smoking history and outcomes in CHILD, FAMILY, and START. Training and testing the score in the same sample (cohort) may result in overfitting and therefore should not be implemented.

The original MRS was constructed using external weights from an independent discovery sample (Joubert et al., 2016; n > 5,000) and the LASSO validation was done in CHILD (n = 352), external testing was in FAMILY and START. This was the lassosum framework whereby we leverage larger sample size from external studies to select more plausible CpGs as candidates to include in the model. Thus, training, validation, and testing were not done in the same samples. We have included a Figure 1 to illustrate the updated analytical flow and a graphical abstract to summarize the methods.

(42) Is it a concern that the findings don't seem to replicate Joubert's results, which came from a much larger study?

Replication is usually done in samples much larger than the discovery samples, thus it is not a concern that we were unable to confirm all signals from Joubert et al., (2016). However, 6/7 of the top associations (FDR adjusted p-value < 0.05) in the meta-analysis were declared as significant in Joubert et al. (2016). In addition, the fact that using Joubert’s summary statistics, we were able to derive MRSs that were strongly associated with both smoking history and weekly exposure suggests shared signals. Also see response to  R1 comment #16 for a comparison of effect consistency.

(43) Please check that all analysis scripts have been uploaded to Github and that the EWAS results are publicly available.

We thank the reviewer for this suggestion. All updated scripts and EWAS results are available on Github. We are working to have the results also submitted to EWAS catalog.

Reviewer #2 (Recommendations For The Authors):

The impact of this study is reduced due to previous findings:

(1) Previous studies have already shown that DNA methylation may mediate the effect of maternal smoking on birth size/weight (see e.g.https://doi.org/10.1098/rstb.2018.0120; https://doi.org/10.1093/ije/dyv048).

We thank the reviewer for this point and would like to take the opportunity to clarify that it was not our objective to examine whether there was a causal relationship, between DNA methylation and birth size that was mediated by maternal smoking. One of the key messages of our study is to evaluate whether epigenetic associations – at individual CpGs and aggregated as a score – are consistent between white European and South Asian populations. One way to examine this is through using established DNAm signatures of maternal smoking, which is known to mediate birth size and weight in white Europeans (these references were cited in the original manuscript), and confirm whether they also carry the same effect on birth outcomes in the South Asian population.

Indeed, our results support that maternal smoking methylation score was consistently associated with negative outcomes in newborns of both white European and South Asian mothers despite no maternal smoking was present in South Asian mothers. These collective point to the possibility that the maternal smoking MRS was capturing a lot more than just smoking and second-hand smoking, but potentially other environmental exposures that also lead to oxidative stress. These together are associated with health consequences, including reduced birth size/weight. One of the candidates for such exposure is air pollution as some of the maternal smoking CpGs were previously linked to air pollution. However, we were unable to assess this hypothesis directly without the air pollution data, and the air pollution methylation score was not associated with smoking history (Supplementary Figure 5) nor smoking exposure (p > 0.4 in CHILD, FAMILY and START).

The following was added to Materials and Methods under the subsection Using DNA Methylation to Construct Predictive Models for Maternal Smoking:

“To benchmark and compare with existing maternal smoking MRSs, we calculated the Reese score using 28 CpGs (48,49),  Richmond score using 568 CpGs (49), Rauschert score using 204 CpGs (50), Joubert score using all 2,620 CpGs with evidence of association for maternal smoking (19), and finally a three-CpG score for air pollution (51). The details of these scores and score weight can be found in Supplementary Table 4.”

The following was added to Results

“Both produced methylation scores that were significantly associated with maternal smoking history (ANOVA F-test p-values =1.0×10-6 and 2.4×10-14 in CHILD and  6.9×10-16 and <2.2×10-16 in FAMILY), and the best among alternative scores for CHILD and FAMILY (Supplementary Table 5). With the exception of the air pollution MRS, all remaining scores were marginally associated with smoking history in both CHILD and FAMILY (Supplementary Figure 5).”

(2) Due to the small study size and low levels of prenatal smoke exposure, the model derived here is of little value and is, in fact, superseded by a previously published model (PMID: 27323799). At the very least, the model should be evaluated here. A novel aspect of this study is the inclusion of a South Asian cohort. Unfortunately, smoke exposure is practically non-existent, so it is unclear how it can be used. The more interesting finding in this study is the possibility that environmental factors such as second-hand smoke or pollution may have similar effects on pregnancies as maternal smoking. Are these available? If so, they could be evaluated for associations with DNA methylation. This would be novel. 

In the revised manuscript, we included the Reese score (Reese et al., 2017) and a few other maternal smoking scores for comparison. In the CHILD cohort, the performance was comparable to our derived score (AUC of 0.95 vs. 0.94 for Reese score), but its applicability was limited since the FAMILY dataset was profiled using a targeted array and only 7 out of 28 of the CpGs in the Reese score were available (AUC of 0.89 vs. 0.72 for Reese). As compared to the remaining scores from literature (see the new Supplementary Table 5 for complete results), Reese’s score has generally favorable performance.

We did examine second-hand smoking in the original manuscript, showing a significant association with weekly maternal smoking exposure (original Table 3 and Supplementary Table 8). However, air pollution data is not available for assessment.

(3) The other novel aspect is the evaluation of associations with outcomes later in life. Height and weight are interesting but impact could be gained by including other relevant outcomes such as birth complications, asthma, and intellectual impairment which are known to be associated with prenatal smoking. 

We thank the reviewer for bring up this point. One of the key health outcomes in the CHILD study was asthma, and data at later time points are available. However, we do not have similar outcomes collected in the other two studies (FAMILY and START), which focused on cardiometabolic health in young children. Thus, we did not initially include outcomes that were not available across all cohorts as the intention was to contrast the effects between populations.

We recognize that this is an important question and decided to provide the association results for mother reported asthma and allergy, but based on different definitions as these outcomes cannot be harmonized across the cohorts. We also included mode of delivery via emergency C-section as an additional proxy outcome of birth complication.

The following was added to Materials and Methods:

“Mode of delivery (emergency c-section vs. other) was collected at the time of delivery.”

“Additional phenotypes included smoking exposures (hours per week) at home, potential allergy based on mother reporting any of: eczema, hay fever, wheeze, asthma, food allergy (egg, cow milk, soy, other) for her child in FAMILY and START, and asthma based on mother’s opinion in CHILD (“In your opinion, does the child have any of the following? Asthma”).”

The following was added to Results:

“The maternal smoking MRS was consistently associated with increasing weekly smoking exposure in children reported by mothers at the 1-year (0.51±0.15, FDR adjusted p= 0.0052) , 3-year (0.53±0.16, FDR adjusted p= 0.0052), and 5-year (0.40±0.15, FDR adjusted p= 0.021) visits with similar effects.”

“We did not find any association with self-reported allergy or asthma in children at later visits (Supplementary Table 8). Further, there was no evidence of association between the MRS and any maternal outcomes (Supplementary Table 8).”

REFERENCES:

Gondalia, R., Baldassari, A., Holliday, K. M., Justice, A. E., Stewart, J. D., Liao, D., . . . Whitsel, E. A. (2021). Epigenetically mediated electrocardiographic manifestations of sub-chronic exposures to ambient particulate matter air pollution in the Women's Health Initiative and Atherosclerosis Risk in Communities Study. Environ Res, 198, 111211. doi:10.1016/j.envres.2021.111211

Joubert, B. R., Felix, J. F., Yousefi, P., Bakulski, K. M., Just, A. C., Breton, C., . . . London, S. J. (2016). DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis. Am J Hum Genet, 98(4), 680-696. doi:10.1016/j.ajhg.2016.02.019

Martin, J. A., Osterman, M. J. K., & Driscoll, A. K. (2023). Declines in Cigarette Smoking During Pregnancy in the United States, 2016-2021. NCHS Data Brief(458), 1-8. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/36723453

Reese, S. E., Zhao, S., Wu, M. C., Joubert, B. R., Parr, C. L., Haberg, S. E., . . . London, S. J. (2017). DNA Methylation Score as a Biomarker in Newborns for Sustained Maternal Smoking during Pregnancy. Environ Health Perspect, 125(4), 760-766. doi:10.1289/EHP333

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The individual roles of both cosolvents and intrinsically disordered proteins (IDPs) in desiccation have been well established, but few studies have tried to elucidate how these two factors may contribute synergistically. The authors quantify the synergy for the model and true IDPs involved with desiccation and find that only the true IDPs have strong desiccation tolerance and synergy with cosolvents. Using these as model systems, they quantify the local (secondary structure vis-a-vi CD spectroscopy) and global dimensions (vis-a-vi the Rg of SAXS experiments) and find no obvious changes with the co-solvents. Instead, they focus on the gelation of one of the IDPs and, using theory and experiments, suggest that the co-solvents may enable desiccation tolerance, an interesting hypothesis to guide future in vivo desiccation studies. A few minor points that remain unclear to this reviewer are noted.

Strengths:

This paper is quite extensive and has significant strengths worth highlighting. Notably, the number and type of methods employed to study IDPs are quite unusual, employing CD spectroscopy, SAXS measurements, and DSC. The use of the TFE is an exciting integration of the physical chemistry of cosolvents into the desiccation field is a nice approach and a clever way of addressing the gap of the lack of conformational changes depending on the cosolvents. Furthermore, I think this is a major point and strength of the paper; the underlying synergy of cosolvents and IDPs may lie in the thermodynamics of the dehydration process.

Figure S6A is very useful. I encourage readers who are confused about the DSC analysis, interpretation, and calculation to refer to it.

Weaknesses:

Overall, the paper is sound and employs strong experimental design and analysis. However, I wish to point out a few minor weaknesses.

Perhaps the largest, in terms of reader comprehension, focuses on the transition between the model peptides and real IDPs in Figures 1 and 2. Notably, little is discussed with respect to the structure of the IDPs and what is known. Notably, I was confused to find out when looking at Table 1 that many of the IDPs are predicted to be largely unordered, which seemed to contrast with some of the CD spectroscopy data. I wonder if the disorder plots are misleading for readers. Can the authors comment more on this confusion? What are these IDPs structurally?

We apologize for the confusion caused here and thank the reviewer for this astute observation. Our CD spectroscopy data suggests all LEA proteins are almost entirely disordered under aqueous conditions, with a single major minimum at 200 nm, although most have a small inflection around 220 nm, indicating a small proportion of helicity (Fig. 3A). The notable exception here is CAHS D, which – in line with our work and the work of many others – possesses a substantial degree of transient helicity in the linker region (residues 100-200), giving rise to a more pronounced minimum at 220 nm. These conclusions are consistent with our SAXS data (Fig. 4), which predict a radius of gyration far larger than a globular folded protein of the same number of residues should have (15-20 Å). The structural predictions (both Metapredict and AlphaFold2), however, imply several of the proteins to be ordered; AvLEA1C and HeLEA68614 are both predicted to have large folded regions based on metapredict disorder scores. We believe this is an erroneous prediction driven by these regions' propensity to acquire helicity in the context of desiccation (Fig 3B) and/or when interacting with clients. As such, our computational analysis is at odds with the experimental data because these proteins are all poised to undergo a coil-to-helix transition, an effect our parallel work has proposed is important for their function (see Biswas et al. Prot. Sci. 2024). The ability of AlphaFold2 to predict bound-state or transient helices has been previously documented (Alderson et al PNAS 2023)

To address this discrepancy, the caption for Table 1 reads: “We note that the reason many of these profiles contain large folded regions is because the amphipathic LEA and CAHS proteins are predicted to form helices, which metapredict infers and incorrectly highlights these regions as ‘folded’ when really they are disordered in isolation”. We have also added additional context and information to the caption for Fig. S9 “We note that the structural predictions from AlphaFold2 contain largely ordered structures. We believe this is due to the propensity of these proteins to form helices in the context of drying or when interacting with a client. This has been shown in cases where an IDR contains residual helicity or is folded upon binding [70].”

Related to the above thoughts, the alpha fold structures for the LEA proteins are predicted (unconfidently) as being alpha-helical in contrast to the CD data. Does this complicate the TFE studies and eliminate the correlation for the LEA proteins?

AlphaFold2 predicted helicity in disordered regions is commonly observed, and thought to indicate a possible “bound” helical state (Alderson et al. PNAS 2023). As shown by the CD data, in aqueous conditions no secondary structure exists. It is only in the desiccated state - the path to which requires proteins to reach excessively high concentrations - that this secondary structure appears. Underlying our TFE model is that the AlphaFold2 predicted secondary structure is indicative of the state the proteins are in at high abundance, which occurs as cells ramp up protectant expression and as water is removed from the system. Under these assumptions, the CD data is in agreement with the AlphaFold2 predictions, and our analysis holds. This is explained in the methods under “Transfer Free Energy (TFE) Calculations” - but we have now also added an additional sentence to this effect in the main text: “Using a similar AlphaFold2-based approach for LEA proteins and for BSA, we can make correlations between the Gtr of the disorder-to-order transition and synergy (Fig. S8F-K). Interestingly, AlphaFold2 predictions of our LEA proteins were broadly helical, which is in contrast to our experimental characterization of these proteins in aqueous solutions. However, this is not unusual for AlphaFold2 predictions and could possibly represent a “bound” conformation for the proteins [70].”

Additionally, the notation that the LEA and BSA proteins do not correlate is unclear to this reviewer, aren't many of the correlations significant, having both a large R^2 and significant p-value?

We thank the reviewer for pointing this out. While BSA and some LEA proteins have values that correlate with synergy, there’s more to consider in assessing the relevance of these correlations. For example, we cannot claim that the value is physiologically relevant without observing an actual structural change in the protein. Furthermore, several of these proteins (BSA and AvLEA1C) were found to be not significantly synergistic in the LDH assay, and any correlation should, therefore, also be considered non-significant. We have added a sentence to the results to clarify this: “For a subset of these proteins, we see a statistically significant correlation between G and synergy. However, this data is purely computational. For CAHS D, we saw our predictions recapitulated in changes in the protein structure, and for the LEA proteins we do not. Thus, we conclude that cosolutes do not induce synergy in our LEA proteins through a change in folding.”

The calculation of synergy seems too simplistic or even problematic to me. While I am not familiar with the standards in the desiccation field, I think the approach as presented may be problematic due to the potential for higher initial values of protection to have lower synergies (two 50%s for example, could not yield higher than 100%).

We acknowledge the reviewer’s concern about our synergy calculation. We would like to highlight the use of sub-optimal protective concentrations in our synergy assays similar to studies previously reported in the desiccation field (Nguyen et al. 2022; Kim et al. 2018).

As the reviewer pointed out, we agree that there is a theoretical 100% threshold in our experiments which if we hit, we cannot distinguish between individual additive vs synergistic effects. To avoid the situation of reaching the near maximal protection levels (~100%), we intentionally select a sub-optimal concentration of the protectants that are below the maximum efficacy level for individual protectants to use in our assays. This limits the potential for initial higher values of the protectants so that their combined effect is not maximized, and there is always the potential for synergy. We would also like to point out that we never actually hit that 100% threshold in any of our synergy experiments, which warrants that any observed increase in protection is attributed to a true synergistic effect between the protectants.

Instead, I would think one would need to really think of it as an apparent equilibrium constant between functional and non-functional LDH (Kapp = [Func]/[Not Func] and frac = Kapp/(1+Kapp) or Kapp = frac/(1-frac) ) Then after getting the apparent equilibrium constants for the IDP and cosolvent (KappIDP and KappCS), the expected additive effect would be frac = (KappIDP+KappCS)/(1+KappIDP+KappCS).

Consequently, the extent of synergy could be instead calculated as KappBOTH-KappIDP-KappCS. Maybe this reviewer is misunderstanding. It is recommended that the authors clarify why the synergy calculation in the manuscript is reasonable.

We thank the reviewer for this suggestion. In the desiccation field, the synergy calculations that we used is the standard method that people use, so that’s what we present in our main manuscript. However, we have now quantified synergy through two new approaches: one, as suggested by the reviewer, using the equilibrium constant (Kapp) as a metric, and the other using the Bliss Independent model, which is a common approach for calculating synergy in drug combination studies. We see minimal differences in terms of the synergy scores using these different methods. We have included the results for these additional methods in supplemental figure S3.

Related to the above, the authors should discuss the utility of using molar concentration instead of volume fraction or mass concentration. Notably, when trehalose is used in concentration, the volume fraction of trehalose is much smaller compared to the IDPs used in Figure 2 or some in Figure 1. Would switching to a different weighted unit impact the results of the study, or is it robust to such (potentially) arbitrary units?

We thank the reviewer for this comment. Indeed, in studies of cosolute effect, concentration units can alter the conclusions of the study (Auton and Bolen 2004). In our case, the relevant figures where we use a concentration scale (1B and 2B) are not germane to the main conclusions: The only use of these PD50 values is to determine a sub-optimal concentration at which ~30% of the LDH is protected. While it is true that the number for the concentration of e.g., trehalose will be dramatically different if we were to use mass fraction units, the rest of the work and all our conclusions would be exactly the same.

Additionally, our use of a molar ratio when discussing synergy is a direct result of the way we think about such synergy: Since the concentration of both protein and cosolute can change by orders of magnitude during drying, it is the copy numbers of both proteins and cosolute that are conserved in this process, and it is this unit that we think is important to the protective effect (rather than the partial molar volume, for example, which would be changing as the system dries).

Reviewer #2 (Public Review):

Summary:

The paper aims to investigate the synergies between desiccation chaperones and small molecule cosolutes, and describe its mechanistic basis. The paper reports that IDP chaperones have stronger synergies with the cosolutes they coexist with, and in one case suggests that this is related to oligomerization propensity of the IDP.

Strengths:

The study uses a lot of orthogonal methods and the experiments are technically well done. They are addressing a new question that has not really been addressed previously.

Weaknesses:

The conclusions are based on a few examples and only partial correlations. While the data support mechanistic conclusions about the individual proteins studied, it is not clear that the conclusions can be generalized to the extent proposed by the authors due to small effect sizes, small numbers of proteins, and only partial correlations.

Thank you for bringing this up. We agree that we should not generalize our results to other systems based on the evidence we have for the proteins used in our study. We have altered our discussion to highlight that this may apply to other IDPs, and that future experiments must be done to support this: “Additionally, we want to point out that our results cannot necessarily be generalized to all desiccation-related IDPs. More experiments will be needed to assess the relevance of cosolute effects to functional synergy and IDP folding in the context of desiccation and beyond. This remains an important future direction for the field.”

The authors pose relevant questions and try to answer them through a systematic series of experiments that are all technically well-conducted. The data points are generally interpreted appropriately in isolation, however, I am a little concerned about a tendency to over-generalize their findings. Many of the experiments give negative or non-conclusive results (not a problem in itself), which means that the overall storyline is often based on single examples.

We agree with the reviewer’s point. As mentioned earlier, we have modified our manuscript to reflect that our findings are based on the six proteins that we studied, and we can only speculate about other desiccation-related IDPs based on our results.

For example, the central conclusion that IDPs interact synergistically with their endogenous co-solute (Figure 2E) is largely driven by one outlier from Arabidopsis. The rest are relatively close to the diagonal, and one could equally well suggest that the cosolutes affect the IDPs equally (which is also the conclusion in 1F).

We appreciate the reviewer’s concern regarding our conclusion in Figures 2E and 1F. We would like to highlight that our conclusions that IDPs interact synergistically with their endogenous cosolute are based on statistical analysis. Our data shows that full-length proteins that were synergistic with both cosolutes are always significantly more synergistic with the endogenous cosolute (Fig. 2E, Fig. S2C-E). For example, the nematode protein is synergistic with both trehalose and sucrose, but is significantly more synergistic with trehalose, the endogenous nematode cosolute, than with sucrose (Fig S2D).

This is not the case in 1F. In Fig. 1F, it is to note that not only are the points close to the diagonal, but most points are close to zero along both axes indicating no synergy. In fact, many points have negative synergy (antagonistic effect).

We do recognize that our conclusions are based on the study of a specific set of six IDPs, and we do not want to overreach in our conclusions. To acknowledge this, we have now added text to emphasize that our conclusion is based on the six proteins that we tested, and we speculate it might apply to other systems: “Our data shows that these six IDPs synergize best with their endogenous cosolute to promote desiccation tolerance and we speculate that this may apply to other desiccation-related IDPs”.

Similarly, the mechanistic explanations tend to be based on single examples. This is somewhat unavoidable as biophysical studies cannot be done on thousands of proteins, but the text should be toned down to reflect the strength of the conclusions.

We acknowledge the reviewer’s concern. We have modified our manuscript accordingly to reflect that the mechanistic insights we gained are for the six proteins we tested empirically. These changes can be found throughout the manuscript. None of our experiments rule out the possibility that other LEA proteins or CAHS proteins may show different structural transitions, or that other IDPs may take on structural changes in response to the cosolutes.

The central hypothesis revolves around the interplay between cosolutes and IDP chaperones comparing chaperones from species with different complements of cosolutes. In Table 1, it is mentioned that Arabidopsis uses both trehalose and sucrose as a cosolute, yet experiments are only done with either of these cosolutes and Arabidopsis is counted in the sucrose column. While it makes sense to compare them separately from a biophysical point of view, the ability to test the co-evolution of these systems is somewhat diminished by this. At least it should be discussed clearly.

We appreciate the reviewer’s comment. As is mentioned in Table 1, Arabidopsis uses both trehalose and sucrose as cosolute. As such, we would predict that the Arabidopsis proteins would respond positively to both cosolutes. We would like to point out that Arabidopsis is counted in both trehalose and sucrose columns.

We would also like to emphasize that multiple osmolytes exist in all organisms as a desiccation response and a simple IDP-cosolute system is far from a true recapitulation of a desiccating system. We have touched on this in the discussion and explicitly addressed the presence of both cosolutes in Arabidopsis and the need for further experiments to test for synergistic interactions using both or multiple mediators to illustrate synergy in multiple cosolute systems: “It is important to note that desiccation-tolerant organisms employ multiple cosolutes to counteract the effects of desiccation. The use of a single cosolute-IDP system in our in vitro experiments does not accurately mirror the diverse cosolute changes in desiccating systems. For instance, Arabidopsis seeds enrich both trehalose and sucrose, among other cosolutes. This demands the necessity of future experiments that incorporate both or multiple cosolutes and assess their synergistic effects, thus elucidating the intricate synergy in multi-cosolute systems.”

It would be helpful if the authors could spell out the theoretical basis of how they quantify synergy. I understand what they are doing - and maybe there are no better ways to do it - but it seems like an approach with limitations. The authors identify one in that the calculation only works far from 100%, but to me, it seems there would be an equally strict requirement to be significantly above 0%. This would suggest that it is used wrongly in Figure 6H, where there is no effect of betaine (at least as far as the color scheme allows one to distinguish the different bars). In this case, the authors cannot really conclude synergy or not, it could be a straight non-synergistic inhibition by betaine.

We appreciate the reviewer’s concern about the theoretical basis of how we quantify synergy. We do acknowledge the limitation of our LDH protection/synergy assay only produces interpretable data when our protectant/mixture yields protection levels within the range 0 and below 100%. Betaine was not protective in any of the concentrations we tested in this study. In line with the reviewer’s comment, we also acknowledge that within our experimental procedures, the inhibitory effects of betaine cannot be accurately captured, considering that LDH activity is ~0% without protectants. However, in our positive control in which LDH is co-incubated with betaine or betaine and CAHS D overnight in the hydrated state, we do not see a loss of enzymatic function of LDH nullifying a direct inhibition by betaine. We have added this text in our manuscript: “Glycine betaine on its own is not protective to LDH during drying nor does it inhibit LDH activity (Fig. S8E)”.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The conclusion in lines 195-196 seems overstated as the length dependence could be strongly changed in non-tested concentrations or those that are not possible experimentally. Notably, the IDPs in Figure 2 are around 200AA and only transition in the ranges tested for these peptides. Some other conclusions around this point seem a little overstated.

We acknowledge the reviewer’s concern about the potential variability of the length dependence of the motifs at concentrations beyond those tested. However, we would like to highlight that higher concentrations of the tandem repeats (At22 and At44) inactivated LDH during the incubation period, as was seen with  the 11-mer motifs. This meant we could not evaluate protection by these motifs at concentrations beyond those plotted in Fig. 1A. This behavior was not observed for the full-length proteins. Regardless, we have toned down the conclusion in lines 195-196 to only reflect our results for the 2X and 4X repeats of At11 which now reads “We synthesized 2X (At22) and 4X (At44) tandem repeats of the A. thaliana 11-mer LEA_4 motif (At11). At22 and At44 show minimal potency in preserving in vitro LDH function during drying (Fig. 1A, Fig. S1A).”

Reviewer #2 (Recommendations For The Authors):

Figure 3: The focus on the ratio 222/210 seems inappropriate. That would indeed be useful for telling apart e.g. an alpha-to-beta transition, or formation of coiled coils. However, for a helix-to-coil equilibrium, which is likely to dominate here, it will not be especially sensitive as demonstrated e.g. by BSA in the dry state.

We thank the reviewer for this comment. The use of ratios to measure structural transition is primarily to eliminate the effects of concentrations on the graph. It is clear from Fig. 3A and Fig. 3B that a structural transition occurs between the aqueous and the desiccated state. This is also very clear from the 222/210 ratio that we use (Fig. 3C), for every construct other than BSA - which indeed does not seem to undergo a dramatic structural change in the desiccated state. We have clarified this now in the description of the results: “Using this metric, all LEAs and CAHS D display a clear increase in helical propensity upon being desiccated (Fig. 3C). On the other hand, the helical propensity of BSA remains very similar to its hydrated state, indicating that no dramatic structural change took place (Fig. 3C).

Minor comments:

Figure 1F is not mentioned in the text.

We have included Fig. 1F in the text.

Some technical details missing for SAXS experiments.

We thank the reviewer for pointing this out. We’ve added additional technical details to the main text, and directed readers to the methods for more information.

It is well known that BSA is in a monomer-dimer equilibrium and this is normally taken into account in data analysis as this is often a calibration sample.

We’ve calculated for BSA, and correlated the resulting data with synergy. This can be found in figure S7M and figure S8I.

Line 247: "BSA, which comes from cows, which of course have no capacity for anhydrobiosis" - This seems like a rather strong statement without a reference. Did the authors consider reanimating beef jerky by soaking it in water? ;-)

This is a great idea, and we hope to assign this project to our next rotation student.

Minor suggestions for figures (that are generally very well done):

Figure 1-4: Consider using the color scheme to indicate what the endogenous cosolutes are. Even though this info is in table one, it would still improve readability.

We have added the colored organismal icons for all figures in which the plain black ones were previously used, including supplementals.

Figure 4: consider adding some white space between the two concentration series of solutes to avoid being read as a single concentration series.

We have updated this figure to clearly separate each sample by osmolyte.

Figure 6H: Consider changing the colors for Betaine and CAHS D, so they are easier to distinguish. They are hard to tell apart on a printout.

We have adjusted the colors for betaine and CAHS D.

Authorr Response

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

Reviewer #1 (Public Review):

The objective of this investigation was to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). Previous TMS investigations of pain perception had focused on motor evoked potentials (MEPs), which reflect a combination of cortical, spinal, and peripheral activity, as well as restricting the focus to M1. The main strength of this investigation is the combined use of TMS and EEG in the context of experimental pain. More specifically, Experiment 1 investigated whether acute pain altered cortical excitability, reflected in the modulation of TEPs. The main outcome of this study is that relative to non-painful warm stimuli, painful thermal stimuli led to an increase on the amplitude of the TEP N45, with a larger increase associated with higher pain ratings. Because it has been argued that a significant portion of TEPs could reflect auditory potentials elicited by the sound (click) of the TMS, Experiment 2 constituted a control study that aimed to disentangle the cortical response related to TMS and auditory activity. Finally, Experiment 3 aimed to disentangle the cortical response to TMS and reafferent feedback from muscular activity elicited by suprathreshold TMS applied over M1. The fact that the authors accompanied their main experiment with two control experiments strengthens the conclusion that the N45 TEP peak could be implicated in the perception of painful stimuli.

Perhaps, the addition of a highly salient but non-painful stimulus (i.e. from another modality) would have further ruled out that the effects on the N45 are not predominantly related to intensity/saliency of the stimulus rather than to pain per se.

We thank the reviewer for their comment on the possibility of whether stimulus intensity influences the N45 as opposed to pain per se. We agree that the ideal experiment would have included multiple levels of stimulation. We would argue, however, that that in Experiment 1, despite the same level of stimulus intensity for all participants (46 degrees), individual differences in pain ratings were associated with the change in the N45 amplitude, suggesting that the results cannot be explained by stimulus intensity, but rather by pain intensity.

Reviewer #2 (Public Review):

The authors have used transcranial magnetic stimulation (TMS) and motor evoked potentials (MEPs) and TMS-electroencephalography (EEG) evoked potentials (TEPs) to determine how experimental heat pain could induce alterations in these metrics.
In Experiment 1 (n = 29), multiple sustained thermal stimuli were administered over the forearm, with the first, second, and third block of stimuli consisting of warm but non-painful (pre-pain block), painful heat (pain block) and warm but non-painful (post-pain block) temperatures respectively. Painful stimuli led to an increase in the amplitude of the fronto-central N45, with a larger increase associated with higher pain ratings. Experiments 2 and 3 studied the correlation between the increase in the N45 in pain and the effects of a sham stimulation protocol/higher stimulation intensity. They found that the centro-frontal N45 TEP was decreased in acute pain. The study comes from a very strong group in the pain fields with long experience in psychophysics, experimental pain, neuromodulation, and EEG in pain. They are among the first to report on changes in cortical excitability as measured by TMS-EEG over M1. While their results are in line with reductions seen in motor-evoked responses during pain and effort was made to address possible confounding factors (study 2 and 3), there are some points that need attention. In my view the most important are:

1) The method used to calculate the rest motor threshold, which is likely to have overestimated its true value : calculating highly abnormal RMT may lead to suprathreshold stimulations in all instances (Experiment 3) and may lead to somatosensory "contamination" due to re-afferent loops in both "supra" and "infra" (aka. less supra) conditions.

The method used to assess motor threshold was the TMS motor threshold Assessment Tool (MTAT) which estimates motor threshold using maximum likelihood parametric estimation by sequential testing (Awiszus et al., 2003; Awiszus and Borckardt, 2011). This was developed as a quicker alternative for calculating motor threshold compared to the traditional Rossini-Rothwell method which involves determining the lowest intensity that evokes at least 5/10 MEPs of at least 50 microvolts. The method has been shown to achieve the same accuracy of determining motor threshold as the traditional Rossini-Rothwell method, but with fewer pulses (Qi et al., 2011; Silbert et al., 2013).

We have now made this clearer in the manuscript:

“The RMT was determined using the TMS motor thresholding assessment tool, which estimates the TMS intensity required to induce an MEP of 50 microvolts with a 50% probability using maximum likelihood parametric estimation by sequential testing (Awiszus, 2003; Awiszus & Borckardt, 2011). This method has been shown to achieve the accuracy of methods such as the Rossini-Rothwell method (Rossini et al., 1994; Rothwell et al., 1999) but with fewer pulses (Qi, Wu, & Schweighofer, 2011; Silbert, Patterson, Pevcic, Windnagel, & Thickbroom, 2013). The test stimulus intensity was set at 110% RMT to concurrently measure MEPs and TEPs during pre-pain, pain and post-pain blocks.”

Therefore, the high RMTs in our study cannot be explained by the threshold assessment method. Instead, they are likely explained by aspects of the experimental setup that increased the distance between the TMS coil and the scalp, including the layer of foam placed over the coil, the EEG cap and the fact that the electrodes we used had a relatively thick profile. This has been explained in the paper:

“We note that the relatively high RMTs are likely due to aspects of the experimental setup that increased the distance between the TMS coil and the scalp, including the layer of foam placed over the coil, the EEG cap and relatively thick electrodes (6mm)”

Awiszus, F. (2003). TMS and threshold hunting. In Supplements to Clinical neurophysiology (Vol. 56, pp. 13-23). Elsevier.

Qi, F., Wu, A. D., & Schweighofer, N. (2011). Fast estimation of transcranial magnetic stimulation motor threshold. Brain stimulation, 4(1), 50-57.

Silbert, B. I., Patterson, H. I., Pevcic, D. D., Windnagel, K. A., & Thickbroom, G. W. (2013). A comparison of relative-frequency and threshold-hunting methods to determine stimulus intensity in transcranial magnetic stimulation. Clinical Neurophysiology, 124(4), 708-712.

2) The low number of pulses used for TEPs (close to ⅓ of the usual and recommended)

We agree that increasing the number of pulses can increase the signal to noise ratio. During piloting, participants were unable to tolerate the painful stimulus for long periods of time and we were required to minimize the number of pulses per condition.

We note that there is no set advised number of trials in TMS-EEG research. According to the recommendations paper, the number of trials should be based on the outcome measure e.g., TEP peaks vs. frequency domain measures vs. other measures and based on previous studies investigating test-retest reliability (Hernandez-Pavon et al., 2023). The choice of 66 pulses per condition was based on the study by Kerwin et al., (2018) showing that optimal concordance between TEP peaks can be found with 60-100 TMS pulses delivered in the same run (as in the present study). The concordance was particularly higher for the N40 peak at prefrontal electrodes, which was the key peak and electrode cluster in our study. We have made this clearer:

“Current recommendations (Hernandez-Pavon et al., 2023) suggest basing the number of TMS trials per condition on the key outcome measure (e.g., TEP peaks vs. frequency measures) and based on previous test-retest reliability studies. In our study the number of trials was based on a test-retest reliability study by (Kerwin, Keller, Wu, Narayan, & Etkin, 2018) which showed that 60 TMS pulses (delivered in the same run) was sufficient to obtain reliable TEP peaks (i.e., sufficient within-individual concordance between the resultant TEP peaks of each trial).”

Further supporting the reliability of the TEP data in our experiment, we note that the scalp topographies of the TEPs for active TMS at various timepoints (Figures 5, 7 and 9) were similar across all three experiments, especially at 45 ms post-TMS (frontal negative activity, parietal-occipital positive activity).

In addition to this, the interclass correlation coefficient (Two-way fixed, single measure) for the N45 to active suprathreshold TMS across timepoints for each experiment was 0.90 for Experiment 1 (across pre-pain, pain, post-pain time points), 0.74 for Experiment 2 (across pre-pain and pain conditions), and 0.95 for Experiment 3 (across pre-pain conditions). This suggests that even with the fluctuations in the N45 induced by pain, the N45 for each participant was stable across time, further supporting the reliability of our data. These ICCs are now reported in the supplementary material (subheading: Test-retest reliability of N45 Peaks).

Hernandez-Pavon, J. C., Veniero, D., Bergmann, T. O., Belardinelli, P., Bortoletto, M., Casarotto, S., ... & Ilmoniemi, R. J. (2023). TMS combined with EEG: Recommendations and open issues for data collection and analysis. Brain Stimulatio, 16(3), 567-593

Kerwin, L. J., Keller, C. J., Wu, W., Narayan, M., & Etkin, A. (2018). Test-retest reliability of transcranial magnetic stimulation EEG evoked potentials. Brain stimulation, 11(3), 536-544.

Lack of measures to mask auditory noise.

In TMS-EEG research, various masking methods have been proposed to suppress the somatosensory and auditory artefacts resulting from TMS pulses, such as white noise played through headphones to mask the click sound (Ilmoniemi and Kičić, 2010), and a thin layer of foam placed between the TMS coil and EEG cap to minimize the scalp sensation (Massimini et al., 2005). However, recent studies have shown that even when these methods are used, sensory contamination of TEPs is still present, as shown by studies that show commonalities in the signal between active and sensory sham conditions that mimic the auditory/somatosensory aspects of real TMS (Biabani et al., 2019; Conde et al., 2019; Rocchi et al., 2021). This has led many authors (Biabani et al., 2019; Conde et al., 2019) to recommend the use of sham conditions to control for sensory contamination. To separate the direct cortical response to TMS from sensory evoked activity, Experiment 2 included a sham TMS condition that mimicked the auditory/somatosensory aspects of active TMS to determine whether any alterations in the TEP peaks in response to pain were due to changes in sensory evoked activity associated with TMS, as opposed to changes in cortical excitability. Therefore, the lack of auditory masking does not impact the main conclusions of the paper.

We have made this clearer:

“… masking methods have been used to suppress these sensory inputs, (Ilmoniemi and Kičić, 2010; Massimini et al., 2005). However recent studies have shown that even when these methods are used, sensory contamination of TEPs is still present, as shown by commonalities in the signal between active and sensory sham conditions that mimic the auditory/somatosensory aspects of real TMS (Biabani et al., 2019; Conde et al., 2019; Rocchi et al., 2021). This has led many leading authors (Biabani et al., 2019; Conde et al., 2019) to recommend the use of sham conditions to control for sensory contamination.”

Ilmoniemi, R. J., & Kičić, D. (2010). Methodology for combined TMS and EEG. Brain topography, 22, 233-248.

Massimini, M., Ferrarelli, F., Huber, R., Esser, S. K., Singh, H., & Tononi, G. (2005). Breakdown of cortical effective connectivity during sleep. Science, 309(5744), 2228-2232.

Biabani, M., Fornito, A., Mutanen, T. P., Morrow, J., & Rogasch, N. C. (2019). Characterizing and minimizing the contribution of sensory inputs to TMS-evoked potentials. Brain stimulation, 12(6), 1537-1552.

Conde, V., Tomasevic, L., Akopian, I., Stanek, K., Saturnino, G. B., Thielscher, A., ... & Siebner, H. R. (2019). The non-transcranial TMS-evoked potential is an inherent source of ambiguity in TMS-EEG studies. Neuroimage, 185, 300-312.

Rocchi, L., Di Santo, A., Brown, K., Ibáñez, J., Casula, E., Rawji, V., ... & Rothwell, J. (2021). Disentangling EEG responses to TMS due to cortical and peripheral activations. Brain stimulation, 14(1), 4-18.

3) A supra-stimulus heat stimulus not based on individual HPT, that oscillates during the experiment and that lead to large variations in pain intensity across participants is unfortunate.

The choice of whether to calibrate or fix stimulus intensity is a contentious question in experimental pain research. A recent discussion by Adamczyk et al., (2022) explores the pros and cons of each approach and recommends situations where one method may be preferred over the other. That paper suggests that the choice of the methodology is related to the research question – when the main outcome of the research is objective (neurophysiological measures) and researchers are interested in the variability in pain ratings, the fixed approach is preferrable. Given we explored the relationship between MEP/N45 modulation by pain and pain intensity, this question is better explored by using the same stimulus intensity for all participants, as opposed to calibrating the intensity to achieve a similar level of pain across participants.

We have made this clearer:

“Given we were interested in the individual relationship between pain and excitability changes, the fixed temperature of 46ºC ensured larger variability in pain ratings as opposed to calibrating the temperature of the thermode for each participant (Adamczyk et al., 2022).”.

Adamczyk, W. M., Szikszay, T. M., Nahman-Averbuch, H., Skalski, J., Nastaj, J., Gouverneur, P., & Luedtke, K. (2022). To calibrate or not to calibrate? A methodological dilemma in experimental pain research. The Journal of Pain, 23(11), 1823-1832.

So is the lack of report on measures taken to correct for a fortuitous significance (multiple comparison correction) in such a huge number of serial paired tests.

Note that we used a Bayesian approach for all analyses as opposed to the traditional frequentist approach. In contrast to the frequentist approach, the Bayesian approach does not require corrections for multiple comparisons (Gelman et al., 2000) given that they provide a ratio representing the strength of evidence for the null vs. alternative hypotheses as opposed to accepting or rejecting the null hypothesis based on p-values. As such, throughout the paper, we frame our interpretations and conclusions based on the strength of evidence (e.g. anecdotal/weak, moderate, strong, very strong) as opposed to referring to the significance of the effects.

Gelman A, Tuerlinckx F. (2000). Type S error rates for classical and Bayesian single and multiple comparison procedures. Computational statistics, 15(3):373-90.

Reviewer #3 (Public Review):

The present study aims to investigate whether pain influences cortical excitability. To this end, heat pain stimuli are applied to healthy human participants. Simultaneously, TMS pulses are applied to M1 and TMS-evoked potentials (TEPs) and pain ratings are assessed after each TMS pulse. TEPs are used as measures of cortical excitability. The results show that TEP amplitudes at 45 msec (N45) after TMS pulses are higher during painful stimulation than during non-painful warm stimulation. Control experiments indicate that auditory, somatosensory, or proprioceptive effects cannot explain this effect. Considering that the N45 might reflect GABAergic activity, the results suggest that pain changes GABAergic activity. The authors conclude that TEP indices of GABAergic transmission might be useful as biomarkers of pain sensitivity.

Pain-induced cortical excitability changes is an interesting, timely, and potentially clinically relevant topic. The paradigm and the analysis are sound, the results are mostly convincing, and the interpretation is adequate. The following clarifications and revisions might help to improve the manuscript further.

1) Non-painful control condition. In this condition, stimuli are applied at warmth detection threshold. At this intensity, by definition, some stimuli are not perceived as different from the baseline. Thus, this condition might not be perfectly suited to control for the effects of painful vs. non-painful stimulation. This potential confound should be critically discussed.

In Experiment 3, we also collected warmth ratings to confirm whether the pre-pain stimuli were perceived as different from baseline. This detail has been added to them methods:

“In addition to the pain rating in between TMS pulses, we collected a second rating for warmth of the thermal stimulus (0 = neutral, 10 = very warm) to confirm that the participants felt some difference in sensation relative to baseline during the pre-pain block. This data is presented in the supplementary material”.

We did not include these data in the initial submission but have now included it in the supplemental material. These data showed warmth ratings were close to 2/10 on average. This confirms that the non-painful control condition produced some level of non-painful sensation.

2) MEP differences between conditions. The results do not show differences in MEP amplitudes between conditions (BF 1.015). The analysis nevertheless relates MEP differences between conditions to pain ratings. It would be more appropriate to state that in this study, pain did not affect MEP and to remove the correlation analysis and its interpretation from the manuscript.

The interindividual relationship between changes in MEP amplitude and individual pain rating is statistically independent from the overall group level effect of pain on MEP amplitude. Therefore, conclusions for the individual and group level effects can be made independently.

It is also important to note that in the pain literature, there is now increasing emphasis placed on investigating the individual level relationship between changes in cortical excitability and pain as opposed to the group level effect (Seminowicz et al., 2019; Summers et al., 2019). As such, it is important to make these results readily available for the scientific community.

We have made this clearer:

‘As there is now increasing emphasis placed on investigating the individual level relationship between changes in cortical excitability and pain and not only the group level effect, (Chowdhury et al., 2022; Seminowicz et al., 2018; Seminowicz, Thapa, & Schabrun, 2019; Summers et al., 2019) we also investigated the correlations between pain ratings and changes in MEP (and TEP) amplitude”

Chowdhury, N. S., Chang, W. J., Millard, S. K., Skippen, P., Bilska, K., Seminowicz, D. A., & Schabrun, S. M. (2022). The Effect of Acute and Sustained Pain on Corticomotor Excitability: A Systematic Review and Meta-Analysis of Group and Individual Level Data. The Journal of Pain, 23(10), 1680-1696.

Summers, S. J., Chipchase, L. S., Hirata, R., Graven-Nielsen, T., Cavaleri, R., & Schabrun, S. M. (2019). Motor adaptation varies between individuals in the transition to sustained pain. Pain, 160(9), 2115-2125.

Seminowicz, D. A., Thapa, T., & Schabrun, S. M. (2019). Corticomotor depression is associated with higher pain severity in the transition to sustained pain: a longitudinal exploratory study of individual differences. The Journal of Pain, 20(12), 1498-1506.

3) Confounds by pain ratings. The ISI between TMS pulses is 4 sec and includes verbal pain ratings. Considering this relatively short ISI, would it be possible that verbal pain ratings confound the TEP? Moreover, could the pain ratings confound TEP differences between conditions, e.g., by providing earlier ratings when the stimulus is painful? This should be carefully considered, and the authors might perform control analyses.

It is unlikely that the verbal ratings contaminated the TEP response as the subsequent TMS pulse was not delivered until the verbal rating was complete and given that each participant was cued by the experimenter to provide the pain rating after each pulse (rather than the participant giving the rating at any time). As such, it would not be possible for participants to provide earlier ratings to more painful stimuli.

We have made this clearer:

"To avoid contamination of TEPs by verbal ratings, the subsequent TMS pulse was not delivered until the verbal rating was complete, and the participant was cued by the experimenter to provide the pain rating after each pulse.”

4) Confounds by time effects. Non-painful and painful conditions were performed in a fixed order. Potential confounds by time effects should be carefully considered.

Previous research suggests that pain alters neural excitability even after pain has subsided. In a recent meta-analysis (Chowdhury et al., 2022) we found effect sizes of 0.55-0.9 for MEP reductions 0-30 minutes after pain had resolved. As such, we avoided intermixing pain and warm blocks given subsequent warm blocks would not serve as a valid baseline, as each subsequent warm block would have residual effects from the previous pain blocks.

Chowdhury, N. S., Chang, W. J., Millard, S. K., Skippen, P., Bilska, K., Seminowicz, D. A., & Schabrun, S. M. (2022). The Effect of Acute and Sustained Pain on Corticomotor Excitability: A Systematic Review and Meta-Analysis of Group and Individual Level Data. The Journal of Pain, 23(10), 1680-1696.

At the same time, given there was no conclusive evidence for a difference in N45 amplitude between pre-pain and post-pain conditions of Experiment 1 (Supplementary Figure 1), it is unlikely that the effect of pain was an artefact of time i.e., the explanation that successive thermal stimuli applied to the skin results an increase in the N45, regardless of whether the stimuli are painful or not. We will make this point in our next revision.

We have discussed this issue:

“Lastly, future research should consider replicating our experiment using intermixed pain and no pain blocks, as opposed to fixed pre-pain and pain blocks, to control for order effects i.e., the explanation that successive thermal stimuli applied to the skin results an increase in the N45 peak, regardless of whether the stimuli are painful or not. However, we note that there was no conclusive evidence for a difference in N45 peak amplitude between pre-pain and post-pain conditions of Experiment 1 (Supplementary Figure 1), suggesting it is unlikely that the observed effects were an artefact of time.”

5) Data availability. The authors should state how they make the data openly available.

We have uploaded the MEP, TEP and pain data on the Open science framework https://osf.io/k3psu/

Reviewer #1 (Recommendations For The Authors):

I think the study is quite solid and I only have very minor recommendations for the authors:

• Introduction, p. 3: "Functional magnetic resonance imaging has helped us understand where in the brain pain is processed". This is an overstatement. fMRI provides us with potential biomarkers (e.g. "the pain signature"), but the specificity of these responses for pain is debated and we still do not know where in the brain pain is processed.

We have amended to:

“functional magnetic resonance imaging has assisted in the localization of brain structures implicated in pain processing”

• Introduction, p. 5: "neural baseline" should be "neutral baseline"?

We thank the reviewer for identifying this – this has now been amended.

Reviewer #2 (Recommendations For The Authors):

INTRODUCTION

The introduction mentions how important extra-motor areas can be explored by TMS-EEG, then the effects of DLPFC rTMS on TEPs ... but you do not explore the DLPFC... Perhaps the introduction should be reframed.

The current work explores cortical excitability throughout the brain (as shown in our cluster-based permutation and source localization analyses), so our investigations are in line with the introductions statement about the importance of studying non-motor areas.

The reference to DLPFC rTMS was to highlight current existing research that has applied TMS-EEG to understand pain. It was not used as a methodological rationale to investigate the DLPFC in the present study. To make the research gap clearer, we state:

“While these studies assist us in understanding whether TEPs might mediate rTMS-induced pain reductions, no study has investigated whether TEPs are altered in direct response to pain”

Lignes 63-65 the term "TMS" is used to refer to motor corticospinal excitability measures, in contrast to TMS-EEG measures of TEPs. Then the authors come back to TMS-EEG and then again back to MEPs. This is rather confusing: TMS means TMS... the concept of MEP/ motor corticospinal excitability measures is not intuitive when using the term "TMS". I suggest using motor corticospinal excitability measures when referring to MEP/MEP-based measures of cortical excitability...) and M1TMS-EEG-evoked potentials (usually abbreviated to TEPs) to refer to TMS-EEG responses as measured here.

Throughout the manuscript, we now use the term TEPs when referring to TMS-EEG measures, and MEPs when referring to TMS-EMG measure. The use of TEPs vs. MEPs will make it easier for readers to follow which measures we are referring to.

Line 83: "As such, the precise origin of the pain mechanism cannot be localized." Please rephrase, the sentence conveys the idea that it is indeed possible to localize the origin of a pain mechanism with a different approach, and we know this is not currently possible, irrespective of the methodological setup.

We have replaced this with:

“This makes it unclear as to whether pain processes occur at the cortical, spinal or peripheral level.”

How can one predetermine the temperature that will be perceived as painful by someone else, and not base it on individual HPT? This is against principles of psychophysics. Please comment. Attesting all participants had HPT below 46 is important, but then being stimulated at 46C when our HPT is 45C is different from when our HPT is 39C. Please explain why the pain intensity was not standardised based on individual HPT.

Please refer to our response to the public review related to the issue

Line 38: "if we had used an alternative design with blocks of warm stimuli intermixed with blocks of painful stimuli, the warm stimuli blocks would not serve as a valid non-painful baseline". I do not understand why it is not possible to have a pain-free baseline, followed by a pain/warm sequence.

In our study, we had the choice of either intermixing blocks or to use a fixed sequence. Previous research suggests that pain alters neural excitability even after pain has subsided. In a recent meta-analysis (Chowdhury et al., 2022) we found effect sizes of 0.55-0.9 for MEP reductions 0-30 minutes after pain had resolved. As such, we avoided intermixing pain and warm blocks given subsequent warm blocks would not serve as a valid baseline, as each subsequent warm block would have residual effects from the previous pain blocks.

We have updated the manuscript to be clearer about why we used a fixed sequence:

“The pre-pain/pain/post-pain design has been commonly used in the TMS-MEP pain literature, as many studies have demonstrated strong changes in corticomotor excitability that persist beyond the painful period. Indeed, in a systematic review, we showed effect sizes of 0.55-0.9 for MEP reductions 0-30 minutes after pain had resolved (Chowdhury et al., 2022). As such, if we had used an alternative design with blocks of warm stimuli intermixed with blocks of painful stimuli, the warm stimuli blocks would not serve as a valid non-painful baseline”

Chowdhury, N. S., Chang, W. J., Millard, S. K., Skippen, P., Bilska, K., Seminowicz, D. A., & Schabrun, S. M. (2022). The Effect of Acute and Sustained Pain on Corticomotor Excitability: A Systematic Review and Meta-Analysis of Group and Individual Level Data. The Journal of Pain, 23(10), 1680-1696.

Please explain, and provide evidence that stimulation of people with predetermined temperatures is able to create warm/pain/warm sensations, without entraining pain in the last warm stimulation.

A previous study by Dube et al. (2011) used sequences of warm (36°C), painful and neutral (32° C) and found that participants did not experience pain at any time when the temperature was at a warm temperature of 36°C. We have now cited this study:

“Based on a previous study (Dubé & Mercier, 2011) which also used sequences of painful (50ºC) and warm (36°C) thermal stimuli, we did not anticipate that the stimulus in the pain block would entrain pain in the post-pain block”

Dubé, J. A., & Mercier, C. (2011). Effect of pain and pain expectation on primary motor cortex excitability. Clinical neurophysiology, 122(11), 2318-2323.

METHODS

It is not clear if participants with chronic pain, present in 20% of the general population, were excluded. If they were, please provide "how" in methods.

We excluded participants with a history or presence of acute/chronic pain. This has now been clarified:

“Participants were excluded if they had a history of chronic pain condition or any current acute pain”

Line 489: the definition of warm detection threshold is unusual, please provide a reference.

We used an identical method to Furman et al., (2020). We have made the reference to this clearer: “Warmth, cold and pain thresholds were assessed in line with a previous study (Furman et al., 2020)”

Furman, A. J., Prokhorenko, M., Keaser, M. L., Zhang, J., Chen, S., Mazaheri, A., & Seminowicz, D. A. (2020). Sensorimotor peak alpha frequency is a reliable biomarker of prolonged pain sensitivity. Cerebral Cortex, 30(12), 6069-6082.

In Experiment 2, please explain how the lack of randomisation between "pre-pain" and "pain" may have influenced results.

Given we tried to replicate Experiment 1’s methodology as close as possible (to isolate the source of the effect from Experiment 1) we chose to repeat the same sequence of blocks as Experiment 1: pre-pain followed by pain.

Given there was no conclusive evidence for a difference in N45 amplitude between pre-pain and post-pain conditions of Experiment 1 (Supplementary Figure 1), it is unlikely that the effect of pain was an order effect i.e., the explanation that successive thermal stimuli applied to the skin results an increase in the N45, regardless of whether the stimuli are painful or not.

We now discuss the issue of randomization:

“Lastly, future research should consider replicating our experiment using intermixed pain and no pain blocks, as opposed to fixed pre-pain and pain blocks, to control for order effects i.e. the explanation that successive thermal stimuli applied to the skin results an increase in the N45 peak, regardless of whether the stimuli are painful or not. However, we note that there was no conclusive evidence for a difference in N45 peak amplitude between pre-pain and post-pain conditions of Experiment 1 (Supplementary Figure 1), suggesting it is unlikely that the observed effects were an artefact of time”

Also, in Methods in general, disclose how pain intensity was assessed, and how.

Pain intensity was assessed using a verbal rating scale (0 = no pain, and 10 = most pain imaginable). We have provided more detail:

“During each 40 second thermal stimulus, TMS pulses were manually delivered, with a verbal pain rating score (0 = no pain, and 10 = worst pain imaginable) obtained between pulses. To avoid contamination of TEPs by verbal ratings, the subsequent TMS pulse was not delivered until the verbal rating was complete, and the participant was cued by the experimenter to provide the pain rating after each pulse”

Please explain how auditory masking was made during data collection.

Auditory masking noise was not played through the headphones, given that Experiment 2 controlled for auditory evoked potentials. We have made this clearer:

“Auditory masking was not used. Instead, auditory evoked potentials resulting from the TMS click sound were controlled for in Experiment 2”

Please explain if online TEP monitoring was used during data collection

Online TEP monitoring was not available with our EEG software. We have made this clearer in the manuscript:

“Online TEP monitoring was not available with the EEG software”

Line 499: what is subthreshold TMS here? You are measuring TEPs, and not MEPs initially, so you may have a threshold for MEPs and TEPs, which are not the same.

The intensity was calibrated relative to the MEP response (rather than TEP response) - this has now been clarified:

“… and the inclusion of a subthreshold TMS (90% of resting motor threshold) condition intermixed within both the pre-pain and pain blocks.”

Please provide a reference and a figure to illustrate the electric stimulation used in the sham procedure in Study 2

The apparatus for the electrical stimulation is shown in Figure 7A, and was based on previous papers using electrical stimulation over motor cortex to simulate the somatosensory aspect of real TMS (Chowdhury et al., 2022; Gordon et al., 2022; Rocchi et al., 2021). We have made this clearer:

“Electrical stimulation was based on previous studies attempting to simulate the somatosensory component of active TMS (Chowdhury et al., 2022; Gordon et al., 2022; Rocchi et al., 2021)”

Gordon, P. C., Jovellar, D. B., Song, Y., Zrenner, C., Belardinelli, P., Siebner, H. R., & Ziemann, U. (2021). Recording brain responses to TMS of primary motor cortex by EEG–utility of an optimized sham procedure. Neuroimage, 245, 118708.

Chowdhury, N. S., Rogasch, N. C., Chiang, A. K., Millard, S. K., Skippen, P., Chang, W. J., ... & Schabrun, S. M. (2022). The influence of sensory potentials on transcranial magnetic stimulation–Electroencephalography recordings. Clinical Neurophysiology, 140, 98-109.

Rocchi, L., Di Santo, A., Brown, K., Ibánez, J., Casula, E., Rawji, V., ... & Rothwell, J. (2021). Disentangling EEG responses to TMS due to cortical and peripheral activations. Brain stimulation, 14(1), 4-18.

It is not so common to use active electrodes for TMS-EEG. Please confirm the electrodes used and if they are c-ring TMS compatible and provide reference if otherwise (or actual papers recommending active ones)

To be more specific about the electrode type we have indicated:

“Signals were recorded from 63 TMS-compatible active electrodes (6mm height, 13mm width), embedded in an elastic cap (ActiCap, Brain Products, Germany), in line with the international 10-10 system”

A paper directly comparing TEPs between active and passive electrodes found no difference between the two and concluded TEPs can be reliably obtained using active electrodes (Mancuso et al., 2021). There is also evidence that active electrodes have better signal quality than passive electrodes at higher impedance levels (Laszlo et al., 2014).

This information has now been added to the paper:

“Active electrodes result in similar TEPs (both magnitude and peaks) to more commonly used passive electrodes (Mancuso et al., 2021). There is also evidence that active electrodes have higher signal quality than passive electrodes at higher impedance levels (Laszlo, Ruiz-Blondet, Khalifian, Chu, & Jin, 2014).”

There is a growing literature showing that monophonic pulses are not reliable for TEPs when compared to biphasic ones, please provide references. https://doi.org/10.1016/j.brs.2023.02.009

The reference provided by the reviewer states that biphasic and monophasic pulses both have advantages and disadvantages, rather than stating “monophonic pulses are not reliable for TEPs”. While there is some evidence that the artefacts resulting from monophasic pulses are larger than biphasic pulses, the EEG signal still returns to baseline levels within 5ms of the TMS pulse (Rogasch et al., 2013). Moreover, one paper (Casula et al. 2018) found that the resultant TEPs evoked by monophasic pulses are larger than those resulting from biphasic pulses. The authors postulated that monophasic pulses are more effective at activating widespread cortical areas than biphasic pulses. Ultimately the reference provided by the reviewer concludes that “effect of pulse shape on TEPs has not been systematically investigated and more studies are needed”.

Rogasch, N. C., Thomson, R. H., Daskalakis, Z. J., & Fitzgerald, P. B. (2013). Short-latency artifacts associated with concurrent TMS–EEG. Brain stimulation, 6(6), 868-876.

Casula, E. P., Rocchi, L., Hannah, R., & Rothwell, J. C. (2018). Effects of pulse width, waveform and current direction in the cortex: A combined cTMS-EEG study. Brain stimulation, 11(5), 1063-1070.

In most heads, a pulse in the PA direction is not obtained by a coil oriented 45o to the midline. The later induced later-medial pulses, good to obtain MEPs

We followed previous studies measuring MEPs from the ECRB elbow muscle (Schabrun et al., 2016; de Martino et al., 2019) whereby the TMS coil handle was angled at 45 degrees relative to the midline in order to induce a posterior-anterior current. We are not aware of literature that shows that the 45 degrees orientation does not induce a posterior anterior current in most heads.

Schabrun, S. M., Christensen, S. W., Mrachacz-Kersting, N., & Graven-Nielsen, T. (2016). Motor cortex reorganization and impaired function in the transition to sustained muscle pain. Cerebral Cortex, 26(5), 1878-1890.

De Martino, E., Seminowicz, D. A., Schabrun, S. M., Petrini, L., & Graven-Nielsen, T. (2019). High frequency repetitive transcranial magnetic stimulation to the left dorsolateral prefrontal cortex modulates sensorimotor cortex function in the transition to sustained muscle pain. Neuroimage, 186, 93-102.

The definition of RMT is (very) unusual. RMT provides small 50microV MEPs in 50% of times. If you obtain MEPs at 50microV you are supra threshold!

The TMS motor threshold assessment tool calculates threshold in the same manner as other threshold tools – it calculates the intensity that elicits an MEP of 50 microvolts, 50% of the time. We have made this clearer:

“The RMT was determined using the TMS motor thresholding assessment tool, which estimates the TMS intensity required to induce an MEP of 50 microvolts with a 50% probability using maximum likelihood parametric estimation by sequential testing (Awiszus and Borckardt, 2011). This method has been shown to achieve the accuracy of methods such as the Rossini-Rothwell method (Rossini et al., 1994; Rothwell et al., 1999) but with fewer pulses (Qi et al., 2011; Silbert et al., 2013).”

Please inform the inter TMS pulse interval used of TEPs and whether they were randomly generated.

The pulses were delivered manually – the interval was not randomly generated – as stated:

“As TMS was delivered manually, there was no set interpulse interval. However, the 40 second stimulus duration allowed for 11 pulses for each heat stimulus …. (~ 4 seconds in between …)”

Why have you stimulated suprathreshold on M1 when assessing TEP´s? The whole idea is that large TEPs can be obtained at lower intensities below real RMT and that prevents re-entering loops of somatosensory and joint movement inputs that insert "noise" to the TEPs.

The suprathreshold intensity was used to concurrently measure MEPs during pre-pain, pain and post-pain blocks.

We have made this clearer:

“The test stimulus intensity was set at 110% RMT to concurrently measure MEPs and TEPs during pre-pain, pain and post-pain blocks.”

The influence of re-afferent muscle activity was controlled for in Experiment 3.

Did you assess pain intensity after each of the TEP pulses? Please discuss how such a cognitive task may have influenced results

Pain intensity was assessed after each TMS pulse, as stated:

“TMS pulses were manually delivered, with a verbal pain rating score (0 = no pain, and 10 = most pain imaginable) obtained between pulses”

Reviewer 3 also brought up a concern of whether the verbal rating task might have influenced the TEPs. However, it is unlikely that the task contaminated the TEP response as the subsequent TMS pulse was not delivered until the verbal rating was complete and given that each participant was cued by the experimenter to provide the pain rating after each pulse (rather than the participant giving the rating at any time). We have made this clearer where we state:

“To avoid contamination of TEPs by verbal ratings, the subsequent TMS pulse was not delivered until the verbal rating was complete, and the participant was cued by the experimenter to provide the pain rating after each pulse”

The QST approach is unusual. Please confirm the sequence of CDT, WDT and HPT were not randomised and that no interval beyond 6sec were used. Proper references are welcome.

In line with a previous study (Furman et al., 2020), the sequence of the CPT, WDT and HPT were not randomized, and the interval was not more than 6 seconds.

We have made this clearer:

“A total of three trials was conducted for each test to obtain an average, with an interstimulus interval of six seconds. The sequence of cold, warmth and pain threshold was the same for all participants (Furman et al. 2020)”

Performing 60 pulses for TEPs is unusual, and against the minimum number in recommendations

Please explain and comment.https://doi.org/10.1016/j.brs.2023.02.009

Please refer to our previous response to this concern in the public reviews.

Line 578: when you refer to "heat" the reader may confound warm/heat with heat meaning suprathreshold. Please revise the wording.

We have now replaced the word heat stimulus with thermal stimulus.

Why were Bayesian statistics used instead as frequentist ones?

We have made this clearer:

“Given we were interested in determining the evidence for pain altering TEP peaks in certain conditions (e.g., active TMS) and pain not altering TEP peaks in other conditions (sham TMS), we used a Bayesian approach as opposed to a frequentist approach, which considers the strength of the evidence for the alternative vs. null hypothesis”

RESULTS

There is a huge response with high power after 100ms- Please discuss if you believe auditory potentials may have influenced it.

It is indeed possible that auditory potentials were present at 100ms. We now state:

“Indeed, the signal at ~100ms post-TMS from Experiment 1 may reflect an auditory N100 response”

The presence of auditory contamination does not impact the main conclusions of the paper given this was controlled for in Experiment 2.

Please discuss how pain ranging from 3-10 may have influenced results in the "PAIN" situation,

It is anticipated that the fixed thermal stimulus intensity approach would lead to large variations in pain ratings (Adamczyk et al., 2022). This is a recommended approach when the aim of the research is to determine relationships between neurophysiological measures and individual differences in pain sensitivity (Adamczyk et al., 2022). Indeed, we were interested in whether alterations in neurophysiological measures were associated with pain intensity, and we found that higher pain ratings were associated with smaller reductions in MEP amplitude and larger increases in N45 amplitude.

Adamczyk, W. M., Szikszay, T. M., Nahman-Averbuch, H., Skalski, J., Nastaj, J., Gouverneur, P., & Luedtke, K. (2022). To calibrate or not to calibrate? A methodological dilemma in experimental pain research. The Journal of Pain, 23(11), 1823-1832.

Please indicate if any participants offered pain after warm stimulation ( possible given secondary hyperalgesia after so many plateaux of heat stimulation).

As stated in the results “All participants reported 0/10 pain during the pre-pain and post-pain blocks”.

Please discuss the potential effects of having around 10% of "bad channels) In average per experiment per participants, its impacts in source localisation and in TEP measurement. Same for >5 epochs excluded by participant.

The number of bad channels has been incorrectly stated by the reviewer as being 10% on average per experiment per participant, whereas the correct number of reported bad channels was 3%, 4.7% and 9.8% for Experiment 1, 2 and 3 respectively (see supplementary material). These numbers are below the accepted number of bad channels to interpolate (10%) in EEG pipelines (e.g., Debnath et al., 2020; Kayhan et al., 2022), so it is unlikely that our channel exclusions significantly influenced the quality of our source localization an TEP data.

Debnath, R., Buzzell, G. A., Morales, S., Bowers, M. E., Leach, S. C., & Fox, N. A. (2020). The Maryland analysis of developmental EEG (MADE) pipeline. Psychophysiology, 57(6), e13580.

Kayhan, E., Matthes, D., Haresign, I. M., Bánki, A., Michel, C., Langeloh, M., ... & Hoehl, S. (2022). DEEP: A dual EEG pipeline for developmental hyperscanning studies. Developmental cognitive neuroscience, 54, 101104.

The number of excluded epochs is unlikely to have influenced the results given there was evidence for no difference in the number of rejected epochs between conditions (E1 BF10 = 0.145, E2 BF10 = 0.27, E3 BF10 = 0.169 – these BFs have now been reported in the supplementary material), and given the reliability of the N45 was high (see response to previous comment on the number of trials per condition).

HPT of 42.9 {plus minus} 2.5{degree sign}C means many participants had HPT close to 46oC. Please discuss

While some participants did indeed have pain thresholds close to 46 degrees, they nonetheless reported pain during the test blocks. While such participants may have reported less pain compared to others, we aimed for larger variations in pain ratings, given one of the research questions was to determine why pain intensity differs between individuals (given the same noxious stimulus). Indeed, we showed that this variation was meaningful (pain intensity was related to alterations in N45 and MEP amplitude).

Please explain the sentence : line 139 "As such, if we had used an alternative design with blocks of warm stimuli intermixed with blocks of painful stimuli, the warm stimuli blocks would not serve as a valid non-painful baseline." I cannot see why.

Please refer to our previous point on why the fixed sequence was included.

And on the top of that heat was not individualised according to HPT.

Please refer to our previous point on why we used a fixed stimulus approach.

Sequences of warm/heat were not randomised. Please refer to our previous point on the why the sequence of blocks was not randomized.

Line 197: "However, as this is the first study investigating the effects of experimental pain on TEPsamplitude, there were no a priori regions or timepoints of interest to compare betweenconditions". This is not clear. It means you have not measured the activity (size of the N45) under the electrode closest to the TMS coil? The TEP is supposed to by higher under the stimulated target/respective corresponding electrode…

We are not aware of any current recommendations that state that the region of interest should be based on the site of stimulation. The advantage of TMS-EEG is that it allows characterisation of cortical excitability changes throughout the brain, not just the site of stimulation. We based our region of interest on a cluster-based permutation analysis, as recommended by Frömer, Maier, & Abdel Rahman, (2018)

Frömer, R., Maier, M., & Abdel Rahman, R. (2018). Group-level EEG-processing pipeline for flexible single trial-based analyses including linear mixed models. Frontiers in neuroscience, 12, 48.

Please explain where N45 values came from.

The N45 was calculated using the TESA peak function (Rogasch et al., 2017) which identifies a data point which is larger/smaller than +/- 5 data points within a specified time window (e,g, 40-70ms post-TMS as in the present study). Where multiple peaks are found, the amplitude of the largest peak is returned. Where no peak is found, the amplitude at the specified latency is returned.

Rogasch, N. C., Sullivan, C., Thomson, R. H., Rose, N. S., Bailey, N. W., Fitzgerald, P. B., ... & Hernandez-Pavon, J. C. (2017). Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software. Neuroimage, 147, 934-951.

If only the cluster assessment was made please provide the comparison between P45 from the target TMS channel location in pre pain vs pain.

We assume the reviewer is referring to the N45 rather than P45, and that by “target” TMS channel they are referring to the stimulated region.

We first clarify that there is no “target” channel given the motor hotspot differs between individuals and so the channel that is closest to the site of stimulation will always differ.

Secondly, as stated above, we are not aware of any current recommendations in TMS-EEG research that states that the region of interest for TEP analysis should be based on the site of stimulation. The advantage of TMS-EEG is that it allows characterisation of cortical excitability throughout the brain, not just the site of stimulation. If we based our ROI on the target channel only, we would lose valuable information about excitability changes occurring in other brain regions.

Lastly, the N45 was localized at frontocentral electrodes, which is also where the cluster differences emerged. As such, we do not believe it would be informative to compare N45 peak amplitude at the region of stimulation.

Also explain how correction for multiple comparisons was made

Please refer to our response to the public review related to this issue.

And report data from pain vs post-pain.

The pain vs. post-pain comparisons are now reported in the Supplementary material.

There is a strong possibility the response at N85 is an auditory /muscle signal. Please provide the location of this response.

We have opted not to include the topography at 85ms in the main paper as it would introduce too much clutter into the figures (which are already very dense), and because the topography was very similar to the topography at 100ms. As an example, for the reviewer, in Author response image 1 we have shown the topography for the pre-pain condition of Experiment 1.

Author response image 1.

Experiment 2: I have a strong impression both active TEPs and sham TEPs were contaminated by auditory (and muscle) noise. Please explain.

While it possible that auditory noise may have influenced TEPs in the active and sham groups, it does not impact the main conclusions of the paper, given that the purpose of the sham condition was to control for auditory and somatosensory stimulation resulting from TMS.

While muscle activity may also affect have influenced the TEPs in active and sham conditions, we used fastICA in all conditions to suppress muscle activity. The fastICA algorithm (Rogasch et al., 2017) runs an independent component analysis on the data, and classifies components as neural, TMS-evoked muscle, eye movements and electrode noise, based on a set of heuristic thresholding rules (e.g., amplitude, frequency and topography of the components). Components classified as TMS-evoked muscle/other muscle artefacts are then removed. In the supplementary material, we further report that the number of components removed did not differ between conditions, suggesting the impact of muscle artefacts are not larger in some conditions vs. others.

Rogasch, N. C., Sullivan, C., Thomson, R. H., Rose, N. S., Bailey, N. W., Fitzgerald, P. B., ... & Hernandez-Pavon, J. C. (2017). Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software. Neuroimage, 147, 934-951.

Experiment 3: One interpretation can be that both supra and sub-threshold TMS were leading to somatosensory re-afferent responses, based on the way RMT was calculated, which hyper estimate the RMT and delivers in reality 2 types of supra-threshold stimulations. Please discuss

Please refer to our response to the public review related to this issue.

Please provide correlation between N45 size and MEPs amplitudes.

This has now been included:

“There was no conclusive evidence of any relationship between alterations in MEP amplitude during pain, and alterations in N100, N45 and P60 amplitude during pain (see supplementary material).”<br /> The supporting statistics for these analyses have been included in the supplementary material.

DISCUSSION

Line 303: " The present study determined whether acute experimental pain induces alterations in cortical inhibitory and/or facilitatory activity observed in TMS-evoked potentials".

Well, no. The study assessed the N45, and was based on it. It did not really explore other metrics in a systematic fashion. P60 and N100 changes were not replicated in experiments 2 and 3..

We assume the reviewer is stating that we did not assess other TEP peaks (such as the N15, P30 and P180). However, we did indeed assess these peaks in a systematic fashion. First, we identified the ROI by using a cluster-based analysis. This is a recommended approach when the ROI is unclear (Frömer, Maier, & Abdel Rahman, 2018). We then analysed the TEP representing the mean voltage across the electrodes within the cluster, and then identified any differences in all peaks between conditions (not just the N45). This has been made clearer in the manuscript.

This has now been included:

“For all experiments, the mean TEP waveform of any identified clusters from Experiment 1 were plotted, and peaks (e.g., N15, P30, N45, P60, N100) were identified using the TESA peak function (Rogasch et al., 2017)”

Frömer, R., Maier, M., & Abdel Rahman, R. (2018). Group-level EEG-processing pipeline for flexible single trial-based analyses including linear mixed models. Frontiers in neuroscience, 12, 48.

And the N45 is not related to facilitatory or inhibitory activity, it is a measure of an evoked response indicating excitability

Evidence suggests the N45 is mediated by GABAAergic neurotransmission (inhibitory activity), as drugs which increase GABAA receptor activity increase the amplitude of the N45 (Premoli et al., 2014) and drugs which decrease GABAA receptor activity decrease the amplitude of the N45 (Darmani et al., 2016). As such, we and various other empirical papers (e.g., Bellardinelli et al., 2021; Noda et al., 2021; Opie at 2019 ) and review papers (Farzan & Bortoletto, 2022; Tremblay et al., 2019) have interpreted changes in the N45 peak as reflecting changes in cortical inhibitory/GABAA mediated activity.

Premoli, I., Castellanos, N., Rivolta, D., Belardinelli, P., Bajo, R., Zipser, C., ... & Ziemann, U. (2014). TMS-EEG signatures of GABAergic neurotransmission in the human cortex. Journal of Neuroscience, 34(16), 5603-5612.

Belardinelli, P., König, F., Liang, C., Premoli, I., Desideri, D., Müller-Dahlhaus, F., ... & Ziemann, U. (2021). TMS-EEG signatures of glutamatergic neurotransmission in human cortex. Scientific reports, 11(1), 8159.

Darmani, G., Zipser, C. M., Böhmer, G. M., Deschet, K., Müller-Dahlhaus, F., Belardinelli, P., ... & Ziemann, U. (2016). Effects of the selective α5-GABAAR antagonist S44819 on excitability in the human brain: a TMS–EMG and TMS–EEG phase I study. Journal of Neuroscience, 36(49), 12312-12320.

Noda, Y., Barr, M. S., Zomorrodi, R., Cash, R. F., Lioumis, P., Chen, R., ... & Blumberger, D. M. (2021). Single-pulse transcranial magnetic stimulation-evoked potential amplitudes and latencies in the motor and dorsolateral prefrontal cortex among young, older healthy participants, and schizophrenia patients. Journal of Personalized Medicine, 11(1), 54.

Farzan, F., & Bortoletto, M. (2022). Identification and verification of a'true'TMS evoked potential in TMS-EEG. Journal of neuroscience methods, 378, 109651.

Opie, G. M., Foo, N., Killington, M., Ridding, M. C., & Semmler, J. G. (2019). Transcranial magnetic stimulation-electroencephalography measures of cortical neuroplasticity are altered after mild traumatic brain injury. Journal of Neurotrauma, 36(19), 2774-2784.

Tremblay, S., Rogasch, N. C., Premoli, I., Blumberger, D. M., Casarotto, S., Chen, R., ... & Daskalakis, Z. J. (2019). Clinical utility and prospective of TMS–EEG. Clinical Neurophysiology, 130(5), 802-844.

Line 321: why have you not measured SEPs in experiment 3?

It is not possible to directly measure the somatosensory evoked potentials resulting from a TMS pulse, given that the TMS pulse produces a range of signals including cortical activity, muscle/eye blink responses, auditory responses, somatosensory responses and other artefacts. While some researchers attempt to isolate the SEP from TMS using pre-processing methods such as ICA, others use control conditions such as sensory sham conditions (to control for the “tapping” artefact) or subthreshold intensity conditions (to control for reafferent muscle activity), as we have done in Experiment 2 and 3 of our study.

We have now stated this in the manuscript:

“As it is extremely challenging to isolate and filter these auditory and somatosensory evoked potentials using pre-processing pipelines, masking methods have been used to suppress these sensory inputs, (Ilmoniemi and Kičić, 2010; Massimini et al., 2005). However recent studies have shown that even when these methods are used, sensory contamination of TEPs is still present, as shown by commonalities in the signal between active and sensory sham conditions that mimic the auditory/somatosensory aspects of real TMS (Biabani et al., 2019; Conde et al., 2019; Rocchi et al., 2021). This has led many leading authors (Biabani et al., 2019; Conde et al., 2019) to recommend the use of sham conditions to control for sensory contamination”

Line 365: SICI is dependent on GABAa activity. But the way the text is written if conveys the idea that TMS pulses "activate" GABA receptors, which is weird...Please rephrase.

This has now been reworded.

“SICI refers to the reduction in MEP amplitude to a TMS pulse that is preceded 1-5ms by a subthreshold pulse, with this reduction believed to be mediated by GABAA neurotransmission (Chowdhury et al., 2022)”

Reviewer #3 (Recommendations For The Authors):

-Key references Ye et al., 2022 and Che et al., 2019 need to be included in the reference list.

These references have now been included in the reference list.

-Heat pain stimuli and TMS stimuli are applied simultaneously. Sometimes the term "stimulus" is used without specifying whether it refers to TMS pulses or heat pain stimuli. Clarifying this whenever the word "stimulus" is used would enhance clarity for the reader.

We have now clarified the use of the word “stimulus” throughout the paper.

-Panels A-D in Figure 6 should be correctly labeled in the text and the figure legend.

Figure 6 Panel labels have now been amended.

Author Response

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

Reviewer #1:

Watanuki et al used metabolomic tracing strategies of U-13C6-labeled glucose and 13C-MFA to quantitatively identify the metabolic programs of HSCs during steady-state, cell-cycling, and OXPHOS inhibition. They found that 5-FU administration in mice increased anaerobic glycolytic flux and decreased ATP concentration in HSCs, suggesting that HSC differentiation and cell cycle progression are closely related to intracellular metabolism and can be monitored by measuring ATP concentration. Using the GO-ATeam2 system to analyze ATP levels in single hematopoietic cells, they found that PFKFB3 can accelerate glycolytic ATP production during HSC cell cycling by activating the rate-limiting enzyme PFK of glycolysis. Additionally, by using Pfkfb3 knockout or overexpressing strategies and conducting experiments with cytokine stimulation or transplantation stress, they found that PFKFB3 governs cell cycle progression and promotes the production of differentiated cells from HSCs in proliferative environments by activating glycolysis. Overall, in their study, Watanuki et al combined metabolomic tracing to quantitatively identify metabolic programs of HSCs and found that PFKFB3 confers glycolytic dependence onto HSCs to help coordinate their response to stress. Even so, several important questions need to be addressed as below:

We sincerely appreciate the constructive feedback from the reviewer. Additional experiments and textual improvements have been made to the manuscript based on your valuable suggestions. In particular, the major revisions are as follows: First, we investigated the extent to which other metabolites, not limited to the glycolytic system, affect metabolism in HSCs after 5-FU treatment. Second, the extent to which PFKFB3 contributes to the expansion of the HSPC pool in the bone marrow was adjusted to make the description more accurate based on the data. Finally, we overexpressed PFKFB3 in HSCs derived from GO-ATeam2 mice and confirmed that PRMT1 inhibition did not reduce the ATP concentration. We believe that the reviewer's valuable comments have further deepened our knowledge of the significance of glycolytic activation by PFKFB3 that we have demonstrated. Our response to the "Recommendations for Authors" is listed first, followed by our responses to all "Public Review" comments as follows:

(Recommendations For The Authors):

1. The methods used in key experiments should be described in more detail. For example, in the section on ‘Conversion of GO-ATeam2 fluorescence to ATP concentration’, the knock-in strategy for GO-ATeam2 should be described, as well as U-13C6 -glucose tracer assays.

As per your recommendation, we have described the key experimental method in more detail in the revised manuscript: the GO-ATeam2 knock-in method was reported by Yamamoto et al. 1. Briefly, they used a CAG promoter-based knock-in strategy targeting the Rosa26 locus to generate GO-ATeam2 knock-in mice. A description of the method has been added to Methods and the reference has been added to the citation.

For the U-13C6-glucose tracer analysis, the following points were added to describe the details of the analysis: First, a note was added that the number of cells used for the in vitro tracer analysis was the number of cells used for each sample. Second, we added the solution from which the cells were collected by sorting. We added that the incubation was performed under 1% O2 and 5% CO2.

1. Confusing image label of Supplemental Figure 1H should be corrected in line 253.

We have corrected the incorrect figure caption on line 217 in the revised manuscript to "Supplemental Figure 1N" as you suggested.

1. The percentage of the indicated cell population should also be shown in Figure S1B.

As you indicated, we have included the percentages for each population in Supplemental Figure 1B.

Author response image 1.

1. Please pay attention to the small size of the marks in the graph, such as in Figure S1F and so on.

As you indicated, we have corrected the very small text contained in Figure S1F. Similar corrections have been made to Figures S1B and S5A.

1. Please pay attention to the label of line in Figure S6A-D.

Thank you very much for the advice. We have added line labels to the graph in the original Figures S6A–D.

(Specific comments)

1. Based on previous reports, the authors expanded the LSK gate to include as many HSCs as possible (Supplemental Figure 1B). However, while they showed the gating strategy on Day 6 after 5-FU treatment, results from other time-points should also be displayed to ensure the strict selection of time-points.

Thank you for pointing this out. First, we did not enlarge the Sca-1 gating in this study. We apologize for any confusion caused by the incomplete description. The gating of c-Kit is based on that shown by Umemoto et al (Figure EV1A) 2, who used 250 mg/kg 5-FU, so their c-Kit reduction is more pronounced than ours.

We followed this study and compared c-Kit expression in Lin-Sca-1+CD150+CD48-EPCR+ gates to BMMNCs on day 6 after 5-FU administration (150 mg/kg). The results are shown below.

Author response image 2.

Since the MFI of c-Kit was downregulated, we used gating that extended the c-Kit gate to lower-expression regions on day 6 after 5-FU administration (revised Figure S1C). At other time points, LSK gating was the same as in the PBS-treated group, as noted in the Methods.

1. In Figure 1, the authors examined the metabolite changes on Day 6 after 5-FU treatment. However, it is important to consider whether there are any dynamic adjustments to metabolism during the early and late stages of 5-FU treatment in HSCs compared to PBS treatment, in order to coordinate cell homeostasis despite no significant changes in cell cycle progression at other time-points.

Thank you for pointing this out. Below are the results of the GO-ATeam2 analysis during the very early phase (day 3) and late phase (day 15) after 5-FU administration (revised Figures S7A–H).

Author response image 3.

In the very early phase, such as day 3 after 5-FU administration, cell cycle progression had not started (Figure S1C) and was not preceded by metabolic changes. Meanwhile, in the late phase, such as day 15 after 5-FU administration, the cell cycle and metabolism returned to a steady state. In summary, the timing of the metabolic changes coincided with that of cell cycle progression. This point is essential for discussing the cell cycle-dependent metabolic system of HSCs and has been newly included in the Results (page 11, lines 321-323).

1. As is well known, ATP can be produced through various pathways, including glycolysis, the TCA cycle, the PPP, NAS, lipid metabolism, amino acid metabolism and so on. Therefore, it is important to investigate whether treatment with 5-FU or oligomycin affects these other metabolic pathways in HSCs.

As the reviewer pointed out, ATP production by systems other than the glycolytic system of HSCs is also essential. In this revised manuscript, we examined the effects of the FAO inhibitor (Etomoxir, 100 µM) and the glutaminolysis inhibitor 6-diazo-5-oxo-L-norleucine (DON, 2mM) alone or in combination on the ATP concentration of HSCs after PBS or 5-FU treatment. As shown below, there was no apparent decrease in ATP concentration (revised Figures S7J–M).

Author response image 4.

Fatty acid β-oxidation activity was also measured in 5-FU-treated HSCs using the fluorescent probe FAOBlue and was unchanged compared to PBS-treated HSCs (revised Figure S7N).

Author response image 5.

Notably, the addition of 100 µM etomoxir plus glucose and Pfkfb3 inhibitors resulted in a rapid decrease in ATP concentration in HSCs (revised Figures S7O–P). This indicates that etomoxir partially mimics the effect of oligomycin, suggesting that at a steady state, OXPHOS is driven by FAO, but can be compensated by the acceleration of the glycolytic system by Pfkfb3. Meanwhile, the exposure of HSCs to Pfkfb3 inhibitors in addition to 2 mM DON, which is an extremely high dose considering that the Ki value of DON for glutaminase is 6 µM, did not reduce ATP (revised Figures S7O–P). This suggests that ATP production from glutaminolysis is limited in HSCs at a steady state.

Author response image 6.

These points suggest that OXPHOS is driven by fatty acids at a steady state, but unlike the glycolytic system, FAO is not further activated by HSCs after 5-FU treatment. The results of these analyses and related descriptions are included in the revised manuscript (page 11, lines 332-344).

1. In part 2, they showed that oligomycin treatment of HSCs exhibited activation of the glycolytic system, but what about the changes in ATP concentration under oligomycin treatment? Are other metabolic systems affected by oligomycin treatment?

Thank you for your thoughtful comments. The relevant results we have obtained so far with the GO-ATeam2 system are as follows: First, OXPHOS inhibition in the absence of glucose significantly decreases the ATP concentration of HSCs (Figure 4C). Meanwhile, OXPHOS inhibition in the presence of glucose maintains the ATP concentration of HSCs (Figure 5B). Since it is difficult to imagine a completely glucose-free environment in vivo, it is thought that ATP concentration is maintained by the acceleration of the glycolytic system even under hypoxic or other conditions that inhibit OXPHOS.

Meanwhile, glucose tracer analysis shows that OXPHOS inhibition suppresses nucleic acid synthesis (NAS) except for the activation of the glycolytic system (Figures 2C–F). This is because phosphate groups derived from ATP are transferred to nucleotide mono-/di-phosphate in NAS, but OXPHOS, the main source of ATP production, is impaired, along with the enzyme conjugated with OXPHOS in the process of NAS (dihydroorotate dehydrogenase, DHODH). We have added a new paragraph in the Discussion section (page 17, lines 511-515) to provide more insight to the reader by summarizing and discussing these points.

1. In Figure 5M, it would be helpful to include a control group that was not treated with 2-DG. Additionally, if Figure 5L is used as the control, it is unclear why the level of ATP does not show significant downregulation after 2-DG treatment. Similarly, in Figure 5O, a control group with no glucose addition should be included.

Thank you for your advice. The experiments corresponding to the control groups in Figures 5M and O were in Figures 5L and N, respectively, but we have combined them into one graph (revised Figures 5L–M). The results more clearly show that PFKFB3 overexpression enhances sensitivity to 2-DG, but also enhances glycolytic activation upon oligomycin administration.

Author response image 7.

1. In this study, their findings suggest that PFKFB3 is required for glycolysis of HSCs under stress, including transplantation. In Figure 7B, the results showed that donor-derived chimerism in PB cells decreased relative to that in the WT control group during the early phase (1 month post-transplant) but recovered thereafter. Although the transplantation cell number is equal in two groups of donor cells, it is unclear why the donor-derived cell count decreased in the 2-week post-transplantation period and recovered thereafter in the Pfkgb3 KO group. Therefore, they should provide an explanation for this. Additionally, they only detected the percentage of donor-derived cells in PB but not from BM, which makes it difficult to support the argument for Increasing the HSPC pool.

As pointed out by the reviewer, it is interesting to note that the decrease in peripheral blood chimerism in the PFKFB3 knockout is limited to immediately after transplantation and then catches up with the control group (Figure 7B). We attribute this to the fact that HSPC proliferation is delayed immediately after transplantation in PFKFB3 deficiency, but after a certain time, PB cells produced by the delayed proliferating HSPCs are supplied. In support of this, the PFKFB3 knockout HSPCs did not exhibit increased cell death after transplantation (Figure 7K), while a delayed cell cycle was observed (Figures 7G–J). A description of this point has been added to the Discussion (page 19, lines 573-579).

In addition, the knockout efficiency in bone marrow cells could not be verified because the number of cells required for KO efficiency analysis was not available. Therefore, we have added a statement on this point and have toned down our overall claim regarding the extent to which PFKFB3 is involved in the expansion of the HSPC pool (page 15, lines 474-476).

1. In Figure 7E, they collected the BM reconstructed with Pfkfb3- or Rosa-KO HSPCs two months after transplantation, and then tested their resistance to 5-FU. However, the short duration of the reconstruction period makes it difficult to draw conclusions about the effects on steady-state blood cell production.

We agree that we cannot conclude from this experiment alone that PFKFB3 is completely unnecessary in steady state because, as you pointed out, the observation period of the experiment in Figure 7E is not long. We have toned down the claim by stating that PFKFB3 is only less necessary in steady-state HSCs compared to proliferative HSCs (page 15, lines 460-461).

1. PFK is allosterically activated by PFKFB, and other members of the PFKFB family could also participate in the glycolytic program. Therefore, they should investigate their function in contributing to glycolytic plasticity in HSCs during proliferation. Additionally, they should also analyze the protein expression and modification levels of other members. Although PFKFB3 is the most favorable for PFK activation, the role of other members should also be explored in HSC cell cycling to provide sufficient reasoning for choosing PFKFB3.

To further justify why we chose PFKFB3 among the PFKFB family members, we reviewed our data and the publicly available Gene Expression Commons (GEXC) 3. PFKFB3 is the most highly expressed member of the PFKFB family in HSCs (revised Figure 4F), and its expression increases with proliferation (Author response image 9). In addition to this, we have also cited the literature 4 indicating that AZ PFKFB3 26 is a Pfkfb3-specific inhibitor that we used in this paper, and added a note to this point (that it is specific) (page 11, lines 327-329). Through these revisions, we sought to strengthen the rationale for Pfkfb3 as the primary target of the analysis.

Author response image 8.

Author response image 9.

1. In this study, the authors identified PRMT1 as the upstream regulator of PFKFB3 that is involved in the glycolysis activation of HSCs. However, PRMT1 is also known to participate in various transcriptional activations. Thus, it is important to determine whether PRMT1 affects glycolysis through transcriptional regulation or through its direct regulation of PFKFB3? Additionally, the authors should investigate whether PRMT1i inhibits ATP production in normal HSCs. Moreover, could we combine Figure 6I and 6J for analysis. Finally, the authors could conduct additional rescue experiments to demonstrate that the effect of PRMT1 inhibitors on ATP production can be rescued by overexpression of PFKFB3.

Although PRMT1 inhibition reduced m-PFKFB3 levels in HSCs, 5-FU treatment also reduced or did not alter Pfkfb3 transcript levels (Figures 6B, G) and the expression of genes such as Hoxa7/9/10, Itga2b, and Nqo1, which are representative transcriptional targets of PRMT1, in proliferating HSCs after 5-FU treatment (revised Figure S9).

Author response image 10.

These results suggest that PRMT1 promotes PFKFB3 methylation, which increases independently of transcription in HSCs after 5-FU treatment.

A summary analysis of the original Figures 6I and 6J is shown below (revised Figure 6I).

Author response image 11.

Finally, we tested whether the inhibition of the glycolytic system and the decrease in ATP concentration due to PRMT1 inhibition could be rescued by the retroviral overexpression of PFKFB3. We found that PFKFB3 overexpression did not decrease the ATP concentration in HSCs due to PRMT1 inhibition (revised Figure 6J). Therefore, PFKFB3 overexpression mitigated the decrease in ATP concentration caused by PRMT1 inhibition. These data and related statements have been added to the revised manuscript (page 14, lines 427-428).

Author response image 12.

Reviewer #2:

In the manuscript Watanuki et al. want to define the metabolic profile of HSCs in stress/proliferative (myelosuppression with 5-FU), and mitochondrial inhibition and homeostatic conditions. Their conclusions are that during proliferation HSCs rely more on glycolysis (as other cell types) while HSCs in homeostatic conditions are mostly dependent on mitochondrial metabolism. Mitochondrial inhibition is used to demonstrate that blocking mitochondrial metabolism results in similar features of proliferative conditions.

The authors used state-of-the-art technologies that allow metabolic readout in a limited number of cells like rare HSCs. These applications could be of help in the field since one of the major issues in studying HSCs metabolism is the limited sensitivity of the“"standard”" assays, which make them not suitable for HSC studies.

However, the observations do not fully support the claims. There are no direct evidence/experiments tackling cell cycle state and metabolism in HSCs. Often the observations for their claims are indirect, while key points on cell cycle state-metabolism, OCR analysis should be addressed directly.

We sincerely appreciate the reviewer's constructive comments. Thank you for highlighting the importance of the highly sensitive metabolic assay developed in this study and the findings based on it. Meanwhile, the reviewer's comments have made us aware of areas where we can further improve this manuscript. In particular, in the revised manuscript, we have performed further studies to demonstrate the link between the cell cycle and metabolic state. Specifically, we further subdivided HSCs by the uptake of in vivo-administered 2-NBDG and performed cell cycle analysis. Next, HSCs after PBS or 5-FU treatment were analyzed by a Mito Stress test using the Seahorse flux analyzer, including ECAR and OCR, and a more direct relationship between the cell cycle state and the metabolic system was found. We believe that the reviewer's valuable suggestions have helped us clarify more directly the importance of the metabolic state of HSCs in response to cell cycle and stress that we wanted to show and emphasize the usefulness of the GO-ATeam2 system. Our response to "Recommendations For The Authors" is listed first, followed by our responses to all comments in "Public Review" as follows:

(Recommendations For The Authors):

In general, I believe it would be important:

1. to directly associate cell cycle state with metabolic state. For example, by sorting HSC (+/- 5FU) based on their cell cycle state (exploiting the mouse model presented in the manuscript or by defining G0/G1/G2-S-M via Pyronin/Hoechst staining which allow to sort live cells) and follow the fate of radiolabeled glucose.

Thank you for raising these crucial points. Unfortunately, it was difficult to perform the glucose tracer analysis by preparing HSCs with different cell cycle states as you suggested due to the amount of work involved. In particular, in the 5-FU group, more than 60 mice per group were originally required for an experiment, and further cell cycle-based purification would require many times that number of mice, which we felt was unrealistic under current technical standards. As an alternative, we administered 2-NBDG to mice and fractionated HSCs at the 2-NBDG fluorescence level for cell cycle analysis. The results are shown below (revised Figure S1M). Notably, even in the PBS-treated group, HSCs with high 2-NBDG uptake were more proliferative than those with low 2-NBDG uptake and are comparable to HSCs after 5-FU treatment, although the overall population of HSCs exiting the G0 phase and entering the G1 phase increased after 5-FU treatment. In both PBS/5-FU-treated groups, these large differences in cell cycle glucose utilization suggest a direct link between HSC proliferation and glycolysis activation. If a more sensitive type of glucose tracer analysis becomes available in the future, it may be possible to directly address the reviewer's comments. We see this as a topic for the future. The descriptions of the above findings and perspectives have been added to the Results and Discussion section (page 7, lines 208-214, page 20, lines 607-610).

Author response image 13.

1. Use other radio labeled substrates (fatty acid, glutamate)

Thank you very much for your suggestion. While this is an essential point for future studies, we believe it is not the primary focus of the paper. We are planning another research project on tracer analysis using labeled fatty acids and glutamates, which we will report on in the near future. We have clearly stated in the Abstract and Introduction of the revised manuscript, that the focus of this study is on changes in glucose metabolism when HSCs are stressed (page 3, line 75 and 87, page 5, lines 135).

Instead, we added the following analyses of metabolic changes in fatty acids and glutamate using the GO-ATeam2 system. HSCs derived from GO-ATeam2 mice treated with PBS or 5-FU were used to measure changes in ATP concentrations after exposure to the fatty acid beta-oxidation (FAO) inhibitor etomoxir and the glutaminolysis inhibitor 6-diazo-5-oxo-L-norleucine (DON). Etomoxir was used at 100 µM, a concentration that inhibits FAO without inhibiting mitochondrial electron transfer complex I, as previously reported 5. DON was used at 2 mM, a concentration that sufficiently inhibits the enzyme as the Ki for glutaminase is 6 µM. In this experiment, etomoxir alone, DON alone, or etomoxir and DON in combination did not decrease the ATP concentration of HSCs in the PBS and 5-FU groups (revised Figures S7J–M), suggesting that FAO and glutaminolysis were not essential for ATP production in HSCs in the short term. Thus, according to the analysis using the GO-Ateam2 system, HSCs exposed to acute stresses change the efficiency of glucose utilization (accelerated glycolytic ATP production) rather than other energy sources. Since there are reports that FAO and glutaminolysis are required for HSC maintenance in the long term 5,6, compensatory pathways may be able to maintain ATP levels in the short term. A description of these points has been added to the Discussion (page 11, lines 332-344).

Author response image 14.

1. Include OCR analyses.

In addition to the ECAR data of the Mito Stress test (original Figures 2G–H), OCR data were added to the revised manuscript (revised Figures 2H, S3D). Compared to c-Kit+ myeloid progenitors (LKS- cells), HSC showed a similar increase in ECAR, while the decrease in OCR was relatively limited. A possible explanation for this is that glycolytic and mitochondrial metabolism are coupled in c-Kit+ myeloid progenitors, whereas they are decoupled in HSCs. This is also suggested by the glucose plus oligomycin experiment in Figures 5B, C, and S6A–D (orange lines). In summary, in HSCs, glycolytic and mitochondrial ATP production are decoupled and can maintain ATP levels by glycolytic ATP production alone, whereas in progenitors including GMPs, the two ATP production systems are constantly coupled, and glycolysis alone cannot maintain ATP concentration. We have added descriptions of these points in the Results and Discussion section (page 8, lines 240-243, page 18, lines 558-561).

Author response image 15.

Next, a Mito Stress test was performed using HSCs derived from PBS- or 5-FU-treated mice in the presence or absence of oligomycin (revised Figures 1G–H, S3A–B). Without oligomycin treatment, ECAR in 5-FU-treated HSCs was higher than in PBS-treated HSCs, and OCR was unchanged. Oligomycin treatment increased ECAR in both PBS- and 5-FU-treated HSCs, whereas OCR was unchanged in PBS-treated HSCs, but significantly decreased in 5-FU-treated HSCs. Changes in ECAR in response to oligomycin differed between HSC proliferation or differentiation: ECAR increased in 5-FU-treated HSCs but not in LKS- progenitors (original Figures 2G–H). This suggests a metabolic feature of HSCs in which the coupling of OXPHOS with glycolysis seen in LKS- cells is not essential in HSCs even after cell cycle entry. The results and discussion of this experiment have been added to page 7, lines 194-201 and page 18, lines 558-561).

Author response image 16.

1. Correlate proliferation-mitochondrial inhibition-metabolic state

We agree that it is important to clarify this point. First, OXPHOS inhibition and proliferation similarly accelerate glycolytic ATP production with PFKFB3 (Figures 4G, I, and 5F–I). Meanwhile, oligomycin treatment rapidly decreases ATP in HSCs with or without 5-FU administration (Figure 4C). These results suggest that OXPHOS is a major source of ATP production both at a steady state and during proliferation, even though the analysis medium is pre-saturated with hypoxia similar to that in vivo. This has been added to the Discussion section (page 17, lines 520-523).

1. Tune down the claim on HSCs in homeostatic conditions since from the data it seems that HSCs rely more on anaerobic glycolysis.

Thanks for the advice. The original Figures S2C, D, F, and G show that HSC is dependent on the anaerobic glycolytic system even at a steady state, so we have toned down our claims (page 7, lines 192-194).

1. For proliferative HSCs mitochondrial are key. When you block mitochondria with oligomycin there's the biggest drop in ATP.

In the revised manuscript, we have tried to highlight the key findings that you have pointed out. First, we mentioned in the Discussion (page 17, lines 523-525) that previous studies suggested the importance of mitochondria in proliferating HSCs. Meanwhile, the GO-ATeam2 and glucose tracer analyses in this study newly revealed that the glycolytic system activated by PFKFB3 is activated during the proliferative phase, as shown in Figure 4C. We also confirmed that mitochondrial ATP production is vital in proliferating HSCs, and we hope to clarify the balance between ATP-producing pathways and nutrient sources in future studies.

1. To better clarify this point authors, authors should do experiments in hypoxic conditions and compare it to oligomycin treatment and showing that mito-inhibition acts differently on HSCs (considering that all these drugs are toxic for mitochondria and induce rapidly stress responses ex: mitophagy).

We apologize for any confusion caused by not clearly describing the experimental conditions. As pointed out by the reviewer, we also recognize the importance of experiments in a hypoxic environment. All GO-ATeam2 analyses were performed in a medium saturated sufficiently under hypoxic conditions and analyzed within minutes, so we believe that the medium did not become oxygenated (page S5-S6, lines 160-163 in the Methods). Despite being conducted under such hypoxic conditions, the substantial decrease in ATP after oligomycin treatment is intriguing (original Figures 4C, 5B, 5C). The p50 value of mitochondria (the partial pressure of oxygen at which respiration is half maximal) is 0.1 kPa, which is less than 0.1% of the oxygen concentration at atmospheric pressure 7. Thus, biochemically, it is consistent that OXPHOS can maintain sufficient activity even in a hypoxic environment like the bone marrow. We are currently embarking on a study to determine ATP concentration in physiological hypoxic conditions using in vivo imaging within the bone marrow, which we hope to report in a separate project. We have discussed these points, technical limitations, and perspectives in the Discussion section (page 20, lines 610-612).

• In Figure 1 C, D, E and F, the comparison should be done as unpaired t test and the control group should not be 1 as the cells comes from different individuals.

Thank you very much for pointing this out. We have reanalyzed and revised the figures (revised Figures 1C–F)

Author response image 17.

• In Figure S2A, the post-sorting bar of 6PG, R5P and S7P are missing.

Metabolites below the detection threshold (post-sorting samples of 6PG, R5P, and S7P) are now indicated as N.D. (not detected) (revised Figure S2A).

Author response image 18.

• In the 2NBDG experiments, authors should add the appropriate controls, since it has been shown that 2NBDG cellular uptake do not correctly reflect glucose uptake (Sinclair LV, Immunometabolism 2020) (a cell type dependent variations) thus inhibitors of glucose transporters should be added as controls (cytochalasin B; 4,6-O-ethylidene-a-D-glucose) it would be quite challenging to test it in vivo but it would be sufficient to show that in vitro in the different HSPCs analyzed.

We appreciate the essential technical point raised by the reviewer. In the revised manuscript, we performed a 2-NBDG assay with cytochalasin B and phloretin as negative controls. After PBS treatment, 2-NBDG uptake was higher in 5-FU-treated HSCs compared to untreated HSCs. This increase was inhibited by both cytochalasin B and phloretin. In PBS-treated HSCs, cytochalasin B did not downregulate 2-NBDG uptake, whereas phloretin did. Although cytochalasin B inhibits glucose transporters (GLUTs), it is also an inhibitor of actin polymerization. Therefore, its inhibitory effect on GLUTs may be weaker than that of phloretin. We have revised the figure (revised Figure S1L) and added the corresponding description (page 7, lines 207-208).

Author response image 19.

• S5C: authors should show the cell number for each population. If there's a decreased in % in Lin- that will be reflected in all HSPCs. Comparing the proportion of the cells doesn't show the real impact on HSPCs.

Thank you for your insightful point. In the revision, we compared the numbers, not percentages, of HSPCs and found no difference in the number of cells in the major HSPC fractions in Lin-. The figure has been revised (revised Figure S6C) and the corresponding description has been added (page 10, lines 296-299).

Author response image 20.

Minor:

1. In S1 F-G is not indicated in which day post 5FU injection is done the analysis. I assume on day 6 but it should be indicated in the figure legend and/or text.

Thank you for pointing this out. As you assumed, the analysis was performed on day 6. The description has been added to the legend of the revised Figure S1G.

1. S1K is not described in the text. What are proliferative and quiescence-maintaining conditions? The analyses are done by flow using LKS SLAM markers after culture? How long was the culture?

Thank you for your comments. First, the figure citation on line 250 was incorrect and has been corrected to Figure S1N. Regarding the proliferative and quiescence-maintaining conditions, we have previously reported on these 8. In brief, these are culture conditions that maintain HSC activity at a high level while allowing for the proliferation or maintenance of HSCs in quiescence, achieved by culturing under fatty acid-rich, hypoxic conditions with either high or low cytokine concentrations. Analysis was performed after one week of culture, with the HSC number determined by flow cytometry based on the LSK-SLAM marker. While these are mentioned in the Methods section, we have added a description in the main text to highlight these points for the reader (page 7, lines 214-217).

1. In Figure 5G, why does the blue line (PFKFB3 inhibitor) go up in the end of the real-time monitoring? Does it mean that other compensatory pathway is turned on?

As you have pointed out, we cannot rule out the possibility that other unknown compensatory ATP production pathways were activated. We have added a note in the Discussion section to address this (page 18, lines 555-556).

1. In Figure S6H&J, the reduction is marginal. Does it mean that PKM2 is not important for ATP production in HSCs?

The activity of the inhibitor is essential in the GO-ATeam2 analysis. The commercially available PKM2 inhibitors have a higher IC50 value (IC50 = 2.95 μM in this case). Nevertheless, the effect of reducing the ATP concentration was observed in progenitor cells, but not in HSCs. The report by Wang et al. 9 on the analysis using a PKM2-deficient model suggests a stronger effect on progenitor cells than on HSCs. Our results are similar to those of the previous report.

(Specific comments)

Specifically, there are several major points that rise concerns about the claims:

1. The gating strategy to select HSCs with enlarged Sca1 gating is not convincing. I understand the rationale to have a sufficient number of cells to analyze, however this gating strategy should be applied also in the control group. From the FACS plot seems that there are more HSCs upon 5FU treatment (Figure S1b). How that is possible? Is it because of the 20% more of cycling cells at day 6? To prove that this gating strategy still represents a pure HSC population, authors should compare the blood reconstitution capability of this population with a "standard" gated population. If the starting population is highly heterogeneous then the metabolic readout could simply reflect cell heterogeneity.

Thank you for pointing this out. First, we did not enlarge the Sca-1 gating in this study. We apologize for any confusion caused by the incomplete description. The gating of c-Kit is based on that shown by Umemoto et al (Figure EV1A) 2, who used 250 mg/kg 5-FU, so their c-Kit reduction is more pronounced than ours.

We followed this study and compared c-Kit expression in the Lin-Sca-1+CD150+CD48-EPCR+ gates to BMMNCs on day 6 after 5-FU administration (150 mg/kg). The results are shown below.

Author response image 21.

Since the MFI of c-Kit was downregulated, we used gating that extended the c-Kit gate to lower expression regions on day 6 after 5-FU administration (revised Figure S1C).

At other time points, LSK gating was the same as in the PBS-treated group, as noted in the Methods.

The reason why the number of HSCs appears to be higher in the 5-FU group is because most of the differentiated blood cells were lost due to 5-FU administration and the same number of cells as in the PBS group were analyzed by FACS, resulting in a relatively higher number of HSCs. The legend of Figure S1 shows that the number of HSCs in both the PBS and 5-FU groups appeared to increase because the same number of BMMNCs was obtained at the time of analysis (page S22, lines 596-598).

Regarding cellular heterogeneity, from a metabolic point of view, the heterogeneity in HSCs is rather reduced by 5-FU administration. As shown in Figure S3A–C, this is simulated under stress conditions, such as after 5-FU administration or during OXPHOS inhibition, where the flux variability in each enzymatic reaction is significantly reduced. GO-ATeam2 analysis after 5-FU treatment showed no increase in cell population variability. After 2-DG treatment, ATP concentrations in HSCs were widely distributed from 0 mM to 0.8 mM in the PBS group, while more than 80% of those in the 5-FU group were less than 0.4 mM (Figures 4B, D). HSCs may have a certain metabolic diversity at a steady state, but under stress conditions, they may switch to a more specialized metabolism with less cellular heterogeneity in order to adapt.

1. S2 does not show major differences before and after sorting. However, a key metabolite like Lactate is decreased, which is also one of the most present. Wouldn't that mean that HSCs once they move out from the hypoxic niche, they decrease lactate production? Do they decrease anaerobic glycolysis? How can quiescent HSC mostly rely on OXPHOS being located in hypoxic niche?

2. Since HSCs in the niche are located in hypoxic regions of the bone marrow, would that not mimic OxPhos inhibition (oligomycin)? Would that not mean that HSCs in the niche are more glycolytic (anaerobic glycolysis)?

3. In Figure 5B, the orange line (Glucose+OXPHOS inhibition) remains stable, which means HSCs prefer to use glycolysis when OXPHOS is inhibited. Which metabolic pathway would HSCs use under hypoxic conditions? As HSCs resides in hypoxic niche, does it mean that these steady-state HSCs prefer to use glycolysis for ATP production? As mentioned before, mitochondrial inhibition can be comparable at the in vivo condition of the niche, where low pO2 will "inhibit" mitochondria metabolism.

Thank you for the first half of comment 2 on the technical features of our approach. First, as you have pointed out, there is minimal variation and stable detection of many metabolites before and after sorting (Figure S2A), suggesting that isolation from the hypoxic niche and sorting stress do not significantly alter metabolite detection performance. This is consistent with a previous report by Jun et al. 10. Meanwhile, lactate levels decreased by sorting. Therefore, if the activity of anaerobic glycolysis was suppressed in stressed HSCs, it may be difficult to detect these metabolic changes with our tracer analysis. However, in this study, several glycolytic metabolites, including an increase in lactate, were detected in HSCs from 5-FU-treated mice compared with HSCs from PBS-treated mice that were similarly sorted and prepared, suggesting an increase in glycolytic activity. In other words, we may have been fortunate to detect the stress-induced activation of the glycolytic system beyond the characteristic of our analysis system that lactate levels tend to appear lower than they are. Given that damage to the bone marrow hematopoiesis tends to alleviate the low-oxygen status of the niche 11, we postulate that this upregulated aerobic glycolysis arises intrinsically in HSCs rather than from external conditions.

The second half of comment 2, and comments 7 and 10, are essential and overlapping comments and will be answered together. Although genetic analyses have shown that HSCs produce ATP by anaerobic glycolysis in low-oxygen environments 9,12, our GO-ATeam2 analysis in this study confirmed that HSCs also generate ATP via mitochondria. This is also supported by Ansó's prior findings where the knockout of the Rieske iron–sulfur protein (RISP), a constituent of the mitochondrial electron transport chain, impairs adult HSC quiescence and bone marrow repopulation 13. Bone marrow is a physiologically hypoxic environment (9.9–32.0 mmHg 11). However, the p50 value of mitochondria (the partial pressure of oxygen at which respiration is half maximal) is below 0.1% oxygen concentration at atmospheric pressure (less than 1 mmHg) 7. This suggests that OXPHOS can retain sufficient activity even under physiologically hypoxic conditions. We are currently initiating efforts to discern ATP concentrations in vivo within the bone marrow under physiological hypoxia. This will be reported in a separate project in the future. Admittedly, when we began this research, we did not anticipate the significant mitochondrial reliance of HSCs. As we previously reported, the metabolic uncoupling of glycolysis and mitochondria 12 may enable HSCs to activate only glycolysis, and not mitochondria, under stress conditions such as post-5-FU administration, suggesting a unique metabolic trait of HSCs. We have included these technical limitations and perspectives in the Discussion section (page 17, lines 520-523).

1. The authors performed challenging experiments to track radiolabeled glucose, which are quite remarkable. However, the data do not fully support the conclusions. Mitochondrial metabolism in HSCs can be supported by fatty acid and glutamate, thus authors should track the fate of other energy sources to fully discriminate the glycolysis vs mito-metabolism dependency. From the data on S2 and Fig1 1C-F, the authors can conclude that upon 5FU treatment HSCs increase glycolytic rate.

2. FIG.2B-C: Increase of Glycolysis upon oligomycin treatment is common in many different cell types. As explained before, other radiolabeled substrates should be used to understand the real effect on mitochondria metabolism.

Thank you for your suggestion. While this is essential for future studies, we believe it is not the primary focus of the paper. We are planning another research project on tracer analysis using labeled fatty acids and glutamates, which we will report on in the near future. We have clearly stated in the Abstract and Introduction of the revised manuscript that the focus of this study is on changes in glucose metabolism when HSCs are stressed (page 3, line 75 and 87, page 5, lines 135).

Instead, we have added the following analyses of metabolic changes in fatty acids and glutamate using the GO-ATeam2 system: HSCs derived from GO-ATeam2 mice treated with PBS or 5-FU were used to measure changes in ATP concentrations after exposure to the fatty acid beta-oxidation (FAO) inhibitor etomoxir and the glutaminolysis inhibitor 6-diazo-5-oxo-L-norleucine (DON). Etomoxir was used at 100 µM, a concentration that inhibits FAO without inhibiting mitochondrial electron transfer complex I, as previously reported 5. DON was used at 2 mM, a concentration that sufficiently inhibits the enzyme as the Ki for glutaminase is 6 µM. In this experiment, etomoxir alone, DON alone, or etomoxir and DON in combination did not decrease the ATP concentration of HSCs in the PBS and 5-FU groups (revised Figures S7J–M), suggesting that FAO and glutaminolysis were not essential for ATP production in HSCs in the short term. Thus, according to the analysis using the GO-Ateam2 system, HSCs exposed to acute stresses change the efficiency of glucose utilization (accelerated glycolytic ATP production) rather than other energy sources. Since there are reports that FAO and glutaminolysis are required for HSC maintenance in the long term 5,6, compensatory pathways may be able to maintain ATP levels in the short term. A description of these points has been added to the Discussion (page 17, lines 525-527).

Author response image 22.

Fatty acid β-oxidation activity was also measured in 5-FU-treated HSCs using the fluorescent probe FAOBlue and was unchanged compared to PBS-treated HSCs (revised Figure S7N).

Author response image 23.

Notably, the addition of 100 µM etomoxir plus glucose and Pfkfb3 inhibitors resulted in a rapid decrease in ATP concentration in HSCs (revised Figures S7O–P). This indicates that etomoxir partially mimics the effect of oligomycin, suggesting that at a steady state, OXPHOS is driven by FAO, but can be compensated by the acceleration of the glycolytic system by Pfkfb3. Meanwhile, the exposure of HSCs to Pfkfb3 inhibitors in addition to 2 mM DON did not reduce ATP (revised Figures S7O–P). This suggests that ATP production from glutaminolysis is limited in HSCs at a steady state.

Author response image 24.

These points suggest that OXPHOS is driven by fatty acids at a steady state, but unlike the glycolytic system, FAO is not further activated by HSCs after 5-FU treatment. The results of these analyses and related descriptions are included in the revised manuscript (page 11, lines 332-344).

1. In Figure S1, 5-FU leads to the induction of cycling HSCs and in figure 1, 5-FU results in higher activation of glycolysis. Would it be possible to correlate these two phenotypes together? For example, by sorting NBDG+ cells and checking the cell cycle status of these cells?

We appreciate the reviewer’s insightful comments. We administered 2-NBDG to mice and fractionated HSCs at the 2-NBDG fluorescence level for cell cycle analysis. The results are shown below (revised Figure S1M). Notably, even in the PBS-treated group, HSCs with high 2-NBDG uptake were more proliferative than HSCs with low 2-NBDG uptake and were comparable to HSCs after 5-FU treatment, although the overall population of HSCs that exited the G0 phase and entered the G1 phase increased after 5-FU treatment. In both PBS/5-FU-treated groups, these profound differences in cell cycle glucose utilization suggest a direct link between HSC proliferation and glycolysis activation. Descriptions of the above findings and perspectives have been added to the Results and Discussion section (page 7, lines 208-214, page 20, lines 607-610).

Author response image 25.

1. Why are only ECAR measurements (and not OCR measurements) shown? In Fig.2G, why are HSCs compared with cKit+ myeloid progenitors, and not with MPP1? The ECAR increased observed in HSC upon oligomycin treatment is shared with many other types of cells. However, cKit+ cells have a weird behavior. Upon oligo treatment cKit+ cells decrease ECAR, which is quite unusual. The data of both HSCs and cKit+ cells could be clarified by adding OCR curves. Moreover, it is recommended to run glycolysis stress test profile to assess the dependency to glycolysis (Glucose, Oligomycin, 2DG).

In addition to the ECAR data of the Mito Stress test (original Figures 2G–H), OCR data were added in the revised manuscript (revised Figures 2H, S3D). Compared to c-Kit+ myeloid progenitors (LKS- cells), HSC exhibited a similar increase in ECAR, while the decrease in OCR was relatively limited. This may be because glycolytic and mitochondrial metabolism are coupled in c-Kit+ myeloid progenitors, whereas they are decoupled in HSCs. This is also suggested by the glucose plus oligomycin experiment in Figures 5B, C, and S6A–D (orange lines). In summary, in HSCs, glycolytic and mitochondrial ATP production are decoupled and can maintain ATP levels by glycolytic ATP production alone, whereas in progenitors including GMPs, the two ATP production systems are constantly coupled, and glycolysis alone cannot maintain the ATP concentration. While we could not conduct a glycolysis stress test, we believe that Pfkfb3-dependent glycolytic activation, which is evident in the oligomycin+glucose+Pfkfb3i experiment, is only apparent in HSCs when subjected to glucose+oligomycin treatment (original Figures 5F–I). We have added descriptions of these points in the Results and Discussion section (page 8, lines 240-243, page 18, lines 558-561).

Author response image 26.

FIG.3 A-C. As mentioned previously, the flux analyses should be integrated with data using other energy sources. If cycling HSCs are less dependent to OXPHOS, what happen if you inhibit OXHPHOS in 5-FU condition? Since the authors are linking OXPHOS inhibition and upregulation of Glycolysis to increase proliferation, do HSCs proliferate more when treated with oligomycin?

First, please see our response to comments 3 and 5 regarding the first part of this comment about the flux analysis of other energy sources. According to the analysis using the GO-Ateam2 system, stressed HSCs change the efficiency of glucose utilization (accelerated glycolytic ATP production) rather than other energy sources. The change in ATP concentration after OXPHOS inhibition for 5-FU-treated HSCs is shown in Figures 4C and E, suggesting that the activity of OXPHOS itself does not increase. HSCs after oligomycin treatment and HSCs after 5-FU treatment are similar in that they activate glycolytic ATP production. However, inhibition of OXPHOS did not induce the proliferation of HSCs (original Figure S1K). This suggests that proliferation activates glycolysis and not that activation of the glycolytic system induces proliferation. This similarity and dissimilarity of glycolytic activation upon proliferation and OXPHOS inhibition is discussed in the Discussion section (page 16-17, lines 505-515).

1. FIG.4 shows that in vivo administration of radiolabeled glucose especially marks metabolites of TCA cycle and Glycolysis. The authors interpret enhanced anaerobic glycolysis, but I am not sure this is correct; if more glycolysis products go in the TCA cycle, it might mean that HSC start engaging mitochondrial metabolism. What do the authors think about that?

Thank you for pointing this out. We believe that the data are due to two differences in the experimental features between in vivo (Figure S5) and in vitro (Figures 1 and S2) tracer analysis. The first difference is that in in vivo tracer analysis, unlike in vitro, all cells can metabolize U-13C6-glucose. Another difference is that after glucose labeling in vivo, it takes approximately 120–180 minutes to purify HSCs to extract metabolites, and processing on ice may result in a gradual progression of metabolic reactions within HSCs. As a result, in vivo tracer analysis may detect an increased influx of labeled carbon derived from U-13C6-glucose into the TCA cycle over an extended period. However, it is difficult to interpret whether this influx of labeled carbon is derived from the direct influx of glycolysis or the re-uptake by HSCs of metabolites that have been metabolized to other metabolites in other cells. Meanwhile, as shown in Figure 4C using the GO-ATeam2 system, ATP production from mitochondria is not upregulated by 5-FU treatment. This suggests that even if the direct influx from glycolysis into the TCA cycle is increased, the rate of ATP production does not exceed that of glycolysis. Despite these technical caveats in interpretation, the results of in vivo and in vitro tracer analyses are considered essential. In particular, we consider the increased labeling of metabolites involved in glycolysis and nucleotide synthesis to be crucial. We have added a discussion of these points, including experimental limitations (page 17-18, lines 530-545).

1. FIG.4: the experimental design is not clear. Are BMNNCs stained and then put in culture? Is it 6-day culture or BMNNCs are purified at day 6 post 5FU? FIG-4B-C The difference between PBS vs 5FU conditions are the most significant; however, the effect of oligomycin in both conditions is the most dramatic one. From this readout, it seems that HSCs are more dependent on mitochondria for energy production both upon 5FU treatment and in PBS conditions.

We apologize for the incomplete description of the experimental details. The experiment involved dispensing freshly stained BMMNC with surface antigens into the medium and immediately subjecting them to flow cytometry analysis. For post-5-FU treatment HSCs, mice were administered with 5-FU (day 1), and freshly obtained BMMNCs were analyzed on day 6. The analysis of HSCs and progenitors was performed by gating each fraction within the BMMNC (original Figure S5A). We have added these details to ensure that readers can grasp these aspects more clearly (page S5, lines 155-158).

As pointed out by the reviewer, we understand that HSCs produce more ATP through OXPHOS. However, ATP production by glycolysis, although limited, is observed under steady-state conditions (post-PBS treatment HSC), and its reliance increases during the proliferation phase (post-5-FU treatment HSC) (original Figures 4B, D). Until now, discussions on energy production in HSCs have focused on either glycolysis or mitochondrial functions. However, with the GO-ATeam2 system, it has become possible for the first time to compare their contributions to ATP production and evaluate compensatory pathways. As a result, it became evident that while OXPHOS is the main source of ATP production, the reliance on glycolysis plastically increases in response to stress. This has led to a better understanding of HSC metabolism. These points are included in the Discussion as well (page 16, lines 479-488).

1. FIG.6H should be extended with cell cycle analyses. There are no differences between 5FU and ctrl groups. If 5FU induces HSCs cycling and increases glycolysis I would expect higher 2-NBDG uptake in the 5FU group. How do the authors explain this?

Thank you for your comments. In the original Figure 6H, we found that 2-NBDG uptake correlated with mPFKFB3 levels in both the 5-FU and PBS groups. mPfkfb3 levels remained low in the few HSCs with low 2-NBDG uptake in the 5-FU group.

In the revised manuscript, to directly relate glucose utilization to the cell cycle, we administered 2-NBDG to mice and fractionated HSCs at the 2-NBDG fluorescence level for cell cycle analysis. The results are shown below (revised Figure S1M). Notably, even in the PBS-treated group, HSCs with high 2-NBDG uptake were more proliferative than those with low 2-NBDG uptake and are comparable to HSCs after 5-FU treatment, although the overall population of HSCs that exited the G0 phase and entered the G1 phase increased after 5-FU treatment. The large differences in glucose utilization per cell cycle observed in both PBS/5-FU-treated groups suggest a direct link between HSC proliferation and glycolysis activation. Descriptions of the above findings have been added to the Results and Discussion ((page 7, lines 208-214, page 20, lines 607-610).

Author response image 27.

1. In S7 the experimental design is not clear. What are quiescent vs proliferative conditions? What does it mean "cell number of HSC-derived colony"? Is it a CFU assay? Then you should show colony numbers. When HSCs proliferate, they need more energy thus inhibition of metabolism will impact proliferation. What happens if you inhibit mitochondrial metabolism with oligomycin?

Regarding the proliferative and quiescence-maintaining conditions, we have previously reported on these 8. In brief, these are culture conditions that maintain HSC activity at a high level while allowing for the proliferation or maintenance of HSCs in quiescence, achieved by culturing under fatty acid-rich, hypoxic conditions with either high or low cytokine concentrations. Analysis was performed after one week of culture, with the HSC number determined by flow cytometry based on the LSK-SLAM marker. While these are mentioned in the Methods section, we have added a description in the main text to highlight these points for the reader (page 7, lines 214-217).

In vitro experiments with the oligomycin treatment of HSCs showed that OXPHOS inhibition activates the glycolytic system, but does not induce HSC proliferation (original Figure S1K). This suggests that proliferation activates glycolysis and not that activation of the glycolytic system induces proliferation. This similarity and dissimilarity of glycolytic activation upon proliferation and OXPHOS inhibition is discussed in the Discussion (page 16-17, lines 505-515).

1. In FIG 7 since homing of HSCs is influenced by the cell cycle state, should be important to show if in the genetic model for PFKFB3 in HSCs there's a difference in homing efficiency.

In response to the reviewer's comments, we knocked out PFKFB3 in HSPCs derived from Ubc-GFP mice, transplanted 200,000 HSPCs into recipients (C57BL/6 mice) post-8.5Gy irradiation, and harvested the bone marrow of recipients after 16 h to compare homing efficiency (revised Figure S10H). Even with the knockout of PFKFB3, no significant difference in homing efficiency was detected compared to the control group (Rosa knockout group). These results suggest that the short-term reduction in chimerism due to PFKFB3 knockout is not due to decreased homing efficiency or cell death by apoptosis (Figure 7K) but a transient delay in cell cycle progression. We have added descriptions regarding these findings in the Results and Discussion sections (page 15, lines 470-471, page 19, lines 576-578).

Author response image 28.

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Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Tobón and Moser reveal a remarkable amount of presynaptic diversity in the fundamental Ca dependent exocytosis of synaptic vesicles at the afferent fiber bouton synapse onto the pilar or mediolar sides of single inner hair cells of mice. These are landmark findings with profound implications for understanding acoustic signal encoding and presynaptic mechanisms of synaptic diversity at inner hair cell ribbon synapses. The paper will have an immediate and long-lasting impact in the field of auditory neuroscience.

Main findings: 1) Synaptic delays and jitter of masker responses are significantly shorter (synaptic delay: 1.19 ms) at high SR fibers (pilar) than at low SR fibers (mediolar; 2.57 ms). 2) Masked evoked EPSC are significantly larger in high SR than in low SR. 3) Quantal content and RRP size are 14 vesicles in both high and low SR fibers. 4) Depression is faster in high SR synapses suggesting they have a higher release probability and tighter Ca nanodomain coupling to docked vesicles. 5) Recovery of master-EPSCs from depletion is similar for high and low SR synapses, although there is a slightly faster rate for low SR synapses that have bigger synaptic ribbons, which is very interesting. 6) High SR synapses had larger and more compact (monophasic) sEPSCs, well suited to trigger rapidly and faithfully spikes. 7) High SR synapses exhibit lower voltage (~sound pressure in vivo) dependent thresholds of exocytosis.

Strengths:

Great care was taken to use physiological external pH buffers and physiological external Ca concentrations. Paired recordings were also performed at higher temperatures with IHCs at physiological resting membrane potentials and in more mature animals than previously done for paired recordings. This is extremely challenging because it becomes increasingly difficult to visualize bouton terminals when myelination becomes more prominent in the cochlear afferents.

In addition, perforated patch recordings were used in the IHC to preserve its intracellular milieu intact and thus extend the viability of the IHCs. The experiments are tour-de-force and reveal several novel aspects of IHC ribbon synapses. The data set is rich and extensive. The analysis is detailed and compelling.

We would like to thank the reviewer for the appreciation of our work and the comments that helped us to improve our manuscript. We detail our responses to the comments below.

Weaknesses:

(1) Materials and Methods: Please provide whole-cell Rs (series resistance ) and Cm (membrane capacitance) average +/- S.E.M. (or SD) values for IHC and afferent fiber bouton recordings. The Cm values for afferents have been estimated to be about 0.1 pF (Glowatzki and Fuchs, 2002) and it would be interesting to know if there are differences in these numbers for high and low SR afferents. Is it possible to estimate Cm from the capacitative transient time constant? Minimal electronic filtering would be required for that to work, so I realize the authors may not have this data and I also realize that the long cable of the afferents do not allow accurate Cm measurements, but some first order estimate would be very interesting to report, if possible.

In response to the reviewer’s comment, we now added the estimates of series resistance and membrane capacitance for IHC and bouton recordings in Material and Methods and in the Figure 1 – figure supplement 1. Our estimate for bouton Cm is on average 1.7 ± 0.09 pF, a value that compares well to the literature. For example, Glowatzki and Fuchs (2002) provided estimates ranging 0.5-2 pF for recordings from afferent inner hair cell synapses in rats that showed a capacitance transient. In own prior work on afferent inner hair cell synapses of pre-hearing mice, we found estimates of 2.6 ± 0.5 pF (Chapochnikov et al., 2014) and 1.9 ± 0.2 pF (Takago et al., 2019). Keen and Hudspeth (2006) reported capacitances of 1–4 pF for afferent terminals in the bullfrog amphibian papilla. There was no difference in bouton Cm between high SR (1.78 ± 0.19 pF) and low SR synapses (1.68 ± 0.11 pF; p = 0.6575, unpaired t test).

(2) Page 20, 26 and Figure 4: With regard to synaptic delays at auditory hair cell synapses: please see extensive studies done in Figure 11 of Chen and von Gersdorff (JNeurosci., 2019); this showed that synaptic delays are 1.26 ms in adult bullfrog auditory hair cells at 31oC, which is very similar to the High SR fibers (1.19 ms; Fig.4B and page 20). During ongoing depolarizations (e.g. during a sustained sine wave) the synaptic delay can be reduced to just 0.72 ms for probe EPSCs, which is a more usual number for mature fast synapses. This paper should, thus, be cited and briefly discussed in the Discussion. So a significant shortening of delay occurs for the probe response and this is also observed in young rat IHC synapses (see Goutman and Glowatzki, 2011).

We thank the reviewer for this comment. We have analysed the synaptic delay of the probe response and included it in Figure 4 – figure supplement 1. Contrary to the findings from Goutman and Glowatzki (2011) and Chen and von Gersdorff (2019), we did not observe a shortening of the synaptic delay for the probe response compared to the masker response. This difference might arise from the duration of the masker stimulus and/or the IHC holding potential. Synaptic facilitation in hair cells seems to occur only when the RRP is not depleted by the first stimulus (Cho et al., 2011). Our 100 ms masker depolarization from a holding potential of -58 mV effectively depleted the synapse RRP (Figure 4D), while both studies mentioned above used relatively short depolarizations (2 in rat and 20 ms in bullfrog) from a holding potential around -90 mV, which most likely didn’t deplete the RRP. Indeed, when using partially RRP depleting stimuli of 10 ms, Goutman (2011) observed longer synaptic latencies and smaller responses to the second stimulus. We have included this discussion in the last paragraph of the results section.

Additionally, we would also like to note that we referred to the important work on frog hair cell synapses in the manuscript, yet aimed to focus on relating synaptic heterogeneity of mammalian inner hair cell synapses to the functional diversity of type I spiral ganglion neurons that unlike the frog afferents show little branching of their peripheral neurites (in only ~15% of the neurons). We think it will be very interesting to study the aspect of presynaptic heterogeneity in the bullfrog amphibian papilla, but assume that the converging input of several active zones onto a single afferent might provide a different encoding scheme than in the mammalian cochlea.

(3) Gaussian-like (and/or multi-peak) EPSC amplitude distributions were obtained in more mature rat IHCs by Grant et al. (see their Figure 4G; JNeurosci. 2010; postnatal day 19-21). The putative single quanta peak was at 50 pA and the main peak was at 375 pA. The large mean suggests a low CV (probably < 0.4). However, Fig. 2F shows a mean of about 100 pA and CV = 0.7 for spontaneous EPSCs. This major difference deserves some more discussion. I suppose that one possible explanation may be that the current paper holds the IHC membrane potential fixed at -58 mV, whereas Grant et al. (2010) did not control the IHC membrane potential and spontaneous fluctuations in the Vm may have depolarized the IHC, thus producing larger evoked EPSCs that are triggered by Ca channel openings. Some discussion that compares these differences and possible explanations would be quite useful for the readers.

We understand the reviewer’s concern. We have now included the amplitude distribution of sEPSCs recorded from 12 boutons without patch-clamping the IHC (Figure 2–figure supplement 1, panel A). The rest of the recording conditions (i.e., artificial perilymph-like solution, physiological temperature and age) were identical to the conditions used for the paired recordings. Both the range of spontaneous rate (0 up to 16.33 sEPSC/s) and the amplitude distribution (peak at -40 pA and CV of 0.66) were comparable to the values we obtained when clamping the IHC resting potential at -58 mV. In addition, for two of our pairs, we established the bouton recording first, measured the spontaneous release, then established the perforated patch-clamp of the IHC and measured the spontaneous release again with IHC held at -58 mV. For pair #l300321_1, the SR before clamping the IHC was 0.0125 sEPSC/s, with a maximal AmpsEPSC of -110 pA (avg. -52 pA). The SR while holding the IHC at -58 mV was 0.36 sEPSCs/s, with a maximal AmpsEPSC of -140 pA (avg. -46 pA). For pair #l200522_2, the SR changed from 0.07 sEPSC/s to 0. The maximal AmpsEPSC before clamping the IHC was -70 pA (avg. -31 pA). Overall, our data recorded without controlling the IHC argues against the resting potential of -58 mV as a major source of differences in EPSC rate and amplitudes compared to previous studies.

Nonetheless, it is important to note that the experimental conditions used in our study differ from previous reports in several aspects. Our extracellular solution contains the physiological pH buffer bicarbonate instead of the fast buffer HEPES, as well as TEA and Cs+ for proper isolation of the Ca2+ currents. Both pH and potassium channel blockers can alter the excitability of the cell and, consequently, the spontaneous and evoked release. For instance, despite maintaining a similar extracellular pH (7.3 to 7.4), the choice of bicarbonate or HEPES for the extracellular solution can influence differently the regulation of the intracellular pH of the cell (Michl et al., 2019). Indeed, the activity of ion channels and receptors (e.g., AMPAR), and the resting potential can change depending on the extracellular buffer used (Hare and Owen, 1998, Vincent et al., 2019, Cho and von Gersdorff, 2014; and review Sinning and Hübner, 2013). Additionally, the animal model and the age range could be a source of difference. In rats, the EPSC amplitude distribution seems to change with maturation but not with K+ stimulation (Grant et al., 2010) or voltage depolarizations (Goutman and Glowatzki, 2007). This however does not seem to be the case for afferent boutons recorded from mice. In resting conditions (i.e. 5.8 mM extracellular K+), average EPSC amplitudes are around -100 to -150 pA for both prehearing (Chapochnikov et al., 2014) and hearing mice (Niwa et al., 2021 and the present study). Upon stimulation (40 mM K+ or voltage depolarizations), the mean EPSC amplitude does not change in prehearing mice (Jing et al., 2013; Takaba et al., 2019), but it significantly increases in hearing mice (Niwa et al., 2021 and the present study). In p20 and p30 mice, the mean EPSC amplitude was predominantly below -100 pA at rest and only increased above -100 pA after stimulation with 40 mM K+ (Niwa et al., 2021). Similarly, our reported avg. AmpsEPSC is below -150 pA, while the evoked EPSCs reached average amplitudes above -200 pA (Figure 1–figure supplement 1, panel F and Figure 4 – figure supplement 1, panel F).

We have included the aforementioned points in the discussion under the section "Diversity of spontaneous release and their topographical segregation”.

Reviewer #2 (Public Review):

Summary:

The study by Jaime-Tobon & Moser is a truly major effort to bridge the gap between classical observations on how auditory neurons respond to sounds and the synaptic basis of these phenomena. The so-called spiral ganglion neurons (SGNs) are the primary auditory neurons connecting the brain with hair cells in the cochlea. They all respond to sounds increasing their firing rates, but also present multiple heterogeneities. For instance, some present a low threshold to sound intensity, whereas others have high threshold. This property inversely correlates with the spontaneous rate, i.e., the rate at which each neuron fires in the absence of any acoustic input. These characteristics, along with others, have been studied by many reports over the years. However, the mechanisms that allow the hair cells-SGN synapses to drive these behaviors are not fully understood.

Strengths:

The level of experimental complexity described in this manuscript is unparalleled, producing data that is hardly found elsewhere. The authors provide strong proof for heterogeneity in transmitter release thresholds at individual synapses and they do so in extremely complex experimental settings. In addition, the authors found other specific differences such as in synaptic latency and max EPSCs. A reasonable effort is put into bridging these observations with those extensively reported in in vivo SGNs recordings. Similarities are many and differences are not particularly worrying as experimental conditions cannot be perfectly matched, despite the authors' efforts in minimizing them.

We would like to thank the reviewer for the appreciation of our work and the comments that helped us to improve our manuscript. We detail our responses to the comments below.

Weaknesses:

Some concern surges in relation to mismatches with previous reports of IHC-SGN synapses function. EPSCs at these synapses present a peculiar distribution of amplitudes, shapes, and rates. These characteristics are well-established and some do not seem to be paralleled in this study. Here, amplitude distributions are drastically shifted to smaller values, and rates of events are very low, all compared with previous evidence. The reasons for these discrepancies are unclear. The rate at which spontaneous EPSCs appear is an especially sensitive matter. A great part of the conclusions relies on the definition of which of the SGNs (or should say synapses) belong to the low end and which to the high end in the spectrum of spontaneous rates. The data presented by the authors seem a bit off and the criteria used to classify recordings are not well justified. The authors should clarify the origin of these differences since they do not seem to come from obvious reasons such as animal ages, recording techniques, mouse strain, or even species.

We understand the reviewer’s concern. We have now included the amplitude distribution of sEPSCs recorded from 12 boutons without patch-clamping the IHC (Figure 2–figure supplement 1, panel A). The rest of the recording conditions (i.e., artificial perilymph-like solution, physiological temperature and age) were identical to the conditions used for the paired recordings. Both the range of spontaneous rate (0 up to 16.33 sEPSC/s) and the amplitude distribution (peak at -40 pA and CV of 0.66) were comparable to the values we obtained when clamping the IHC resting potential at -58 mV. In addition, for two of our pairs, we established the bouton recording first, measured the spontaneous release, then established the perforated patch-clamp of the IHC and measured the spontaneous release again with IHC held at -58 mV. For pair #l300321_1, the SR before clamping the IHC was 0.0125 sEPSC/s, with a maximal AmpsEPSC of -110 pA (avg. -52 pA). The SR while holding the IHC at -58 mV was 0.36 sEPSCs/s, with a maximal AmpsEPSC of -140 pA (avg. -46 pA). For pair #l200522_2, the SR changed from 0.07 sEPSC/s to 0. The maximal AmpsEPSC before clamping the IHC was -70 pA (avg. -31 pA). Overall, our data recorded without controlling the IHC argues against the resting potential of -58 mV as a major source of differences in EPSC rate and amplitudes compared to previous studies.

Additionally, as noted on the section “Diversity of spontaneous release and their topographical segregation”, our SR values also agree with the range of 0.1 – 16.42 spikes/s reported by Wu et al., (2016) using loose patch recordings from p15-p17 rats. 90% of the paired recordings (and 60% of the bouton recordings) of our dataset were obtained from mice between p14-p17, where spontaneous activity is still low compared to older age groups (p19-p21: 0 – 44.22 spikes/s; p29p32: 0.11 – 54.9 spikes/s Wu et al., 2016; p28: 0 – 47.94 spikes/s, Siebald at al., 2023). There are two additional aspects to consider: i) about 40% of the SGN spikes seem to be generated intrinsically (not activated by an EPSP, ergo an EPSC) at p15-p18 (Wu et al., 2016); and ii) the presence of a spike or EPSC is the sole determinant of a successful recording when the IHC is not stimulated (either by K+ or voltage), thus, these type of experiments undersample fibers with low SR.

We have included the aforementioned points in the discussion under the section "Diversity of spontaneous release and their topographical segregation”.

Reviewer #3 (Public Review):

Summary:

"Bridging the gap between presynaptic hair cell function and neural sound encoding" by Jaime Tobon and Moser uses patch-clamp electrophysiology in cochlear preparations to probe the pre- and post-synaptic specializations that give rise to the diverse activity of spiral ganglion afferent neurons (SGN). The experiments are quite an achievement! They use paired recordings from pre-synaptic cochlear inner hair cells (IHC) that allow precise control of voltage and therefore calcium influx, with post-synaptic recordings from type I SGN boutons directly opposed to the IHC for both presynaptic control of membrane voltage and post-synaptic measurement of synaptic function with great temporal resolution.

Strengths

Any of these techniques by themselves are challenging, but the authors do them in pairs, at physiological temperatures, and in hearing animals, all of which combined make these experiments a real tour de force. The data is carefully analyzed and presented, and the results are convincing. In particular, the authors demonstrate that post-synaptic features that contribute to the spontaneous rate (SR) of predominantly monophasic post-synaptic currents (PSCs), shorter EPSC latency, and higher PSC rates are directly paired with pre-synaptic features such as a lower IHC voltage activation and tighter calcium channel coupling for release to give a higher probability of release and subsequent increase in synaptic depression. Importantly, IHCs paired with Low and High SR afferent fibers had the same total calcium currents, indicating that the same IHC can connect to both low and high SR fibers. These fibers also followed expected organizational patterns, with high SR fibers primarily contacting the pillar IHC face and low SR fibers primarily contacting the modiolar face. The authors also use in vivo-like stimulation paradigms to show different RRP and release dynamics that are similar to results from SGN in vivo recordings. Overall, this work systematically examines many features giving rise to specializations and diversity of SGN neurons.

We would like to thank the reviewer for the appreciation of our work and the comments that helped us to improve our manuscript. We detail our responses to the comments below.

Weaknesses / Comments / edits:

(1) The careful analysis of calcium coupling and EPSC metrics is especially nice. Can the authors speculate as to why different synapses (likely in the same IHC) would have different calcium cooperativity?

The finding of different apparent Ca2+ cooperativities among IHC synapses is intriguing. Paired pre- and postsynaptic patch-clamp recordings (this work and (Jaime Tobón and Moser, 2023)) and single synapse imaging of presynaptic Ca2+ signals and glutamate release (Özçete and Moser, 2021) jointly support this notion. Both methodologies complement each other. Imaging allows to assess the presynaptic Ca2+ of the specific synapse, while in paired recordings release is related to the whole cell Ca2+ influx. Paired recordings, on the other hand, provide the sensitivity and temporal resolution to assess the initial release rate with short stimuli (2 to 10 ms), which avoids an impact of RRP depletion and ongoing SV replenishment that needs to be considered for the longer stimuli used in imaging (50 ms). Both approaches agree on the finding of tighter coupling of Ca2+ channels and release sites (i.e., lower apparent Ca2+ cooperativity during depolarization within the range of receptor potentials) at pillar synapses. Moreover, the present study took advantage of recording individual release events [which was not achieved by imaging] and further supported the hypothesis that high SR SGNs receive input from active zones with tighter coupling than low SR SGNs. However, our two non-overlapping data sets for paired patch-clamp recordings (this work and (Jaime Tobón and Moser, 2023)) found a narrower range of apparent Ca2+ cooperativities compared to results from single synapse imaging (Özçete and Moser, 2021). This might reflect the technical differences described above. Future studies, potentially combining paired patch-clamp recordings with imaging of presynaptic Ca2+ signals will be needed to scrutinize this aspect.

We think that the different Ca2+ cooperativities reflect subtle differences in the topography of presynaptic Ca2+ channels and vesicular release sites at the specific IHC active zones. The work of Özçete and Moser (2021) indicated that indeed, apparent Ca2+ cooperativities differ among active zones even within the same inner hair cell. Synaptic heterogeneity within one individual cell can expand its coding capacity. In the case of IHCs, differences in the Ca2+ dependence of synaptic release, in addition to the heterogeneous voltage dependence, appears to diversify the response properties (i.e., synaptic vesicle release probability) of individual synapses to the same stimulus. This is particularly important for sound intensity and temporal coding.

We have included the aforementioned points in the discussion under the section "Candidate mechanisms distinguishing evoked release at low and high SR synapses”.

(2) On the bottom of page 6 it would be helpful to mention earlier how many pillar vs modiolar fibers were recorded from, otherwise the skewness of SRs (figure 2H could be thought to be due to predominantly recordings from modiolar fibers. As is, it reads a bit like a cliff-hanger.

Done!

(3) The contrasts for some of the data could be used to point out that while significant differences occur between low and high SR fibers, some of these differences are no longer apparent when comparing modiolar vs pillar fibers (eg by contrasting Figure 2C and 2K). This can indicate that indeed there are differences between the fiber activity, but that the activity likely exists in a gradient across the hair cell faces. Pointing this out at the top of page 10 (end of the first paragraph) would be helpful, it would make the seemingly contradictory voltage dependence data easier to understand on first read (voltage-dependence of release is significantly different between different SR fibers (figure 3) but is not significantly different between fibers on different HC faces (figure S3).

Done!

(4) It should be acknowledged that although the use of post-hearing animals here (P14-23) ensures that SGN have begun to develop more mature activity patterns (Grant et al 2010), the features of the synapses and SGN activity may not be completely mature (Wu et al 2016 PMID: 27733610). Could this explain some of the 'challenges' (authors' section title) detailed on page 28, first full paragraph?

Done!

(5) In the discussion on page 24, the authors compare their recorded SR of EPSCs to measure values in vivo which are higher. Could this indicate that in vivo, the resting membrane potential of IHCs is more depolarized than is currently used for in vitro cochlear experiments?

That is indeed one possible explanation among others. We have expanded the discussion about the factors that could affect the SR in ex vivo experiments.

(6) The results showing lower calcium cooperativity of high SR fibers are powerful, but do the authors have an explanation for why the calcium cooperativity of < 2 is different from that (m = 3-4) observed in other manuscripts?

We assume this question to potentially result from a misunderstanding. Using membrane capacitance measurements and Ca2+ uncaging, Beutner et al. (2001) reported a high intrinsic Ca2+ cooperativity of inner hair cell exocytosis (m = 4-5). Based on this data, it has been proposed that the binding of 4-5 Ca2+ ions is required to trigger the fusion of a synaptic vesicle in IHCs. However, given the shortcoming of Ca2+ uncaging, we and others aimed to further study this aspect using alternative methods. By varying the current of single Ca2+ channels in apical IHCs of hearing mice, several studies reported a high apparent Ca2+ cooperativity (m = 3-5) that is thought to reflect the high intrinsic cooperativity (Brandt et al., 2005; Wong et al., 2014; Özçete and Moser, 2021; Jaime Tobón and Moser, 2023).

On the other hand, the apparent Ca2+ cooperativity observed upon changing the number of open Ca2+ channels would also reflect the active zone topography (i.e., number and distance of Ca2+ channels to the vesicular release site). In the present study, we used different depolarizations within the range of receptor potentials and found a low apparent Ca2+ cooperativity (m < 2) in 93% of the studied synapses. Other studies in apical IHCs from hearing mice used similar and alternative methods to change the number of open Ca2+ channels and also estimated an apparent cooperativity of < 2 (Brandt et al., 2005; Johnson et al., 2005; Johnson et al., 2007; Wong et al., 2014; Özçete and Moser, 2021; Jaime Tobón and Moser, 2023). The fact that these estimates are smaller than those seen upon changes in single Ca2+ current has been taken to indicate that SV release is governed by one or few Ca2+ channels in nanometer proximity (Ca2+ nanodomain-like control of SV exocytosis), building on classical synapse work (Augustine et al., 1991). 

In contrast, comparable recordings from mouse IHCs before the onset of hearing (Wong et al., 2014) revealed more similar apparent Ca2+ cooperativities (m ~3) for both changes in the number of open Ca2+ channels and changes in single Ca2+ channel current. This suggests that IHCs before the onset of hearing employ a Ca2+ microdomain-like control of SV exocytosis in which release is governed by the combined activity of several Ca2+ channels in >100 nm distance to the release site. A Ca2+ microdomain-like control of SV exocytosis was also reported for basocochlear IHCs (Johnson et al., 2017).

Recommendations for the authors:

As explained in the public reviews of Reviewers 1 and 2, some mismatches between the data presented here and previous reports from the literature have been identified. It is recommended that you discuss those mismatches, perhaps in relation to the choice of patchclamping the hair cells at -58mV.

We have addressed this point thoroughly in the revised MS. Please see our response to the public review.

Reviewer #1 (Recommendations For The Authors):

Minor suggestions and corrections:

(1) Figures 3 and 4 show beautiful data with paired recordings. Figure 3 shows 10 ms pulses, whereas Fig. 4 shows 100 ms depolarizing pulses. The example in Fig. 3A shows asynchronous release after Ca channel closure, whereas Fig. 4 does not show this so prominently. Was there quite a bit of variability in the asynchronous release from different cell pairs, or was this correlated with pulse duration?

The asynchronous release is also present after 100 ms depolarizing pulses (please see the updated panel A of Figure 4). However, we have not analysed asynchronous release and think that this would be beyond the scope of the current MS. For clarity, we have added dashed lines in the EPSC traces of Figs. 3 and 4 to indicate the on and off-set of the depolarization.

(2) Differences in apex and basal IHC ribbon synapse nanodomain to microdomain Ca channel coupling to exocytosis-sensor have been reported also for gerbil IHCs (see Johnson et al., JNeurosci., 2017). This may be worth mentioning since it is another indication of major synaptic diversity in the mammalian cochlea, this time from low to frequency-located IHCs.

Done

(3) Page 22: change "hight SR" to "high SR".

Done

(4) Page 27: change "addess" to "addressed".

Done

Reviewer #2 (Recommendations For The Authors):

Major points:

(1) As indicated in methods, recording stretches of 5-10 seconds were used to determine the SR of a given SGN. This seems too short for a reasonable estimate of the SR in these neurons. Also, the reported SRs for these mature mice are not only much lower than those measured in in-vivo SGN extracellular recordings but also compared to those reported in ex-vivo rat recordings. Why this discrepancy? The authors decided to estimate SR by voltage-clamping IHCs at a fixed value of - 58 mV, which they take from Johnson, 2015. I wonder if it is not more reasonable to use a range of IHC holdings and measure SR at those, instead of using a single one. It is hard to visualize a very strong argument for using strictly -58 mV. In addition, mapping out a range of holding potentials could provide additional information on IHCs resting membrane potential in physiological conditions.

Related to this point, considering that SR values found in the ex-vivo preparation are much lower than those described in in-vivo situations, is it fair to use the same 1 sp/s criteria, as in Taberner & Liberman, to segregate low and high? Shouldn't this value be adjusted to the overall lower SR? This criterion is naturally critical for the consequent evaluation of other SGN properties.

Finally, on this same problem of IHC Vh, does -58 mV estimate include the 19 mV liquid junction potential? How does it compare with the activation threshold of calcium influx at modiolar vs pillar synapses (see imaging studies)?

We had proactively discussed the challenges of relating ex vivo and in vivo data in the preprint provided for review. While we consider the outcome of our study helpful for better understanding the relation of afferent synaptic heterogeneity and diverse firing properties of SGNs, we do not claim that the assumptions based on literature (such as on the physiological resting potential) represent ground truth.

When carefully revising the MS, we have expanded on the discussion to address the points raised here, particularly regarding the lower SR and sEPSC amplitudes. As this and the other reviewer commented in the public review, these experiments were hard to achieve and we consider repeating them with a range of IHC holding potentials (then not only for spontaneous rate of transmission, but also for in depth characterization of evoked release) to be beyond the scope of the present study.

We do appreciate the suggestion to adjust the distinction between low and high SR given the overall lower rates. However, we would like to refrain from it, as i) we consider it quite arbitrary to define another criterium and ii) we would like to avoid any apparent cherry-picking bias.

Finally, yes, of course, the -58 mV represent the liquid junction potential corrected holding potential. Our average IHC whole-cell Vhalf ICa (-38.86 mV for high SR and -37.60 mV for low SR) compares well with previous reports of average whole-cell Vhalf ICa (-35.44 mV) and average synaptic Vhalf Rhod-FF (-41.15 mV) (Özçete and Moser, 2021). Additionally, our Vhalf QEPSC distribution (ranging from -53.97 to -31.72 mV) also compares well with the Vhalf iGluSnFR distribution (ranging from -45.25 to -29.86 mV) obtained by imaging of synaptic glutamate release (Özçete and Moser, 2021).

2) EPSCs amplitude distributions in Figure 2 seem very different from those reported before by Grant et al., 2010 and Niwa et al., 2021 (even Chapochnikov et al., 2014; although not sure if the animal ages match). The average amplitudes of EPSCs reported here, for either pillar or modiolar SGNs, seem way smaller than those reported previously. The authors should provide a convincing explanation for this critical deviation from the consensual results.

Please refer to our response to the public review (point #3).

3) Rise time analysis in Fig. 2 supp 1. The actual values seem too long, again, compared to reported values. Also, what would these differences between modiolar and pillar represent?

Previous reports on mouse, rat, turtle and bullfrog focused mainly on the rise times (or time to peak) of monophasic EPSCs: about 0.39 ms (p8-p11 mouse; Chapochnikov et al., 2014, Takago et al., 2019), 0.33-0.58 ms (p7-p14 rat; Yi at al., 2010, Grant et al., 2010, Glowatzki and Fuchs, 2002), 0.17-0.29 ms (p15-p21 rat; Chapochnikov et al., 2014, Huang and Moser, 2018, Grant et al., 2010), 0.1-0.2 ms (turtle auditory papilla; Schnee et al., 2013) and 0.15-0.2 ms (bullfrog 31ºC and 22ºC; Li et al., 2009, Chen and von Gersdorff, 2019). Regarding multiphasic EPSCs, some studies have reported rise times (or times to peak) of about 1.5 ms (p8-p11 mouse; Takago et al., 2019), 1.1 ms (p8-p11 rat; Grant et al., 2010) and 0.6-0.8 ms (p15-p21 rats; Huang and Moser, 2018, Chapochnikov et al., 2014, Grant et al., 2010). When we factor in the waveform of the sEPSCs, our rise times are comparable to the literature:

Author response table 1.

Thus, IHC synapses with higher SR and predominantly located at the pillar side appear to have sEPSCs with faster rise times regardless of their waveform. This might be a consequence of the fusion kinetics of the synaptic vesicles which are tightly influenced by the Ca2+ influx (Huang and Moser, 2018). Additionally, differences in the composition and density of the postsynaptic AMPA receptors could play a role in the rise time of the EPSC (Rubio et al., 2017). 

4) One of the most impressive observations of the in-vivo SGN physiology is the difference in sound threshold among specific fibers. This can vary over tens of dB of sound pressure levels.

The representation of this phenomenon when using an ex-vivo preparation is not obvious. Overall, it has been reported that IHC Vm is a good proxy for stimulus intensity. Consequently, the authors reported an 'IHC Vm threshold' at the start of SGN synaptic activity for each recording. This can be found in Figure 3 Eii, where values vary between -65 to -30 mV. This is already an important finding. However, the representative traces on panel A only diverge by 5 mV. It would be very interesting to the reader to have represented in the figure recordings that can better illustrate this wide range of values.

We agree with the reviewer regarding the impressive difference in the sound thresholds recorded in vivo. To illustrate better illustrate our findings, we have chosen a different representative trace for the high SR synapse.

5) On the masker-probe experiments it would be interesting to look at the synaptic delay of the probe pulses. Are they different between high and low SR synapses?

We have now included the results of the synaptic delay of the probe response (Figure 4– supplementary figure 1). Despite not being statistically significant, the eEPSC probe latency of high SR is on average faster than low SR.

Reviewer #3 (Recommendations For The Authors):

(1) The terms monophasic and compact are used interchangeably. This is fine, but perhaps compact could be defined earlier, otherwise, readers may think that 'compact' means 'short' (as is sometimes euphemistically used to describe short people), which then makes phrasing such as the figure legend for figure 2 a bit confusing. This could be included at first use in a figure as well, in figure 1B where the two types of EPSCs are first shown.

Done, now explained and preferentially used monophasic.

(2) Check for mention of figure panels in the results text - for example, there is no mention in the results text of figure 2A, 2I,

Done

(3) The locations of some of the statistics are inconsistent. This is fine if the authors have a reason for including the stats where they did, but in some cases, the stats are duplicated (for example figure 2J, 2K, 2L, the stats are in both the figure legend and the results text, then check throughout).

Done

(4) The color coding in figure 4 is confusing in panel A - does orange still mean a high SR fiber here? The legend indicates that orange is for EPSCs, but does not specify charge. It could be helpful to show both a high and low SR response, both for EPSCs and for charge. 

Thanks for pointing us to this aspect: we have carefully revised the figure and figure legend for clarity. We also included an exemplary response of a low SR synapse in the figure.

Author response:

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

Recommendations For The Authors:

Reviewer #1:

●      It might help the reader if you make it explicit that mDES allows you to create an approximate amalgam of different kinds of experiences by assuming that, across individuals, there is a general consensus of experiences at particular points in the movie. Whether this assumption is an accurate reflection of the way in which each individual's brain is an important, testable prediction that could be discussed/examined in different projects. For instance, in other projects there are clear idiosyncratic responses to the same naturalistic stimuli: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064646/.

Thank you, this is an excellent point. We have included this article in our revision and expanded on the introduction to emphasize how this study relates to our work. Additionally, we have included an additional figure that helps illustrate how mDES can be used to evaluate the idiosyncrasy for each respective thought component to visually display the variance across moments in the film:

Page 6-7 [137-148] In our study, we used multi-dimensional experience sampling (mDES) to describe ongoing thought patterns during the movie-watching experience [8]. mDES is an experience sampling method that identifies different features of thought by probing participants about multiple dimensions of their experiences. mDES can provide a description of a person’s thoughts, generating reliable thought patterns across laboratory cognitive tasks [22, 32, 33] and in daily life [34, 35], and is sensitive to accompanying changes in brain activity [24, 36]. Studies that use mDES to describe experience ask participants to provide experiential reports by answering a set of questions about different features of their thought on a continuous scale from 1 (Not at all) to 10 (Completely) [24, 32-41]. Each question describes a different feature of experience such as if their thoughts are oriented in the future or the past, about oneself or other people, deliberate or intrusive in nature, and more (See methods for a full list of questions used in the current study).

●      A cartoon describing the mDES technique could be helpful for uninitiated readers.

Thank you for your suggestion, we have added an additional figure (Figure 3) that illustrates the process of mDES in the laboratory during this experiment, clarifying that participants answer mDES items using a slider to indicate their score (rather than expressing it verbally).

●      Did the authors check for any measures of reliability across mDES estimates other than split-half reliability? For instance, the authors could demonstrate construct validity by showing that engagement with certain features of the thought-sampling space aligned with specific points in the movies. If so, the start of the Results section would be a great place to demonstrate the reliability of the approach. For instance, did any two participants sample the same 15-second window of time in a particular stimulus? If so, you could compare their experience samples to determine whether the method was extensible across subjects.

This is a great point, thank you very much for highlighting this. We have eight individuals at each time point in our analysis, which is probably not enough to calculate meaningful reliability measures. However, we have added a time series analysis of experience in each clip to our revision (Figure 3). In these time plots, it is possible to see clear moments in the film in which scores do not straddle 0 (using 95% CI), and often, these persist across successive moments (Figure 3; see time-series plot four for the clearest example).  When the confidence intervals of a sampling epoch do not overlap with zero, this suggests a high degree of agreement in thought content across participants. At the same time, our analysis shows that individual differences do exist since the relative presence of each component for each participant was linked to objective measures of movie watching (in this case, comprehension). In this revision we have specifically addressed this question by conducting ANOVAs to determine how scores on each component across the clip (See also supplementary table 11). This additional analysis shows that mDES effectively captures shared aspects of movie-watching and is also sensitive to individual variation (since it can describe individual differences).

Page 15 [304-323]: Next, we examined how each pattern of thought changes across each movie clip. For this analysis, we conducted separate ANOVA for each film clip for the four components (see Table 1 and Figure 3). Clear dynamic changes were observed in several components for different films. We analyzed these data using an Analysis of Variance (ANOVA) in which the time in each clip were explanatory variables of interest. This identified significant change in “Episodic Social Cognition” scores across Little Miss Sunshine, F(1, 712) = 10.80, p = .001, , η2 = .03, and Citizenfour, F(1, 712) = 5.23, p = .023, , η2 = .02. There were also significant change in “Verbal Detail” scores across Little Miss Sunshine, F(1, 712) = 31.79, p <.001, η2 = .09. Lastly, there were significant changes in “Sensory Engagement” scores for both Citizenfour, F(1, 712) = 6.22, p = .013, η2 = .02, and 500 Days of Summer, F(1, 706) = 80.41, p <.001, η2 = .18. These time series are plotted in Figure 3 and highlight how mDES can capture the dynamics of different types of experience across the three movie clips. Moreover, in several of these time series plots, it is clear that thought patterns reported extend beyond adjacent time periods (e.g. scores above zero between time periods 150 to 400 for Sensory Engagement in 500 days of Summer and for time periods between 175 and 225 for Verbal Detail in Little Miss Sunshine). It is important to note that no participant completed experience sampling reports during adjacent sampling points (see Supplementary Figure 7), so the length of these intervals indicates agreement in how specific scenes within a film were experienced and conserved across different individuals. Notably, the component with the least evidence for temporal dynamics was “Intrusive Distraction.”

●      P10: "Generation of the thought-space" - how stable are these word clouds to individual subjects? If there are subject-specific differences, are there ways to account for this with some form of normalization?

Thank you for bringing up this point. Our current goal was to show how the average experience of one group of participants relates to the brain activity of a second group. In this regard it is important to seek the patterns of similarity across individuals in how they experience the film. However, as is normal in our studies using mDES, we can also use the variation from the mean to predict other cognitive measures and, in this way, account for the variability that individuals have in their movie-watching experience. In other words, the word clouds reflect the mean of a particular dimension, so when an individual score is close to 0, their thought content does not align with this dimension -- however, deviating scores, positive or negative, indicating that this dimension provides meaningful information about the individual's experience. Evidence of the meaningful nature of this variation can be seen in the links between the reported thoughts and the individuals’ comprehension (e.g. individuals whose thoughts do not contain strong evidence of “Intrusive Distraction”, or in other words, a negative score, tended to do better on comprehension tests of information in the movies they watched).

●      P11: "Variation in thought patterns" - can the authors use a null model here to demonstrate that the associations they've observed would occur above chance levels (e.g., for a comparison of time series with similar temporal autocorrelation but non-preserved semantic structure)? Further, were there any pre-defined hypotheses over whether any of the three different movies would engage any of the 4 observed dimensions?

This is a great point. We chose to sample from three distinctly different films to help us understand if mDES was sensitive to different semantic and affective features of films. Our analysis, therefore, shows that at a broad level, mDES is able to discriminate between films, highlighting its broad sensitivity to variation in semantic or affective content. Armed with this knowledge, researchers in the future could derive mechanistic insights into how the semantic features may influence the mDES data. For example, future studies could ask participants to watch movies in a scrambled order to understand how varying the structure of semantics or information breaks the mapping between brains and ongoing experience. In this revision we have amended the text to reflect this possibility:

Page 34 [674-679]. Our analysis shows that mDES is able to discriminate between films, highlighting its broad sensitivity to variation in semantic or affective content. Armed with this knowledge, we propose that in the future, researchers could derive mechanistic insights into how the semantic features may influence the mDES data. For example, it may be possible to ask participants to watch movies in a scrambled order to understand how the structure of semantic or information influences the mapping between brains and ongoing experience as measured by mDES.

●      P14: "Brain - Thought Mappings: Voxel-space Analysis" - this is a cool analysis, and a nice validation of the authors' approach. I would personally love to see some form of reliability analysis on these approaches - e.g., do the same locations in the cerebral cortex align with the four features in all three movies? Across subjects?

This is another great point, and we thank you for your enthusiasm. The data we have has only sampled mDES during a relatively short period of brain activity which we suspect would make an individual-by-individual analysis underpowered. In the future, however, it may be possible to adopt a precision mapping approach in which we sample mDES during longer periods of movie watching and identify how group-level mappings of experience relate to brain activity within a single subject. To reflect this possibility, we have amended the text in this revision in the following way:

Page 34-35 [672-687]: In addition, our study is correlational in nature, and in the future, it could be useful to generate a more mechanistic understanding of how brain activity maps onto the participants' experience. Our analysis shows that mDES is able to discriminate between films, highlighting its broad sensitivity to variation in semantic or affective content. Armed with this knowledge, we propose that in the future, researchers could derive mechanistic insights into how the semantic features may influence the mDES data. For example, it may be possible to ask participants to watch movies in a scrambled order to understand how the structure of semantic or information influences the mapping between brains and ongoing experience as measured by mDES. Finally, our study focused on mapping group-level patterns of experience onto group-level descriptions of brain activity. In the future, it may be possible to adopt a “precision-mapping” approach by measuring longer periods of experience using mDES and determining how the neural correlates of experience vary across individuals who watched the same movies while brain activity was collected [1]. In the future, we anticipate that the ease with which our method can be applied to different groups of individuals and different types of media will make it possible to build a more comprehensive and culturally inclusive understanding of the links between brain activity and movie-watching experience

Reviewer #2:

(1) The three-dimensional scatter plot in Figure 2 does not represent "Intrusive Distraction." Would it make sense to color-code dots by this important dimension?

Thank you for this suggestion. Although it could be possible to indicate the location of each film in all four dimensions, we were worried that this would make the already complex 3-D space confusing to a naive reader. In this case, we prefer to provide this information in the form of bar graphs, as we did in the previous submission.

(2) The coloring of neural activation patterns in Figure 3 is not distinct enough between the different dimensions of thought. Please reconsider color intensities or coding. The same applies to the left panel in Figure 4.

Thanks for this comment; we found it quite difficult to find a colour mapping that allows us to show the distinction between four states in a simple manner, yet we believe it is valuable to show all of the results on a similar brain. Nonetheless, to provide a more fine-grained viewing of our results in this revision we have provided a supplementary figure (Supplementary Figure 6) that shows each of the observed patterns of activity in isolation.

(3) The new method (mDES) is mentioned too often without explanation, making it hard to follow without referring to the methods section. It would be helpful to state prominently that participants rated their thoughts on different dimensions instead of verbalizing them.

Thank you for this point, we have adjusted the Introduction to clarify and expand on the mDES method. We have also included an example of the mDES method in an additional figure that we have now included to visually express how participants respond to mDES probes (Figure 3).

Page 6-7 [136-148]: In our study, we used multi-dimensional experience sampling (mDES) to describe ongoing thought patterns during the movie-watching experience [2]. mDES is an experience sampling method that identifies different features of thought by probing participants about multiple dimensions of their experiences. mDES can provide a description of a person’s thoughts, generating reliable thought patterns across laboratory cognitive tasks [3-5] and in daily life [6, 7], and is sensitive to accompanying changes in brain activity when reports are gained during scanning [8, 9]. Studies that use mDES to describe experience ask participants to provide experiential reports by answering a set of questions about different features of their thought on a continuous scale from 1 (Not at all) to 10 (Completely) [3, 5-14]. Each question describes a different feature of experience, such as if their thoughts are oriented in the future or the past, about oneself or other people, deliberate or intrusive in nature, and more (See Methods for a full list of questions used in the current study).

Author response image 1.

(4) Reporting of single-movie thought patterns seems quite extensive. Could this be condensed in the main text?

Thank you for this point, upon re-visiting the manuscript, we have adjusted the text to be more concise.

Reviewer #3:

●      This is a very elegant experiment and seems like a very promising approach. The text is currently hard to read.

Thank you for this point, we have since revisited the text and adjusted the manuscript to be more concise and add more clarity.

●      The introduction (+ analysis goals) fails to explain the basic aspects of the analysis and dataset. It is not clear how many participants and datapoints were used to establish the group-level thought patterns, nor is it entirely clear that the fMRI data is a separate existing dataset. Some terms are introduced and highlighted and never revisited (e.g decoupled states and the role of the DMN).

Thank you for this critique, we have since adjusted the introduction to clearly explain the difference between Sample 1 and Sample 2 and further clarify that the fMRI data is an entirely separate, independent sample compared to the laboratory mDES sample:

Page 7-8 [158-174]: Thus, to overcome this obstacle, we developed a novel methodological approach using two independent sample participants. In the current study, one set of 120 participants was probed with mDES five times across the three ten-minute movie clips (11 minutes total, no sampling in the first minute). We used a jittered sampling technique where probes were delivered at different intervals across the film for different people depending on the condition they were assigned. Probe orders were also counterbalanced to minimize the systematic impact of prior and later probes at any given sampling moment. We used these data to construct a precise description of the dynamics of experience for every 15 seconds of three ten-minute movie clips. These data were then combined with fMRI data from a different sample of 44 participants who had already watched these clips without experience sampling [15]. By combining data from two different groups of participants, our method allows us to describe the time series of different experiential states (as defined by mDES) and relate these to the time series of brain activity in another set of participants who watched the same films with no interruptions. In this way, our study set out to explicitly understand how the patterns of thoughts that dominate different moments in a film in one group of participants relate to the brain activity at these time points in a second set of participants and, therefore, better understand the contribution of different neural systems to the movie-watching experience.

Page 8-9 [177-188] The goal of our study, therefore, was to understand the association between patterns of brain activity over time during movie clips in one group of participants and the patterns of thought that participants reported at the corresponding moment in a different set of participants (see Figure 1). This can be conceptualized as identifying the mapping between two multi-dimensional spaces, one reflecting the time series of brain activity and the other describing the time series of ongoing experience (see Figure 1 right-hand panel). In our study, we selected three 11-minute clips from movies (Citizenfour, Little Miss Sunshine and 500 Days of Summer) for which recordings of brain data in fMRI already existed (n = 44) [15] (Figure 1, Sample 1). A second set of participants (n = 120) viewed the same movie clips, providing intermittent reports on their thought patterns using mDES (Figure 1, Sample 2). Our goal was to understand the mapping between the patterns of brain activity at each moment of the film and the reports of ongoing thought recorded at the same point in the movies.

●      It is unclear what the utility of the method is - is it meant to be done in fMRI studies on the same participants? Or is the idea to use one sample to model another?

Great point, thank you for highlighting this important question. This paper aimed to interrogate the relationship between experience and neural states while preserving the novelty of movie-watching. Although it could be done in the same sample, it may be difficult to collect frequent reports of experience without interrupting the dynamics of the brain. However, in the future it could be possible to collect mDES and brain activity in the same individuals while they watched movies. For example, our prior studies (e.g. [9]) where we combined mDES with openly-available brain data activity during tasks. In the future, this online method could also be applied during movie watching to identify direct mapping between brain activity and films. However, this online approach would make it very expensive to produce the time series of experience across each clip given that it would require a large number of participants (e.g. 200 as we used in our current study). The following has been included in our manuscript:

Page 7 [149-159] One challenge that arises when attempting to map the dynamics of thought onto brain activity during movie watching is accounting for the inherently disruptive nature of experience sampling: to measure experience with sufficient frequency to map the dynamics of thoughts during movies would disrupt the natural dynamics of the brain and would also alter the viewer’s experience (for example, by pausing the film at a moment of suspense). Therefore, if we periodically interrupt viewers to acquire a description of their thoughts while recording brain activity, this could impact capturing important dynamic features of the brain. On the other hand, if we measured fMRI activity continuously over movie-watching (as is usually the case), we would lack the capacity to directly relate brain signals to the corresponding experiential states. Thus, to overcome this obstacle, we developed a novel methodological approach using two independent sample participants

●      The conclusions currently read as somewhat trivial (e.g "Our study, therefore, establishes both sensory and association cortex as core features of the movie-watching experience", "Our study supports the hypothesis that perceptual coupling between the brain and external input is a core feature of how we make sense of events in movies").

Thank you for this comment. In this revision we have attempted to extend the theoretical significance of our work in the discussion (for example, in contrasting the links between Intrusive distraction and the other components). To this end we have amended the text in this revision by including the following sections:

Page 33-35 [654-687]: Importantly, our study provides a novel method for answering these questions and others regarding the brain basis of experiences during films that can be applied simply and cost-effectively. As we have shown mDES can be combined with existing brain activity allowing information about both brain activity and experience to be determined at a relatively low cost.  For example, the cost-effective nature of our paradigm makes it an ideal way to explore the relationship between cognition and neural activity during movie-watching during different genres of film. In neuroimaging, conclusions are often made using one film in naturalistic paradigm studies [16]. Although the current study only used three movie clips, restraining our ability to form strong conclusions regarding how different patterns of thought relate to specific genres of film, in the future, it will be possible to map cognition across a more extensive set of movies and discern whether there are specific types of experience that different genres of films engage. One of the major strengths of our approach, therefore, is the ability to map thoughts across groups of participants across a wide range of movies at a relatively low cost.

Nonetheless, this paradigm is not without limitations. This is the first study, as far as we know, that attempts to compare experiential reports in one sample of participants with brain activity in a second set of participants, and while the utility of this method enables us to understand the relationship between thought and brain activity during movies, it will be important to extend our analysis to mDES data during movie watching while brain activity is recorded. In addition, our study is correlational in nature, and in the future, it could be useful to generate a more mechanistic understanding of how brain activity maps onto the participants experience. Our analysis shows that mDES is able to discriminate between films, highlighting its broad sensitivity to variation in semantic or affective content. Armed with this knowledge, we propose that in the future, researchers could derive mechanistic insights into how the semantic features may influence the mDES data. For example, it may be possible to ask participants to watch movies in a scrambled order to understand how the structure of semantic or information influences the mapping between brains and ongoing experience as measured by mDES. Finally, our study focused on mapping group-level patterns of experience onto group-level descriptions of brain activity. In the future it may be possible to adopt a “precision-mapping” approach by measuring longer periods of experience using mDES and determining how the neural correlates of experience vary across individuals who watched the same movies while brain activity was collected [1]. In the future, we anticipate that the ease with which our method can be applied to different groups of individuals and different types of media will make it possible to build a more comprehensive and culturally inclusive understanding of the links between brain activity and movie-watching experience

●      The beginning of the discussion is very clear and explains the study very well. Some of it could be brought up in the intro/analysis goal sections.

Thank you for this comment, this is an excellent idea. We have revisited the introduction and analysis goals section to mirror this clarity across the manuscript.

●      The different components are very interesting, and not entirely clear. Some examples in the text could help. Especially regarding your thought that verbal components would refer to a "decoupled" mental verbal analysis participants might be performing in their thoughts.

Thank you for this point. We would prefer not to elaborate on this point since, at present, it would simply be conjecture based on our correlational design. However, we have included a section in the discussion which explains how, in principle, we would draw more mechanistic conclusions (for example, by shuffling the order of scenes in a movie as suggested by another reviewer). In the current revision, we have amended the text in the following way:

Page 34 [674-679]: Our analysis shows that mDES is able to discriminate between films, highlighting its broad sensitivity to variation in semantic or affective content. Armed with this knowledge, we propose that in the future, researchers could derive mechanistic insights into how the semantic features may influence the mDES data. For example, it may be possible to ask participants to watch movies in a scrambled order to understand how the structure of semantic or information influences the mapping between brains and ongoing experience as measured by mDES

●      The reference to using neurosynth as performing a meta-analysis seems a little stretched.

We have adjusted the manuscript to remove ‘meta-analysis’ when referring to the analysis computed with neurosynth. Thank you for bringing this to our attention.

●      State-space is defined as brain-space in the methods.

Thank you, we have since updated this.

●      It could be useful to remind the reader what thought and brain spaces are at the top of the state-space results section.

This is an excellent point, and it has since been updated to remind the reader of thought- and brain-space. Thank you for this comment.

Page 24 [458-467]: Our next analysis used a “state-space” approach to determine how brain activity at each moment in the film predicted the patterns of thoughts reported at these moments (for prior examples in the domain of tasks, see [12, 17], See Methods). In this analysis, we used the coordinates of the group average of each TR in the “brain-space” and the coordinates of each experience sampling moment in the “thought-space.”. To clarify, the location of a moment in a film in “brain-space” is calculated by projecting the grand mean of brain activity for each volume of each film against the first five dimensions of brain activity from a decomposition of the Human Connectome Project (HCP) resting state data, referred to as Gradients 1-5. “Thought-space” is the decomposition of mDES items to create thought pattern components, referred to as “Episodic Knowledge”, “Intrusive Distraction”, “Verbal Detail” and “Sensory Engagement.”

●      DF missing from the t-test for episodic knowledge/grad 4.

Thank you for catching this, the degrees of freedom has since been included in this revision.

Page 24 [474-476]: First, we found a significant main effect of Gradient 4 (DAN to Visual), which predicted the similarity of answers to the “Episodic Knowledge” component, t(2046) = 2.17, p = .013, η2 = .01.

Public Reviews:

Reviewer #1:

●      The lack of direct interrogation of individual differences/reliability of the mDES scores warrants some pause.

Our study's goal was to understand how group-level patterns of thought in one group of participants relate to brain activity in a different group of participants. To this end, we decomposed trial-level mDES data to show dimensions that are common across individuals, which demonstrated excellent split-half reliability. Then we used these data in two complementary ways. First, we established that these ratings reliably distinguished between the different films (showing that our approach is sensitive to manipulations of semantic and affective features in a film) and that these group-level patterns were also able to predict patterns of brain activity in a different group of participants (suggesting that mDES dimensions are also sensitive to broad differences in how brain activity emerges during movie watching). Second, we established that variation across individuals in their mDES scores predicted their comprehension of information from the films. This establishes that when applied to movie-watching, mDES is sensitive to individual differences in the movie-watching experience (as determined by an individual's comprehension). Given the success of this study and the relative ease with which mDES can be performed, it will be possible in the future to conduct mDES studies that hone in on the common and distinct features of the movie-watching experience.

Reviewer #2:

(1) The dimensions of thought seem to distinguish between sensory and executive processing states. However, it is unclear if this effect primarily pertains to thinking. I could imagine highly intrusive distractions in movie segments to correlate with stagnating plot development, little change in scenery, or incomprehensible events. Put differently, it may primarily be the properties of the movies that evoke different processing modes, but these properties are not accounted for. For example, I'm wondering whether a simple measure of engagement with stimulus materials could explain the effects just as much. How can the effects of thinking be distinguished from the perceptual and semantic properties of the movie, as well as attentional effects? Is the measure used here capturing thought processes beyond what other factors could explain?

Our study used mDES to identify four distinct components of experience, each of which had distinct behavioural and neural correlates and relationships to comprehension. Together this makes it unlikely that a single measure of engagement would be able to capture the range of effects we observed in our study. For example, “Intrusive Distraction” was associated with regions of association cortex, while the other three components highlighted regions of sensory cortex. Behaviorally, we found that some components had a common effect on comprehension (e.g. “Intrusive distraction” was related to worse comprehension across all films), while others were linked to clear benefits to comprehension in specific films (e.g. “Episodic Knowledge” was associated with better comprehension in only one of the films). Given the complex nature of these effects, it would be difficult for a single metric of engagement to explain this pattern of results, and even if it did, this could be misleading because our analysis implies that they are better explained by a model of movie-watching experience in which there are several relatively orthogonal dimensions upon which our experience can vary.

At the same time, we also found that films vary in the general types of experience they can engender. For example, Citizenfour was high on “Intrusive Distraction” and participants performed relatively low on comprehension. This shows that manipulations of the semantic and affective content of films also have implications for the movie-watching experience. This pattern is consistent with laboratory studies that applied mDES during tasks and found that different tasks evoke different types of experience (for example, patterns of ‘intrusive’ thoughts were common in movie clips that were suspenseful, [18]). At the same time, in the same study, patterns of intrusive thought across the tasks were also associated with trait levels of dysphoria reported by participants. Other studies using mDES in daily life have shown that the data can be described by multiple dimensions and that each of these types of thought is more prevalent in certain activities than others ([19]). For example, in daily life, patterns of ‘intrusive distraction’ thoughts were more prevalent when individuals were engaged in activities that were relatively unengaging (such as resting). Collectively, therefore, studies using mDES suggest that is likely that human thought is multidimensional in nature and that these dimensions vary in a complex way in terms of (a) the contexts that promote them, and (b) how they are impacted by features of the individual (whether they be traits like anxiety or depression or memory for information in a film).

(2) I'm skeptical about taking human thought ratings at face value. Intrusive distraction might imply disengagement from stimulus materials, but it could also be an intended effect of the movie to trigger higher-level, abstract thinking. Can a label like intrusive distraction be misleading without considering the actual thought and movie content?

Our method uses a data-driven approach to identify the dimensions that best describe the range of answers that our participants provided to describe their experience. We use these dimensions to understand how these patterns of thought emerge in different contexts and how they vary across individuals (in this case, in different movies, but in other studies, laboratory tasks [3, 8, 9, 12, 20-22] or activities in daily life[6, 7]). These context relationships help constrain interpretations of what the components mean. For example, “Intrusive Distraction” scores were highest in the film with the most real-world significance for the participants (Citizenfour) and were associated with worse comprehension. In daily life, however, patterns of “Intrusive Distraction” thoughts tend to occur when activities engage in non-demanding activities, like resting. Psychological perspectives on thoughts that arise spontaneously occur in this manner since there is evidence that they occur in non-demanding tasks with no semantic content (when there is almost no external stimulus to explain the occurrence of the experience, see [23]), however, other studies have shown that specific cues in the environment can also cue the experience (see [23]). Consistent with this perspective, and our current data, patterns of ‘Intrusive Distraction’ thought are likely to arise for multiple reasons, some of which are more intrinsic in nature (the general association with poor comprehension across all films) and others which are extrinsic in nature (the elevation of intrusive distraction in Citizenfour).

It is also important to note that our data-driven approach also found patterns of experience that provide more information about the content of their experience, for example, the dimension of “Episodic Knowledge” is characterized by thoughts based on prior knowledge, involving the past, and concerning oneself, and was most prevalent in the romance film (500 Days of Summer). Likewise, “Sensory Engagement” was associated with experiences related to sensory input and positive emotionality and occurred more during the romance movie (500 Days of Summer) than in the documentary (Citizenfour) and was linked to increased brain activity across the sensory systems. This shows that mDES can also provide information about the content of that experience, and discriminate between different sources of experience. In the future, it will be possible to improve the level of detail regarding the content of experiences by changing the questions used to interrogate experience.     

(3) A jittered sampling approach is used to acquire thought ratings every 15 seconds. Are ratings for the same time point averaged across participants? If so, how consistent are ratings among participants? High consistency would suggest thoughts are mainly stimulus-evoked. Low consistency would question the validity of applying ratings from one (group of) participant(s) to brain-related analyses of another participant.

In this experiment, we sampled experience every 15 seconds in each clip, and in each sampling epoch, we gained mDES responses from eight participants. Furthermore, no participant was sampled at an adjacent time point, as our approach jittered probes approximately 2 minutes apart (See Supplementary Figure 7). To illustrate the consistency of mDES data, we have included an additional figure (Figure 3) highlighting how experience varies over time in each clip. It is evident from these plots that there are distinct moments in which group-averaged reported thoughts across participants are stable and that these can extend across adjacent sampling points (i.e. when the confidence intervals of the score at a timepoint do not overlap with zero). Therefore, in some cases, adjacent sampling points, consisting of different sets of eight participants, describe their experiences as having similar positions on the same mDES dimension. This suggests that there is agreement among individuals regarding how they experienced a specific moment in a film, and in some cases, this agreement was apparent in successive sets of eight participants. Together, our findings indicate a conservation of agreement across participants that spans multiple moments in a film. A clear example of agreement on experience across multiple sets of 10 participants can be seen between 150-400 seconds in the clip from 500 Days of Summer for the dimension of “Sensory Engagement” (time series plot 4 in Figure 3).

(4) Using three different movies to conclude that different genres evoke different thought patterns (e.g., line 277) seems like an overinterpretation with only one instance per genre.

We found that mDES was able to distinguish between each film on at least one dimension of experience. In other words, information encoded in the mDES dimensions was sensitive to variation in semantic and affective experiences in the different movie clips. This provides evidence that is necessary but not sufficient to conclude that we can distinguish different genres of films (i.e. if we could not distinguish between films, then we would not be able to distinguish genres). However, it is correct that to begin answering the broader question about experiences in different genres then it would be necessary to map cognition across a larger set of movies, ideally with multiple examples of each genre.

(5) I see no indication that results were cross-validated, and no effect sizes are reported, leaving the robustness and strength of effects unknown.

Thank you for drawing this to our attention. We have re-run the LMMs and ANOVA models to include partial eta-squared values to clarify the strength of the effects in each of our reported outcomes.

Reviewer #3:

●      What are the considerations for treating high-order thought patterns that occur during film viewing as stable enough to be used across participants? What would be the limitations of this method? (Do all people reading this paper think comparable thoughts reading through the sections?)

It is likely, based on our study, that films can evoke both stereotyped thought patterns (i.e. thoughts that many people will share) and others that are individualistic. It is clear that, in principle, mDES is capable of capturing empirical information on both stereotypical thoughts and idiosyncratic thoughts. For example, clear differences in experiences across films and, in particular, during specific periods within a film, show that movie-watching can evoke broadly similar thought patterns in different groups of participants (see Figure 3 right-hand panel). On the other hand, the association between comprehension and the different mDES components indicate that certain individuals respond to the same film clip in different ways and that these differences are rooted in objective information (i.e. their memory of an event in a film clip). A clear example of these more idiosyncratic features of movie watching experience can be seen in the association between “Episodic Knowledge” and comprehension. We found that “Episodic Knowledge” was generally high in the romance clip from 500 Days of Summer but was especially high for individuals who performed the best, indicating they remembered the most information. Thus good comprehends responded to the 500 Days of Summer clip with responses that had more evidence of “Episodic Knowledge” In the future, since the mDES approach can account for both stereotyped and idiosyncratic features of experience, it will be an important tool in understanding the common and distinct features that movie watching experiences can have, especially given the cost effective manner with which these studies can be run.   

●      How does this approach differ from collaborative filtering, (for example as presented in Chang et al., 2021)?

Our study is very similar to the notion of collaborative filtering since we can use an approach that is similar to crowd-sourcing as a tool for understanding brain activity. One of its strengths is its generalizability since it is also a method that can be used to understand cognition because it is not limited to movie-watching. We can use the same mDES method to sample cognition in multiple situations in daily life ([6, 19]), while performing tasks in the behavioural lab [18, 24], and while brain activity is being acquired [8, 25, 26]. In principle, therefore, we can use mDES to understand cognition in different contexts in a common analytic space (see [27] for an example of how this could work)

Page 5 [106-110]: In our study, we acquired experiential data in one group of participants while watching a movie clip and used these data to understand brain activity recorded in a second set of participants who watched the same clip and for whom no experiential data was recorded. This approach is similar to what is known as “collaborative filtering” [28].

●      In conclusion, this study tackles a highly interesting subject and does it creatively and expertly. It fails to discuss and establish the utility and appropriateness of its proposed method.

Thank you very much for your feedback and critique. In our revision and our responses to these questions, we provided more information about the method's robustness utility and application to understanding cognition.

References

(1) Gordon, E.M., et al., Precision Functional Mapping of Individual Human Brains. Neuron, 2017. 95(4): p. 791-807.e7.

(2) Smallwood, J., et al., The neural correlates of ongoing conscious thought. Iscience, 2021. 24(3).

(3) Konu, D., et al., Exploring patterns of ongoing thought under naturalistic and conventional task-based conditions. Consciousness and Cognition, 2021. 93.

(4) Smallwood, J., et al., The default mode network in cognition: a topographical perspective. Nature Reviews Neuroscience, 2021. 22(8): p. 503-513.

(5) Turnbull, A., et al., Age-related changes in ongoing thought relate to external context and individual cognition. Consciousness and Cognition, 2021. 96: p. 103226.

(6) McKeown, B., et al., The impact of social isolation and changes in work patterns on ongoing thought during the first COVID-19 lockdown in the United Kingdom. Proceedings of the National Academy of Sciences, 2021. 118(40): p. e2102565118.

(7) Mulholland, B., et al., Patterns of ongoing thought in the real world. Consciousness and Cognition, 2023. 114: p. 103530.

(8) Konu, D., et al., A role for the ventromedial prefrontal cortex in self-generated episodic social cognition. NeuroImage, 2020. 218: p. 116977.

(9) Turnbull, A., et al., Left dorsolateral prefrontal cortex supports context-dependent prioritisation of off-task thought. Nature Communications, 2019. 10.

(10) Ho, N.S.P., et al., Facing up to the wandering mind: Patterns of off-task laboratory thought are associated with stronger neural recruitment of right fusiform cortex while processing facial stimuli. NeuroImage, 2020. 214: p. 116765.

(11) Karapanagiotidis, T., et al., Tracking thoughts: Exploring the neural architecture of mental time travel during mind-wandering. NeuroImage, 2017. 147: p. 272-281.

(12) McKeown, B., et al., Experience sampling reveals the role that covert goal states play in task-relevant behavior. Scientific Reports, 2023. 13(1): p. 21710.

(13) Vatansever, D., et al., Distinct patterns of thought mediate the link between brain functional connectomes and well-being. Network Neuroscience, 2020. 4(3): p. 637-657.

(14) Wang, H.-T., et al., Dimensions of Experience: Exploring the Heterogeneity of the Wandering Mind. Psychological Science, 2017. 29(1): p. 56-71.

(15) Aliko, S., et al., A naturalistic neuroimaging database for understanding the brain using ecological stimuli. Scientific Data, 2020. 7(1).

(16) Yang, E., et al., The default network dominates neural responses to evolving movie stories. Nature Communications, 2023. 14(1): p. 4197.

(17) Turnbull, A., et al., Reductions in task positive neural systems occur with the passage of time and are associated with changes in ongoing thought. Scientific Reports, 2020. 10(1): p. 9912.

(18) Konu, D., et al., Exploring patterns of ongoing thought under naturalistic and conventional task-based conditions. Consciousness and cognition, 2021. 93: p. 103139.

(19) Mulholland, B., et al., Patterns of ongoing thought in the real world. Consciousness and cognition, 2023. 114: p. 103530.

(20) Christoff, K., et al., Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proc Natl Acad Sci U S A, 2009. 106(21): p. 8719-24.

(21) Zhang, M., et al., Perceptual coupling and decoupling of the default mode network during mind-wandering and reading. eLife, 2022. 11: p. e74011.

(22) Zhang, M.C., et al., Distinct individual differences in default mode network connectivity relate to off-task thought and text memory during reading. Scientific Reports, 2019. 9.

(23) Smallwood, J. and J.W. Schooler, The science of mind wandering: Empirically navigating the stream of consciousness. Annual review of psychology, 2015. 66(1): p. 487-518.

(24) Turnbull, A., et al., The ebb and flow of attention: Between-subject variation in intrinsic connectivity and cognition associated with the dynamics of ongoing experience. Neuroimage, 2019. 185: p. 286-299.

(25) Turnbull, A., et al., Left dorsolateral prefrontal cortex supports context-dependent prioritisation of off-task thought. Nature communications, 2019. 10(1): p. 3816.

(26) Mckeown, B., et al., Experience sampling reveals the role that covert goal states play in task-relevant behavior. Scientific reports, 2023. 13(1): p. 21710.

(27) Chitiz, L., et al., Mapping cognition across lab and daily life using experience-sampling. 2023.

(28) Chang, L.J., et al., Endogenous variation in ventromedial prefrontal cortex state dynamics during naturalistic viewing reflects affective experience. Science Advances, 2021. 7(17): p. eabf7129.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This is an interesting study investigating the mechanisms underlying membrane targeting of the NLRP3 inflammasome and reporting a key role for the palmitoylation-depalmitoylation cycle of cys130 in NRLP3. The authors identify ZDHHC3 and APT2 as the specific ZDHHC and APT/ABHD enzymes that are responsible for the s-acylation and de-acylation of NLRP3, respectively. They show that the levels of ZDHHC3 and APT2, both localized at the Golgi, control the level of palmitoylation of NLRP3. The S-acylation-mediated membrane targeting of NLRP3 cooperates with polybasic domain (PBD)-mediated PI4P-binding to target NLRP3 to the TGN under steady-state conditions and to the disassembled TGN induced by the NLRP3 activator nigericin.

However, the study has several weaknesses in its current form as outlined below.

(1) The novelty of the findings concerning cys130 palmitoylation in NLRP3 is unfortunately compromised by recent reports on the acylation of different cysteines in NLRP3 (PMID: 38092000), including palmitoylation of the very same cys130 in NLRP3 (Yu et al https://doi.org/10.1101/2023.11.07.566005), which was shown to be relevant for NLRP3 activation in cell and animal models. What remains novel and intriguing is the finding that NLRP3 activators induce an imbalance in the acylation-deacylation cycle by segregating NLRP3 in late Golgi/endosomes from de-acylating enzymes confined in the Golgi. The interesting hypothesis put forward by the authors is that the increased palmitoylation of cys130 would finally contribute to the activation of NLRP3. However, the authors should clarify the trafficking pathway of acylated-NLRP3. This pathway should, in principle, coincide with that of TGN46 which constitutively recycles from the TGN to the plasma membrane and is trapped in endosomes upon treatment with nigericin. 

We think the data presented in our manuscript are consistent with the majority of S-acylated NLRP3 remaining on the Golgi via S-acylation in both untreated and nigericin treated cells. We have performed an experiment with BrefeldinA (BFA), a fungal metabolite that disassembles the Golgi without causing dissolution of early endosomes, that further supports the conclusion that NLRP3 predominantly resides on Golgi membranes pre and post activation. Treatment of cells with BFA prevents recruitment of NLRP3 to the Golgi in untreated cells and blocks the accumulation of NLRP3 on the structures seen in the perinuclear area after nigericin treatment (see new Supplementary Figure 4A-D). We do see some overlap of NLRP3 signal with TGN46 in the perinuclear area after nigericin treatment (see new Supplementary Figure 2E), however this likely represents TGN46 at the Golgi rather than endosomes given that the NLRP3 signal in this area is BFA sensitive.  As with 2-BP and GFP-NLRP3C130S, GFP-NLRP3 spots also form in BFA / nigericin co-treated cells but not with untagged NLRP3. These spots also do not show any co-localisation with EEA1, suggesting that under these conditions, endosomes don’t appear to represent a secondary site of NLRP3 recruitment in the absence of an intact Golgi. However, we cannot completely rule out that some NLRP3 may recruited to endosomes at some point during its activation.

(2) To affect the S-acylation, the authors used 16 hrs treatment with 2-bromopalmitate (2BP). In Figure 1f, it is quite clear that NLRP3 in 2-BP treated cells completely redistributed in spots dispersed throughout the cells upon nigericin treatment. What is the Golgi like in those cells? In other words, does 2-BP alter/affect Golgi morphology? What about PI4P levels after 2-BP treatment? These are important missing pieces of data since both the localization of many proteins and the activity of one key PI4K in the Golgi (i.e. PI4KIIalpha) are regulated by palmitoylation.

We thank the reviewer for highlighting this point and agree that it is possible the observed loss of NLRP3 from the Golgi might be due to an adverse effect of 2-BP on Golgi morphology or PI4P levels. We have tested the effect of 2-BP on the Golgi markers GM130, p230 and TGN46. 2BP has marginal effects on Golgi morphology with cis, trans and TGN markers all present at similar levels to untreated control cells (Supplementary Figure 2B-D). We also tested the effect of 2-BP on PI4P levels using mCherry-P4M, a PI4P biosensor. Surprisingly, as noted by the reviewer, despite recruitment of PI4K2A being dependent on S-acylation, PI4P was still present on the Golgi after 2-BP treatment, suggesting that a reduction in Golgi PI4P levels does not underly loss of NLRP3 from the Golgi (Supplementary Figure 2A). The pool of PI4P still present on the Golgi following 2-BP treatment is likely generated by other PI4K enzymes that localise to the Golgi independently of S-acylation, such as PI4KIIIB. We have included this data in our manuscript as part of a new Supplementary Figure 2. 

(3) The authors argue that the spots observed with NLRP-GFP result from non-specific effects mediated by the addition of the GFP tag to the NLRP3 protein. However, puncta are visible upon nigericin treatment, as a hallmark of endosomal activation. How do the authors reconcile these data? Along the same lines, the NLRP3-C130S mutant behaves similarly to wt NLRP3 upon 2-BP treatment (Figure 1h). Are those NLRP3-C130S puncta positive for endosomal markers? Are they still positive for TGN46? Are they positive for PI4P?

This is a fair point given the literature showing overlap of NLRP3 puncta formed in response to nigericin with endosomal markers and the similarity of the structures we see in terms of size and distribution to endosomes after 2BP + nigericin treatment. We have tested whether these puncta overlap with EEA1, TGN46 or PI4P (Supplementary Figure 2A, E-G). The vast majority of spots formed by GFP-NLRP3 co-treated with 2-BP and nigericin do not co-localise with EEA1, TGN46 or PI4P. This is consistent with these spots potentially being an artifact, although it has recently been shown that human NLRP3 unable to bind to the Golgi can still respond to nigericin (Mateo-Tórtola et al., 2023). These puncta might represent a conformational change cytosolic NLRP3 undergoes in response to stimulation, although our results suggest that this doesn’t appear to happen on endosomes.

(4) The authors expressed the minimal NLRP3 region to identify the domain required for NLRP3 Golgi localization. These experiments were performed in control cells. It might be informative to perform the same experiments upon nigericin treatment to investigate the ability of NLRP3 to recognize activating signals. It has been reported that PI4P increases on Golgi and endosomes upon NG treatment. Hence, all the differences between the domains may be lost or preserved. In parallel, also the timing of such recruitment upon nigericin treatment (early or late event) may be informative for the dynamics of the process and of the contribution of the single protein domains.

This is an interesting point which we thank the reviewer for highlighting. However, we think that each domain on its own is not capable of responding to nigericin as shown by the effect of mutations in helix115-125 or the PB region in the full-length NLRP3 protein. NLRP3HF, which still contains a functional PB region, isn’t capable of responding to nigericin in the same way as wild type NLRP3 (Supplementary Figure 6C-D). Similarly, mutations in the PB region of full length NLRP3 that leave helix115-125 intact show that helix115-125 is not sufficient to allow enhanced recruitment of NLRP3 to Golgi membranes after nigericin treatment (Supplementary Figure 9A). We speculate that helix115-125, the PB region and the LRR domain all need to be present to provide maximum affinity of NLRP3 for the Golgi prior to encounter with and S-acylation by ZDHHC3/7. Mutation or loss of any one of the PB region, helix115-125 or the LRR lowers NLRP3 membrane affinity, which is reflected by reduced levels of NLRP3 captured on the Golgi by S-acylation at steady state and in response to nigericin. 

(5) As noted above for the chemical inhibitors (1) the authors should check the impact of altering the balance between acyl transferase and de-acylases on the Golgi organization and PI4P levels. What is the effect of overexpressing PATs on Golgi functions?

We have checked the effect of APT2 overexpression on Golgi morphology and can show that it has no noticeable effect, ruling out an impact of APT on Golgi integrity as the reason for loss of NLRP3 from the Golgi in the presence of overexpressed APT2. We have included these images as Supplementary Figure 11H-J. 

It is plausible that the effects of ZDHHC3 or ZDHHC7 on enhanced recruitment of NLRP3 to the Golgi may be via an effect on PI4P levels since, as mentioned above, both enzymes are involved in recruitment of PI4K2A to the Golgi and have previously been shown to enhance levels of PI4K2A and PI4P on the Golgi when overexpressed (Kutchukian et al., 2021). However, NLRP3 mutants with most of the charge removed from the PB region, which are presumably unable to interact with PI4P or other negatively charged lipids, are still capable of being recruited to the Golgi by excess ZDHHC3. This would suggest that the effect of overexpressed ZDHHC3 on NLRP3 is largely independent of changes in PI4P levels on the Golgi and instead driven by helix115-125 and S-acylation at Cys-130. The latter point is supported by the observation that NLRP3HF and NLRP3Cys130 are insensitive to ZDHHC3 overexpression.

At the levels of HA-ZDHHC3 used in our experiments with NLRP3 (200ng pEF-Bos-HAZDHHC3 / c.a. 180,000 cells) we don’t see any adverse effect on Golgi morphology (Author response image 1), although it has been noted previously by others that higher levels of ZDHHC3 can have an impact on TGN46 (Ernst et al., 2018). ZDHHC3 overexpression surprisingly has no adverse effects on Golgi function and in fact enhances secretion from the Golgi (Ernst et al., 2018).  

Author response image 1.

Overexpression of HA-ZDHHC3 does not impact Golgi morphology. A) Representative confocal micrographs of HeLaM cells transfected with 200 ng HA-ZDHHC3 fixed and stained with antibodies to STX5 or TGN46. Scale bars = 10 µm. 

Reviewer #2 (Public Review):

Summary:

This paper examines the recruitment of the inflammasome seeding pattern recognition receptor NLRP3 to the Golgi. Previously, electrostatic interactions between the polybasic region of NLRP3 and negatively charged lipids were implicated in membrane association. The current study reports that reversible S-acylation of the conserved Cys-130 residue, in conjunction with upstream hydrophobic residues plus the polybasic region, act together to promote Golgi localization of NLRP3, although additional parts of the protein are needed for full Golgi localization. Treatment with the bacterial ionophore nigericin inhibits membrane traffic and prevents Golgi-associated thioesterases from removing the acyl chain, causing NLRP3 to become immobilized at the Golgi. This mechanism is put forth as an explanation for how NLRP3 is activated in response to nigericin.

Strengths:

The experiments are generally well presented. It seems likely that Cys-130 does indeed play a previously unappreciated role in the membrane association of NLRP3.

Weaknesses:

The interpretations about the effects of nigericin are less convincing. Specific comments follow.

(1) The experiments of Figure 4 bring into question whether Cys-130 is S-acylated. For Cys130, S-acylation was seen only upon expression of a severely truncated piece of the protein in conjunction with overexpression of ZDHHC3. How do the authors reconcile this result with the rest of the story?

Providing direct evidence of S-acylation at Cys-130 in the full-length protein proved difficult. We attempted to detect S-acylation of this residue by mass spectrometry. However, the presence of the PB region and multiple lysines / arginines directly after Cys-130 made this approach technically challenging and we were unable to convincingly detect S-acylation at Cys-130 by M/S. However, Cys-130 is clearly important for membrane recruitment as its mutation abolishes the localisation of NLRP3 to the Golgi. It is feasible that it is the hydrophobic nature of the cysteine residue itself which supports localisation to the Golgi, rather than S-acylation of Cys-130. A similar role for cysteine residues present in SNAP-25 has been reported (Greaves et al., 2009). However, the rest of our data are consistent with Cys-130 in NLRP3 being S-acylated. We also refer to another recently published study which provides additional biochemical evidence that mutation of Cys-130 impacts the overall levels of NLRP3 S-acylation (Yu et al., 2024). 

(2) Nigericin seems to cause fragmentation and vesiculation of the Golgi. That effect complicates the interpretations. For example, the FRAP experiment of Figure 5 is problematic because the authors neglected to show that the FRAP recovery kinetics of nonacylated resident Golgi proteins are unaffected by nigericin. Similarly, the colocalization analysis in Figure 6 is less than persuasive when considering that nigericin significantly alters Golgi structure and could indirectly affect colocalization. 

We agree that it is likely that the behaviour of other Golgi resident proteins are altered by nigericin. This is in line with a recent proteomics study showing that nigericin alters the amount of Golgi resident proteins associated with the Golgi (Hollingsworth et al., 2024) and other work demonstrating that changes in organelle pH can influence the membrane on / off rates of Rab GTPases (Maxson et al., 2023). However, Golgi levels of other peripheral membrane proteins

that associate with the Golgi through S-acylation, such as N-Ras, appear unaltered (Author response image 2.), indicating a degree of selectivity in the proteins affected. Our main point here is that NLRP3 is amongst those proteins whose behaviour on the Golgi is sensitive to nigericin and that this change in behaviour may be important to the NLRP3 activation process, although this requires further investigation and will form the basis of future studies. 

The reduction in co-localisation between NLRP3 and APT2, due to alterations in Golgi organisation and trafficking, was the point we were trying to make with this figure, and we apologise if this was not clear. We think that the changes in Golgi structure and function caused by nigericin potentially affect the ability of APT2 to encounter NLRP3 and de-acylate it. We have added a new paragraph to the results section to hopefully explain this more clearly. We recognise that our results supporting this hypothesis are at present limited and we have toned down the language used in the results section to reflect the nature of these findings..  

Author response image 2.

S-acylated peripheral membrane proteins show differential sensitivity to nigericin. A) Representative confocal micrographs of HeLaM cells coexpressing GFP-NRas and an untagged NLRP3 construct. Cells were left untreated or treated with 10 µM nigericin for 1 hour prior to fixation. Scale bars = 10 µm. B) Quantification of GFP-NRas or NLRP3 signal in the perinuclear region of cells treated with or without nigericin

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

(1) Does overnight 2-BP treatment potentially have indirect effects that could prevent NLRP3 recruitment? It would be useful here to show some sort of control confirming that the cells are not broadly perturbed.

Please see our response to point (2) raised by reviewer #1 which is along similar lines. 

(2) In Figure 5, "Veh" presumably is short for "Vehicle". This term should be defined in the legend.

We have now corrected this.

References

Ernst, A.M., S.A. Syed, O. Zaki, F. Bottanelli, H. Zheng, M. Hacke, Z. Xi, F. Rivera-Molina, M. Graham, A.A. Rebane, P. Bjorkholm, D. Baddeley, D. Toomre, F. Pincet, and J.E. Rothman. 2018. SPalmitoylation Sorts Membrane Cargo for Anterograde Transport in the Golgi. Dev Cell. 47:479-493 e477.

Greaves, J., G.R. Prescott, Y. Fukata, M. Fukata, C. Salaun, and L.H. Chamberlain. 2009. The hydrophobic cysteine-rich domain of SNAP25 couples with downstream residues to mediate membrane interactions and recognition by DHHC palmitoyl transferases. Mol Biol Cell. 20:1845-1854.

Hollingsworth, L.R., P. Veeraraghavan, J.A. Paulo, J.W. Harper, and I. Rauch. 2024. Spatiotemporal proteomic profiling of cellular responses to NLRP3 agonists. bioRxiv.

Kutchukian, C., O. Vivas, M. Casas, J.G. Jones, S.A. Tiscione, S. Simo, D.S. Ory, R.E. Dixon, and E.J. Dickson. 2021. NPC1 regulates the distribution of phosphatidylinositol 4-kinases at Golgi and lysosomal membranes. EMBO J. 40:e105990.

Mateo-Tórtola, M., I.V. Hochheiser, J. Grga, J.S. Mueller, M. Geyer, A.N.R. Weber, and A. TapiaAbellán. 2023. Non-decameric NLRP3 forms an MTOC-independent inflammasome. bioRxiv:2023.2007.2007.548075.

Maxson, M.E., K.K. Huynh, and S. Grinstein. 2023. Endocytosis is regulated through the pHdependent phosphorylation of Rab GTPases by Parkinson’s kinase LRRK2. bioRxiv:2023.2002.2015.528749.

Yu, T., D. Hou, J. Zhao, X. Lu, W.K. Greentree, Q. Zhao, M. Yang, D.G. Conde, M.E. Linder, and H. Lin. 2024. NLRP3 Cys126 palmitoylation by ZDHHC7 promotes inflammasome activation. Cell Rep. 43:114070.

Author response:

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

We are grateful for these balanced, nuanced evaluations of our work concerning the observed epistatic trends and our interpretations of their mechanistic origins. Overall, we think the reviewers have done an excellent job at recognizing the novel aspects of our findings while also discussing the caveats associated with our interpretations of the biophysical effects of these mutations. We believe it is important to consider both of these aspects of our work in order to appreciate these advances and what sorts of pertinent questions remain.

Notably, both reviewers are concerned that our lack of experimental approaches to compare the conformational properties of GnRHR variants weakens our claims. We would first humbly suggest that this constitutes a more general caveat that applies to nearly all investigations of the cellular misfolding of α-helical membrane proteins. Whether or not any current in vitro folding measurements report on conformational transitions that are relevant to cellular protein misfolding reactions remains an active area of debate (discussed further below). Nevertheless, while we concede that our structural and/ or computational evaluations of various mutagenic effects remain speculative, prevailing knowledge on the mechanisms of membrane protein folding suggest our mutations of interest (V276T and W107A) are highly unlikely to promote misfolding in precisely the same way. Thus, regardless of whether or not we were able experimentally compare the relevant folding energetics of GnRHR variants, we are confident that the distinct epistatic interactions formed by these mutations reflect variations in the misfolding mechanism and that they are distinct from the interactions that are observed in the context of stable proteins. In the following, we provide detailed considerations concerning these caveats in relation to the reviewers’ specific comments.

Reviewer #1 (Public Review):

The paper carries out an impressive and exhaustive non-sense mutagenesis using deep mutational scanning (DMS) of the gonadotropin-releasing hormone receptor for the WT protein and two single point mutations that I) influence TM insertion (V267T) and ii) influence protein stability (W107A), and then measures the effect of these mutants on correct plasma membrane expression (PME).

Overall, most mutations decreased mGnRHR PME levels in all three backgrounds, indicating poor mutational tolerance under these conditions. The W107A variant wasn't really recoverable with low levels of plasma membrane localisation. For the V267T variant, most additional mutations were more deleterious than WT based on correct trafficking, indicating a synergistic effect. As one might expect, there was a higher degree of positive correlation between V267T/W107A mutants and other mutants located in TM regions, confirming that improper trafficking was a likely consequence of membrane protein co-translational folding. Nevertheless, context is important, as positive synergistic mutants in the V27T could be negative in the W107A background and vice versa. Taken together, this important study highlights the complexity of membrane protein folding in dissecting the mechanism-dependent impact of disease-causing mutations related to improper trafficking.

Strengths

This is a novel and exhaustive approach to dissecting how receptor mutations under different mutational backgrounds related to co-translational folding, could influence membrane protein trafficking.

Weaknesses

The premise for the study requires an in-depth understanding of how the single-point mutations analysed affect membrane protein folding, but the single-point mutants used seem to lack proper validation.

Given our limited understanding of the structural properties of misfolded membrane proteins, it is unclear whether the relevant conformational effects of these mutations can be unambiguously validated using current biochemical and/ or biophysical folding assays. X-ray crystallography, cryo-EM, and NMR spectroscopy measurements have demonstrated that many purified GPCRs retain native-like structural ensembles within certain detergent micelles, bicelles, and/ or nanodiscs. However, helical membrane protein folding measurements typically require titration with denaturing detergents to promote the formation of a denatured state ensemble (DSE), which will invariably retain considerable secondary structure. Given that the solvation provided by mixed micelles is clearly distinct from that of native membranes, it remains unclear whether these DSEs represent a reasonable proxy for the misfolded conformations recognized by cellular quality control (QC, see https://doi.org/10.1021/acs.chemrev.8b00532). Thus, the use and interpretation of these systems for such purposes remains contentious in the membrane protein folding community. In addition to this theoretical issue, we are unaware of any instances in which GPCRs have been found to undergo reversible denaturation in vitro- a practical requirement for equilibrium folding measurements (https://doi.org/10.1146/annurev-biophys-051013-022926). We note that, while the resistance of GPCRs to aggregation, proteolysis, and/ or mechanical unfolding have also been probed in micelles, it is again unclear whether the associated thermal, kinetic, and/ or mechanical stability should necessarily correspond to their resistance to cotranslational and/ or posttranslational misfolding. Thus, even if we had attempted to validate the computational folding predictions employed herein, we suspect that any resulting correlations with cellular expression may have justifiably been viewed by many as circumstantial. Simply put, we know very little about the non-native conformations are generally involved in the cellular misfolding of α-helical membrane proteins, much less how to measure their relative abundance. From a philosophical standpoint, we prefer to let cells tell us what sorts of broken protein variants are degraded by their QC systems, then do our best to surmise what this tells us about the relevant properties of cellular DSEs.

Despite this fundamental caveat, we believe that the chosen mutations and our interpretation of their relevant conformational effects are reasonably well-informed by current modeling tools and by prevailing knowledge on the physicochemical drivers of membrane protein folding and misfolding. Specifically, the mechanistic constraints of translocon-mediated membrane integration provide an understanding of the types of mutations that are likely to disrupt cotranslational folding. Though we are still learning about the protein complexes that mediate membrane translocation (https://doi.org/10.1038/s41586-022-05336-2), it is known that this underlying process is fundamentally driven by the membrane depth-dependent amino acid transfer free energies (https://doi.org/10.1146/annurev.biophys.37.032807.125904). This energetic consideration suggests introducing polar side chains near the center of a nascent TMDs should almost invariably reduce the efficiency of topogenesis. To confirm this in the context of TMD6 specifically, we utilized a well-established biochemical reporter system to confirm that V276T attenuates its translocon-mediated membrane integration (Fig. S1)- at least in the context of a chimeric protein. We also constructed a glycosylation-based topology reporter for full-length GnRHR, but ultimately found its’ in vitro expression to be insufficient to detect changes in the nascent topological ensemble.

In contrast to V276T, the W107A mutation is predicted to preserve the native topological energetics of GnRHR due to its position within a soluble loop region. W107A is also unlike V276T in that it clearly disrupts tertiary interactions that stabilize the native structure. This mutation should preclude the formation of a structurally conserved hydrogen bonding network that has been observed in the context of at least 25 native GPCR structures (https://doi.org/10.7554/eLife.5489). However, without a relevant folding assay, the extent to which this network stabilizes the native GnRHR fold in cellular membranes remains unclear. Overall, we admit that these limitations have prevented us from measuring how much V276T alters the efficiency of GnRHR topogenesis, how much the W107A destabilizes the native fold, or vice versa. Nevertheless, given these design principles and the fact that both reduce the plasma membrane expression of GnRHR, as expected, we are highly confident that the structural defects generated by these mutations do, in fact, promote misfolding in their own ways. We also concede that the degree to which these mutagenic perturbations are indeed selective for specific folding processes is somewhat uncertain. However, it seems exceedingly unlikely that these mutations should disrupt topogenesis and/ or the folding of the native topomer to the exact same extent. From our perspective, this is the most important consideration with respect to the validity of the conclusions we have made in this manuscript.

Furthermore, plasma membrane expression has been used as a proxy for incorrect membrane protein folding, but this not necessarily be the case, as even correctly folded membrane proteins may not be trafficked correctly, at least, under heterologous expression conditions. In addition, mutations can affect trafficking and potential post-translational modifications, like glycosylation.

While the reviewer is correct that the sorting of folded proteins within the secretory pathway is generally inefficient, it is also true that the maturation of nascent proteins within the ER generally bottlenecks the plasma membrane expression of most α-helical membrane proteins. Our group and several others have demonstrated that the efficiency of ER export generally appears to scale with the propensity of membrane proteins to achieve their correct topology and/ or to achieve their native fold (see https://doi.org/10.1021/jacs.5b03743 and https://doi.org/10.1021/jacs.8b08243). Notably, these investigations all involved proteins that contain native glycosylation and various other post-translational modification sites. While we cannot rule out that certain specific combinations of mutations may alter expression through their perturbation of post-translational GnRHR modifications, we feel confident that the general trends we have observed across hundreds of variants predominantly reflect changes in folding and cellular QC. This interpretation is supported by the relationship between observed trends in variant expression and Rosetta-based stability calculations, which we identified using unbiased unsupervised machine learning approaches (compare Figs. 6B & 6D).

Reviewer #2 (Public Review):

Summary:

In this paper, Chamness and colleagues make a pioneering effort to map epistatic interactions among mutations in a membrane protein. They introduce thousands of mutations to the mouse GnRH Receptor (GnRHR), either under wild-type background or two mutant backgrounds, representing mutations that destabilize GnRHR by distinct mechanisms. The first mutant background is W107A, destabilizing the tertiary fold, and the second, V276T, perturbing the efficiency of cotranslational insertion of TM6 to the membrane, which is essential for proper folding. They then measure the surface expression of these three mutant libraries, using it as a proxy for protein stability, since misfolded proteins do not typically make it to the plasma membrane. The resulting dataset is then used to shed light on how diverse mutations interact epistatically with the two genetic background mutations. Their main conclusion is that epistatic interactions vary depending on the degree of destabilization and the mechanism through which they perturb the protein. The mutation V276T forms primarily negative (aggravating) epistatic interactions with many mutations, as is common to destabilizing mutations in soluble proteins. Surprisingly, W107A forms many positive (alleviating) epistatic interactions with other mutations. They further show that the locations of secondary mutations correlate with the types of epistatic interactions they form with the above two mutants.

Strengths:

Such a high throughput study for epistasis in membrane proteins is pioneering, and the results are indeed illuminating. Examples of interesting findings are that: (1) No single mutation can dramatically rescue the destabilization introduced by W107A. (2) Epistasis with a secondary mutation is strongly influenced by the degree of destabilization introduced by the primary mutation. (3) Misfolding caused by mis-insertion tends to be aggravated by further mutations. The discussion of how protein folding energetics affects epistasis (Fig. 7) makes a lot of sense and lays out an interesting biophysical framework for the findings.

Weaknesses:

The major weakness comes from the potential limitations in the measurements of surface expression of severely misfolded mutants. This point is discussed quite fairly in the paper, in statements like "the W107A variant already exhibits marginal surface immunostaining" and many others. It seems that only about 5% of the W107A makes it to the plasma membrane compared to wild-type (Figures 2 and 3). This might be a low starting point from which to accurately measure the effects of secondary mutations.

The reviewer raises an excellent point that we considered at length during the analysis of these data and the preparation of the manuscript. Though we remain confident in the integrity of these measurements and the corresponding analyses, we now realize this aspect of the data required further discussion and documentation which we have provided in the revised version of the manuscript as is described in the following.

Still, the authors claim that measurements of W107A double mutants "still contain cellular subpopulations with surface immunostaining intensities that are well above or below that of the W107A single mutant, which suggests that this fluorescence signal is sensitive enough to detect subtle differences in the PME of these variants". I was not entirely convinced that this was true.

We made this statement based on the simple observation that the surface immunostaining intensities across the population of recombinant cells expressing the library of W107A double mutants was consistently broader than that of recombinant cells expressing W107A GnRHR alone (see Author response image 1 for reference). Given that the recombinant cellular library represents a mix of cells expressing ~1600 individual variants that are each present at low abundance, the pronounced tails within this distribution presumably represent the composite staining of many small cellular subpopulations that express collections of variants that deviate from the expression of W107A to an extent that is significant enough to be visible on a log intensity plot.

Author response image 1.

Firstly, I think it would be important to test how much noise these measurements have and how much surface immunostaining the W107A mutant displays above the background of cells that do not express the protein at all.

For reference, the average surface immunostaining intensity of HEK293T cells transiently expressing W107A GnRHR was 2.2-fold higher than that of the IRES-eGFP negative, untransfected cells within the same sample- the WT immunostaining intensity was 9.5-fold over background by comparison. Similarly, recombinant HEK293T cells expressing the W107A double mutant library had an average surface immunostaining intensity that was 2.6-fold over background across the two DMS trials. Thus, while the surface immunostaining of this variant is certainly diminished, we were still able to reliably detect W107A at the plasma membrane even under distinct expression regimes. We have included these and other signal-to-noise metrics for each experiment in the Results section of the revised manuscript.

Beyond considerations related to intensity, we also previously noticed the relative intensity values for W107A double mutants exhibited considerable precision across our two biological replicates. If signal were too poor to detect changes in variant expression, we would have expected a plot of the intensity values across these two replicates to form a scatter. Instead, we found DMS intensity values for individual variants to be highly correlated from one replicate to the next (Pearson’s R2 = 0.95, see Author response image 2 for reference). This observation empirically demonstrates that this assay consistently differentiated between variants that exhibit slightly enhanced immunostaining from those that have even lower immunostaining than W107A GnRHR. We have included these discussion points in the Results section as well as scatter plots for replicate variant intensities within all three genetic backgrounds in Figure S3 of the revised manuscript.

Author response image 2.

But more importantly, it is not clear if under this regimen surface expression still reports on stability/protein fitness. It is unknown if the W107A retains any function or folding at all. For example, it is possible that the low amount of surface protein represents misfolded receptors that escaped the ER quality control.

While we believe that such questions are outside the scope of this work, we certainly agree that it is entirely possible that some of these variants bypass QC without achieving their native fold. This topic is quite interesting to us but is quite challenging to assess in the context of GPCRs, which have complex fitness landscapes that involve their propensity to distinguish between different ligands, engage specific components associated with divergent downstream signaling pathways, and navigate between endocytic recycling/ degradation pathways following activation. In light of the inherent complexity of GPCR function, we humbly suggest our choice of a relatively simple property of an otherwise complex protein may be viewed as a virtue rather than a shortcoming. Protein fitness is typically cast as the product of abundance and activity. Rather than measuring an oversimplified, composite fitness metric, we focused on one variable (plasma membrane expression) and its dominant effector (folding). We believe restraining the scope in this manner was key for the elucidation of clear mechanistic insights.

The differential clustering of epistatic mutations (Fig. 6) provides some interesting insights as to the rules that dictate epistasis, but these too are dominated by the magnitude of destabilization caused by one of the mutations. In this case, the secondary mutations that had the most interesting epistasis were exceedingly destabilizing. With this in mind, it is hard to interpret the results that emerge regarding the epistatic interactions of W107A. Furthermore, the most significant positive epistasis is observed when W107A is combined with additional mutations that almost completely abolish surface expression. It is likely that either mutation destabilizes the protein beyond repair. Therefore, what we can learn from the fact that such mutations have positive epistasis is not clear to me. Based on this, I am not sure that another mutation that disrupts the tertiary folding more mildly would not yield different results. With that said, I believe that the results regarding the epistasis of V276T with other mutations are strong and very interesting on their own.

We agree with the reviewer. In light of our results we believe it is virtually certain that the secondary mutations characterized herein would be likely to form distinct epistatic interactions with mutations that are only mildly destabilizing. Indeed, this insight reflects one of the key takeaway messages from this work- stability-mediated epistasis is difficult to generalize because it should depend on the extent to which each mutation changes the stability (ΔΔG) as well as initial stability of the WT/ reference sequence (ΔG, see Figure 7). Frankly, we are not so sure we would have pieced this together as clearly had we not had the fortune (or misfortune?) of including such a destructive mutation like W107A as a point of reference.

Additionally, the study draws general conclusions from the characterization of only two mutations, W107A and V276T. At this point, it is hard to know if other mutations that perturb insertion or tertiary folding would behave similarly. This should be emphasized in the text.

We agree. Our findings suggest different mutations may not behave similarly, which we believe is a key finding of this work. We have emphasized this point in the Discussion section of the revised manuscript as follows:

“These findings suggest the folding-mediated epistasis is likely to vary among different classes of destabilizing mutations in a manner that should also depend on folding efficiency and/ or the mechanism(s) of misfolding in the cell.”

Some statistical aspects of the study could be improved:

(1) It would be nice to see the level of reproducibility of the biological replicates in a plot, such as scatter or similar, with correlation values that give a sense of the noise level of the measurements. This should be done before filtering out the inconsistent data.

We thank the reviewer for this suggestion and will include scatters for each genetic background like the one shown above in Figure S3 of the revised version of the manuscript.

(2) The statements "Variants bearing mutations within the C- terminal region (ICL3-TMD6-ECL3-TMD7) fare consistently worse in the V276T background relative to WT (Fig. 4 B & E)." and "In contrast, mutations that are 210 better tolerated in the context of W107A mGnRHR are located 211 throughout the structure but are particularly abundant among residues 212 in the middle of the primary structure that form TMD4, ICL2, and ECL2 213 (Fig. 4 C & F)." are both hard to judge. Inspecting Figures 4B and C does not immediately show these trends, and importantly, a solid statistical test is missing here. In Figures 4E and F the locations of the different loops and TMs are not indicated on the structure, making these statements hard to judge.

We apologize for this oversight and thank the reviewer for pointing this out. We utilized paired Wilcoxon-Signed Rank Tests to evaluate the statistical significance of these observations and modified the description of these findings in the revised version of the results section as follows:

“Variants bearing mutations within the C-terminal regions including ICL3, TMD6, and TMD7 fare consistently worse in the V276T background relative to WT (paired Wilcoxon-Signed Rank Test p-values of 0.0001, 0.02, and 0.005, respectively) (Fig. 4 B & E). Given that V276T perturbs the cotranslational membrane integration of TMD6 (Fig. S1, Table S1), this directional bias potentially suggests that the apparent interactions between these mutations manifest during the late stages of cotranslational folding. In contrast, mutations that are better tolerated in the context of W107A mGnRHR are located throughout the structure but are particularly abundant among residues in the middle of the primary structure that form ICL2, TMD4, and ECL2 (paired Wilcoxon-Signed Rank Test p-values of 0.0005, 0.0001, and 0.004, respectively) (Fig. 4 C & F).”

(3) The following statement lacks a statistical test: "Notably, these 98 variants are enriched with TMD variants (65% TMD) relative to the overall set of 251 variants (45% TMD)." Is this enrichment significant? Further in the same paragraph, the claim that "In contrast to the sparse epistasis that is generally observed between mutations within soluble proteins, these findings suggest a relatively large proportion of random mutations form epistatic interactions in the context of unstable mGnRHR variants". Needs to be backed by relevant data and statistics, or at least a reference.

We thank the reviewer for this reasonable suggestion. In the revised manuscript, we included the results of a paired Wilcoxon-Signed Rank Test that confirms the statistical significance of this observation and modified the Results section to reflect this as follows:

“Notably, these 98 variants are enriched with TMD variants (65% TMD) relative to the overall set of 251 variants (45% TMD, Fisher’s Exact Test p = 0.0019). These findings suggest random mutations form epistatic interactions in the context of unstable mGnRHR variants in a manner that depends on the specific folding defect (V276T vs. W107A) and topological context.”

Reviewer #1 (Recommendations for the Authors):

As far as this reviewer is aware, the effect of the V267T variant on MP insertion has not been measured directly; its position corresponds to T277 in TMD6 of human GnRHR that has been measured for TM insertion, but given the clear lack of conservation (threonine vs valine) the mutation in TM6 could potentially have a different impact on the mouse homologue. Please clarify what the predicted delta TM for insertion is between human and mouse GnRHR is? Moreover, I would argue that single TM insertion by tethering to Lep is insufficient to understand MP insertion/folding, as neighbouring TM helices could help to drive TM6 insertion. Has ER microsome experiments for mouse GnRHR also been carried out in the context of neighbouring helices?

We included measurements (and predictions) of the impact of the V276T substitution on the translocon-mediated membrane integration of the mouse TMD6 in the context of a chimeric Lep protein (see Fig. S1 & Table S1). Our results reveal that this substitution decreases the efficiency of TMD6 membrane integration by ~10%. Though imperfect, this prevailing biochemical assay remains popular for a variety of theoretical and technical reasons. Importantly, extensive experimental testing of this system has shown that these measurements report apparent equilibrium constants that are well-described by two-state equilibrium partitioning models (see DOIs 10.1038/nature03216 and 10.1038/nature06387). This observation provides a reasonable rationale to interpret these measurements using energetic models as we have in this work (see Table S1). From a technical perspective, the Lep system is also advantageous due to the fact that this protein is generally well expressed in the context of in vitro translation systems containing native membranes, which generally ensures a consistent signal to noise and dynamic range for membrane integration measurements. Nevertheless, the reviewers are correct that membrane integration efficiencies are likely distinct in the context of the native mGnRHR protein. For these reasons, we attempted to develop a glycosylation-based topology reporter prior to the posting and submission of this manuscript. However, all GnRHR reporters we tested were poorly expressed in vitro and the resulting 35S-labeled proteins only generated faint smears on our phosphorimaging screens that could not be interpreted. For these reasons, we chose to rely the Lep measurements for these investigations.

The lack of a more relevant topological reporter is one of many challenges we faced in our investigations of this unstable, poorly behaved protein. We share the reviewer’s frustrations concerning the speculative aspects of this work. Nevertheless, there is increasing appreciation for the fact that our perspectives on protein biophysics have been skewed by our continuing choice to focus on the relatively small set of model proteins that are compatible with our favored methodologies (doi: 10.1016/j.tibs.2013.05.001). We humbly suggest this work represents an example of how we can gain a deeper understanding of the limits of biochemical systems when we instead choose to study the unsavory bits of cellular proteomes. But this choice requires a willingness to make some reasonable assumptions and to lean on energetic/ structural modeling from time to time. Despite this limitation, we believe there is still tremendous value in this compromise.

What is the experimental evidence the W107A variant affects the protein structure? Has its melting temperature with and without inverse agonist binding for WT vs the W107A variant been measured, for example? Even heat-FSEC of detergent-solubilised membranes would be informative to know how unstable the W107A variant is. If is very unstable in detergent, then it could be that recovery mutants are going to be unlikely as you are already starting with a poor construct showing poor folding/localisation.

We again understand the rationale for this concern, but do not believe that thermal melting measurements are likely to report the same sorts of conformational transitions involved in cellular misfolding. Heating up a protein to the point in which membranes (or micelles) are disrupted and the proteins begin to form insoluble aggregates is a distinct physical process from those that occur during co- and post-translational folding within intact ER membranes at physiological temperatures (discussed further in the Response to the Reviews). Indeed, as the reviewer points out below, there seems to be little evidence that secretion is linked to thermal stability or various other metrics that others have attempted to optimize for the sake of purification and/ or structural characterization. Thus, we believe it would be just as speculative to suggest thermal aggregation represents a relevant metric for the propensity of membrane proteins to fold in the cell. The physical interpretation of membrane protein misfolding reaction remains contentious in our field due to the key fact that the denatured states of helical membrane proteins remain highly structured in a manner that is hard to generalize beyond the fact that the denatured states retain α-helical secondary structure (doi: 10.1146/annurev-biophys-051013-022926). This is in stark contrast to soluble proteins, where random coil reference states have proven to be generally useful for energetic interpretations of protein stability. For reference, our lab is currently working to leverage epistatic measurements like this to map the prevailing physiological denatured states of an integral membrane protein. Our current findings suggest that non-native electrostatic interactions form in the context of misfolded states. We hope that more information on the structural aspects of these states will help us to develop and interpret meaningful folding measurements within the membrane.

For reference, even in cases when quantitative folding measurements can be achieved, their relevance remains actively debated. As a point of reference, the corresponding author of this work previously worked on the stability and misfolding of another human α-helical membrane protein (PMP22). Like GnRHR, PMP22 is prone to misfolding in the secretory pathway and is associated with dozens of pathogenic mutations that cause protein misfolding. To understand how the thermodynamic stability of this protein is linked to secretion, the corresponding author purified PMP22, reconstituted it into n-Dodecyl-phosphocholine (DPC) micelles, and measured its resistance to denaturation by an anionic denaturing detergent (Lauryl Sarcosine, LS). The results were initially perplexing due to the fact that equilibrium unfolding curves manifested as an exponential decay (rather than a sigmoid) and relaxation kinetics appeared to be dominated by the rate constant for unfolding (doi: 10.1021/bi301635f). Unfortunately, these data could not be fit with existing folding models due to the lack of a folded protein baseline and the absence of a folding arm in the chevron plot. We eventually found that a full sigmoidal unfolding transition and refolding kinetics could be measured upon addition of 15% (v/v) glycerol. Our measurements revealed that the free energy of unfolding in DPC micelles was 0 kcal/ mol (without glycerol). This shocking lack of WT stability made it impossible to directly measure the effects of destabilizing mutations that enhance misfolding- you can’t measure the unfolding of a protein that is already unfolded. We ultimately had to instead infer the energetic effects of such mutations from the thermodynamic coupling between cofactor binding and folding (doi: 10.1021/jacs.5b03743). Finally, after demonstrating the resulting ΔΔGs correlated with both cellular trafficking and disease phenotype, we still faced justified scrutiny about the relevance of these measurements due to the fact that they were carried out in micelles. For these reasons, we do not feel that additional biophysical measurements will add much to this work until more is understood about the nature of misfolding reactions in the membrane and how to effectively recapitulate it in vitro. We also note that PMP22 is secreted with 20% efficiency in mammalian cell lines, which is 20-fold more efficient than human GnRHR under similar conditions (doi: 10.1016/j.celrep.2021.110046). Thus, we suspect equilibrium unfolding measurements are likely out of reach using previously described measurements.

Our greatest evidence suggesting W107A destabilizes the protein has to do with the fact that it deletes a highly conserved structural contact and that this structural modification kills its secretion. The fact that this mutation clearly reduces the escape of GnRHR from ER quality control is a classic indicator of misfolding that represents the cell’s way of telling us that the mutation compromises the folding of the nascent protein in some way or another. Precisely how this mutation remodels the nascent conformational ensemble of nascent GnRHR and how this relates to the free energy difference between the native and non-native portions of its conformational ensemble under cellular conditions is a much more challenging question that lies beyond the scope of this investigation (and likely beyond the scope of what’s currently possible). Indeed, there is an entire field dedicated to understanding such. Nevertheless, the difference in the epistatic interactions formed by W107A and V276T is at the very least consistent with our speculative interpretation that these two mutations vary in their misfolding mechanism and/ or in the extent to which they destabilize the protein. For these reasons, we feel the main conclusions of this manuscript are well-justified.

Please clarify if the protein is glycosylated or not and, if it is, how would this requirement affect the conclusions of your analysis?

As we noted in the Response to the Reviewers, which also constitutes a published portion of the final manuscript, this protein is indeed glycosylated. We were well aware of this aspect of the protein since inception of this project and do not think this changes our interpretation at all. Most membrane proteins are glycosylated, and several groups have demonstrated in various ways that the secretion efficiency of glycoproteins is proportional to certain stability metrics for secreted soluble proteins and membrane proteins alike. Generally, mutations that enhance misfolding do not change the propensity of the nascent chain to undergo N-linked glycosylation, which occurs during translation before protein synthesis and/ or folding is complete. Misfolded proteins typically carry lower weight glycans, which reflects their failure to advance from the ER to the Golgi, where N-linked glycans are modified and O-linked glycans are added. From our perspective, glycosyl modifications just ensure that nascent proteins are engaged by calnexin and other lectin chaperones involved in QC. It does not decouple folding from secretion efficiency. In the case of PMP22 (described above), we found that removal of its glycosylation site allows the nascent protein to bypass the lectin chaperones in a manner that enhances its plasma membrane expression eight-fold (doi: 10.1016/j.jbc.2021.100719). Similar to WT, the expression of several misfolded PMP22 variants also significantly increases upon removal of the glycosylation site. Nevertheless, their expression is still significantly lower than the un-glycosylated WT protein, and the expression patterns of the mutants relative to WT was quite similar across this panel of un-glycosylated proteins. Thus, while glycosylation certainly impacts secretion, it does not change its dependence on folding efficiency within the ER. There are many layers of partially redundant QC within the ER, and it seems that folding imposes a key bottleneck to secretion regardless of which QC proteins are involved. For these reasons, we do not think glycosylation (or other PTMs) should factor into our interpretation of these results.

One caveat with the study is that there is a poor understanding of the factors that decide if the protein should be trafficked to the PM or not. Even secretory proteins not going through the calnexin/reticulum cycle (as they have no N-linked glycans), might still get stuck in the ER, despite the fact they are functional. Could this be a technical issue of heterologous expression overloading the Sec system?

While we agree that there is much to be learned about this topic, we disagree with the notion that our understanding of folding and secretion is insufficient to generally interpret the molecular basis of the observed trends. In collaboration with various other groups, the corresponding author of this paper has shown for several other proteins that the stability of the native topology and the native tertiary structure can constrain secretion efficiency (see dois: 10.1021/jacs.8b08243, 10.1021/jacs.5b03743, and 10.1016/j.jbc.2021.100423). Moreover, the Balch and Kelly groups demonstrated many years ago that relatively simple models for the coupling between folding and chaperone binding can recapitulate the observed effects of mutations on the secretion efficiency of various proteins (doi: 10.1016/j.cell.2007.10.025). Given a wide body of prevailing knowledge in this area, we believe it is entirely reasonable to assume that the conformational effects of these mutation have a dominant effect on plasma membrane expression.

Whether or not some of the proteins retained in the ER are folded and/ or functional is an interesting question, but is outside the scope of this work. Various lines of evidence concerning approaches to rescue misfolded membrane proteins suggest many of these variants are likely to retain residual function once they escape the ER, which may suggest there are pockets of foldable/ folded proteins within the ER. But it seems generally clear that the efficiency of folding in the ER bottlenecks secretion regardless of whether or not the ER contains some fraction of folded/ functional protein. We note that it is certainly possible, if not likely, that secretion efficiency is likely to be higher at lower expression levels (doi: 10.1074/jbc.AC120.014940). However, the mutational scanning platform used in this work was designed such that all variants are expressed from an identical promoter at the same location within the genome. Thus, for the purposes of these investigations, we believe it is entirely fair to draw “apples-to-apples” comparisons of their relative effects on plasma membrane expression.

Please see Francis Arnold's paper on this point and their mutagenesis library of the channelrhodopsin (https://www.pnas.org/doi/10.1073/pnas.1700269114), which further found that 20% of mutations improved WT trafficking. Some general comparisons to this paper might be informative.

We agree that it may be interesting to compare the results from this paper to those in our own. Indeed, we find that 20% of the point mutations characterized herein also enhance the expression of WT mGnRHR, as mentioned in the Results section. However, we think it might be a bit premature to suggest this is a more general trend in light of the fact that the channelrhodopsins engineered in those studies were not of eukaryotic origin and have likely resulted from distinct evolutionary constraints. We ultimately decided against adding more on this to our already lengthy discussion in order to maintain focus on the mechanisms of epistasis.

Chris Tate and others have shown that there is a high frequency of finding stabilising point mutations in GPCRs and this is the premise of the StAR technology used to thermostabilise GPCRs in the presence of different ligands, i.e. agonist vs inverse agonists. As far as I am aware, there is a poor correlation between expression levels and thermostability (measured by ligand binding to detergent-solubilised membranes). As such, it is possible that some of the mutants might be more stable than WT even though they have lower levels of PME.

We believe the disconnect between thermostability and expression precisely speaks to our main point about the suitability of current membrane protein folding assays for the questions we address herein. The degradative activity of ER quality control has not necessarily selected for proteins that are resistant to thermal degradation and/ or are suitable for macromolecular crystallography. For this reason, it is often not so difficult to engineer proteins with enhanced thermal stability. We do not believe this disconnect signals that quality control is insensitive to protein folding and stability, but rather that it is more likely to recognize conformational defects that are distinct from those involved in thermal degradation and/ or aggregation. Indeed, recent work from the Fluman group, which builds on a wider body of previous observations, has shown that the exposure of polar groups within the membrane is a key factor that recruits degradation machinery (doi: 0.1101/2023.12.12.571171). It is hard to imagine that these sorts of conformational defects are the same as those involved in thermal aggregation.

Reviewer #2 (Recommendations For The Authors):

(1) I believe that by focusing more on the epistasis with V276T, and less on W107A, the paper could be strengthened significantly.

We appreciate this sentiment. But we believe the comparison of these two mutants really drive home the point that destabilizing mutations are not equivalent with respect to the epistatic interactions they form.

(2) In the abstract - please define the term epistasis in a simple way, to make it accessible to a general audience. For example - negative epistasis means that... this should be explicitly explained.

We thank the reviewer for this suggestion. To meet eLife formatting, we had to cut down the abstract significantly. We simplified this as best we could in the following statement:

“Though protein stability is known to shape evolution, it is unclear how cotranslational folding constraints modulate the synergistic, epistatic interactions between mutations.”

We also define positive and negative epistasis in the results section as follows:

“Positive Ɛ values denote double mutants that have greater PME than would be expected based on the effects of single mutants. Negative Ɛ values denote double mutants that have lower PME than would be expected based on the effects of single mutants. Pairs of mutations with Ɛ values near zero have additive effects on PME.”

(3) The title is quite complex and might deter readers from outside the protein evolution field. Consider simplifying it.

We thank the reviewer for this suggestion. We have simplified the title to the following:

“Divergent Folding-Mediated Epistasis Among Unstable Membrane Protein Variants”

(4) The paper could benefit from a simple figure explaining the different stages of membrane protein folding (stages 1+2) to make it more accessible to readers from outside the membrane protein field.

This is a great suggestion. We incorporated a new schematic in the revised manuscript that outlines the nature of these processes (see Fig. 1A in the revised manuscript).

(5) For the FACS-Seq experiment - it was not clear to me if and when all cells are pulled together. For example - are the 3 libraries mixed together already at the point of transfection, or are the transfected cells pulled together at any point before sorting? This could have some implications on batch effects and should, therefore, be explicitly mentioned in the main text.

We thank the reviewer for this suggestion. We modified the description of the DNA library assembly to emphasize that the mutations were generated in the context of three mixed plasmid pools, which were then transfected into the cells and sorted independently:

“We then generated a mixed array of mutagenic oligonucleotides that collectively encode this series of substitutions (Table S3) and used nicking mutagenesis to introduce these mutations into the V276T, W107A, and WT mGnRHR cDNAs (Medina-Cucurella et al., 2019), which produced three mixed plasmid pools.”

(6) The following description in the text is quite confusing. It would be better to simplify it considerably or remove it: "scores (Ɛ) were then determined by taking the log of the double mutant fitness value divided by the difference between the single mutant fitness values (see Methods)."

We thank the reviewer for this valuable feedback and have simplified the text as follows:

“To compare epistatic trends in these libraries, we calculated epistasis scores (Ɛ) for the interactions that these 251 mutations form with V276T and W107A by comparing their relative effects on PME of the WT, V276T, and W107A variants using a previously described epistasis model (product model, see Methods) (Olson et al. 2014).”

Author response:

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

We would like to thank both Editors and reviewers for their valuable time, careful reading, and constructive comments. The comments have been highly valuable and useful for improving the quality of our study, as well as important in guiding the direction of our present and future research. In the revised manuscript, we have incorporated the necessary changes including additional experimental data as suggested; please find our detailed pointby-point response to the reviewer’s comments and the changes we have made in the manuscript as follows.

Reviewer #1 (Public Review):

In this work, the authors have explored how treating C. albicans fungal cells with EDTA affects their growth and virulence potential. They then explore the use of EDTA-treated yeast as a whole-cell vaccine in a mouse model of systemic infection. In general, the results of the paper are unsurprising. Treating yeast cells with EDTA affects their growth and the addition of metals rescues the phenotype. Because of the significant growth defects of the cells, they don't infect mice and you see reduced virulence. Injection with these cells effectively immunises the mice, in the same way that heatkilled yeast cells would. The data is fairly sound and mostly well-presented, and the paper is easy to follow. However, I feel the data is an incremental advance at best, and the immune analysis in the paper is very basic and descriptive.

Strengths:

Detailed analysis of EDTA-treated yeast cells

Weaknesses:

• Basic immune data with little advance in knowledge.

• No comparison between their whole-cell vaccine and others tried in the field.

• The data is largely unsurprising and not novel.

Reply: Thank you so much for appreciating our effort to generate a whole cell anti-fungal vaccine by treating C. albicans cells with EDTA. Also, we appreciate your comment that the manuscript is sound and well-presented. However, we are afraid that the respected reviewer assumed the CAET cells as dead cells while they only divide relatively slower than the untreated cells. In the revised manuscript, we have presented additional evidence to show that CAET are live cells (Supp. Figs 2) and based on the new data, we expect a positive change in the reviewer’s opinion. Since CAET is a live strain, the data presented here is novel.

Reviewer #2 (Public Review):

Summary:

Invasive fungal infections are very difficult to treat with limited drug options. With the increasing concern of drug resistance, developing an antifungal vaccine is a high priority. In this study, the authors studied the metal metabolism in Candida albicans by testing some chelators, including EDTA, to block the metal acquisition and metabolism by the fungus. Interestingly, they found EDTAtreated yeast cells grew poorly in vitro and non-pathogenic in vivo in a murine model. Mice immunized by EDTA-treated Candida (CAET) were protected against challenge with wild-type Candida cells. RNA-Seq analysis to survey the gene expression profile in response to EDTA treatment in vitro revealed upregulation of genes in metal homeostasis and downregulation of ribosome biogenesis. They also revealed an induction of both pro- and anti-inflammatory cytokines involved in Th1, Th2 and Th17 host immune response in response to CAET immunization. Overall, this is an interesting study with translational potential.

Strengths:

The main strength of the report is that the authors identified a potential whole-cell live vaccine strain that can provide full protection against candidiasis. Abundant data both on in vitro phenotype, gene expression profile, and host immune response have been presented.

Weaknesses:

A weakness is that the immune mechanism of CAET-mediated host protection remains unclear. The immune data is somewhat confusing. The authors only checked cytokines and chemokines in blood. The immune response in infected tissues and antibody response may be investigated.

Reply: Thank you very much for appreciating our work and finding our strain to be a live whole-cell anti-fungal vaccine strain with translational potential. Since the current study focused on the identification and detailed characterizations of a non-genetically modified live-attenuated strain and determination of its safety and efficacy as a potential vaccine candidate in the preclinical model, we have excluded the possible immune mechanisms involving CAET. In a separate study, we are currently investigating both cellular and molecular mechanisms that provide protective immunity in CAET-vaccinated mice.

Reviewer #3 (Public Review):

Summary:

The authors are trying to find a vaccine solution for invasive candidiasis.

Strengths:

The testing of the antifungal activity of EDTA on Candida is not new as many other papers have examined this effect. The novelty here is the use of this EDTA-treated strain as a vaccine to protect against a secondary challenge with wild-type Candida.

Weaknesses:

However, data presented in Figure 5 and Figure 6 are not convincing and need further experimental controls and analysis as the authors do not show a time-dependent effect on the CFU of their vaccine formulation. The methodology used is also an issue. As it stands, the impact is minor.

Reply: Thank you so much for appreciating our efforts to develop a novel vaccine against fungal infections. We are extremely sorry for the lack of clarity in our writing related to Figs. 5 and 6, we have now modified the text and hope that the respected reviewer will find these convincing.

Recommendations for the authors:

Although the reviewers recognize the importance of the manuscript, they would like to see: 1) comparisons between their whole-cell vaccine and others tried in the field, 2) an investigation of the immune response in infected tissues and antibody response, and 3) more controls in Figures 5 and 6, and a time-dependent effect on the colony-forming units of their vaccine formulation. Please, address the questions and submit a revised version together with a rebuttal letter addressing point-by-point raised by each reviewer.

Reply: (1) We are afraid that a comparative study of a live and heat-killed cell vaccines will mislead the information presented here. This is the only non-genetically modified antifungal vaccine candidate therefore a comparison with a dead strain at present is unwarranted. We have now added supporting data to confirm that, the survivability of C. albicans cells was unaffected at 6 hr of EDTA treatment (CAET, Supp. Fig. S2). (2) Since the current study focused on the identification and a detailed characterization of a non-genetically modified live attenuated strain and its safety and efficacy as a potential vaccine candidate in the preclinical model, we have excluded the possible immune mechanisms involving CAET. However, in a separate study, we are currently investigating both cellular and molecular mechanisms that provide protective immunity in CAET-vaccinated mice. (3) The results of Figs 5 and 6 were misinterpreted by the respected reviewer, please see the explanation below.

Reviewer #1 (Recommendations For The Authors):

Some specific comments/suggestions for the authors: (1) What was the viability of the yeast after EDTA treatment? Is the delayed growth response because many cells died and it takes a while for remaining viable cells to catch up? This is important to know because it may mean the dose given to mice is substantially different and that should be accounted for. Some PI staining of the cells after treatment would help.

Reply: The growth curve assays (Fig. 1A and 1E) were initiated with O.D.600nm=0.5 of each cultures (~ 107 cells/mL) and the analyses suggested that the EDTA-treated C. albicans cells grew slower than the untreated cells. Fig. 1B and 1F further demonstrated that EDTA has minimal effect on the survival of the strain up to 8 hrs post-exposure. The proportion of the number of cells increased without and with metal chelators almost remained the same for this duration (0 – 8 hrs). Therefore, for subsequent analyses, 6 hr treatment was selected and such treated cells were considered as CAET, which were actively dividing live cells, albeit slower than untreated cells. As suggested and to strengthen our finding, a time dependent SYTOX Green and Propidium iodide staining of C. albicans cells without and with EDTA treatment was carried out and analysed by flow cytometry and microscopy, respectively. Both analyses revealed that the percentage of dead cells up to 12 hrs of without and with EDTA treatment remained the same. The new data has now been added in the revised version of the manuscript as Supplementary figure 2.

Author response image 1.

(2) In line with the above, what was the viability of the CAET cells after 3h in media? In the macrophage in vitro experiments, how do you know the reduced viability of the CAET cells is macrophage-specific? Did you run a control of CAET cells in media on their own to determine how CFU changed in macrophage-free conditions? Is the proliferation rates of untreated and CAET cells different? That would affect CFSE labelling and results. These experiments would work better with a GFP-expressing C. albicans strain, which is widely available. In the images in Figure 4c, it looks like there are more hyphae in CAET than untreated - was hyphal induction checked/measured? That's important to know because more hyphae usually means more clumping and this can affect CFU counts (giving the impression of less CFU when actually there is more). Because of all the issues above, I'm not fully convinced by the uptake/killing data.

Reply: As explained in response 1, we used actively dividing WT and CAET cells, and equal number of these cells were CFSE labelled. As can be seen in Fig.4A, the rate of phagocytosis was the same in 1 hr of pre-culture, but in the subsequent time points the double-positive cells were reduced in the case of CAET cells and that is due to fungal killing by macrophages. Fungal cells were released from the macrophages by warm water treatment and CFU was determined. Fig. 4B suggested that at 1hr of co-culture, the CFU of both fungal cells (WT and CAET) were the same and the fungal clearance was observed at later time points. Thus, the reduced viability of CAET cells was macrophagespecific. EDTA has minimal effect on hyphal transition without and with the presence of serum and the new data has now been provided in the revised version (Supplementary Fig. 3).

Author response image 2.

(3) Pooled data should be shown for all animal experiments.

Reply: Thank you for the suggestion, wherever it was meaningful pooled data for the animal experiments have now been provided.

(4) Immune cell counts/analysis in the kidney and bone marrow would be hugely helpful and more relevant to understanding immune responses following immunisation/infection. I think a more interesting analysis for the authors to consider would be to immunise with heat-killed yeast vs EDTAtreated yeast and see if there is a qualitative difference or better protection, i.e. is the EDTA-treated whole-cell vaccine superior to the heat-killed version? That is a better question to address. As it stands, the data in the paper is not surprising.

Reply: The studies on cellular and molecular mechanisms underlying protective immunity in CAETvaccinated mice are under progress in a separate study. This study mostly focused on the identification and detailed characterization of a non-genetically modified live-attenuated strain and its safety and efficacy as a potential vaccine candidate in a preclinical model. We are afraid that a comparison of a live cell (CAET) with a dead cell (heat-killed) will dilute the content of the manuscript and will not be meaningful. It is well accepted that the heat-killed C. albicans strain only provides partial short-lived protection to re-challenge (Refs-PMIDs: 12146759, and 9916097), thus, it does not warrant any comparison with CAET.

Reviewer #2 (Recommendations For The Authors):

Overall, this is a highly interesting study. I have the following specific comments for clarification.

(1) In the introduction, the authors mentioned other anti-candida vaccines that are mostly effective against Candida infection by inducing neutralizing antibodies. However, in their CAET vaccine candidate, they only checked the cellular immunity in blood and found a balanced immune response (both pro- and anti-inflammatory responses are induced). How about the antibody production in these mice? It is a bit surprising that both untreated Candida infection and CAET Candida infection produced similar immune activation based on Figure 6, yet the CAET immunization provides protection. Some innate cell recruitment is higher in untreated Ca infection than the CAET infected mice (Figure 5F). The overall results on immune response characterization did not seem to explain why the CAET infection led to host protection while untreated Ca infection cannot. Characterizing infected tissue immune cell differentiation and cytokine production may offer some additional insights.

Reply: We agree with you that in this manuscript we have not provided any mechanistic study on the protective immunity in CAET-vaccinated mice. This will be demonstrated in a subsequent study.

(2) In Figure 5, some critical data seem to be missing in panels B and C. The CFU and histopathological images for CAET-treated mice challenged by Ca should also be shown there for comparison. Although they did show some data in Figure 5E and Figure S4, it is necessary to have that data in 5B and 5C from the same experiment. Figure S4 is a very busy figure and the images are quite small. It may be necessary to use arrows to point out what information authors want to emphasize.

Reply: Fig 5 B and 5C showed the data for mice that succumbed to infection. Since the other mice (saline control groups, CAET infected, CAET vaccinated, and re-challenged groups) survived, they were not sacrificed; therefore, the CFU data was not collected. In addition, we wanted to see the longevity of these survived mice and after 1 year of observations, they were handed over to the animal house for clearance as per the institutional guidelines. However, Figure 5E and Figure S4 (now Fig. S6) included all the mice groups as they were sacrificed at various time points irrespective of humane end points. As suggested FigS6 has now been modified and fungal cells were denoted by yellow arrows.

(3) EDTA-treated yeast cells showed poor growth but also had thicker cell walls with high chitin, glucan, and mannan levels. What leads to its clearance in vivo remains unclear, as usually, cells with thick cell wall structures and low metabolism are more resistant to stress, e.g., dormant cells. Macrophages were shown to contribute to CAET killing in a phagocytosis assay (Figure 4). Checking cytokines produced by macrophages during co-incubation may offer some insights. In all, additional discussion on what caused in vivo clearance would be helpful.

Reply: Mechanistic study on the protective immune responses of CAET will be demonstrated in a separate study. As suggested, the discussion section now contains additional information emphasising the in vivo clearance of CAET cells in the 3rd paragraph of discussion section.

(4) Long paragraphs in the discussion section could be divided into a bigger number of shorter paragraphs.

Reply: Thank you for the suggestion, it has now been modified in the revised version (7 short paragraphs). To make it more comprehensive, some of the content has been removed.

Reviewer #3 (Recommendations For The Authors):

(1) It is unclear how many cells were treated with 250 micromolar of EDTA for 6 hours before preparing the inoculum. It seems that only the OD was measured before adding EDTA. This is not a very rigorous and reproducible method.

Reply: In this manuscript, we have repeatedly used the same protocol to generate CAET cells for various analyses. The O.D.600nm= 0.5 culture is equivalent to 107 C. albicans cells per mL and this information has now been added in the revised manuscript.

(2) Upon treatment with 250 micromolar of EDTA, cells were harvested and counted to prepare the inoculum (5x10e5) for injecting it in mice. However, it appears that CFU of the inoculum was not done. Based on data shown in Fig. 1B, 250 micromolar of EDTA does inhibit Candida cell replication. Thus, the authors may have counted dead cells and, thus, injected dead cells together with live cells for the CAET inoculum. Thus, mice receiving this inoculum may have been infected (and vaccinated) with a lower number of live Candida cells.

Reply: Please see a similar response to reviewer #1. EDTA has minimal effect on the survival of C. albicans cells at 6 hr (also see supp. Fig. S2). We have already mentioned the CFU analysis of untreated and CAET cells in the methodology section related to inoculum preparation.

(3) It is unclear if 6 hours of treatment with 250 micromolar of EDTA is enough to induce a block of Candida cell replication. In Figure 1B, the authors treated for 24h. The authors are encouraged to wash the cells after 6 hours of treatment and see if their cell division will recover upon removal of EDTA.

Reply: Thank you for the suggestion. At 6 hr treatment, survivability of C. albicans cells was unaffected upon EDTA exposure. PI and SYTOX GREEN staining confirmed it (Supp. Fig. 2). Additionally, as suggested a rescue experiment was carried out by exogenous addition of divalent metals after 6 hr EDTA treatment and growth/CFU analyses were followed thereafter. A modified Fig. 1 A and B with new data has been provided.

(4) The data shown in Figure 5A is extremely exciting. However, the number of mice in each group (n=6) is too low. Normally, 10 mice per group are used for virulence studies unless the authors provide a power analysis that 6 mice per group will be sufficient. Also, CFU data were only provided for Ca and saline-Ca groups (Fig. 5B) and not for the other groups. CFU data should be provided for all mice.

Reply: Thank you for the suggestion and a statistical analysis of Fig. 5A was provided in the revised version. The rationale behind not including all mice groups in Fig. 5B is already explained in a response to reviewer #2.

(5) It is unclear how the authors differentiate between CFU arising from CAET or from WT Candida.

Reply: Since the Fig 5 E demonstrated that no CAET cells were detected in the kidney beyond 10 days of inoculation, in the re-challenged mice group (1CAET 2 Ca), the fungal cells those detected in the 3rd and 7th days were from the later inoculated cells (brown colour).

(6) Figure 5E: it is unclear if a 1 saline-2 saline (Figure legend) or if 1 saline-2 Ca (text) group was included. If the latter, where are the CFU? It is impossible that 1 saline-2 Ca mice have no CFU.

Reply: Thank you so much for pointing this out. The legend has now been modified that include 1saline-2saline and 1CAET-2Ca.

(7) It seems that CFU is significantly present in the kidney in the 1 CAET - 2 Ca group at day 7 but not at day 3. How is this possible? This is an extremely invasive model of infection, and the authors are challenging intravenously 500,000 live Candida cells. If by the 3rd day, the authors detect no CFU, then how is it possible that CFUs are arising on day 7?

Reply: We do detect fungal cells on 3rd day in 1CAET 2 WT mice group (~2000 cells), albeit much lower than in 7 days (~11200 cells). A Log10 scale graph has now been provided for better representation.

(8) Most importantly, if the authors are not detecting CFU at day 3, then earlier time points (e.g. day 2, day 1, or even 12 hours post-challenge) must be analyzed. The authors should show that CFU from the organs is decreasing in a time-dependent manner. Also, all CFU should be shown as Log10.

Reply: please see the previous response.

(9) Fig. 6: because it is unclear if the mice were challenged with the same inoculum of live Candida cells (untreated and treated with EDTA), the different cytokine profiles between the two groups could be simply due to the different inoculum sizes and not to the effect of EDTA on Ca.

Reply: please see the previous response as given also for Reviewer 1.

Author Response

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

Reviewer 1

Comment 1: It is worth mentioning that the authors show that there are Arid1a transcripts that escape the Cre system. This might mask the phenotype of the Arid1a knockout, given that many sequencing techniques used here are done on a heterogeneous population of knockout and wild-type spermatocytes.

Response: The proportions of undifferentiated spermatogonia (PLZF+) with detectable (ARID1A+) and non-detectable (ARID1A=) levels of ARID1A protein by immunostaining on testes cryosections obtained from 1-month old Arid1afl/fl (control) and Arid1acKO (CKO) males were 74% ARID1A negative (CKO) and 26% ARID1A positive (CKO) as compared to 95% ARID1A positive and 5% ARID1A negative in WT controls. The manuscript includes these data (page 5, lines 114-116). Furthermore, Western blot analysis of STA-Put purified pachytene WT and mutant spermatocytes showed significantly reduced levels of ARID1A protein in mutant cells (95% reduction). The manuscript has added these data (page 5, line 116 and Fig. S2).

Comment 2: In relation to this, I think that the use of the term "pachytene arrest" might be overstated, since this is not the phenotype truly observed (these mice produce sperm).

Response: Based on the profiling of prophase-I spermatocytes by co-staining for SYCP3 and ARID1A, we observed a marked reduction in mid-late pachytene spermatocytes that lacked ARID1A, indicating a failure to progress beyond pachynema in the absence of ARID1A (Table 1 in manuscript). Furthermore, we were unable to detect diplotene spermatocytes lacking ARID1A protein. Haploid spermatid populations isolated from Arid1acKO males appeared normal, expressing the wild-type allele, suggesting that they originated from spermatocytes that failed to undergo efficient Cre recombination (Fig. S3). Arid1acKO also produces viable sperm at a level equal to their wild-type controls (see page 5, lines 123-126). It is reasonable to conclude that the absence of ARID1A results in a pachynema arrest and that the viable sperm are from escapers. We cannot make any conclusions regarding the requirement of ARID1A for progression beyond pachynema.

Comment 3: ARID1A is present throughout prophase I, and it might have pre-MSCI roles that impact earlier stages of Meiosis I, and cell death might be happening in these earlier stages too.

Response: We did not observe an effect on the frequency of leptotene and zygotene spermatocytes lacking ARID1A. There appeared to be an accumulation of these prophase-I populations in response to the loss of ARID1A, consistent with a failure in progression beyond pachynema in the mutants (Table 1 in the manuscript).

Additionally, we did not detect any significant difference in the numbers of undifferentiated spermatogonia expressing PLZF (also known as ZBTB16) in 1-month-old Arid1acKO relative to Arid1afl/fl males (see Table below, now included in the manuscript as supplemental Table 1). Therefore, the Arid1a conditional knockouts generated with a Stra8-Cre did not appear to impact earlier stages of spermatogenesis. However, potential roles of ARID1A early in spermatogenesis might be revealed using a more efficient and earlier-acting germline Cre transgene. In this case, an inducible Cre transgene would be needed, given the haploinsufficiency associated with Arid1a. Such haploinsufficiency was why we used the Stra8-Cre. The lack of Cre expression in the female germline allowed the transmission of the floxed allele maternally.

Author response table 1.

Comment 4: Overall, the research presented here is solid, adds new knowledge on how sex chromatin is silenced during meiosis, and has generated relevant databases for the field.

Response: We thank the reviewer for this comment.

Reviewer 2

Comment 1: The conditional deletion mouse model of ARIDA using Stra8-cre showed inefficient deletion; spermatogenesis did not appear to be severely compromised in the mutants. Using this data, the authors claimed that meiotic arrest occurs in the mutants. This is obviously a misinterpretation.

Response: As stated in response to Reviewer 1, testes cryosections obtained from 1-month-old control and mutant males showed that 74% are ARID1A negative (CKO) and 26% ARID1A positive (CKO) as compared to 95% ARID1A positive and 5% ARID1A negative in WT controls (page 5, lines 114-116). This difference is dramatic. Western blot analysis of STA-Put purified pachytene WT and mutant spermatocytes also showed a significant reduction of ARID1A protein in mutant cells (Fig. S2). We observed a marked decrease in mid-late pachytene spermatocytes that lacked ARID1A, indicating a failure to progress beyond pachynema without ARID1A (Table 1 from the manuscript). Furthermore, we were unable to detect any diplotene spermatocytes lacking ARID1A protein. These data suggest that the haploid spermatids originated from spermatocytes that failed to undergo efficient Cre recombination (Fig. S3). Comparison of cKO and wild-type littermate yielded nearly identical results (Avg total conc WT = 32.65 M/m; Avg total conc cKO = 32.06 M/ml), indicating that the cKO’s produce viable sperm at a level equal to their wild-type controls. Taken together, the conclusion that the absence of ARID1A results in a pachynema arrest and that the escapers produce the haploid spermatids is firm. By IF, we see that ~70% of the spermatocytes have deleted ARID1A. Therefore, we disagree with the reviewer’s comments that “spermatogenesis did not appear to be severely compromised in the mutants”.

Comment 2: In the later parts, the authors performed next-gen analyses, including ATAC-seq and H3.3 CUT&RUN, using the isolated cells from the mutant mice. However, with this inefficient deletion, most cells isolated from the mutant mice appeared not to undergo Cre-mediated recombination. Therefore, these experiments do not tell any conclusion pertinent to the Arid1a mutation.

Response: We agree that the ATAC-seq and CUT&RUN data were derived from a mixed population of pachytene spermatocytes consisting of mutants and, to a much lesser extent, escapers. As stated, based on our previous study (Menon et al., 2021, Nat. Commun., PMID: 34772938) and additional analyses in this current work, the undifferentiated spermatogonia lacking ARID1A indicates that Stra8-Cre is ~ 70% efficient. With this efficiency, we can detect striking changes in H3.3 occupancy and chromatin accessibility in the mutants relative to wild-type spermatocytes.

Comment 3: Furthermore, many of the later parts of this study focus on the analysis of H3.3 CUT&RUN. However, Fig. S7 clearly suggests that the H3.3 CUT&RUN experiment in the wild-type simply failed. Thus, none of the analyses using the H3.3 CUT&RUN data can be interpreted.

Response: We would like to draw the attention of the reviewer to a recent study (Fointane et al., 2022, NAR, PMID: 35766398) where the authors observed an identical X chromosome-wide spreading of H3.3 in mouse meiotic cells by ChIP-seq. The genomic distribution matches the microscopic observation of H3.3 coating of the sex chromosomes. Therefore, in normal spermatocytes, H3.3 distribution is pervasive across the X chromosome, with very few peaks observed in intergenic regions. Additionally, we detected H3.3 enrichment at TSSs of ARID1A-regulated autosomal genes in wild-type pachytene spermatocytes, albeit reduced relative to the mutants, indicating that the H3.3 CUT&RUN worked. For these reasons, we do not agree with the reviewer’s assessment that the H3.3 CUT&RUN experiment failed in the wild type.

Comment 4: If the author wishes to study the function of ARID2 in spermatogenesis, they may need to try other cre-lines to have more robust phenotypes, and all analyses must be redone using a mouse model with efficient deletion of ARID2.

Response: As noted, we chose Stra8-Cre to conditionally knockout Arid1a because ARID1A is haploinsufficient during embryonic development. The lack of Cre expression in the maternal germline allows for transmission of the floxed allele, allowing for the experiments to progress.

Reviewer 3

Comment 1: A challenge with the author's CKO model is the incomplete efficiency of ARID1A loss, due to incomplete CRE-mediated deletion. The authors effectively work around this issue, but they don't state specifically what percentage of CKO cells lack ARID1A staining. This information should be added.

Response: Our data indicate that Stra8-Cre is ~ 70% efficient. This information has been added.

Comment 2: They refer to cells that retain ARID1A staining in CKO testes as 'internal controls' but this reviewer finds that label inappropriate.

Response: We have dropped ‘internal controls’ and used ‘escapers’ instead.

Comment 3: Although some cells that retain ARID1A won't have undergone CRE-mediated excision, others may have excised but possibly have delayed kinetics of deletion or ARID1A RNA/protein turnover and loss. Such cells likely have partial ARID1A depletion to different extents and, therefore, in some cases, are no longer wild-type. In subsequent figures in which co-staining for ARID1A is done, it would be appropriate for the authors to specify if they are quantifying all cells from CKO testes, or only those that lack ARID1A staining.

Response: We were unable to detect any diplotene spermatocytes lacking ARID1A protein. The data suggest that the haploid spermatids originated from spermatocytes that failed to undergo efficient Cre recombination (Fig. S3). Thus, we conclude that the absence of ARID1A results in a pachynema arrest and that the escapers produce haploid spermatids. In figures displaying quantification data, we indicate whether the quantification was performed on spermatocytes lacking or containing ARID1A from cKO testes. By IF, we see that ~70% of the spermatocytes have deleted ARID1A.

Comment 4: The authors don't see defects in a few DDR markers in ARID1A CKO cells and conclude that the role of ARID1A in silencing is 'mutually exclusive to DDR pathways' (p 12) and 'occurs independently of DDR signaling' (p30). The data suggest that ARID1A may not be required for DDR signaling, but do not rule out the possibility that ARID1A is downstream of DDR signaling (and the authors even hypothesize this on p30). The data provided do not justify the conclusion that ARID1A acts independently of DDR signaling.

associated DDR factors such as: H2Ax; ATR; and MDC1. We observed an abnormal persistence of elongating RNA polymerase II on the mutant XY body in response to the loss of ARID1A, emphasizing its role in the transcriptional repression of the XY during pachynema. The loss of ARID1A results in a failure to silence sex-linked genes and does so in the presence of DDR signaling factors in the XY body. As the reviewer notes, we highlighted the possibility that DDR pathways might influence ARID1A recruitment to the XY, evidenced by the hyperaccumulation of ARID1A on the sex body late in diplonema. Therefore, whether ARID1A is dependent on DDR signaling remains an open question.

Comment 5: After observing no changes in levels or localization of H3.3 chaperones, the authors conclude that 'ARID1A impacts H3.3 accumulation on the sex chromosomes without affecting its expression or incorporation during pachynema.' It's not clear to this reviewer what the authors mean by this. Aside from the issue of not having tested DAXX or HIRA activity, are they suggesting that some other process besides altered incorporation leads to H3.3 accumulation, and if so, what process would that be?

Response: The loss of ARID1A might result in an abnormal redistribution of DAXX or HIRA on the XY, potentially contributing to the defects in H3.3 accumulation and canonical H3.1/3.2 eviction on the XY. While speculative at this point, it is also possible that the persistence of elongating RNAPII in response to the loss of ARID1A might prevent the sex chromosome-wide coating of H3.3. Addressing the mechanism underlying ARID1A-governed H3.3 accumulation on the XY body remains a topic for future investigation.

Comment 6: The authors find an interesting connection between certain regions that gained chromatin accessibility after ARID1A loss (clusters G1 and G3) and the presence of the PRDM9 sequence motif. The G1 and G3 clusters also show DMC1 occupancy and H3K4me3 enrichment. However, an additional cluster with gained accessibility (G4) also shows DMC1 occupancy and H3K4me3 enrichment but has modest H3.3 accumulation. The paper would benefit for additional discussion about the G4 cluster (which encompasses 960 peak calls). Is there any enrichment of PRDM9 sites in G4? If H3.3 exclusion governs meiotic DSBs, how does cluster G4 fit into the model?

Response: We agree that, compared to G1+G3, cluster G4 shows an insignificant increase in H3.3 occupancy in the absence of ARID1A (Figure 6B). The plot profile associated with the heatmap confirms this result (Figure 6B). Therefore, cluster G4 is very distinct in its chromatin composition from G1+G3 upon the loss of ARID1A and, as such, is not inconsistent with our model of H3.3 antagonism with DSB sites. Additionally, we did not observe an enrichment of PRDM9 sites in G4. Since G4 does not display similar dynamics in H3.3 occupancy to G1+G3, DMC1 association might not be perturbed at G4 in response to the loss of ARID1A. Future studies will be required to determine the genomic associations of DMC1 and H3K4me3 in response to the loss of ARID1A.

Comment 7: The impacts of ARID1A loss on DMC1 focus formation (reduced sex chromosome association) are very interesting and also raise additional questions. Are DMC1 foci on autosomes also affected during pachynema? The corresponding lack of apparent effect on RAD51 implies that breaks are still made and resected, enabling RAD51 filament formation. A more thorough quantitative assessment of RAD51 focus formation will be interesting in the long run, enabling determination of the number of break sites and the kinetics of repair, which the authors suggest is perturbed by ARID1A loss but doesn't directly test. It isn't clear how a nucleosomal factor (H3.3) would influence loading of recombinases onto ssDNA, especially if the alteration is not at the level of resection and ssDNA formation. Additional discussion of this point is warranted. Lastly, there currently are various notions for the interplay between RAD51 and DMC1 in filament formation and break repair, and brief discussion of this area and the implications of the new findings from the ARID1A CKO would strengthen the paper further.

Response: The impact of H3.3 on the loading of recombinases might be an indirect consequence of ARID1A-governed sex-linked transcriptional repression. In a recent study, Alexander et al. (Nat. Commun, 2023, PMID: 36990976) showed that transcriptional activity and meiotic recombination are spatially compartmentalized during meiosis. Therefore, the persistence of elongating RNA polymerase II on a sex body depleted for H3.3 in the absence of ARID1A might contribute to the defect in DMC1 association. RAD51 and DMC1 are known to bind ssDNA at PRDM9/SPO11 designated DSB hotspots. However, these recombinases occupy unique domains. DMC1 localizes nearest the DSB breakpoint, promoting strand exchange, whereas RAD51 is further away (Hinch et al., PMID32610038). We show that loss of Arid1a decreases DMC1 foci on the XY chromosomes without affecting RAD51. These findings indicate that BAF-A plays a role in the loading and/or retention of DMC1 to the XY chromosomes. This information has been added to the discussion.

Author response:

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

eLife assessment

This work presents valuable information about the specificity and promiscuity of toxic effector and immunity protein pairs. The evidence supporting the claims of the authors is currently incomplete, as there is concern about the methodology used to analyze protein interactions, which did not take potential differences in expression levels, protein folding, and/or transient interaction into account. Other methods to measure the strength of interactions and structural predictions would improve the study. The work will be of interest to microbiologists and biochemists working with toxin-antitoxin and effector-immunity proteins.

We thank the reviewers for considering this manuscript. We agree that this manuscript provides a valuable and cross-discipline introduction to new EI pair protein families where we focus on the EI pair’s flexibility and impacts on community structure. As such, we believe we have provided a solid foundation for future studies to examine non-cognate interactions and their possible effects on microbial communities. This, by definition, leaves some areas “incomplete” and, therefore, open for further investigations. While the methods we show do consider potential differences in binding assays, we have more explicitly addressed how “expression, protein folding, and/or transient binding” may play into this expanded EI pair model. We have also tempered the discussion of the proposed model, while also clearly highlighting other published evidence of non-cognate binding interactions between effector and immunity proteins. We have responded to the reviewers’ public comments (italicized below). 

In this revised manuscript, we have updated the main text, particularly the Discussion section, to include more careful language, explain past research better, and add new references to works showing non-cognate immunity proteins protecting against effectors in other systems. We have also updated the supplemental files with more analyses; the relevant procedures are in the Materials and Methods.

Public Reviews:

Note: Reviewer 1, who appeared to focus on a subset of the manuscript rather than the whole, based their comments on several inaccuracies, which we discuss below. We found the tone in this reviewer's comments to be, at times, inappropriate, e.g., using "harsh" and "simply too drastic" to imply that common structure-function analyses were outside of the field-standard methods. We also note that the reviewer took a somewhat atypical step in reviewing this manuscript by running and analyzing the potential protein-complex data in AlphaFold2 but did not discuss areas of low confidence within that model that may contradict their conclusions. We are concerned their approach muddled valid scientific criticisms with problematic conclusions.

Reviewer #1 (Public Review):

In this manuscript, Knecht, Sirias et al describe toxin-immunity pair from Proteus mirabilis. Their observations suggest that the immunity protein could protect against non-cognate effectors from the same family. They analyze these proteins by dissecting them into domains and constructing chimeras which leads them to the conclusion that the immunity can be promiscuous and that the binding of immunity is insufficient for protective activity.

Strengths:<br />  The manuscript is well written and the data are very well presented and could be potentially interesting. The phylogenetic analysis is well done, and provides some general insights.

Weaknesses:<br /> (1) Conclusions are mostly supported by harsh deletions and double hybrid assays. The later assays might show binding, but this method is not resolutive enough to report the binding strength. Proteins could still bind, but the binding might be weaker, transient, and out-competed by the target binding.

The phrasing of structure-function analyses as “harsh” is a bit unusual, as other research groups regularly use deletions and hybrid studies. Given the known caveats to deletion and domain substitutions, we included point-mutation analyses for both the effector and immunity proteins, as found on lines 105 - 113 and 255 - 261 in the current manuscript. These caveats are also why we coupled the in vitro binding analyses with in vivo protection experiments in two distinct experimental systems (E. coli and P. mirabilis). Based on this manuscript’s introductory analysis (where we define and characterize the genes, proteins, interactions, phylogenetics, and incidences in human microbiomes), the next apparent questions are beyond the scope of this study. Future approaches would include analyzing purified proteins from the effector (E) and immunity (I) protein families using biochemical assays, such as X-ray crystallography, circular dichroism spectroscopy, among others. 

Interestingly, most papers in the EI field do not measure EI protein affinity (Jana et al., 2019, Yadav et al., 2021). Notable exceptions are earlier colicin research (Wallis et al., 1995) and a new T6SS EI paper (Bosch et al., 2023) published as we first submitted this manuscript.

(2) While the authors have modeled the structure of toxin and immunity, the toxin-immunity complex model is missing. Such a model allows alternative, more realistic interpretation of the presented data. Firstly, the immunity protein is predicted to bind contributing to the surface all over the sequence, except the last two alpha helices (very high confidence model, iPTM>0.8). The N terminus described by the authors contributes one of the toxin-binding surfaces, but this is not the sole binding site. Most importantly, other parts of the immunity protein are predicted to interact closer to the active site (D-E-K residues). Thus, based on the AlphaFold model, the predicted mechanism of immunization remains physically blocking the active site. However, removing the N terminal part, which contributes large interaction surface will directly impact the binding strength. Hence, the toxin-immunity co-folding model suggests that proper binding of immunity, contributed by different parts of the protein, is required to stabilize the toxin-immunity complex and to achieve complete neutralization. Alternative mechanisms of neutralization might not be necessary in this case and are difficult to imagine for a DNase.

In response to the reviewer’s comment, we again reviewed the RdnE-RdnI AlphaFold2 complex predictions with the most updated version of ColabFold (1.5.2-patch with PDB100 and MMseq2) and have included them at the end of these responses [1].

However, the literature reports that computational predictions of E-I complexes often do not match experimental structural results (Hespanhol et al., 2022, Bosch et al., 2023). As such, we chose not to include the predicted cognate and non-cognate RdnE-I complexes from ColabFold (which uses AlphaFold2) and have not included this data in the revised manuscript. (It is notable that reviewer 1 found the proposed expanded model and research so interesting as to directly input and examine the AI-predicted RdnE-RdnI protein interactions in AlphaFold2.)

Discussion of the prevailing toxin-immunity complex model is in the introduction (lines 45-48) and Figure 5E. Further, there are various known mechanisms for neutralizing nucleases and other T6SS effectors, which we briefly state in the discussion (lines 359 - 361). More in-depth, these molecular mechanisms include active-site blocking (Benz et al., 2012), allosteric-site binding (Kleanthous et al., 1999 and Lu et al., 2014), enzymatic neutralization of the target (Ting et al., 2021), and structural disruption of both the active and binding sites (Bosch et al., 2023). Given this diversity of mechanisms, we did not presume to speculate on the as-of-yet unknown mechanism of RdnI protection. We have expanded discussion of these items in the revised manuscript.

(3) Dissection of a toxin into two domains is also not justified from a structural point of view, it is probably based on initial sequence analyses. The N terminus (actually previously reported as Pone domain in ref 21) is actually not a separate domain, but an integral part of the protein that is encased from both sides by the C terminal part. These parts might indeed evolve faster since they are located further from the active site and the central core of the protein. I am happy to see that the chimeric toxins are active, but regarding the conservation and neutralization, I am not surprised, that the central core of the protein fold is highly conserved. However, "deletion 2" is quite irrelevant - it deletes the central core of the protein, which is simply too drastic to draw any conclusions from such a construct - it will not fold into anything similar to an original protein, if it will fold properly at all.

The reviewer’s comment highlights why we turned to the chimera proteins to dissect the regions of RdnE (formerly IdrD-CT), as the deletions could result in misfolded proteins. (We initially examined RdnE in the years before the launch of AlphaFold2.) However, the reviewer is incorrect regarding the N-terminus of RdnE. The PoNe domain, while also a subfamily of the PD-(D/E)XK superfamily, forms a distinct clade of effectors from the PD-(D/E)XK domain in RdnE (formally IdrD-CT) as seen in Hespanhol et al., 2022; this is true for other DNase effectors as well. Many studies analyzing effectors within the PD-(D/E)XK superfamily only focus on the PD-(D/E)XK domain, removing just this domain from the context of the whole protein (Hespanhol et al., 2022; Jana et al., 2019). Of note, in RdnE, this region alone (containing the DNA-binding domain) is insufficient for DNase activity (unlike in PoNe). We have clarified this distinction in the results section of the current manuscript, visible in figure 2 .

(4) Regarding the "promiscuity" there is always a limit to how similar proteins are, hence when cross-neutralization is claimed authors should always provide sequence similarities. This similarity could also be further compared in terms of the predicted interaction surface between toxin and immunity.

Reviewer 1 points out a fundamental property of protein-protein interactions that has been isolated away from the impacts of such interactions on bacterial community structure. We have provided the whole protein alignments in figure 3 supplemental figure 3, the summary images in Figure 3D, and the protein phylogenetic trees in Figure 3C. We encourage others to consider the protein alignments as percent amino acid sequence similarity is not necessarily a good gauge for protein function and interactions. These data are publicly available on the OSF website associated with this manuscript https://osf.io/scb7z/, and we hope the community explores the data there.

In consideration of the enthusiasm to deeply dive into the primary research data, we have included the pairwise sequence identities across the entire proteins here: Proteus RdnI vs. Rothia RdnI: 23.6%; Proteus RdnI vs. Prevotella RdnI: 16.3%, Proteus RdnI vs. Pseudomonas RdnI: 14.6%; Rothia RdnI vs. Prevotella RdnI: 22.4%, Rothia RdnI vs. Pseudomonas RdnI: 17.6%; Prevotella RdnI vs. Pseudomonas RdnI: 19.5%. (As stated in response to reviewer 1 comment 2, we did not find it appropriate to make inferences based on AlphaFold2-predicted protein complexes.)

Overall, it looks more like a regular toxin-immunity couple, where some cross-reactions with homologues are possible, depending on how far the sequences have deviated. Nevertheless, taking all of the above into account, these results do not challenge toxin-immunity specificity dogma.

In this manuscript, we did not intend to dismiss the E-I specificity model but rather point out its limitations and propose an important expansion of that model that accounts for cross-protection and survival against attacks from other genera. We agree that it is commonly considered that deviations in amino acid sequence over time could result in cross-binding and protection (see lines 364-368). However, the impacts of such cross-binding on community structure, bacterial survival, and strain evolution were rarely addressed in prior literature, with exceptions such as in Zhang et al., 2013 and Bosch et al., 2023 among others. One key insight we propose and show in this manuscript is that cross-binding can be a fitness benefit in mixed communities; therefore, it could be selected for evolutionarily (lines 378-380), even potentially in host microbiomes.

Reviewer #2 (Public Review):

Summary:

The manuscript by Knecht et al entitled "Non-cognate immunity proteins provide broader defenses against interbacterial effectors in microbial communities" aims at characterizing a new type VI secretion system (T6SS) effector immunity pair using genetic and biochemical studies primarily focused on Proteus mirabilis and metagenomic analysis of human-derived data focused on Rothia and Prevotella sequences. The authors provide evidence that RdnE and RdnI of Proteus constitute an E-I pair and that the effector likely degrades nucleic acids. Further, they provide evidence that expression of non-cognate immunity derived from diverse species can provide protection against RdnE intoxication. Overall, this general line of investigation is underdeveloped in the T6SS field and conceptually appropriate for a broad audience journal. The paper is well-written and, aside from a few cases, well-cited. As detailed below however, there are several aspects of this paper where the evidence provided is somewhat insufficient to support the claims. Further, there are now at least two examples in the literature of non-cognate immunity providing protection against intoxication, one of which is not cited here (Bosch et al PMID 37345922 - the other being Ting et al 2018). In general therefore I think that the motivating concept here in this paper of overturning the predominant model of interbacterial effector-immunity cognate interactions is oversold and should be dialed back.

We agree that analyses focusing on flexible non-cognate interactions and protection are underdeveloped within the T6SS field and are not fully explored within a community structure. These ideas are rapidly growing in the field, as evidenced by the references provided by the reviewer. As stated earlier, we did not intend to overturn the prevailing model but rather have proposed an expanded model that accounts for protection against attacks from foreign genera.

Strengths:

One of the major strengths of this paper is the combination of diverse techniques including competition assays, biochemistry, and metagenomics surveys. The metagenomic analysis in particular has great potential for understanding T6SS biology in natural communities. Finally, it is clear that much new biology remains to be discovered in the realm of T6SS effectors and immunity.

Weaknesses:

The authors have not formally shown that RdnE is delivered by the T6SS. Is it the case that there are not available genetics tools for gene deletion for the BB2000 strain? If there are genetic tools available, standard assays to demonstrate T6SS-dependency would be to interrogate function via inactivation of the T6SS (e.g. by deleting tssC).

Our research group showed that the T6SS secretes RdnE (previously IdrD) in Wenren et al., 2013 (cited in lines 71-73). We later confirmed T6SS-dependent secretion by LC-MS/MS (Saak et al., 2017).  

For swarm cross-phyla competition assays (Figure 4), at what level compared to cognate immunity are the non-cognate immunity proteins being expressed? This is unclear from the methods and Figure 4 legend and should be elaborated upon. Presumably these non-cognate immunity proteins are being overexpressed. Expression level and effector-to-immunity protein stoichiometry likely matters for interpretation of function, both in vitro as well as in relevant settings in nature. It is important to assess if native expression levels of non-cognate cross-phyla immunity (e.g. Rothia and Prevotella) protect similarly as the endogenously produced cognate immunity. This experiment could be performed in several ways, for example by deleting the RdnE-I pair and complementing back the Rothia or Prevotella RdnI at the same chromosomal locus, then performing the swarm assay. Alternatively, if there are inducible expression systems available for Proteus, examination of protection under varying levels of immunity induction could be an alternate way to address this question. Western blot analysis comparing cognate to non-cognate immunity protein levels expressed in Proteus could also be important. If the authors were interested in deriving physical binding constants between E and various cognate and non-cognate I (e.g. through isothermal titration calorimetry) that would be a strong set of data to support the claims made. The co-IP data presented in supplemental Figure 6 are nice but are from E. coli cells overexpressing each protein and do not fully address the question of in vivo (in Proteus) native expression.

P. mirabilis strain ATCC29906 does not encode the rdnE and rdnI genes on the chromosome (NCBI BioSample: SAMN00001486) (line 151). Production of the RdnI proteins, including the cognate Proteus RdnI, comes from equivalent transgenic expression vectors. Specifically, the rdnI genes were expressed under the flaA promoter in P. mirabilis strain ATCC29906 (Table 1) for the swarm competition assays found in Figure 2C and Figure 4. This promoter results in constitutive expression in swarming cells (Belas et al., 1991; Jansen et al., 2003). In the revised manuscript, figure 4 Supplement Figure 2 shows the relative RdnI protein levels in these strains; we also clarified the expression constructs in the text (see reviewer 3, comment 1).

Lines 321-324, the authors infer differences between E and I in terms of read recruitment (greater abundance of I) to indicate the presence of orphan immunity genes in metagenomic samples (Figure 5A-D). It seems equally or perhaps more likely that there is substantial sequence divergence in E compared to the reference sequence. In fact, metagenomes analyzed were required only to have "half of the bases on reference E-I sequence receiving coverage". Variation in coverage again could reflect divergent sequence dipping below 90% identity cutoff. I recommend performing metagenomic assemblies on these samples to assess and curate the E-I sequences present in each sample and then recalculating coverage based on the exact inferred sequences from each sample.

This comment raises the challenges with metagenomic analyses. It was difficult to balance specificity to a particular species’ DNA sequence with the prevalence of any homologous sequence in the sample. Given the distinction in binding interactions among the examined four species, we opted to prioritize specificity, accepting that we were losing access to some rdnE and rdnI sequences in that decision. We chose a 90% identity cutoff, which, through several in silica controls, ensured that each sequence we identified was the rdnE or rdnI gene from that specific species. For the Version of Record, we have included analysis with a 70% cutoff in the supplemental information to try to account for sequence divergence by lowering the identity cutoffs as suggested. The data from the 70% identity cutoff was consistent with the original data from the 90% identity cutoff.

A description of gene-level read recruitment in the methods section relating to metagenomic analysis is lacking and should be provided.

Noted. We included the raw code and sequences on the OSF website associated with this manuscript https://osf.io/scb7z/.

Reviewer #3 (Public Review):

Summary:<br /> The authors discovered that the RdnE effector possesses DNase activity, and in competition, P. mirabilis having RdnE outcompetes the null strain. Additionally, they presented evidence that the RdnI immunity protein binds to RdnE, suppressing its toxicity. Interestingly, the authors demonstrated that the RdnI homolog from a different phylum (i.e., Actinomycetota) provides cross-species protection against RdnE injected from P. mirabilis, despite the limited identity between the immunity sequences. Finally, using metagenomic data from human-associated microbiomes, the authors provided bioinformatic evidence that the rdnE/rdnI gene pair is widespread and present in individual microbiomes. Overall, the discovery of broad protection by non-cognate immunity is intriguing, although not necessarily surprising in retrospect, considering the prolonged period during which Earth was a microbial battlefield/paradise.

Strengths:<br /> The authors presented a strong rationale in the manuscript and characterized the molecular mechanism of the RdnE effector both in vitro and in the heterologous expression model. The utilization of the bacterial two-hybrid system, along with the competition assays, to study the protective action of RdnI immunity is informative. Furthermore, the authors conducted bioinformatic analyses throughout the manuscript, examining the primary sequence, predicted structural, and metagenomic levels, which significantly underscore the significance and importance of the EI pair. 

Weaknesses:<br /> (1) The interaction between RdnI and RdnE appears to be complex and requires further investigation. The manuscript's data does not conclusively explain how RdnI provides a "promiscuous" immunity function, particularly concerning the RdnI mutant/chimera derivatives. The lack of protection observed in these cases might be attributed to other factors, such as a decrease in protein expression levels or misfolding of the proteins. Additionally, the transient nature of the binding interaction could be insufficient to offer effective defenses.

Yes, we agree with the reviewer and hope that grant reviewers’ share this colleague’s enthusiasm for understanding the detailed molecular mechanisms of RdnE-RdnI binding across genera. In the revised manuscript, we have continued to emphasize such caveats as the next frontier is clearly understanding the molecular mechanisms for RdnI cognate or non-cognate protection. In the revised manuscript, figure 4 Supplement Figure 2 shows the RdnI protein levels; we also clarified the expression constructs in the text (see reviewer 2, comment 2).

(2) The results from the mixed population competition lack quantitative analysis. The swarm competition assays only yield binary outcomes (Yes or No), limiting the ability to obtain more detailed insights from the data.

The mixed swam assay is needed when studying T6SS effectors that are primarily secreted during Proteus’ swarming activity (Saak et al. 2017, Zepeda-Rivera et al. 2018). This limitation is one reason we utilize in vitro, in vivo, and bioinformatic analyses. Though the swarm competition assay yields a binary outcome, we are confident that the observed RdnI protection is due to interaction with a trans-cell RdnE via an active T6SS. By contrast, many manuscripts report co-expression of the EI pair (Yadev et al., 2021, Hespanhol et al., 2022) rather than secreted effectors, as we have achieved in this manuscript.

(3) The discovery of cross-species protection is solely evident in the heterologous expression-competition model. It remains uncertain whether this is an isolated occurrence or a common characteristic of RdnI immunity proteins across various scenarios. Further investigations are necessary to determine the generality of this behavior.

We agree, which is why we submitted this paper as a launching point for further investigations into the generality of non-cognate interactions and their potential impact on community structure.

Comments from Reviewing Editor:<br />  - In addition to the references provided by reviewer#2, the first manuscript to show non-cognate binding of immunity proteins was Russell et al 2012 (PMID: 22607806).<br />  - IdrD was shown to form a subfamily of effectors in this manuscript by Hespanhol et al 2022 PMID: 36226828 that analyzed several T6SS effectors belonging to PDDExK, and it should be cited.

We appreciate that the reviewer and eLife staff pointed out missed citations. We have incorporated these studies and cited them in the revised manuscript.

[1] The Proteus RdnE in complex with either the Prevotella or Pseudomonas RdnI showed low confidence at the interface (pIDDT ~50-70%); this AI-predicted complex might support the lack of binding seen in the bacterial two-hybrid assay. On the other hand, the Proteus and Rothia RdnI N-terminal regions show higher confidence at the interface with RdnE. Despite this, the C-terminus of the Proteus RdnI shows especially low confidence (pIDDT ~50%) where it might interact near RdnE’s active site (as suggested by reviewer 1). Given this low confidence and the already stated inaccuracies of AI-generated complexes, we would rather wait for crystallization data to inform potential protection mechanisms of RdnI.

Author response image 1.

Author response:

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

We thank the reviewers for their constructive comments and suggestions. We have prepared a revised manuscript with updated quantification of theta cycle skipping, new statistical comparisons of the difference between the two behavioral tasks, and general improvements to the text and figures.

Reviewer #1 (Public Review):

Summary

The authors provide very compelling evidence that the lateral septum (LS) engages in theta cycle skipping.

Strengths

The data and analysis are highly compelling regarding the existence of cycle skipping.

Weaknesses

The manuscript falls short on in describing the behavioral or physiological importance of the witnessed theta cycle skipping, and there is a lack of attention to detail with some of the findings and figures:

More/any description is needed in the article text to explain the switching task and the behavioral paradigm generally. This should be moved from only being in methods as it is essential for understanding the study.

Following this suggestion, we have expanded the description of the behavioral tasks in the Results section.

An explanation is needed as to how a cell can be theta skipping if it is not theta rhythmic.

A cell that is purely theta skipping (i.e., always fires on alternating theta cycles and never on adjacent theta cycles) will only have enhanced power at half theta frequency and not at theta frequency. Such a cell will therefore not be considered theta rhythmic in our analysis. Note, however, that there is a large overlap between theta rhythmic and theta skipping cell populations in our data (Figure 3 - figure supplement 2), indicating that most cells are not purely theta skipping.

The most interesting result, in my opinion, is the last paragraph of the entire results section, where there is more switching in the alternation task, but the reader is kind of left hanging as to how this relates to other findings. How does this relate to differences in decoding of relative arms (the correct or incorrect arm) during those theta cycles or to the animal's actual choice? Similarly, how does it relate to the animal's actual choice? Is this phenomenon actually behaviorally or physiologically meaningful at all? Does it contribute at all to any sort of planning or decision-making?

We agree that the difference between the two behavioral tasks is very interesting. It may provide clues about the mechanisms that control the cycle-by-cycle expression of possible future paths and the potential impact of goal-directed planning and (recent) experience. In the revised manuscript, we have expanded the analysis of the differences in theta-cycle dynamics between the two behavioral tasks. First, we confirm the difference through a new quantification and statistical comparison. Second, we performed additional analyses to explore the idea that the alternation of non-local representations reflects the number of relevant paths available to the animal (Figure 11 – figure supplements 2 and 3), but this did not appear to be the case. However, these results provide a starting point for future studies to clarify the task dependence of the theta- cycle dynamics of spatial representations and to address the important question of behavioral/physiological relevance.

The authors state that there is more cycle skipping in the alternation task than in the switching task, and that this switching occurs in the lead-up to the choice point. Then they say there is a higher peak at ~125 in the alternation task, which is consistent. However, in the final sentence, the authors note that "This result indicates that the representations of the goal arms alternate more strongly ahead of the choice point when animals performed a task in which either goal arm potentially leads to reward." Doesn't either arm potentially lead to a reward (but different amounts) in the switching task, not the alternation task? Yet switching is stronger in the alternation task, which is not constant and contradicts this last sentence.

The reviewer is correct that both choices lead to (different amounts of) reward in the switching task. As written, the sentence that the reviewer refers to is indeed not accurate and we have rephrased it to: “This result indicates that the representations of the goal arms alternate more strongly ahead of the choice point when animals performed a task in which either goal arm potentially leads to a desirable high-value reward.”.

Additionally, regarding the same sentence - "representations of the goal arms alternate more strongly ahead of the choice point when the animals performed a task in which either goal arm potentially leads to reward." - is this actually what is going on? Is there any reason at all to think this has anything to do with reward versus just a navigational choice?

We appreciate the reviewer’s feedback and acknowledge that our statement needs clarification. At the choice point in the Y-maze there are two physical future paths available to the animal (disregarding the path that the animal took to reach the choice point) – we assume this is what the reviewer refers to as “a navigational choice”. One hypothesis could be that alternation of goal arm representations is present whenever there are multiple future paths available, irrespective of the animal’s (learned) preference to visit one or the other goal arm. However, the reduced alternation of goal arm representations in the switching task that we report, suggests that the animal’s recent history of goal arm visits and reward expectations likely do influence the theta-cycle representations ahead of the choice point. We have expanded our analysis to test if theta cycle dynamics differ for trials before and after a switch in reward contingency in the switching task, but there was no statistical difference in our data. We have rewritten and expanded this part of the results to make our point more clearly.

Similarly, the authors mention several times that the LS links the HPC to 'reward' regions in the brain, and it has been found that the LS represents rewarded locations comparatively more than the hippocampus. How does this relate to their finding?

Indeed, Wirtshafter and Wilson (2020) reported that lateral septum cells are more likely to have a place field close to a reward site than elsewhere in their double-sided T-maze. It is possible that this indicates a shift towards reward or value representations in the lateral septum. In our study we did not look at reward-biased cells and whether they are more or less likely to engage in theta cycle skipping. This could be a topic for future analyses. It should be noted that the study by Wirtshafter and Wilson (2020) reports that a reward bias was predominantly present for place fields in the direction of travel away from the reward site. These reward-proximate LS cells may thus contribute to theta-cycle skipping in the inbound direction, but it is not clear if these cells would be active during theta sweeps when approaching the choice point in the outbound direction.

Reviewer #2 (Public Review)

Summary

Recent evidence indicates that cells of the navigation system representing different directions and whole spatial routes fire in a rhythmic alternation during 5-10 Hz (theta) network oscillation (Brandon et al., 2013, Kay et al., 2020). This phenomenon of theta cycle skipping was also reported in broader circuitry connecting the navigation system with the cognitive control regions (Jankowski et al., 2014, Tang et al., 2021). Yet nothing was known about the translation of these temporally separate representations to midbrain regions involved in reward processing as well as the hypothalamic regions, which integrate metabolic, visceral, and sensory signals with the descending signals from the forebrain to ensure adaptive control of innate behaviors (Carus-Cadavieco et al., 2017). The present work aimed to investigate theta cycle skipping and alternating representations of trajectories in the lateral septum, neurons of which receive inputs from a large number of CA1 and nearly all CA3 pyramidal cells (Risold and Swanson, 1995). While spatial firing has been reported in the lateral septum before (Leutgeb and Mizumori, 2002, Wirtshafter and Wilson, 2019), its dynamic aspects have remained elusive. The present study replicates the previous findings of theta-rhythmic neuronal activity in the lateral septum and reports a temporal alternation of spatial representations in this region, thus filling an important knowledge gap and significantly extending the understanding of the processing of spatial information in the brain. The lateral septum thus propagates the representations of alternative spatial behaviors to its efferent regions. The results can instruct further research of neural mechanisms supporting learning during goal-oriented navigation and decision-making in the behaviourally crucial circuits entailing the lateral septum.

Strengths

To this end, cutting-edge approaches for high-density monitoring of neuronal activity in freely behaving rodents and neural decoding were applied. Strengths of this work include comparisons of different anatomically and probably functionally distinct compartments of the lateral septum, innervated by different hippocampal domains and projecting to different parts of the hypothalamus; large neuronal datasets including many sessions with simultaneously recorded neurons; consequently, the rhythmic aspects of the spatial code could be directly revealed from the analysis of multiple spike trains, which were also used for decoding of spatial trajectories; and comparisons of the spatial coding between the two differently reinforced tasks.

Weaknesses

Possible in principle, with the present data across sessions, longitudinal analysis of the spatial coding during learning the task was not performed. Without using perturbation techniques, the present approach could not identify the aspects of the spatial code actually influencing the generation of behaviors by downstream regions.

Reviewer #3 (Public Review)

Summary

Bzymek and Kloosterman carried out a complex experiment to determine the temporal spike dynamics of cells in the dorsal and intermediate lateral septum during the performance of a Y-maze spatial task. In this descriptive study, the authors aim to determine if inputting spatial and temporal dynamics of hippocampal cells carry over to the lateral septum, thereby presenting the possibility that this information could then be conveyed to other interconnected subcortical circuits. The authors are successful in these aims, demonstrating that the phenomenon of theta cycle skipping is present in cells of the lateral septum. This finding is a significant contribution to the field as it indicates the phenomenon is present in neocortex, hippocampus, and the subcortical hub of the lateral septal circuit. In effect, this discovery closes the circuit loop on theta cycle skipping between the interconnected regions of the entorhinal cortex, hippocampus, and lateral septum. Moreover, the authors make 2 additional findings: 1) There are differences in the degree of theta modulation and theta cycle skipping as a function of depth, between the dorsal and intermediate lateral septum; and 2) The significant proportion of lateral septum cells that exhibit theta cycle skipping, predominantly do so during 'non-local' spatial processing.

Strengths

The major strength of the study lies in its design, with 2 behavioral tasks within the Y-maze and a battery of established analyses drawn from prior studies that have established spatial and temporal firing patterns of entorhinal and hippocampal cells during these tasks. Primary among these analyses, is the ability to decode the animal's position relative to locations of increased spatial cognitive demand, such as the choice point before the goal arms. The presence of theta cycle skipping cells in the lateral septum is robust and has significant implications for the ability to dissect the generation and transfer of spatial routes to goals within and between the neocortex and subcortical neural circuits.

Weaknesses

There are no major discernable weaknesses in the study, yet the scope and mechanism of the theta cycle phenomenon remain to be placed in the context of other phenomena indicative of spatial processing independent of the animal's current position. An example of this would be the ensemble-level 'scan ahead' activity of hippocampal place cells (Gupta et al., 2012; Johnson & Redish, 2007). Given the extensive analytical demands of the study, it is understandable that the authors chose to limit the analyses to the spatial and burst firing dynamics of the septal cells rather than the phasic firing of septal action potentials relative to local theta oscillations or CA1 theta oscillations. Yet, one would ideally be able to link, rather than parse the phenomena of temporal dynamics. For example, Tingley et al recently showed that there was significant phase coding of action potentials in lateral septum cells relative to spatial location (Tingley & Buzsaki, 2018). This begs the question as to whether the non-uniform distribution of septal cell activity within the Y-maze may have a phasic firing component, as well as a theta cycle skipping component. If so, these phenomena could represent another means of information transfer within the spatial circuit during cognitive demands. Alternatively, these phenomena could be part of the same process, ultimately representing the coherent input of information from one region to another. Future experiments will therefore have to sort out whether theta cycle skipping, is a feature of either rate or phase coding, or perhaps both, depending on circuit and cognitive demands.

The authors have achieved their aims of describing the temporal dynamics of the lateral septum, at both the dorsal extreme and the intermediate region. All conclusions are warranted.

Reviewer #1 (Recommendations For The Authors)

The text states: "We found that 39.7% of cells in the LSD and 32.4% of cells in LSI had significantly higher CSI values than expected by chance on at least one of the trajectories." The text in the supplemental figure indicates a p-value of 0.05 was used to determine significance. However, four trajectory categories are being examined so a Bonferroni correction should be used (significance at p<0.0125).

Indeed, a p-value correction for multiple tests should be performed when determining theta cycle skipping behavior for each of the four trajectories. We thank the reviewer for pointing out this oversight. We have implemented a Holm-Sidak p-value correction for the number of tested trajectories per cell (excluding trajectories with insufficient spikes). As a consequence, the number of cells with significant cycle-skipping activity decreased, but overall the results have not changed.

Figure 4 is very confusing as raster plots are displayed for multiple animals but it is unclear which animal the LFP refers to? The bottom of the plot is also referenced twice in the figure caption.

We apologize for the confusion. We have removed this figure in the revised manuscript, as it was not necessary to make the point about the spatial distribution of theta cycle skipping. Instead, we show examples of spatially-resolved cycle skipping in Figure 4 (formerly Figure 5 - supplementary figures 1 and 2) and we have added a plot with the spatially-resolved cycle skipping index for all analyzed cells in Figure 5A.

Figure 6 has, I think, an incorrect caption or figure. Only A and B are marked in the figure but A-G are mentioned in the caption but do not appear to correspond to anything in the figure.

Indeed, the caption was outdated. This has now been corrected.

Figure 8 is also confusing for several reasons: how is the probability scale on the right related to multiple semi-separate (top and middle) figures? In the top and bottom figures, it is not clear what the right and left sides refer to. It is also unclear why a probability of 0.25 is used for position (seems potentially low). The caption also mentions Figure A but there are no lettered "sub" figures in Figure 8.

The color bar on the right applies to both the top plot (directional decoding) and the middle plot (positional decoding). However, the maximum probability that is represented by black differs between the top and middle plots. We acknowledge that a shared color bar may lead to confusion and we have given each of the plots a separate color bar.

As for the maximum probability of 0.25 for position: this was a typo in the legend. The correct maximum value is 0.5. In general, the posterior probability will be distributed over multiple (often neighboring) spatial bins, and the distribution of maximum probabilities will depend on the number of spatial bins, the level of spatial smoothing in the decoding algorithm, and the amount of decodable information in the data. It would be more appropriate to consider the integrated probability over a small section of the maze, rather than the peak probability that is assigned to a single 5 cm bin. Also, note that a posterior probability of 0.5 is many times higher than the probability associated with a uniform distribution, which is in our case.

The left and right sides of the plots represent two different journeys that the animal ran. On the left an outbound journey is shown, and on the right an inbound journey. We have improved the figure and the description in the legend to make this clearer.

The reviewer is correct that there are no panels in Figure 8 and we have corrected the legend.

Some minor concerns

The introduction states that "a few studies have reported place cell-like activity in the lateral septum (Tingley and Buzsaki, 2018; Wirtshafter and Wilson, 2020, 2019)." However, notably and controversially, the Tingley study is one of the few studies to find NO place cell activity in the lateral septum. This is sort of mentioned later but the citation in this location should be removed.

The reviewer is correct, Tingley and Buzsaki reported a spatial phase code but no spatial rate code. We have removed the citation.

Stronger position/direction coding in the dLS consistent with prior studies and they should be cited in text (not a novel finding).

Thank you for pointing out this omission. Indeed, a stronger spatial coding in the dorsal lateral septum has been reported before, for example by Van der Veldt et al. (2021). We now cite this paper when discussing these findings.

Why is the alternation task administered for 30m but the switching task for 45m?

The reason is that rats received a larger reward in the switching task (in the high-reward goal arm) and took longer to complete trials on average. To obtain a more-or-less similar number of trials per session in both tasks, we extended the duration of switching task sessions to 45 minutes. We have added this explanation to the text.

Regarding the percentage of spatially modulated cells in the discussion, it is also worth pointing out that bits/sec information is consistent with previous studies.

Thank you for the suggestion. We now point out that the spatial information in our data is consistent with previous studies.

Reviewer #2 (Recommendations For The Authors)

While the results of the study are robust and timely, further details of behavioural training, additional quantitative comparisons, and improvements in the data presentation would make the study more comprehensible and complete.

Major comments

(1) I could not fully comprehend the behavioural protocols. They require a clearer explanation of both the specific rationale of the two tasks as well as a more detailed presentation of the protocols. Specifically:

(1.1) In the alternation task, were the arms baited in a random succession? How many trials were applied per session? Fig 1D: how could animals reach high choice accuracy if the baiting was random?

We used a continuous version of the alternation task, in which the animals were rewarded for left→home→right and right→home→left visit sequences. In addition, animals were always rewarded on inbound journeys. There was no random baiting of goal arms. Perhaps the confusion stems from our use of the word “trial” to refer to a completed lap (i.e., a pair of outbound/inbound journeys). On average, animals performed 54 of such trials per 30-minute session in the alternation task. We have expanded the description of the behavioral tasks in the Results and further clarified these points in the Methods section.

(1.2) Were they rewarded for correct inbound trials? If there was no reward, why were they considered correct?

Yes, rats received a reward at the home platform for correct inbound trials. We have now explicitly stated this in the text.

(1.3) In the switch alternation protocol, for how many trials was one arm kept more rewarding than the other, and how many trials followed after the rewarding value switch?

A switch was triggered when rats (of their own volition) visited the high-reward goal arm eight times in a row. Following a switch, the animals could complete as many trials as necessary until they visited the new high- reward goal arm in eight consecutive trials, which triggered another switch. As can be seen in Figure 1D, at the population level, animals needed ~13 trials to fully commit to the high-reward goal arm following a switch. We have further clarified the switching task protocol in the Results and Methods sections.

(1.4) What does the phrase "the opposite arm (as 8 consecutive visits)" exactly mean? Sounds like 8 consecutive visits signalled that the arm was rewarded (as if were not predefined in the protocol).

The task is self-paced and the animals initially visit both goal arms, before developing a bias for the high- reward goal arm. A switch of reward size was triggered as soon as the animal visited the high-reward goal arm for eight consecutive trials. We have rewritten the description of the switching task protocol, including this sentence, which hopefully clarifies the procedure.

(1.5) P. 15, 1st paragraph, Theta cycle skipping and alternation of spatial representations is more prominent in the alternation task. Why in the switching task, did rats visit the left and right arms approximately equally often if one was more rewarding than the other? How many switches were applied per recording session, and how many trials were there in total?

Both the left and right goal arms were sampled more or less equally by the animals because both goal arms at various times were associated with a large reward following switches in reward values during sessions. The number of switches per session varied from 1 to 3. Sampling of both goal arms was also evident at the beginning of each session and following each reward value switch, before animals switched their behavior to the (new) highly rewarded goal arm. In Table 1, we have now listed the number of trials and the number of reward-value switches for all sessions.

(1.6) Is the goal arm in figures the rewarded/highly rewarded arm only or are non-baited arms also considered here?

Both left and right arms are considered goal arms and were included in the analyses, irrespective of the reward that was received (or not received).

(2) The spatial navigation-centred behavioural study design and the interpretation of results highlight the importance of the dorsal hippocampal input to the LS. Yet, the recorded LSI cells are innervated by intermediate and ventral aspects of the hippocampus, and LS receives inputs from the amygdala and the prefrontal cortex, which together may together bring about - crucial for the adaptive behaviours regulated by the LS - reward, and reward-prediction-related aspects in the firing of LS cells during spatial navigation. Does success or failure to acquire reward in a trial modify spatial coding and cycle skipping of LSD vs. LSI cells in ensuing inbound and outbound trials?

This is an excellent question and given the length of the current manuscript, we think that exploration of this question is best left for a future extension of our study.

A related question: in Figure 10, it is interesting that cycle skipping is prominent in the goal arm for outbound switching trials and inbound trials of both tasks. Could it be analytically explained by task contingencies and behaviour (e.g. correct/incorrect trial, learning dynamics, running speed, or acceleration)?

Our observation of cycle skipping at the single-cell level in the goal arms is somewhat surprising and, we agree with the reviewer, potentially interesting. However, it was not accompanied by alternation of representations at the population level. Given the current focus and length of the manuscript, we think further investigation of cycle skipping in the goal arm is better left for future analyses.

(3) Regarding possible cellular and circuit mechanisms of cycle skipping and their relation to the alternating representations in the LS. Recent history of spiking influences the discharge probability; e.g. complex spike bursts in the hippocampus are associated with a post-burst delay of spiking. In LS, cycle skipping was characteristic for LS cells with high firing rates and was not uniformly present in all trajectories and arms. The authors propose that cycle skipping can be more pronounced in epochs of reduced firing, yet the opposite seems also possible - this phenomenon can be due to an intermittently increased drive onto some LS cells. Was there a systematic relationship between cycle skipping in a given cell and the concurrent firing rate or a recent discharge with short interspike intervals?

In our discussion, we tried to explain the presence of theta cycle skipping in the goal arms at the single-cell level without corresponding alternation dynamics at the population level. We mentioned the possibility of a decrease in excitatory drive. As the reviewer suggests, an increase in excitatory drive combined with post- burst suppression or delay of spiking is an alternative explanation. We analyzed the spatial tuning of cells with theta cycle skipping and found that, on average, these cells have a higher firing rate in the goal arm than the stem of the maze in both outbound and inbound run directions (Figure 5 – figure supplement 1). In contrast, cells that do not display theta cycle skipping do not show increased firing in the goal arm. These results are more consistent with the reviewer’s suggested mechanism and we have updated the discussion accordingly.

(4) Were the differences between the theta modulation (cycle skipping) of local vs. non-local representations (P.14, line 10-12, "In contrast...", Figure 9A) and between alternation vs. switching tasks (Figure 10 C,D) significantly different?

We have added quantification and statistical comparisons for the auto- and cross-correlations of the local/non-local representations. The results indeed show significantly stronger theta cycle skipping of the non-local representations as compared to the local representations (Figure 10 - figure supplement 1A), a stronger alternation of non-local representations in the outbound direction (Figure 10 - figure supplement 1B), and significant differences between the two tasks (Figure 11E,F).

(5) Regarding the possibility of prospective coding in LS, is the accurate coding of run direction not consistent with prospective coding? Can the direction be decoded from the neural activity in the start arm? Are the cycling representations of the upcoming arms near the choice point equally likely or preferential for the then- selected arm?

The coding of run direction (outbound or inbound) is distinct from the prospective/retrospective coding of the goal arm. As implemented, the directional decoding model does not differentiate between the two goal arms and accurate decoding of direction with this model can not inform us whether or not there is prospective (or retrospective) coding. To address the reviewer’s comments, we performed two additional analyses. First, we analyzed the directional (outbound/inbound) decoding performance as a function of location in the maze (Figure 6 - figure supplement 3E). The results show that directional decoding performance is high in both stem and goal arms. Second, we analyzed how well we can predict the trajectory type (i.e., to/from the left or right goal arm) as a function of location in the maze, and separately for outbound and inbound trajectories (Figure 6 - figure supplement 3C,D). The results show that on outbound journeys, decoding the future goal arm is close to chance when the animals are running along the stem. The decoding performance goes up around the choice point and reaches the highest level when animals are in the goal arm.

(6) Figure 10 seems to show the same or similar data as Figures 5 (A,B) and 9 (C,D).

Figure 10 (figure 11 in revised manuscript) re-analyzes the same data as presented in Figures 5 and 9, but separates the experimental sessions according to the behavioral task. We now explicitly state this.

Minor comments

(1) If cycle skipping in the periodicity of non-local representations was more prominent in alternation than in the switching task, one might expect them to be also prominent in early trials of the switching task, when the preference of a more rewarding arm is not yet established. Was this the case?

The reviewer makes an interesting suggestion. Indeed, if theta cycle skipping and the alternation of non-local representations reflect that there are multiple paths that the animal is considering, one may predict that the theta skipping dynamics are similar between the two tasks in early trials (as the reviewer suggests). Similarly, one may predict that in the switching task, the alternation of non-local representations is weaker immediately before a reward contingency switch (when the animal has developed a bias towards the goal arm with a large reward) as compared to after the switch.

We have now quantified the theta cycle dynamics of spatial representations in the early trials in each session of both tasks (Figure 11 - figure supplement 2) and in the trials before and after each switch in the switching task (Figure 11 - figure supplement 3).

The results of the early trial analysis indicate stronger alternation of non-local representations in the alternation task than in the switching task (consistent with the whole session analysis), which is contrary to the prediction.

The pre-/post-switch analysis did not reveal a significant difference between the trials before and after a reward contingency switch. If anything, there was a trend towards stronger theta cycle skipping/alternation in the trials before a switch, which would be opposite to the prediction.

These results do not appear to support the idea that the alternation of non-local representations reflects the number of relevant paths available to the animal. We have updated the text to incorporate these new data and discuss the implications.

(2) Summary: sounds like the encoding of spatial information and its readout in the efferent regions are equally well established.

Thank you for pointing this out.

(3) Summary: "motivation and reward processing centers such as the ventral tegmental area." How about also mentioning here the hypothalamus, which is a more prominent output of the lateral septum than the VTA?

We have now also mentioned the hypothalamus.

(4) "lateral septum may contribute to the hippocampal theta" - readers not familiar with details of the medial vs. lateral septum research may misinterpret the modest role of LS in theta compared to MS.

We have added “in addition to the strong theta drive originating from the medial septum” to make clear that the lateral septum has a modest role in hippocampal theta generation.

(5) "(Tingley and Buzsáki, 2018) found a lack of spatial rate coding in the lateral septum and instead reported a place coding by specific phases of the hippocampal theta rhythm (Rizzi-Wise and Wang, 2021) " needs rephrasing.

Thank you, we have rephrased the sentence.

(6) Figure 4 is a bit hard to generalize. The authors may additionally consider a sorted raster presentation of the dataset in this main figure.

We have removed this figure in the revised manuscript, as it was not necessary to make the point about the location of theta cycle skipping. Instead, we show examples of spatially-resolved cycle skipping in Figure 4 (formerly Figure 5 - supplementary figures 1 and 2), and, following the reviewer’s suggestion, we have added a plot with the spatially-resolved cycle skipping index for all analyzed cells (Figure 5A).

(7) It would help if legends of Figure 5 (and related supplementary figures) state in which of the two tasks the data was acquired, as it is done for Figure 10.

Thank you for the suggestion. The legends of Figure 4A,B (formerly Figure 5 – supplemental figures 1 and 2) and Figure 5 now include in which behavioral task the data was acquired.

(8) Page 10, "Spatial coding...", 1st Citing the initial report by Leugeb and Mizumori would be appropriate here too.

The reviewer is correct. We have added the citation.

(9) The legend in Figure 6 (panels A-G) does not match the figure (only panels A,B). What is shown in Fig. 6B, the legend does not seem to fully match.

Indeed, the legend was outdated. This has now been corrected.

(10) 7 suppl., if extended to enable comparisons, could be a main figure. Presently, Figure 7C does not account for the confounding effect of population size and is therefore difficult to interpret without complex comparisons with the Supplementary Figure which is revealing per se.

We thank the reviewer for their suggestion. We have changed Figure 7 such that it only shows the analysis of decoding performed with all LSD and LSI cells. Figure 7 – supplemental figure 1 has been transformed into main Figure 8, with the addition of a panel to show a statistical comparison between decoding performance in LSD and LSI with a fixed number of cells.

(11) 14, line 10 there is no Figure 8A

This has been corrected.

(12) 15 paragraph 1, is the discussed here model the one from Kay et al?

From Kay et al. (2020) and also Wang et al. (2020). We have added the citations.

(13) Figure 5 - Figure Supplement 1 presents a nice analysis that, in my view, can merit a main figure. I could not find the description of the colour code in CSI panels, does grey/red refer to non/significant points?

Indeed, grey/red refers to non-significant points and significant points respectively. We have clarified the color code in the figure legend. Following the reviewer’s suggestion, we have made Figure 5 Supplement 1 and 2 a main figure (Figure 4).

(14) Figure 5 -Figure Supplement 2. Half of the cells (255 and 549) seems not to be representative of the typically high SCI in the goal arm in left and right inbound trials combined (Figure 5 A). Were the changes in CSI in the right and left inbound trials similar enough to be combined in Fig 5A? Otherwise, considering left and right inbound runs separately and trying to explain where the differences come from would seem to make sense.

Figure 5 – figure supplement 2 is now part of the new main Figure 4. Originally, the examples were from a single session and the same cells as shown in the old Figure 4. However, since the old Figure 4 has been removed, we have selected examples from different sessions and both left/right trajectories that are more representative of the overall distribution. We have further added a plot with the spatially-resolved cycle skipping for all analyzed cells in Figure 5A.

(15) In the second paragraph of the Discussion, dorso-ventral topography of hippocampal projections to the LS (Risold and Swanson, Science, 90s) could be more explicitly stated here.

Thank you for the suggestion. We have now explicitly mentioned the dorsal-ventral topography of hippocampal-lateral septum projections and cite Risold & Swanson (1997).

(16) Discussion point: why do the differences in spatial information of cells in the ventral/intermediate vs. dorsal hippocampus not translate into similarly prominent differences in LSI vs. LSD?

In our data, we do observe clear differences in spatial coding between LSD and LSI. Specifically, cell activity in the LSD is more directional, has higher goal arm selectivity, and higher spatial information (we have now added statistical comparisons to Figure 6 – figure supplement 1). As a result, spatial decoding performance is much better for LSD cell populations than LSI cell populations (see updated Figure 8, with statistical comparison of decoding performance). Spatial coding in the LS is not as strong as in the hippocampus, likely because of the convergence of hippocampal inputs, which may give the impression of a less prominent difference between the two subregions.

(17) Discussion, last paragraph: citation of the few original anatomical and neurophysiological studies would be fitting here, in addition to the recent review article.

Thank you for the suggestion. We have added selected citations of the original literature.

(18) Methods, what was the reference electrode?

We used an external reference electrode that was soldered to a skull screw, which was positioned above the cerebellum. We have added this to the Methods section.

(19) Methods, Theta cycle skipping: bandwidth = gaussian kerner parameter?

The bandwidth is indeed a parameter of the Gaussian smoothing kernel and is equal to the standard deviation.

Reviewer #3 (Recommendations For The Authors)

Below I offer a short list of minor comments and suggestions that may benefit the manuscript.

(A) I was not able to access the Open Science Framework Repository. Can this be rectified?

Thank you for checking the OSF repository. The data and analysis code are now publicly available.

(B) In the discussion the authors should attempt to flesh out whether they can place theta cycle skipping into context with left/right sweeps or scan ahead phenomena, as shown in the Redish lab.

Thank you for the excellent suggestion. We have now added a discussion of the possible link between theta cycle skipping and the previously reported scan-ahead theta sweeps.

(C) What is the mechanism of cycle skipping? This could be relevant to intrinsic vs network oscillator models. Reference should also be made to the Deshmukh model of interference between theta and delta (Deshmukh, Yoganarasimha, Voicu, & Knierim, 2010).

We had discussed a potential mechanism in the discussion (2nd to last paragraph in the revised manuscript), which now includes a citation of a recent computational study (Chu et al., 2023). We have now also added a reference to the interference model in Deshmukh et al, 2010.

(D) Little background was given for the motivation and expectation for potential differences between the comparison of the dorsal and intermediate lateral septum. I don't believe that this is the same as the dorsal/ventral axis of the hippocampus, but if there's a physiological justification, the authors need to make it.

We have added a paragraph to the introduction to explain the anatomical and physiological differences across the lateral septum subregions that provide our rationale for comparing dorsal and intermediate lateral septum (we excluded the ventral lateral septum because the number of cells recorded in this region was too low).

(E) It would help to label "outbound" and "inbound" on several of the figures. All axes need to be labeled, with appropriate units indicated.

We have carefully checked the figures and added inbound/outbound labels and axes labels where appropriate.

(F) In Figure 6, the legend doesn't match the figure.

Indeed, the legend was outdated. This has now been corrected.

(G) The firing rate was non-uniform across the Y-maze. Does this mean that the cells tended to fire more in specific positions of the maze? If so, how would this affect the result? Would increased theta cycle skipping at the choice point translate to a lower firing rate at the choice point? Perhaps less overdispersion of the firing rate (Fenton et al., 2010)?

Individual cells indeed show a non-uniform firing rate across the maze. To address the reviewer’s comment and test if theta cycle skipping cells were active preferentially near the choice point or other locations, we computed the mean-corrected spatial tuning curves for cell-trajectory pairs with and without significant theta cycle skipping. This additional analysis indicates that, on average, the population of theta cycle skipping cells showed a higher firing rate in the goal arms than in the stem of the maze as compared to non-skipping cells for outbound and inbound directions (shown in Figure 5 - figure supplement 1).

(H) As mentioned above, it could be helpful to look at phase preference. Was there an increased phase preference at the choice point? Would half-cycle firing correlate with an increased or decreased phase preference? Based on prior work, one would expect increased phase preference, at least in CA1, at the choice point (Schomburg et al., 2014). In contrast, other work might predict phasic preference according to spatial location (Tingley & Buzsaki, 2018). Including phase analyses is a suggestion, of course. The manuscript is already sufficiently novel and informative. Yet, the authors should state why phase was not analyzed and that these questions remain for follow-up analyses. If the authors did analyze this and found negative results, it should be included in this manuscript.

We thank the reviewer for their suggestion. We have not yet analyzed the theta phase preference of lateral septum cells or other relations to the theta phase. We agree that this would be a valuable extension of our work, but prefer to leave it for future analyses.

(I) One of the most important aspects of the manuscript, is that there is now evidence of theta cycle skipping in the circuit loop between the EC, CA1, and LS. This now creates a foundation for circuit-based studies that could dissect the origin of route planning. Perhaps the authors should state this? In the same line of thinking, how would one determine whether theta cycle skipping is necessary for route planning as opposed to a byproduct of route planning? While this question is extremely complex, other studies have shown that spatial navigation and memory are still possible during the optogenetic manipulation of septal oscillations (Mouchati, Kloc, Holmes, White, & Barry, 2020; Quirk et al., 2021). However, pharmacological perturbation or lesioning of septal activity can have a more profound effect on spatial navigation (Bolding, Ferbinteanu, Fox, & Muller, 2019; Winson, 1978). As a descriptive study, I think it would be helpful to remind the readers of these basic concepts.

We thank the reviewer for their comment and for pointing out possible future directions for linking theta cycle skipping to route planning. Experimental manipulations to directly test this link would be very challenging, but worthwhile to pursue. We now mention how circuit-based studies may help to test if theta cycle skipping in the broader subcortical-cortical network is necessary for route planning. Given that the discussion is already quite long, we decided to omit a more detailed discussion of the possible role of the medial septum (which is the focus of the papers cited by the reviewer).

Very minor points

(A) In the introduction, "one study" begins the sentence but there is a second reference.

Thank you, we have rephrased the sentence.

(B) Also in the introduction, it could be helpful to have an operational definition of theta cycle skipping (i.e., 'enhanced rhythmicity at half theta frequency').

We followed the reviewer’s suggestion.

(C) The others should be more explicit in the introduction about their main question. Theta cycle skipping exists in CA1, and then import some of the explanations mentioned in the discussion to the introduction (i.e., attractors states of multiple routes). The main question is then whether this phenomenon, and others from CA1, translate to the output in LS.

We have edited the introduction to more clearly state the main question of our study, following the suggestion from the reviewer.

(D) There are a few instances of extra closing parentheses.

We checked the text but did not find instances of erroneous extra closing parentheses. There are instances of nested parentheses, which may have given the impression that closing parentheses were duplicated.

(E) The first paragraph of the Discussion lacks sufficient references.

We have now added references to the first paragraph of the discussion.

(F) At the end of the 2nd paragraph in the Discussion, the comparison is missing. More than what? It's not until the next reference that one can assume that the authors are referring to a dorsal/ventral axis. However, the physiological motivation for this comparison is lacking. Why would one expect a dorsal/intermediate continuum for theta modulation as there is along the dorsal/ventral axis of the hippocampus?

Thank you for spotting this omission. We have rewritten the paragraph to more clearly make the parallel between dorsal-ventral gradients in the lateral septum and hippocampus and how this relates to the topographical connections between the two structures.

Author response:

The following is the authors’ response to the original reviews

Reviewer 1 (Public Review):

Summary

The manuscript uses state-of-the-art analysis technology to document the spatio-temporal dynamics of brain activity during the processing of threats. The authors offer convincing evidence that complex spatio-temporal aspects of brain dynamics are essential to describe brain operations during threat processing.

Strengths

Rigorous complex analyses well suited to the data.

Weaknesses

Lack of a simple take-home message about discovery of a new brain operation.

We have addressed the concern under response to item 1 in Recommendations for the authors of Reviewer 2 below.

Reviewer 1 (Recommendations for the authors):

The paper presents sophisticated analyses of how the spatiotemporal activity of the brain processes threats. While the study is elegant and relevant to the threat processing literature, it could be improved by better clarification of novelty, scope, assumptions and implications. Suggestions are reported below.

(1) Introduction: It is difficult to understand what is unsatisfactory in the present literature and why we need this study. For example, lines 57-64 report what works well in the work of Anderson and Fincham but do not really describe what this approach lacks, either in failing to explain real data in conceptual terms.

We have edited the corresponding lines to better describe what such approaches generally lack:

Introduction; Lines 63-66: However, the mapping between brain signals and putative mental states (e.g., “encoding”) remained speculative. More generally, state-based modeling of fMRI data would benefit from evaluation in contexts where the experimental paradigm affords a clearer mapping between discovered states and experimental manipulation.

(2) Also, based on the introduction it is unclear if the focus is on understanding the processing of threat or in the methodological development of experimental design and analysis paradigms for more ecologically valid situations.

In our present work, we tried to focus on understanding dynamics of threat processing while also contributing to methodological development of analysis of dynamic/ecologically inspired experiments. To that end, we have added a new paragraph at the end of Introduction to clarify the principal focus of our work:

Introduction; Lines 111-118: Is the present contribution focused on threat processing or methodological developments for the analysis of more continuous/ecologically valid paradigms? Our answer is “both”. One goal was to contribute to the development of a framework that considers brain processing to be inherently dynamic and multivariate. In particular, our goal was to provide the formal basis for conceptualizing threat processing as a dynamic process (see (Fanselow and Lester, 1987)) subject to endogenous and exogenous contributions. At the same time, our study revealed how regions studied individually in the past (e.g., anterior insula, cingulate cortex) contribute to brain states with multi-region dynamics.

(3) The repeated statement, based on the Fiete paper, that most analyses or models of brain activity do not include an exogenous drive seems an overstatement. There is plenty of literature that not only includes exogenous drives but also studies and documents them in detail. There are many examples, but a prominent one is the study of auditory processing. Essentially all human brain areas related to hearing (not only the activity of individual areas but also their communication) are entrained by the exogenous drive of speech (e.g. J. Gross et al, PLoS Biology 11 e1001752, 2013).

We have altered the original phrasing, which now reads as:

Introduction; Lines 93-95: Importantly, we estimated both endogenous and exogenous components of the dynamics, whereas some past work has not modeled both contributions (see discussion in (Khona and Fiete, 2022)).

Discussion; Lines 454-455: Work on dynamics of neural circuits in systems neuroscience at times assumes that the target circuit is driven only by endogenous processes (Khona and Fiete, 2022).

(4) Attractor dynamics is used as a prominent descriptor of fMRI activity, yet the discussion of how this may emerge from the interaction between areas is limited. Is it related to the way attractors emerge from physical systems or neural networks (e.g. Hopfield?).

This is an important question that we believe will benefit from computational and mathematical modeling, but we consider it beyond the scope of the present paper.

(5) Fig 4 shows activity of 4 regions, not 2 s stated in lines 201-202. Correct?

Fig. 4 shows activity of two regions and also the average activity of regions belonging to two resting-state networks engaged during threat processing (discussed shortly after lines 201-202). To clarify the above concern, we have changed the following line:

Results; Lines 228-230: In Fig. 4, we probed the average signals from two resting-state networks engaged during threat-related processing, the salience network which is particularly engaged during higher threat, and the default network which is engaged during conditions of relative safety.

(6) It would be useful to state more clearly how Fig 7B, C differs from Fig 2A, B (my understanding it is that in the former they are isolating the stimulus-driven processes)

We have clarified this by adding the following line in the Results:

Results; Lines 290-292: Note that in Fig. 7B/C we evaluated exogenous contributions only for stimuli associated with each state/state transition reported in Fig. 2A/B (see also Methods).

Reviewer 2 (Public Review):

Summary

This paper by Misra and Pessoa uses switching linear dynamical systems (SLDS) to investigate the neural network dynamics underlying threat processing at varying levels of proximity. Using an existing dataset from a threat-of-shock paradigm in which threat proximity is manipulated in a continuous fashion, the authors first show that they can identify states that each has their own linear dynamical system and are consistently associated with distinct phases of the threat-of-shock task (e.g., “peri-shock”, “not near”, etc). They then show how activity maps associated with these states are in agreement with existing literature on neural mechanisms of threat processing, and how activity in underlying brain regions alters around state transitions. The central novelty of the paper lies in its analyses of how intrinsic and extrinsic factors contribute to within-state trajectories and betweenstate transitions. A final set of analyses shows how the findings generalize to another (related) threat paradigm.

Strengths

The analyses for this study are conducted at a very high level of mathematical and theoretical sophistication. The paper is very well written and effectively communicates complex concepts from dynamical systems. I am enthusiastic about this paper, but I think the authors have not yet exploited the full potential of their analyses in making this work meaningful toward increasing our neuroscientific understanding of threat processing, as explained below.

Weaknesses

(1) I appreciate the sophistication of the analyses applied and/or developed by the authors. These methods have many potential use cases for investigating the network dynamics underlying various cognitive and affective processes. However, I am somewhat disappointed by the level of inferences made by the authors based on these analyses at the level of systems neuroscience. As an illustration consider the following citations from the abstract: “The results revealed that threat processing benefits from being viewed in terms of dynamic multivariate patterns whose trajectories are a combination of intrinsic and extrinsic factors that jointly determine how the brain temporally evolves during dynamic threat” and “We propose that viewing threat processing through the lens of dynamical systems offers important avenues to uncover properties of the dynamics of threat that are not unveiled with standard experimental designs and analyses”. I can agree to the claim that we may be able to better describe the intrinsic and extrinsic dynamics of threat processing using this method, but what is now the contribution that this makes toward understanding these processes?

We have addressed the concern under response to item 1 in Recommendations for the authors below.

(2) How sure can we be that it is possible to separate extrinsically and intrinsically driven dynamics?

We have addressed the concern under response to item 2 in Recommendations for the authors below.

Reviewer 2 (Recommendations for the authors):

(1) To address the first point under weaknesses above: I would challenge the authors to make their results more biologically/neuroscientifically meaningful, in particular in the sections (in results and/or discussion) on how intrinsic and extrinsic factors contribute to within-state trajectories and between-state transitions, and make those explicit in both the abstract and the discussion (what exactly are the properties of the dynamics of threat that are uncovered?). The authors may also argue that the current approach lies the groundwork for such efforts, but does not currently provide such insights. If they would take this position, that should be made explicit throughout (which would make it more of a methodological paper).

The SLDS approach provides, we believe, a powerful framework to describe system-level dynamics (of threat processing in the the present case). A complementary type of information can be obtained by studying the contribution of individual components (brain regions) within the larger system (brain), an approach that helps connect our approach to studies that typically focus on the contributions of individual regions, and contributes to providing more neurobiological interpretability to the results. Accordingly, we developed a new measure of region importance that captured the extent to which individual brain regions contributed to driving system dynamics during a given state.

Abstract; Lines 22-25: Furthermore, we developed a measure of region importance that quantifies the contributions of an individual brain region to system dynamics, which complements the system-level characterization that is obtained with the state-space SLDS formalism.

Introduction; Lines 95-99: A considerable challenge in state-based modeling, including SLDS, is linking estimated states and dynamics to interpretable processes. Here, we developed a measure of region importance that provides a biologically meaningful way to bridge this gap, as it quantifies how individual brain regions contribute to steering state trajectories.

Results; Lines 302-321: Region importance and steering of dynamics: Based on time series data and input information, the SLDS approach identifies a set of states and their dynamics. While these states are determined in the latent space, they can be readily mapped back to the brain, allowing for the characterization of spatiotemporal properties across the entire brain. Since not all regions contribute equally to state properties, we propose that a region’s impact on state dynamics serves as a measure of its importance.

We illustrate the concept for STATE 5 (“near miss”) in Fig. 8 (see Fig. S17 for all states). Fig. 8A shows importance in the top row and activity below as a function of time from state entry.The dynamics of importance and activity can be further visualized (Fig. 8B), where some regions of particularly high importance are illustrated together with the ventromedial PFC, a region that is typically not engaged during high-threat conditions. Notably, the importance of the dorsal anterior insula increased quickly in the first time points, and later decreased. In contrast, the importance of the periaqueductal gray was relatively high from the beginning of the state and decreased moderately later.

Fig. 8C depicts the correlation between these measures as a function of time. For all but STATE 1, the correlation increased over time. Interestingly, for STATES 4-5, the correlation was low at the first and second time points of the state (and for STATE 2 at the first time point), and for STATE 3 the measures were actually anticorrelated; both cases indicate a dissociation between activity and importance. In summary, our results illustrate that univariate region activity can differ from multivariate importance, providing a fruitful path to understand how individual brain regions contribute to collective dynamic properties.

Discussion; Lines 466-487: In the Introduction, we motivated our study in terms of determining multivariate and distributed patterns of activity with shared dynamics. At one end of the spectrum, it is possible to conceptualize the whole brain as dynamically evolving during a state; at the other end, we could focus on just a few “key” regions, or possibly a single one (at which point the description would be univariate). Here, we addressed this gap by studying the importance of regions to state dynamics: To what extent does a region steer the trajectory of the system? From a mathematical standpoint, our proposed measure is not merely a function of activity of a region but also of the coefficients of the dynamics matrix capturing its effect on across-region dynamics (Eichler, 2005; Smith et al., 2010).

How distributed should the dynamics of threat be considered? One answer to this question is to consider the distribution of importance values for all states. For STATE 1 (“post shock”), a few regions displayed the highest importance values for a few time points. However, for the other states the distribution of importance values tended to be more uniform at each time point. Thus, based on our proposed importance measure, we conclude that threat-related processing is profitably viewed as substantially distributed. Furthermore, we found that while activity and importance were relatively correlated, they could also diverge substantially. Together, we believe that the proposed importance measure provides a valuable tool for understanding the rich dynamics of threat processing. For example, we discovered that the dorsal anterior insula is important not only during high-anxiety states (such as STATE 5; “near miss”) but also, surprisingly, for a state that followed the aversive shock event (STATE 1; “post shock”). Additionally, we noted that posterior cingulate cortex, widely known to play a central role in the default mode network, to have the highest importance among all other regions in driving dynamics of low-anxiety states (such as STATE 3 and STATE 4; “not near”).

Methods; Lines 840-866: Region importance We performed a “lesion study”, where we quantified how brain regions contribute to state dynamics by eliminating (zeroing) model parameters corresponding to a given region, and observing the resulting changes in system dynamics. According to our approach, the most important regions are those that cause the greatest change in system dynamics when eliminated.

The SLDS model represents dynamics in a low dimensional latent space and model parameters are not readily available at the level of individual regions. Thus, the first step was to project the dynamics equation onto the brain data prior to computing importance values. Thus, the linear dynamics equation in the latent space (Eq. 2) was mapped to the original data space of N = 85 ROIs using the emissions model (Eq. 1):

where C^† represents the Moore-Penrose pseudoinverse of C, and and denote the corresponding dynamics matrix, input matrix, and bias terms in the original data space.

Based on the above, we defined the importance of the i^th ROI at time t based on quantifying the impact of “lesioning” the i^th ROI, i.e., by setting the i^th column of , the i^th row of ,   and the i^th element of to 0, denoted , , and respectively. Formally, the importance of the i^th ROI was defined as:

where ‘∗’ indicates element-wise multiplication of a scalar with a vector, is the activity of i^th ROI at time corresponds to the i^th column of is the inner product between i^th row of and input corresponds to the i^th element of and represents an indicator vector corresponding to the i^th ROI. Note that the term is a function of both the i^th ROI’s activity as well as the coefficients of the dynamics matrix capturing the effect of region i on the one-step dynamics of the entire system (Eichler, 2005; Smith et al., 2010); the remaining terms capture the effect of the external inputs and the bias term on the one-step dynamics of the i^th ROI.

After computing for a given run, the resultant importance time series was normalized to zero mean and unit variance.

(2) To address the second point under the weaknesses above: Given that the distinction between intrinsic and extrinsic dynamics appears central to the novelty of the paper, I would suggest the authors explicitly address this issue in the introduction and/or discussion sections.

The distinction between intrinsic and extrinsic dynamics is a modeling assumption of SLDS. We used such an assumption because in experimental designs with experimenter manipulated inputs one can profitably investigate both types of contribution to dynamics. While we should not reify the model’s assumption, we can gain confidence in our separation of extrinsically and intrinsically driven dynamics through controlled experiments where we can manipulate external inputs, or by demonstrating time-scale separation of intrinsic and extrinsic dynamics and that they operate at different frequencies. This is an important question that requires additional computational/mathematical modeling, but we consider it beyond the scope of the current paper. We have added the following lines in the discussion section:

Discussion; Lines 521-528: A further issue that we wish to discuss is related to the distinction between intrinsic and extrinsic dynamics, which is explicitly modeled in our SLDS approach (see Methods, equation 2). We believe this is a powerful approach because in experimental designs with experimenter manipulated inputs, one can profitably investigate both types of contribution to dynamics. However, complete separation between intrinsic and extrinsic dynamics is challenging to ascertain. More generally, one can gain confidence in their separation through controlled experiments where external inputs are manipulated, or by demonstrating timescale separation of intrinsic and extrinsic dynamics.

(3) In the abstract, the statement “.. studies in systems neuroscience that frequently assume that systems are decoupled from external inputs” sounds paradoxical after first introducing how threat processing is almost exclusively studied using blocked and event-related task designs (which obviously rely on external inputs only). Please clarify this.

In this work, we wished to state that the SLDS framework characterizes both endogenous and exogenous contributions to dynamics, whereas some past work has not modeled both contributions. To clarify, we have changed the corresponding line:

Abstract; Lines 19-20: Importantly, we characterized both endogenous and exogenous contributions to dynamics.

(4) In the abstract, the first mention of circles comes out of the blue; the paradigm needs to be introduced first to make this understandable.

We have rephrased the corresponding text:

Abstract; Lines 14-17: First, we demonstrated that the SLDS model learned the regularities of the experimental paradigm, such that states and state transitions estimated from fMRI time series data from 85 regions of interest reflected threat proximity and threat approach vs. retreat.

(5 In Figure 3, the legend shows z-scores representing BOLD changes associated with states. However, the z-scores are extremely low (ranging between -.4 and .4). Can this be correct, given that maps are thresholded at p < ._001 (i.e., _z > 3_._09)? A similar small range of z-scores is shown in the legend of Fig 5. Please check the z-score ranges.

The p-value threshold used in Fig. 3 is based on the voxelwise t-test conducted between the participantbased bootstrapped maps and null maps (see Methods : State spatial maps : “To identify statistically significant voxels, we performed a paired t-test between the participant-based boostrapped maps and the null maps.”). Thus, the p-value threshold in the figure does not correspond to the z-scores of the groupaveraged state-activation maps. Similarly in Fig. 5, we only visualized the state-wise attractors on a brain surface map without any thresholding. The purpose of using a z-score color bar was to provide a scale comparable to that of BOLD activity.

Author response:

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

Thank you very much for the careful and positive reviews of our manuscript. We have addressed each comment in the attached revised manuscript. We describe the modifications below. To avoid confusion, we've changed supplementary figure and table captions to start with "Supplement Figure" and "Supplementary Table," instead of "Figure" and "Table."

We have modified/added:

● Supplementary Table S1: AUC scores for the top 10 frequent epitope types (pathogens) in the testing set of epitope split.

● Supplementary Table S5: AUCs of TCR-epitope binding affinity prediction models with BLOSUM62 to embed epitope sequences.

● Supplementary Table S6: AUCs of TCR-epitope binding affinity prediction models trained on catELMo TCR embeddings and random-initialized epitope embeddings.

● Supplementary Table S7: AUCs of TCR-epitope binding affinity prediction models trained on catELMo and BLOSUM62 embeddings.

● Supplementary Figure 4: TCR clustering performance for the top 34 abundant epitopes representing 70.55% of TCRs in our collected databases.

● Section Discussion.

● Section 4.1 Data: TCR-epitope pairs for binding affinity prediction.

● Section 4.4.2 Epitope-specific TCR clustering.

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript, the authors described a computational method catELMo for embedding TCR CDR3 sequences into numeric vectors using a deep-learning-based approach, ELMo. The authors applied catELMo to two applications: supervised TCR-epitope binding affinity prediction and unsupervised epitope-specific TCR clustering. In both applications, the authors showed that catELMo generated significantly better binding prediction and clustering performance than other established TCR embedding methods. However, there are a few major concerns that need to be addressed.

(1) There are other TCR CDR3 embedding methods in addition to TCRBert. The authors may consider incorporating a few more methods in the evaluation, such as TESSA (PMCID: PMC7799492), DeepTCR (PMCID: PMC7952906) and the embedding method in ATM-TCR (reference 10 in the manuscript). TESSA is also the embedding method in pMTnet, which is another TCR-epitope binding prediction method and is the reference 12 mentioned in this manuscript.

TESSA is designed for characterizing TCR repertoires, so we initially excluded it from the comparison. Our focus was on models developed specifically for amino acid embedding rather than TCR repertoire characterization. However, to address the reviewer's inquiry, we conducted further evaluations. Since both TESSA and DeepTCR used autoencoder-based models to embed TCR sequences, we selected one used in TESSA for evaluation in our downstream prediction task, conducting ten trials in total. It achieved an average AUC of 75.69 in TCR split and 73.3 in epitope split. Notably, catELMo significantly outperformed such performance with an AUC of 96.04 in TCR split and 94.10 in epitope split.

Regarding the embedding method in ATM-TCR, it simply uses BLOSUM as an embedding matrix which we have already compared in Section 2.1. Furthermore, we have provided the comparison results between our prediction model trained on catELMo embeddings with the state-of-the-art prediction models such as netTCR and ATM-TCR in Table 6 of the Discussion section.

(2) The TCR training data for catELMo is obtained from ImmunoSEQ platform, including SARS-CoV2, EBV, CMV, and other disease samples. Meanwhile, antigens related to these diseases and their associated TCRs are extensively annotated in databases VDJdb, IEDB and McPAS-TCR. The authors then utilized the curated TCR-epitope pairs from these databases to conduct the evaluations for eptitope binding prediction and TCR clustering. Therefore, the training data for TCR embedding may already be implicitly tuned for better representations of the TCRs used in the evaluations. This seems to be true based on Table 4, as BERT-Base-TCR outperformed TCRBert. Could catELMo be trained on PIRD as TCRBert to demonstrate catELMo's embedding for TCRs targeting unseen diseases/epitopes?

We would like to note that catELMo was trained exclusively on TCR sequences in an unsupervised manner, which means it has never been exposed to antigen information. We also ensured that the TCRs used in catELMo's training did not overlap with our downstream prediction data. Please refer to the section 4.1 Data where we explicitly stated, “We note that it includes no identical TCR sequences with the TCRs used for training the embedding models.”. Moreover, the performance gap (~1%) between BERT-Base-TCR and TCRBert, as observed in Table 4, is relatively small, especially when compared to the performance difference (>16%) between catELMo and TCRBert.

To further address this concern, we conducted experiments using the same number of TCRs, 4,173,895 in total, sourced exclusively from healthy ImmunoSeq repertoires. This alternative catELMo model demonstrated a similar prediction performance (based on 10 trials) to the one reported in our paper, with an average AUC of 96.35% in TCR split and an average AUC of 94.03% in epitope split.

We opted not to train catELMo on the PIRD dataset for several reasons. First, approximately 7.8% of the sequences in PIRD also appear in our downstream prediction data, which could be a potential source of bias. Furthermore, PIRD encompasses sequences related to diseases such as Tuberculosis, HIV, CMV, among others, which the reviewer is concerned about.

(3) In the application of TCR-epitope binding prediction, the authors mentioned that the model for embedding epitope sequences was catElMo, but how about for other methods, such as TCRBert? Do the other methods also use catELMo-embedded epitope sequences as part of the binding prediction model, or use their own model to embed the epitope sequences? Since the manuscript focuses on TCR embedding, it would be nice for other methods to be evaluated on the same epitope embedding (maybe adjusted to the same embedded vector length).

Furthermore, the authors found that catELMo requires less training data to achieve better performance. So one would think the other methods could not learn a reasonable epitope embedding with limited epitope data, and catELMo's better performance in binding prediction is mainly due to better epitope representation.

Review 1 and 3 have raised similar concerns regarding the epitope embedding approach employed in our binding affinity prediction models. We address both comments together on page 6 where we discuss the epitope embedding strategies in detail.

(4) In the epitope binding prediction evaluation, the authors generated the test data using TCR-epitope pairs from VDJdb, IEDB, McPAS, which may be dominated by epitopes from CMV. Could the authors show accuracy categorized by epitope types, i.e. the accuracy for TCR-CMV pair and accuracy for TCR-SARs-CoV2 separately?

The categorized AUC scores have been added in Supplementary Table 7. We observed significant performance boosts from catELMo compared with other embedding models.

(5) In the unsupervised TCR clustering evaluation, since GIANA and TCRdist direct outputs the clustering result, so they should not be affected by hierarchical clusters. Why did the curves of GIANA and TCRdist change in Figure 4 when relaxing the hierarchical clustering threshold?

For fair comparisons, we performed GIANA and TCRdist with hierarchical clustering instead of the nearest neighbor search. We have clarified it in the revised manuscript as follows.

“Both methods are developed on the BLOSUM62 matrix and apply nearest neighbor search to cluster TCR sequences. GIANA used the CDR3 of TCRβ chain and V gene, while TCRdist predominantly experimented with CDR1, CDR2, and CDR3 from both TCRα and TCRβ chains. For fair comparisons, we perform GIANA and TCRdist only on CDR3 β chains and with hierarchical clustering instead of the nearest neighbor search.”

(6 & 7) In the unsupervised TCR clustering evaluation, the authors examined the TCR related to the top eight epitopes. However, there are much more epitopes curated in VDJdb, IEDB and McPAS-TCR. In real application, the potential epitopes is also more complex than just eight epitopes. Could the authors evaluate the clustering result using all the TCR data from the databases? In addition to NMI, it is important to know how specific each TCR cluster is. Could the authors add the fraction of pure clusters in the results? Pure cluster means all the TCRs in the cluster are binding to the same epitope, and is a metric used in the method GIANA.

We would like to note that there is a significant disparity in TCR binding frequencies across different epitopes in current databases. For instance, the most abundant epitope (KLGGALQAK) has approximately 13k TCRs binding to it, while 836 out of 982 epitopes are associated with fewer than 100 TCRs in our dataset. Furthermore, there are 9347 TCRs having the ability to bind multiple epitopes. In order to robustly evaluate the clustering performance, we originally selected the top eight frequent epitopes from McPAS and removed TCRs binding multiple epitopes to create a more balanced dataset.

We acknowledge that the real-world scenario is more complex than just eight epitopes. Therefore, we conducted clustering experiments using the top most abundant epitopes whose combined cognate TCRs make up at least 70% of TCRs across three databases (34 epitopes). This is illustrated in Supplementary Figure 5. Furthermore, we extended our analysis by clustering all TCRs after filtering out those that bind to multiple epitopes, resulting in 782 unique epitopes. We found that catELMo achieved the 3rd and 2nd best performance in NMI and Purity, respectively (see Table below). These are aligned with our previous observations of the eight epitopes.

Author response table 1.

Reviewer #2 (Public Review):

In the manuscript, the authors highlighted the importance of T-cell receptor (TCR) analysis and the lack of amino acid embedding methods specific to this domain. The authors proposed a novel bi-directional context-aware amino acid embedding method, catELMo, adapted from ELMo (Embeddings from Language Models), specifically designed for TCR analysis. The model is trained on TCR sequences from seven projects in the ImmunoSEQ database, instead of the generic protein sequences. They assessed the effectiveness of the proposed method in both TCR-epitope binding affinity prediction, a supervised task, and the unsupervised TCR clustering task. The results demonstrate significant performance improvements compared to existing embedding models. The authors also aimed to provide and discuss their observations on embedding model design for TCR analysis: 1) Models specifically trained on TCR sequences have better performance than models trained on general protein sequences for the TCR-related tasks; and 2) The proposed ELMo-based method outperforms TCR embedding models with BERT-based architecture. The authors also provided a comprehensive introduction and investigation of existing amino acid embedding methods. Overall, the paper is well-written and well-organized.

The work has originality and has potential prospects for immune response analysis and immunotherapy exploration. TCR-epitope pair binding plays a significant role in T cell regulation. Accurate prediction and analysis of TCR sequences are crucial for comprehending the biological foundations of binding mechanisms and advancing immunotherapy approaches. The proposed embedding method presents an efficient context-aware mathematical representation for TCR sequences, enabling the capture and analysis of their structural and functional characteristics. This method serves as a valuable tool for various downstream analyses and is essential for a wide range of applications. Thank you.

Reviewer #3 (Public Review):

Here, the authors trained catElMo, a new context-aware embedding model for TCRβ CDR3 amino acid sequences for TCR-epitope specificity and clustering tasks. This method benchmarked existing work in protein and TCR language models and investigated the role that model architecture plays in the prediction performance. The major strength of this paper is comprehensively evaluating common model architectures used, which is useful for practitioners in the field. However, some key details were missing to assess whether the benchmarking study is a fair comparison between different architectures. Major comments are as follows:

• It is not clear why epitope sequences were also embedded using catELMo for the binding prediction task. Because catELMO is trained on TCRβ CDR3 sequences, it's not clear what benefit would come from this embedding. Were the other embedding models under comparison also applied to both the TCR and epitope sequences? It may be a fairer comparison if a single method is used to encode epitope sequence for all models under comparison, so that the performance reflects the quality of the TCR embedding only.

In our study, we indeed used the same embedding model for both TCRs and epitopes in each prediction model, ensuring a consistent approach throughout.

Recognizing the importance of evaluating the impact of epitope embeddings, we conducted experiments in which we used BLOSUM62 matrix to embed epitope sequences for all models. The results (Supplementary Table 5) are well aligned with the performance reported in our paper. This suggests that epitope embedding may not play as critical a role as TCR embedding in the prediction tasks. To further validate this point, we conducted two additional experiments.

Firstly, we used catELMo to embed TCRs while employing randomly initialized embedding matrices with trainable parameters for epitope sequences. It yielded similar prediction performance as when catELMo was used for both TCR and epitope embedding (Supplementary Table 6). Secondly, we utilized BLOSUM62 to embed TCRs but employed catELMo for epitope sequence embedding, resulting in performance comparable to using BLOSUM62 for both TCRs and epitopes (Supplementary Table 4). These experiment results confirmed the limited impact of epitope embedding on downstream performance.

We conjecture that these results may be attributed to the significant disparity in data scale between TCRs (~290k) and epitopes (less than 1k). Moreover, TCRs tend to exhibit high similarity, whereas epitopes display greater distinctiveness from one another. These features of TCRs require robust embeddings to facilitate effective separation and improve downstream performance, while epitope embedding primarily serves as a categorical encoding.

We have included a detailed discussion of these findings in the revised manuscript to provide a comprehensive understanding of the role of epitope embeddings in TCR binding prediction.

• The tSNE visualization in Figure 3 is helpful. It makes sense that the last hidden layer features separate well by binding labels for the better performing models. However, it would be useful to know if positive and negative TCRs for each epitope group also separate well in the original TCR embedding space. In other words, how much separation between these groups is due to the neural network vs just the embedding?

It is important to note that we used the same downstream prediction model, a simple three-linear-layer network, for all the discussed embedding methods. We believe that the separation observed in the t-SNE visualization effectively reflects the ability of our embedding model. Also, we would like to mention that it can be hard to see a clear distinction between positive and negative TCRs in the original embedding space because embedding models were not trained on positive/negative labels. Please refer to the t-SNE of the original TCR embeddings below.

Author response image 1.

• To generate negative samples, the author randomly paired TCRs from healthy subjects to different epitopes. This could produce issues with false negatives if the epitopes used are common. Is there an estimate for how frequently there might be false negatives for those commonly occurring epitopes that most populations might also have been exposed to? Could there be a potential batch effect for the negative sampled TCR that confounds with the performance evaluation?

Thank you for bringing this valid and interesting point up. Generating negative samples is non-trivial since only a limited number of non-binding TCR-pairs are publicly available and experimentally validating non-binding pairs is costly [1]. Standard practices for generating negative pairs are (1) paring epitopes with healthy TCRs [2, 3], and (2) randomly shuffling existing TCR-epitope pairs [4,5]. We used both approaches (the former included in the main results, and the latter in the discussion). In both scenarios, catELMo embeddings consistently demonstrated superior performance.

We acknowledge the possibility of false negatives due to the finite-sized TCR database from which we randomly selected TCRs, however, we believe that the likelihood of such occurrences is low. Given the vast diversity of human TCR clonotypes, which can exceed 10^15[6], the chance of randomly selecting a TCR that specifically recognizes a target epitope is relatively small.

In order to investigate the batch effect, we generated new negative pairs using different seeds and observed consistent prediction performance across these variations. However, we agree that there could still be a potential batch effect for the negative samples due to potential data bias.

We have discussed the limitation of generative negative samples in the revised manuscript.

• Most of the models being compared were trained on general proteins rather than TCR sequences. This makes their comparison to catELMO questionable since it's not clear if the improvement is due to the training data or architecture. The authors partially addressed this with BERT-based models in section 2.4. This concern would be more fully addressed if the authors also trained the Doc2vec model (Yang et al, Figure 2) on TCR sequences as baseline models instead of using the original models trained on general protein sequences. This would make clear the strength of context-aware embeddings if the performance is worse than catElmo and BERT.

We agree it is important to distinguish between the effects of training data and architecture on model performance.

In Section 2.4, as the reviewer mentioned, we compared catELMo with BERT-based models trained on the same TCR repertoire data, demonstrating that architecture plays a significant role in improving performance. Furthermore, in Section 2.5, we compared catELMo-shallow with SeqVec, which share the same architecture but were trained on different data, highlighting the importance of data on the model performance.

To further address the reviewer's concern, we trained a Doc2Vec model on the TCR sequences that have been used for catELMo training. We observed significantly lower prediction performance compared to catELMo, with an average AUC of 50.24% in TCR split and an average AUC of 51.02% in epitope split, making the strength of context-aware embeddings clear.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) It is known that TRB CDR3, the CDR1, CDR2 on TRBV gene and the TCR alpha chain also contribute to epitope recognition, but were not modeled in catELMo. It would be nice for the authors to add this as a current limitation for catELMo in the Discussion section.

We have discussed the limitation in the revised manuscript.

“Our study focuses on modeling the TCRβ chain CDR3 region, which is known as the primary determinant of epitope binding. Other regions, such as CDR1 and CDR2 on the TRB V gene, along with the TCRα chain, may also contribute to specificity in antigen recognition. However, a limited number of available samples for those additional features can be a challenge for training embedding models. Future work may explore strategies to incorporate these regions while mitigating the challenges of working with limited samples.”

(2) I tried to follow the instructions to train a binding affinity prediction model for TCR-epitope pairs, however, the cachetools=5.3.0 seems could not be found when running "pip install -r requirements.txt" in the conda environment bap. Is this cachetools version supported after Python 3.7 so the Python 3.6.13 suggested on the GitHub repo might not work?

This has been fixed. We have updated the README.md on our github page.

Reviewer #2 (Recommendations For The Authors):

The article is well-constructed and well-written, and the analysis is comprehensive.

The comments for minor issues that I have are as follows:

(1) In the Methods section, it will be clearer if the authors interpret more on how the standard deviation is calculated in all tables. How to define the '10 trials'? Are they based on different random training and test set splits?

‘10 trials' refers to the process of splitting the dataset into training, validation, and testing sets using different seeds for each trial. Different trials have different training, validation, and testing sets. For each trial, we trained a prediction model on its training set and measured performance on its testing set. The standard deviation was calculated from the 10 measurements, estimating model performance variation across different random splits of the data.

(2) The format of AUCs and the improvement of AUCs need to be consistent, i.e., with the percent sign.

We have updated the format of AUCs.

Reviewer #3 (Recommendations For The Authors):

In addition to the recommendations in the public review, we had the following more minor questions and recommendations:

• Could you provide some more background on the data, such as overlaps between the databases, and how the training and validation split was performed between the three databases? Also summary statistics on the length of TCR and epitope sequence data would be helpful.

We have provided more details about data in our revision.

• Could you comment on the runtime to train and embed using the catELMo and BERT models?

Our training data is TCR sequences with relatively short lengths (averaging less than 20 amino acid residues). Such characteristic significantly reduces the computational resources required compared to training large-scale language models on extensive text corpora. Leveraging standard machines equipped with two GeForce RTX 2080 GPUs, we were able to complete the training tasks within a matter of days. After training, embedding one sequence can be accomplished in a matter of seconds.

• Typos and wording:

• Table 1 first row of "source": "immunoSEQ" instead of "immuneSEQ"

This has been corrected.

• L23 of abstract "negates the need of complex deep neural network architecture" is a little confusing because ELMo itself is a deep neural network architecture. Perhaps be more specific and add that the need is for downstream tasks.

We have made it more specific in our abstract.

“...negates the need for complex deep neural network architecture in downstream tasks.”

References

(1) Montemurro, Alessandro, et al. "NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data." Communications biology 4.1 (2021): 1060.

(2) Jurtz, Vanessa Isabell, et al. "NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks." BioRxiv (2018): 433706.

(3) Gielis, Sofie, et al. "Detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires." Frontiers in immunology 10 (2019): 2820.

(4) Cai, Michael, et al. "ATM-TCR: TCR-epitope binding affinity prediction using a multi-head self-attention model." Frontiers in Immunology 13 (2022): 893247.

(5) Weber, Anna, et al. "TITAN: T-cell receptor specificity prediction with bimodal attention networks." Bioinformatics 37 (2021): i237-i244.

(6) Lythe, Grant, et al. "How many TCR clonotypes does a body maintain?." Journal of theoretical biology 389 (2016): 214-224.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors seek to establish what aspects of nervous system structure and function may explain behavioral differences across individual fruit flies. The behavior in question is a preference for one odor or another in a choice assay. The variables related to neural function are odor responses in olfactory receptor neurons or in the second-order projection neurons, measured via calcium imaging. A different variable related to neural structure is the density of a presynaptic protein BRP. The authors measure these variables in the same fly along with the behavioral bias in the odor assays. Then they look for correlations across flies between the structure-function data and the behavior.

Strengths:

Where behavioral biases originate is a question of fundamental interest in the field. In an earlier paper (Honegger 2019) this group showed that flies do vary with regard to odor preference, and that there exists neural variation in olfactory circuits, but did not connect the two in the same animal. Here they do, which is a categorical advance, and opens the door to establishing a correlation. The authors inspect many such possible correlations. The underlying experiments reflect a great deal of work, and appear to be done carefully. The reporting is clear and transparent: All the data underlying the conclusions are shown, and associated code is available online.

We are glad to hear the reviewer is supportive of the general question and approach.

Weaknesses:

The results are overstated. The correlations reported here are uniformly small, and don't inspire confidence that there is any causal connection. The main problems are

Our revision overhauls the interpretation of the results to prioritize the results we have high confidence in (specifically, PC 2 of our Ca++ data as a predictor of OCT-MCH preference) versus results that are suggestive but not definitive (such as PC 1 of Ca++ data as a predictor of Air-OCT preference).

It’s true that the correlations are small, with R2 values typically in the 0.1-0.2 range. That said, we would call it a victory if we could explain 10 to 20% of the variance of a behavior measure, captured in a 3 minute experiment, with a circuit correlate. This is particularly true because, as the reviewer notes, the behavioral measurement is noisy.

(1) The target effect to be explained is itself very weak. Odor preference of a given fly varies considerably across time. The systematic bias distinguishing one fly from another is small compared to the variability. Because the neural measurements are by necessity separated in time from the behavior, this noise places serious limits on any correlation between the two.

This is broadly correct, though to quibble, it’s our measurement of odor preference which varies considerably over time. We are reasonably confident that more variance in our measurements can be attributed to sampling error than changes to true preference over time. As evidence, the correlation in sequential measures of individual odor preference, with delays of 3 hours or 24 hours, are not obviously different. We are separately working on methodological improvements to get more precise estimates of persistent individual odor preference, using averages of multiple, spaced measurements. This is promising, but beyond the scope of this study.

(2) The correlations reported here are uniformly weak and not robust. In several of the key figures, the elimination of one or two outlier flies completely abolishes the relationship. The confidence bounds on the claimed correlations are very broad. These uncertainties propagate to undermine the eventual claims for a correspondence between neural and behavioral measures.

We are broadly receptive to this criticism. The lack of robustness of some results comes from the fundamental challenge of this work: measuring behavior is noisy at the individual level. Measuring Ca++ is also somewhat noisy. Correlating the two will be underpowered unless the sample size is huge (which is impractical, as each data point requires a dissection and live imaging session) or the effect size is large (which is generally not the case in biology). In the current version we tried in some sense to avoid discussing these challenges head-on, instead trying to focus on what we thought were the conclusions justified by our experiments with sample sizes ranging from 20 to 60. Our revision is more candid about these challenges.

That said, we believe the result we view as the most exciting — that PC2 of Ca++ responses predicts OCT-MCH preference — is robust. 1) It is based on a training set with 47 individuals and a test set composed of 22 individuals. The p-value is sufficiently low in each of these sets (0.0063 and 0.0069, respectively) to pass an overly stringent Bonferroni correction for the 5 tests (each PC) in this analysis. 2) The BRP immunohistochemistry provides independent evidence that is consistent with this result — PC2 that predicts behavior (p = 0.03 from only one test) and has loadings that contrast DC2 and DM2. Taken together, these results are well above the field-standard bar of statistical robustness.

In our revision, we are explicit that this is the (one) result we have high confidence in. We believe this result convincingly links Ca++ and behavior, and warrants spotlighting. We have less confidence in other results, and say so, and we hope this addresses concerns about overstating our results.

(3) Some aspects of the statistical treatment are unusual. Typically a model is proposed for the relationship between neuronal signals and behavior, and the model predictions are correlated with the actual behavioral data. The normal practice is to train the model on part of the data and test it on another part. But here the training set at times includes the testing set, which tends to give high correlations from overfitting. Other times the testing set gives much higher correlations than the training set, and then the results from the testing set are reported. Where the authors explored many possible relationships, it is unclear whether the significance tests account for the many tested hypotheses. The main text quotes the key results without confidence limits.

Our primary analyses are exactly what the reviewer describes, scatter plots and correlations of actual behavioral measures against predicted measures. We produced test data in separate experiments, conducted weeks to months after models were fit on training data. This is more rigorous than splitting into training and test sets data collected in a single session, as batch/environmental effects reduce the independence of data collected within a single session.

We only collected a test set when our training set produced a promising correlation between predicted and actual behavioral measures. We never used data from test sets to train models. In our main figures, we showed scatter plots that combined test and training data, as the training and test partitions had similar correlations.

We are unsure what the reviewer means by instances where we explored many possible relationships. The greatest number of comparisons that could lead to the rejection of a null hypothesis was 5 (corresponding to the top 5 PCs of Ca++ response variation or Brp signal). We were explicit that the p-values reported were nominal. As mentioned above, applying a Bonferroni correction for n=5 comparisons to either the training or test correlations from the Ca++ to OCT-MCH preference model remains significant at alpha=0.05.

Our revision includes confidence intervals around ⍴signal for the PN PC2 OCT-MCH model, and for the ORN Brp-Short PC2 OCT-MCH model (lines 170-172, 238)

Reviewer #2 (Public Review):

Summary:

The authors aimed to identify the neural sources of behavioral variation in a decision between odor and air, or between two odors.

Strengths:

-The question is of fundamental importance.

-The behavioral studies are automated, and high-throughput.

-The data analyses are sophisticated and appropriate.

-The paper is clear and well-written aside from some strong wording.

-The figures beautifully illustrate their results.

-The modeling efforts mechanistically ground observed data correlations.

We are glad to read that the reviewer sees these strengths in the study. We hope the current revision addresses the strong wording.

Weaknesses:

-The correlations between behavioral variations and neural activity/synapse morphology are (i) relatively weak, (ii) framed using the inappropriate words "predict", "link", and "explain", and (iii) sometimes non-intuitive (e.g., PC 1 of neural activity).

Taking each of these points in turn:

i) It would indeed be nicer if our empirical correlations are higher. One quibble: we primarily report relatively weak correlations between measurements of behavior and Ca++/Brp. This could be the case even when the correlation between true behavior and Ca++/Brp is higher. Our analysis of the potential correlation between latent behavioral and Ca++ signals was an attempt to tease these relationships apart. The analysis suggests that there could, in fact, be a high underlying correlation between behavior and these circuit features (though the error bars on these inferences are wide).

ii) We worked to ensure such words are used appropriately. “Predict” can often be appropriate in this context, as a model predicts true data values. Explain can also be appropriate, as X “explaining” a portion of the variance of Y is synonymous with X and Y being correlated. We cannot think of formal uses of “link,” and have revised the manuscript to resolve any inappropriate word choice.

iii) If the underlying biology is rooted in non-intuitive relationships, there’s unfortunately not much we can do about it. We chose to use PCs of our Ca++/Brp data as predictors to deal with the challenge of having many potential predictors (odor-glomerular responses) and relatively few output variables (behavioral bias). Thus, using PCs is a conservative approach to deal with multiple comparisons. Because PCs are just linear transformations of the original data, interpreting them is relatively easy, and in interpreting PC1 and PC2, we were able to identify simple interpretations (total activity and the difference between DC2 and DM2 activation, respectively). All in all, we remain satisfied with this approach as a means to both 1) limit multiple comparisons and 2) interpret simple meanings from predictive PCs.

No attempts were made to perturb the relevant circuits to establish a causal relationship between behavioral variations and functional/morphological variations.

We did conduct such experiments, but we did not report them because they had negative results that we could not definitively interpret. We used constitutive and inducible effectors to alter the physiology of ORNs projecting to DC2 and DM2. We also used UAS-LRP4 and UAS-LRP4-RNAi to attempt to increase and decrease the extent of Brp puncta in ORNs projecting to DC2 and DM2. None of these manipulations had a significant effect on mean odor preference in the OCT-MCH choice, which was the behavioral focus of these experiments. We were unable to determine if the effectors had the intended effects in the targeted Gal4 lines, particularly in the LRP experiments, so we could not rule out that our negative finding reflected a technical failure.

Author response image 1.

We believe that even if these negative results are not technical failures, they are not necessarily inconsistent with the analyses correlating features of DC2 and DM2 to behavior. Specifically, we suspect that there are correlated fluctuations in glomerular Ca++ responses and Brp across individuals, due to fluctuations in the developmental spatial patterning of the antennal lobe. Thus, the DC2-DM2 predictor may represent a slice/subset of predictors distributed across the antennal lobe. This would also explain how we “got lucky” to find two glomeruli as predictors of behavior, when we were only able to image a small portion of the glomeruli.

Reviewer #3 (Public Review):

Churgin et. al. seeks to understand the neural substrates of individual odor preference in the Drosophila antennal lobe, using paired behavioral testing and calcium imaging from ORNs and PNs in the same flies, and testing whether ORN and PN odor responses can predict behavioral preference. The manuscript's main claims are that ORN activity in response to a panel of odors is predictive of the individual's preference for 3-octanol (3-OCT) relative to clean air, and that activity in the projection neurons is predictive of both 3-OCT vs. air preference and 3-OCT vs. 4-methylcyclohexanol (MCH). They find that the difference in density of fluorescently-tagged brp (a presynaptic marker) in two glomeruli (DC2 and DM2) trends towards predicting behavioral preference between 3-oct vs. MCH. Implementing a model of the antennal lobe based on the available connectome data, they find that glomerulus-level variation in response reminiscent of the variation that they observe can be generated by resampling variables associated with the glomeruli, such as ORN identity and glomerular synapse density.

Strengths:

The authors investigate a highly significant and impactful problem of interest to all experimental biologists, nearly all of whom must often conduct their measurements in many different individuals and so have a vested interest in understanding this problem. The manuscript represents a lot of work, with challenging paired behavioral and neural measurements.

Weaknesses:

The overall impression is that the authors are attempting to explain complex, highly variable behavioral output with a comparatively limited set of neural measurements.

We would say that we are attempting to explain a simple, highly variable behavioral measure with a comparatively limited set of neural measurements, i.e. we make no claims to explain the complex behavioral components of odor choice, like locomotion, reversals at the odor boundary, etc.

Given the degree of behavioral variability they observe within an individual (Figure 1- supp 1) which implies temporal/state/measurement variation in behavior, it's unclear that their degree of sampling can resolve true individual variability (what they call "idiosyncrasy") in neural responses, given the additional temporal/state/measurement variation in neural responses.

We are confident that different Ca++ recordings are statistically different. This is borne out in the analysis of repeated Ca++ recordings in this study, which finds that the significant PCs of Ca++ variation contain 77% of the variation in that data. That this variation is persistent over time and across hemispheres was assessed in Honegger & Smith, et al., 2019. We are thus confident that there is true individuality in neural responses (Note, we prefer not to call it “individual variability” as this could refer to variability within individuals, not variability across individuals.) It is a separate question of whether individual differences in neural responses bear some relation to individual differences in behavioral biases. That was the focus of this study, and our finding of a robust correlation between PC 2 of Ca++ responses and OCT-MCH preference indicates a relation. Because behavior and Ca++ were collected with an hours-to-day long gap, this implies that there are latent versions of both behavioral bias and Ca++ response that are stable on timescales at least that long.

The statistical analyses in the manuscript are underdeveloped, and it's unclear the degree to which the correlations reported have explanatory (causative) power in accounting for organismal behavior.

With respect, we do not think our statistical analyses are underdeveloped, though we acknowledge that the detailed reviewer suggestions included the helpful suggestion to include uncertainty in the estimation of confidence intervals around the point estimate of the strength of correlation between latent behavioral and Ca++ response states – we have added these for the PN PC2 linear model (lines 170-172).

It is indeed a separate question whether the correlations we observed represent causal links from Ca++ to behavior (though our yoked experiment suggests there is not a behavior-to-Ca++ causal relationship — at least one where odor experience through behavior is an upstream cause). We attempted to be precise in indicating that our observations are correlations. That is why we used that word in the title, as an example. In the revision, we worked to ensure this is appropriately reflected in all word choice across the paper.

Recommendations for the Authors:

Reviewer #1 (Recommendations for the Authors):

Detailed comments: Many of the problems can be identified starting from Figure 4, which summarizes the main claims. I will focus on that figure and its tributaries.

Acknowledging that the strength of several of our inferences are weak compared to what we consider the main result (the relationship between PC2 of Ca++ and OCT-MCH preference),we have removed Figure 4. This makes the focus of the paper much clearer and appropriately puts focus on the results that have strong statistical support.

(1) The process of "inferring" correlation among the unobserved latent states for neural sensitivity and behavioral bias is unconventional and risky. The larger the assumed noise linking the latent to the observed variables (i.e. the smaller r_b and r_c) the bigger the inferred correlation rho from a given observed correlation R^2_cb. In this situation, the value of the inferred rho becomes highly dependent on what model one assumes that links latent to observed states. But the specific model drawn in Fig 4 suppl 1 is just one of many possible guesses. For example, models with nonlinear interactions could produce different inference.

We agree with the reviewer’s notes of caution. To be clear, we do not intend for this analysis to be the main takeaway of the paper and have revised it to make this clear. The signal we are most confident in is the simple correlation between measured Ca++ PC2 and measured behavior. We have added more careful language saying that the attempt to infer the correlation between latent signals is one attempt at describing the data generation process (lines 166-172), and one possible estimate of an “underlying” correlation.

(2) If one still wanted to go through with this inference process and set confidence bounds on rho, one needs to include all the uncertainties. Here the authors only include uncertainty in the value of R^2_c,b and they peg that at +/-20% (Line 1367). In addition there is plenty of uncertainty associated also with R^2_c,c and R^2_b,b. This will propagate into a wider confidence interval on rho.

We have replaced the arbitrary +/- 20% window with bootstrapping the pairs of (predicted preference by PN PC2, measured preference) points and getting a bootstrap distribution of R2c,b, which is, not surprisingly, considerably wider. Still, we think there is some value in this analysis as the 90% CI of 𝜌signal under this model is 0.24-0.95. That is, including uncertainty about the R2b,b and R2c,c in the model still implies a significant relationship between latent calcium and behavior signals.

(2.1) The uncertainty in R^2_cb is much greater than +/-20%. Take for example the highest correlation quoted in Fig 4: R^2=0.23 in the top row of panel A. This relationship refers to Fig 1L. Based on bootstrapping from this data set, I find a 90% confidence interval of CI=[0.002, 0.527]. That's an uncertainty of -100/+140%, not +/-20%. Moreover, this correlation is due entirely to the lone outlier on the bottom left. Removing that single fly abolishes any correlation in the data (R^2=0.04, p>0.3). With that the correlation of rho=0.64, the second-largest effect in Fig 4, disappears.

We acknowledge that removal of the outlier in Fig 1L abolishes the correlation between predicted and measured OCT-AIR preference. We have thus moved that subfigure to the supplement (now Figure 1 – figure supplement 10B), note that we do not have robust statistical support of ORN PC1 predicting OCT-AIR preference in the results (lines 177-178), and place our emphasis on PN PC2’s capacity to predict OCT-MCH preference throughout the text.

(2.2) Similarly with the bottom line of Fig 4A, which relies on Fig 1M. With the data as plotted, the confidence interval on R^2 is CI=[0.007, 0.201], again an uncertainty of -100/+140%. There are two clear outlier points, and if one removes those, the correlation disappears entirely (R^2=0.06, p=0.09).

We acknowledge that removal of the two outliers in Fig 1M between predicted and measured OCT-AIR preference abolishes the correlation. We have also moved that subfigure to the supplement (now Figure 1 – figure supplement 10F) and do not claim to have robust statistical support of PN PC1 predicting OCT-AIR preference.

(2.3) Similarly, the correlation R^2_bb of behavior with itself is weak and comes with great uncertainty (Fig 1 Suppl 1, panels B-E). For example, panel D figures prominently in computing the large inferred correlation of 0.75 between PN responses and OCT-MCH choice (Line 171ff). That correlation is weak and has a very wide confidence interval CI=[0.018, 0.329]. This uncertainty about R^2_bb should be taken into account when computing the likelihood of rho.

We now include bootstrapping of the 3 hour OCT-MCH persistence data in our inference of 𝜌signal.

(2.4) The correlation R^2_cc for the empirical repeatability of Ca signals seems to be obtained by a different method. Fig 4 suppl 1 focuses on the repeatability of calcium recording at two different time points. But Line 625ff suggests the correlation R^2_cc=0.77 all derives from one time point. It is unclear how these are related.

Because our calcium model predictors utilize principal components of the glomerulus-odor responses (the mean Δf/f in the odor presentation window), we compute R2c,c through adding variance explained along the PCs, up to the point in which the component-wise variance explained does not exceed that of shuffled data (lines 609-620 in Materials and Methods). In this revision we now bootstrap the calcium data on the level of individual flies to get a bootstrap distribution of R2c,c, and propagate the uncertainty forward in the inference of 𝜌signal.

(2.5) To summarize, two of the key relationships in Fig 1 are due entirely to one or two outlier points. These should not even be used for further analysis, yet they underlie two of the claims in Fig 4. The other correlations are weak, and come with great uncertainty, as confirmed by resampling. Those uncertainties should be propagated through the inference procedure described in Fig 4. It seems possible that the result will be entirely uninformative, leaving rho with a confidence interval that spans the entire available range [0,1]. Until that analysis is done, the claims of neuron-to-behavior correlation in this manuscript are not convincing.

It is important to note that we never thought our analysis of the relationship between latent behavior and calcium signals should be interpreted as the main finding. Instead, the observed correlation between measured behavior and calcium is the take-away result. Importantly, it is also conservative compared to the inferred latent relationship, which in our minds was always a “bonus” analysis. Our revisions are now focused on highlighting the correlations between measured signals that have strong statistical support.

As a response to these specific concerns, we have propagated uncertainty in all R2’s (calcium-calcium, behavior-behavior, calcium-behavior) in our new inference for 𝜌signal, yielding a new median estimate for PN PC 2 underlying OCT-MCH preference of 0.68, with a 90% CI of 0.24-0.95. (Lines 171-172 in results, Inference of correlation between latent calcium and behavior states section in Materials and Methods).

(3) Other statistical methods:

(3.1) The caption of Fig 4 refers to "model applied to train+test data". Does that mean the training data were included in the correlation measurement? Depending on the number of degrees of freedom in the model, this could have led to overfitting.

We have removed Figure 4 and emphasize the key results in Figure 1 and 2 that we see statistically robust signal of PN PC 2 explaining OCT-MCH preference variation in both a training set and a testing set of flies (Fig 2 – figure supplement 1C-D).

(3.2) Line 180 describes a model that performed twice as well on test data (31% EV) as it did on training data (15%). What would explain such an outcome? And how does that affect one's confidence in the 31% number?

The test set recordings were conducted several weeks after the training set recordings, which were used to establish PN PC 2 as a correlate of OCT-MCH preference. The fact that the test data had a higher R2 likely reflects sampling error (these two correlation coefficients are not significantly different). Ultimately this gives us more confidence in our model, as the predictive capacity is maintained in a totally separate set of flies.

(3.340 Multiple models get compared in performance before settling on one. For example, sometimes the first PC is used, sometimes the second. Different weighting schemes appear in Fig 2. Do the quoted p-values for the correlation plots reflect a correction for multiple hypothesis testing?

For all calcium-behavior models, we restricted our analysis to 5 PCs, as the proportion of calcium variance explained by each of these PCs was higher than that explained by the respective PC of shuffled data — i.e., there were at most five significant PCs in that data. We thus performed at most 5 hypothesis tests for a given model. PN PC 2 explained 15% of OCT-MCH preference variation, with a p-value of 0.0063 – this p-value is robust to a conservative Bonferroni correction to the 5 hypotheses considered at alpha=0.05.

The weight schemes in Figure 2 and Figure 1 – figure supplement 10 reflect our interpretations of the salient features of the PCs and are follow-up analysis of the single principal component hypothesis tests. Thus they do not constitute additional tests that should be corrected. We now state in the methods explicitly that all reported p-values are nominal (line 563).

(3.4) Line 165 ff: Quoting rho without giving the confidence interval is misleading. For example, the rho for the presynaptic density model is quoted as 0.51, which would be a sizeable correlation. But in fact, the posterior on rho is almost flat, see caption of Fig 4 suppl 1, which lists the CI as [0.11, 0.85]. That means the experiments place virtually no constraint on rho. If the authors had taken no data at all, the posterior on rho would be uniform, and give a median of 0.5.

We now provide a confidence interval around 𝜌signal for the PN PC 2 model (lines 170-172). But per above, and consistent with the new focus of this revision, we view the 𝜌signal inference as secondary to the simple, significant correlation between PN PC 2 and OCT-MCH preference.

(4) As it stands now, this paper illustrates how difficult it is to come to a strong conclusion in this domain. This may be worth some discussion. This group is probably in a better position than any to identify what are the limiting factors for this kind of research.

We thank the reviewer for this suggestion and have added discussion of the difficulties in detecting signals for this kind of problem. That said, we are confident in stating that there is a meaningful correlation between PC 2 of PN Ca++ responses and OCT-MCH behavior given our model’s performance in predicting preference in a test set of flies, and in the consistent signal in ORN Bruchpilot.

Reviewer #3 (Recommendations for the Authors):

Two major concerns, one experimental/technical and one conceptual:

(1) I appreciate the difficulty of the experimental design and problem. However, the correlations reported throughout are based on neural measurements in only 5 glomeruli (~10% of the olfactory system) at early stages of olfactory processing.

We acknowledge that only imaging 5 glomeruli is regrettable. We worked hard to develop image analysis pipelines that could reliably segment as many glomeruli as possible from almost all individual flies. In the end, we concluded that it was better to focus our analysis on a (small) core set of glomeruli for which we had high confidence in the segmentation. Increasing the number of analyzed glomeruli is high on the list of improvements for subsequent studies. Happily, we are confident that we are capturing a significant, biologically meaningful correlation between PC 2 of PN calcium (dominated by the responses in DC2 and DM2) and OCT-MCH preference.

3-OCT and MCH activate many glomeruli in addition to the five studied, especially at the concentrations used. There is also limited odor-specificity in their response matrix: notably responses are more correlated in all glomeruli within an individual, compared to responses across individuals (they note this in lines 194-198, though I don't quite understand the specific point they make here). This is a sign of high experimental variability (typically the dynamic range of odor response within an individual is similar to the range across individuals) and makes it even more difficult to resolve underlying individual variation.

We respectfully disagree with the reviewer’s interpretation here. There is substantial odor-specificity in our response matrix. This is evident in both the ORN and PN response matrices (and especially the PN matrix) as variation in the brightness across rows. Columns, which correspond to individuals, are more similar than rows, which correspond to odor-glomerulus pairs. The dynamic range within an individual (within a column, across rows) is indeed greater than the variation among individuals (within a row, across columns).

As an (important) aside, the odor stimuli are very unusual in this study. Odors are delivered at extremely high concentrations (variably 10-25% sv, line 464, not exactly sure what "variably' means- is the stimulus intensity not constant?) as compared to even the highest concentrations used in >95% of other studies (usually <~0.1% sv delivered).

We used these concentrations for a variety of reasons. First, following the protocol of Honegger and Smith (2020), we found that dilutions in this range produce a linear input-output relationship, i.e. doubling or halving one odorant yields proportionate changes in odor-choice behavior metrics. Second, such fold dilutions are standard for tunnel assays of the kind we used. Claridge-Chang et al. (2009) used 14% and 11% for MCH and OCT respectively, for instance. Finally, the specific dilution factor (i.e., within the range of 10-25%) was adjusted on a week-by-week basis to ensure that in an OCT-MCH choice, the mean preference was approximately 50%. This yields the greatest signal of individual odor preference. We have added this last point to the methods section where the range of dilutions is described (lines 442-445).

A parsimonious interpretation of their results is that the strongest correlation they see (ORN PC1 predicts OCT v. air preference) arises because intensity/strength of ORN responses across all odors (e.g. overall excitability of ORNs) partially predicts behavioral avoidance of 3-OCT. However, the degree to which variation in odor-specific glomerular activation patterns can explain behavioral preference (3-OCT v. MCH) seems much less clear, and correspondingly the correlations are weaker and p-values larger for the 3-OCT v. MCH result.

With respect, we disagree with this analysis. The correlation between ORN PC 1 and OCT v. air preference (R2 \= 0.23) is quite similar to that of PN PC 2 and OCT vs MCH preference (R2 \= 0.20). However, the former is dependent on a single outlying point, whereas the latter is not. The latter relationship is also backed up by the BRP imaging and modeling. Therefore in the revision we have de-emphasized the OCT v. air preference model and emphasized the OCT v. MCH preference models.

(2) There is a broader conceptual concern about the degree of logical consistency in the authors' interpretation of how neural variability maps to behavioral variability. For instance, the two odors they focus on, 3-OCT and MCH, barely activate ORNs in 4 of the 5 glomeruli they study. Most of the correlation of ORN PC1 vs. behavioral choice for 3-OCT vs. air, then, must be driven by overall glomerular activation by other odors (but remains predictive since responses across odors appear correlated within an individual). This gives pause to the interpretation that 3-OCT-evoked ORN activity in these five glomeruli is the neural substrate for variability in the behavioral response to 3-OCT.

Our interpretation of the ORN PC1 linear model is not that 3-OCT-evoked ORN activity is the neural substrate for variability – instead, it is the general responsiveness of an individual’s AL across multiple odors (this is our interpretation of the the uniformly positive loadings in ORN PC1). It is true that OCT and MCH do not activate ORNs as strongly as other odorants – our analysis rests on the loadings of the PCs that capture all odor/glomerulus combinations available in our data. All that said, since a single outlier in Figure 1L dominates the relationship, therefore we have de-emphasized these particular results in our revision.

This leads to the most significant concern, which is that the paper does not provide strong evidence that odor-specific patterns of glomerular activation in ORNs and PNs underlie individual behavioral preference between different odors (that each drive significant levels of activity, e.g. 3-OCT v. MCH), or that the ORN-PN synapse is a major driver of individual behavioral variability. Lines 26-31 of the abstract are not well supported, and the language should be softened.

We have modified the abstract to emphasize our confidence in PN calcium correlating with odor-vs-odor preference (removing the ORN & odor-vs-air language).

Their conclusions come primarily from having correlated many parameters reduced from the ORN and PN response matrices against the behavioral data. Several claims are made that a given PC is predictive of an odor preference while others are not, however it does not appear that the statistical tests to support this are shown in the figures or text.

For each linear model of calcium dynamics predicting preference, we restricted our analysis to the first 5 principal components. Thus, we do not feel that we correlated many parameters against the behavioral data. As mentioned below, the correlations identified by this approach comfortably survive a conservative Bonferroni correction. In this revision, a linear model with a single predictor – the projection onto PC 2 of PN calcium – is the result we emphasize in the text, and we report R2 between measured and predicted preference for both a training set of flies and for a test set of flies (Figure 1M and Figure 2 – figure supplement 1).

That is, it appears that the correlation of models based on each component is calculated, then the component with the highest correlation is selected, and a correlation and p-value computed based on that component alone, without a statistical comparison between the predictive values of each component, or to account for effectively performing multiple comparisons. (Figure 1, k l m n o p, Figure 3, d f, and associated analyses).

To reiterate, this was our process: 1) Collect a training data set of paired Ca++ recordings and behavioral preference scores. 2) Compute the first five PCs of the Ca++ data, and measure the correlation of each to behavior. 3) Identify the PC with the best correlation. 4) Collect a test data set with new experimental recordings. 5) Apply the model identified in step 3. For some downstream analyses, we combined test and training data, but only after confirming the separate significance of the training and test correlations.

The p-values associated with the PN PC 2 model predicting OCT-MCH preference are sufficiently low in each of the training and testing sets (0.0063 and 0.0069, respectively) to pass a conservative Bonferroni multiple hypothesis correction (one hypothesis for each of the 5 PCs) at an alpha of 0.05.

Additionally, the statistical model presented in Figure 4 needs significantly more explanation or should be removed- it's unclear how they "infer" the correlation, and the conclusions appears inconsistent with Figure 3 - Figure Supplement 2.

We have removed Figure 4 and have improved upon our approach of inferring the strength of the correlation between latent calcium and behavior in the Methods, incorporating bootstrapping of all sources of data used for the inference (lines 622-628). At the same time, we now emphasize that this analysis is a bonus of sorts, and that the simple correlation between Ca++ and behavior is the main result.

Suggestions:

(1) If the authors want to make the claim that individual variation in ORN or PN odor representations (e.g. glomerular activation patterns) underlie differences in odor preference (MCH v. OCT), they should generalize the weak correlation between ORN/PN activity and behavior to additional glomeruli and pair of odors, where both odors drive significant activity. Otherwise, the claims in the abstract should be tempered.

We have modified the abstract to focus on the effect we have the highest confidence in: contrasting PN calcium activation of DM2 and DC2 predicting OCT-MCH preference.

(2) One of the most valuable contributions a study like this could provide is to carefully quantify the amount of measurement variation (across trials, across hemispheres) in neural responses relative to the amount of individual variation (across individuals). Beyond the degree of variation in the amplitude of odor responses, the rank ordering of odor response strength between repeated measurements (to try to establish conditions that account for adaptation, etc.), between hemispheres, and between individuals is important. Establishing this information is foundational to this entire field of study. The authors take a good first step towards this in Figure 1J and Figure 1, supplement 5C, but the plots do not directly show variance, and the comparison is flawed because more comparisons go into the individual-individual crunch (as evidenced by the consistently smaller range of quartiles). The proper way to do this is by resampling.

We do not know what the reviewer means by “individual-individual crunch,” unfortunately. Thus, it is difficult to determine why they think the analysis is flawed. We are also uncertain about the role of resampling in this analysis. The medians, interquartile ranges and whiskers in the panels referenced by the reviewer are not confidence intervals as might be determined by bootstrap resampling. Rather, these are direct statistics on the coding distances as measured – the raw values associated with these plots are visualized in Figure 1H.

In our revision we updated the heatmaps in Figure 1 – figure supplement 3 to include recordings across the lobes and trials of each individual fly, and we have added a new supplementary figure, Figure 1 – figure supplement 4, to show the correspondence between recordings across lobes or trials, with associated rank-order correlation coefficients. Since the focus of this study was whether measured individual differences predict individual behavioral preference, a full characterization of the statistics of variation in calcium responses was not the focus, though it was the focus of a previous study (Honegger & Smith et al., 2019).

To help the reader understand the data, we would encourage displaying data prior to dimensionality reduction - why not show direct plots of the mean and variance of the neural responses in each glomerulus across repeats, hemispheres, individuals?

We added a new supplementary figure, Figure 1 – figure supplement 4, to show the correspondence between recordings across lobes or trials.

A careful analysis of this point would allow the authors to support their currently unfounded assertion that odor responses become more "idiosyncratic" farther from the periphery (line 135-36); presumably they mean beyond just noise introduced by synaptic transmission, e.g. "idiosyncrasy" is reproducible within an individual. This is a strong statement that is not well-supported at present - it requires showing the degree of similarity in the representation between hemispheres is more similar within a fly than between flies in PNs compared to ORNs (see Hige... Turner, 2015).

Here are the lines in question: “PN responses were more variable within flies, as measured across the left and right hemisphere ALs, compared to ORN responses (Figure 1 – figure supplement 5C), consistent with the hypothesis that odor representations become more idiosyncratic farther from the sensory periphery.”

That responses are more idiosyncratic farther from the periphery is therefore not an “unfounded assertion.” It is clearly laid out as a hypothesis for which we can assess consistency in the data. We stand by our original interpretation: that several observations are consistent with this finding, including greater distance in coding space in PNs compared to ORNs, particularly across lobes and across flies. In addition, higher accuracy in decoding individual identity from PN responses compared to ORN responses (now appearing as Figure 1 – figure supplement 6A) is also consistent with this hypothesis.

Still, to make confusion at this sentence less likely, we have reworded it as “suggesting that odor representations become more divergent farther from the sensory periphery.” (lines 139-140)

(3) Figure 3 is difficult to interpret. Again, the variability of the measurement itself within and across individuals is not established up front. Expression of exogenous tagged brp in ORNs is also not guaranteed to reflect endogenous brp levels, so there is an additional assumption at that level.

Figure 3 – figure supplement 1 Panels A-C display the variability of measurements (Brp volume, total fluorescence and fluorescence density) both within (left/right lobes) and across individuals (the different data points). We agree that exogenous tagged Brp levels will not be identical to endogenous levels. The relationship appears significant despite this caveat.

Again there are statistical concerns with the correlations. For instance, the claim that "Higher Brp in DM2 predicted stronger MCH preference... " on line 389 is not statistically supported with p<0.05 in the ms (see Figure 3 G as the closest test, but even that is a test of the difference of DM2 and DC2, not DM2 alone).

We have changed the language to focus on the pattern of the loadings in PC 2 of Brp-Short density and replaced “predict.” (lines 366-369).

Can the authors also discuss what additional information is gained from the expansion microscopy in the figure supplement, and how it compares to brp density in DC2 using conventional methods?

The expansion microscopy analysis was an attempt to determine what specific aspect of Brp expression was predictive of behavior, on the level of individual Brp puncta, as a finer look compared to the glomerulus-wide fluorescence signal in the conventional microscopy approach. Since this method did not yield a large sample size, at best we can say it provided evidence consistent with the observation from confocal imaging that Brp fluorescent density was the best measure in terms of predicting behavior.

I would prefer to see the calcium and behavioral datasets strengthened to better establish the relationship between ORN/PN responses and behavior, and to set aside the anatomical dataset for a future work that investigates mechanisms.

We are satisfied that our revisions put appropriate emphasis on a robust result relating calcium and behavior measurements: the relationship between OCT-MCH preference and idiosyncratic PN calcium responses. Finding that idiosyncratic Brp density has similar PC 2 loadings that also significantly predict behavior is an important finding that increases confidence in the calcium-behavior finding. We agree with the reviewer that these anatomical findings are secondary to the calcium-behavior analyses, but think they warrant a place in the main findings of the study. As the reviewer suggests, we are conducting follow-on studies that focus on the relationship between neuroanatomical measures and odor preference.

(4) The mean imputation of missing data may have an effect on the conclusions that it is possible to draw from this dataset. In particular, as shown in Figure 1, supplemental figure 3, there is a relatively large amount of missing data, which is unevenly distributed across glomeruli and between the cell types recorded from. Strikingly, DC2 is missing in a large fraction of ORN recordings, while it is present in nearly all the PN recordings. Because DC2 is one of the glomeruli implicated in predicting MCH-OCT preference, this lack of data may be particularly likely to effect the evaluation of whether this preference can be predicted from the ORN data. Overall, mean imputation of glomerulus activity prior to PCA will artificially reduce the amount of variance contributed by the glomerulus. It would be useful to see an evaluation of which results of this paper are robust to different treatments of this missing data.

We confirmed that the linear model of predicted OCT-MCH using PN PC2 calcium was minimally altered when we performed imputation via alternating least squares using the pca function with option ‘als’ to infill missing values on the calcium matrix 1000 times and taking the mean infilled matrix (see MATLAB documentation and Figure 1 – figure supplement 5 of Werkhoven et al., 2021). Fitted slope value for model using mean-infilled data presented in article: -0.0806 (SE = 0.028, model R2 \= 0.15), fitted slope value using ALS-imputed model: -0.0806 (SE 0.026, model R2 \= 0.17).

Additional comments:

(1) On line 255 there is an unnecessary condition: "non-negative positive".

Thank you – non-negative has been removed.

(2) In Figure 4 and the associated analysis, selection of +/- 20% interval around the observed $R^2$ appears arbitrary. This could be based on the actual confidence interval, or established by bootstrapping.

We have replaced the +/- 20% rule by bootstrapping the calculation of behavior-behavior R2, calcium-calcium R2, and calcium-behavior R2 and propagating the uncertainties forward (Inference of correlation between latent calcium and behavior states section in Materials and Methods).

(3) On line 409 the claim is made "These sources of variation specifically implicate the ORN-PN synapse..." While the model recapitulates the glomerulus specific variation of activity under PN synapse density variation, it also occurs under ORN identity variation, which calls into question whether the synapse distribution itself is specifically implicated, or if any variation that is expected to be glomerulus specific would be equally implicated.

We agree with this observation. We found that varying either the ORNs or the PNs that project to each glomeruli can produce patterns of PN response variation similar to what is measured experimentally. This is consistent with the idea that the ORN-PN synapse is a key site of behaviorally-relevant variation.

(4) Line 214 "... we conclude that the relative responses of DM2 vs DC2 in PNs largely explains an individual's preference." is too strong of a claim, based on the fact that using the PC2 explains much more of the variance, while using the stated hypothesis noticeable decreases the predictive power ($R^2$ = 0.2 vs $R^2$ = 0.12 )

We have changed the wording here to “we conclude that the relative responses of DM2 vs DC2 in PNs compactly predict an individual’s preference.” (lines 192-193)

Author response:

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

eLife Assessment

This study attempts to resolve an apparent paradox of rapid evolutionary rates of multi-copy gene systems by using a theoretical model that integrates two classic population models. While the conceptual framework is intuitive and thus useful, the specific model is perplexing and difficult to penetrate for non-specialists. The data analysis of rRNA genes provides inadequate support for the conclusions due to a lack of consideration of technical challenges, mutation rate variation, and the relationship between molecular processes and model parameters.

Overall Responses:

Since the eLife assessment succinctly captures the key points of the reviews, the reply here can be seen as the overall responses to the summed criticisms. We believe that the overview should be sufficient to address the main concerns, but further details can be found in the point-by-point responses below. The overview covers the same grounds as the provisional responses (see the end of this rebuttal) but is organized more systematically in response to the reviews. The criticisms together fall into four broad areas. 

First, the lack of engagement with the literature, particularly concerning Cannings models and non-diffusive limits. This is the main rebuttal of the companion paper (eLife-RP-RA-2024-99990). The literature in question is all in the WF framework and with modifications, in particular, with the introduction of V(K). Nevertheless, all WF models are based on population sampling. The Haldane model is an entirely different model of genetic drift, based on gene transmission. Most importantly, the WF models and the Haldane model differ in the ability to handle the four paradoxes presented in the two papers. These paradoxes are all incompatible with the WF models.

Second, the poor presentation of the model that makes the analyses and results difficult to interpret. In retrospect, we fully agree and thank all the reviewers for pointing them out. Indeed, we have unnecessarily complicated the model. Even the key concept that defines the paradox, which is the effective copy number of rRNA genes, is difficult to comprehend. We have streamlined the presentation now. Briefly, the complexity arose from the general formulation permitting V(K) ≠ E(K) even for single copy genes. (It would serve the same purpose if we simply let V(K) = E(K) for single copy genes.) The sentences below, copied from the new abstract, should clarify the issue. The full text in the Results section has all the details.

“On average, rDNAs have C ~ 150 - 300 copies per haploid in humans. While a neutral mutation of a single-copy gene would take 4N generations (N being the population size of an ideal population) to become fixed, the time should be 4NC* generations for rRNA genes (C* being the effective copy number). Note that C* >> 1, but C* < (or >) C would depend on the drift strength. Surprisingly, the observed fixation time in mouse and human is < 4N, implying the paradox of C* < 1.”

Third, the confusion about which rRNA gene is being compared with which homology, as there are hundreds of them. We should note that the effective copy number C* indicates that the rRNA gene arrays do not correspond with the “gene locus” concept. This is at the heart of the confusion we failed to remove clearly. We now use the term “pseudo-population” to clarify the nature of rDNA variation and evolution. The relevant passage is reproduced from the main text shown below.

“The pseudo-population of ribosomal DNA copies within each individual

While a human haploid with 200 rRNA genes may appear to have 200 loci, the concept of "gene loci" cannot be applied to the rRNA gene clusters. This is because DNA sequences can spread from one copy to others on the same chromosome via replication slippage. They can also spread among copies on different chromosomes via gene conversion and unequal crossovers (Nagylaki 1983; Ohta and Dover 1983; Stults, et al. 2008; Smirnov, et al. 2021). Replication slippage and unequal crossovers would also alter the copy number of rRNA genes. These mechanisms will be referred to collectively as the homogenization process. Copies of the cluster on the same chromosome are known to be nearly identical in sequences (Hori, et al. 2021; Nurk, et al. 2022). Previous research has also provided extensive evidence for genetic exchanges between chromosomes (Krystal, et al. 1981; Arnheim, et al. 1982; van Sluis, et al. 2019).

In short, rRNA gene copies in an individual can be treated as a pseudo-population of gene copies. Such a pseudo-population is not Mendelian but its genetic drift can be analyzed using the branching process (see below). The pseudo-population corresponds to the "chromosome community" proposed recently (Guarracino, et al. 2023). As seen in Fig. 1C, the five short arms harbor a shared pool of rRNA genes that can be exchanged among them. Fig. 1D presents the possible molecular mechanisms of genetic drift within individuals whereby mutations may spread, segregate or disappear among copies. Hence, rRNA gene diversity or polymorphism refers to the variation across all rRNA copies, as these genes exist as paralogs rather than orthologs. This diversity can be assessed at both individual and population levels according to the multi-copy nature of rRNA genes.”

Fourth, the lack of consideration of many technical challenges. We have responded to the criticisms point-by-point below. One of the main criticisms is about mutation rate differences between single-copy and rRNA genes. We did in fact alluded to the parity in mutation rate between them in the original text but should have presented this property more prominently as is done now. Below is copied from the revised text:

“We now consider the evolution of rRNA genes between species by analyzing the rate of fixation (or near fixation) of mutations. Polymorphic variants are filtered out in the calculation. Note that Eq. (3) shows that the mutation rate, m, determines the long-term evolutionary rate, l. Since we will compare the l values between rRNA and single-copy genes, we have to compare their mutation rates first by analyzing their long-term evolution. As shown in Table S1, l falls in the range of 50-60 (differences per Kb) for single copy genes and 40 – 70 for the non-functional parts of rRNA genes. The data thus suggest that rRNA and single-copy genes are comparable in mutation rate. Differences between their l values will have to be explained by other means.”

While the overview should address the key issues, we now present the point-by-point response below. 

Public Reviews:

Reviewer #1 (Public Review):

The manuscript by Wang et al is, like its companion paper, very unusual in the opinion of this reviewer. It builds off of the companion theory paper's exploration of the "Wright-Fisher Haldane" model but applies it to the specific problem of diversity in ribosomal RNA arrays.

The authors argue that polymorphism and divergence among rRNA arrays are inconsistent with neutral evolution, primarily stating that the amount of polymorphism suggests a high effective size and thus a slow fixation rate, while we, in fact, observe relatively fast fixation between species, even in putatively non-functional regions.

They frame this as a paradox in need of solving, and invoke the WFH model.

The same critiques apply to this paper as to the presentation of the WFH model and the lack of engagement with the literature, particularly concerning Cannings models and non-diffusive limits. However, I have additional concerns about this manuscript, which I found particularly difficult to follow.

Response 1: We would like to emphasize that, despite the many modified WF models, there has not been a model for quantifying genetic drift in multi-copy gene systems, due to the complexity of two levels of genetic drift – within individuals as well as between individuals of the population. We will address this question in the revised manuscript (Ruan, et al. 2024) and have included a mention of it in the text as follows:

“In the WF model, gene frequency is governed by 1/N (or 1/2_N_ in diploids) because K would follow the Poisson distribution whereby V(K) = E(K). As E(K) is generally ~1, V(K) would also be ~ 1. In this backdrop, many "modified WF" models have been developed(Der, et al. 2011), most of them permitting V(K) ≠ E(K) (Karlin and McGregor 1964; Chia and Watterson 1969; Cannings 1974). Nevertheless, paradoxes encountered by the standard WF model apply to these modified WF models as well because all WF models share the key feature of gene sampling (see below and (Ruan, et al. 2024)). ”

My first, and most major, concern is that I can never tell when the authors are referring to diversity in a single copy of an rRNA gene compared to when they are discussing diversity across the entire array of rRNA genes. I admit that I am not at all an expert in studies of rRNA diversity, so perhaps this is a standard understanding in the field, but in order for this manuscript to be read and understood by a larger number of people, these issues must be clarified.

Response 2: We appreciate the reviewer’s feedback and acknowledge that the distinction between the diversity of individual rRNA gene copies and the diversity across the entire array of rRNA genes may not have been clearly defined in the original manuscript. The diversity in our manuscript is referring to the genetic diversity of the population of rRNA genes in the cell. To address this concern, we have revised the relevant paragraph in the text:

“Hence, rRNA gene diversity or polymorphism refer to the variation across all rRNA copies, as these genes exist as paralogs rather than orthologs. This diversity can be assessed at both individual and population levels according to the multi-copy nature of rRNA genes.”

Additionally, we have updated the Methods section to include a detailed description of how diversity is measured as follows:

“All mapping and analysis are performed among individual copies of rRNA genes.

Each individual was considered as a psedo-population of rRNA genes and the diversity of rRNA genes was calculated using this psedo-population of rRNA genes.”

The authors frame the number of rRNA genes as roughly equivalent to expanding the population size, but this seems to be wrong: the way that a mutation can spread among rRNA gene copies is fundamentally different than how mutations spread within a single copy gene. In particular, a mutation in a single copy gene can spread through vertical transmission, but a mutation spreading from one copy to another is fundamentally horizontal: it has to occur because some molecular mechanism, such as slippage, gene conversion, or recombination resulted in its spread to another copy. Moreover, by collapsing diversity across genes in an rRNA array, the authors are massively increasing the mutational target size.   

For example, it's difficult for me to tell if the discussion of heterozygosity at rRNA genes in mice starting on line 277 is collapsed or not. The authors point out that Hs per kb is ~5x larger in rRNA than the rest of the genome, but I can't tell based on the authors' description if this is diversity per single copy locus or after collapsing loci together. If it's the first one, I have concerns about diversity estimation in highly repetitive regions that would need to be addressed, and if it's the second one, an elevated rate of polymorphism is not surprising, because the mutational target size is in fact significantly larger.

Response 3: As addressed in previous Response2, the measurement of diversity or heterozygosity of rRNA genes is consistently done by combining copies, as there is no concept of single gene locus for rDNAs. We agree that by combining the diversity across multiple rRNA gene copies into one measurement, the mutational target size is effectively increased, leading to higher observed levels of diversity than one gene. This is in line with our text:

“If we use the polymorphism data, it is as if rDNA array has a population size 5.2 times larger than single-copy genes. Although the actual copy number on each haploid is ~ 110, these copies do not segregate like single-copy genes and we should not expect N* to be 100 times larger than N. The HS results confirm the prediction that rRNA genes should be more polymorphic than single-copy genes.”

Under this consensus, the reviewer points out that the having a large number of rRNA genes is not equivalent to having a larger population size, because the spreading of mutations among rDNA copies within a species involves two stages: within individual (horizontal transmission) and between individuals (vertical transmission). Let’s examine how the mutation spreading mechanisms influence the population size of rRNA genes.

First, an increase in the copy number of rRNA genes dose increase the actual population size (CN) of rRNA genes. If reviewer is referring to the effective population size of rRNA genes in the context of diversity (N* = CN/V*(K)), then an increase in C would also increase N*. In addition, the linkage among copies would reduce the drift effect, leading to increase diversity. Conversely, homogenization mechanism, like gene conversion and unequal crossing-over would reduce genetic variations between copies and increase V*(K), leading to lower diversity. Therefore, the C* =C/V*(K) in mice is about 5 times larger for rRNA genes than the rest of the genome (which mainly single-copy genes), even though the actual copy number is about 110, indicating a high homogenization rate.

Even if these issues were sorted out, I'm not sure that the authors framing, in terms of variance in reproductive success is a useful way to understand what is going on in rRNA arrays. The authors explicitly highlight homogenizing forces such as gene conversion and replication slippage but then seem to just want to incorporate those as accounting for variance in reproductive success. However, don't we usually want to dissect these things in terms of their underlying mechanism? Why build a model based on variance in reproductive success when you could instead explicitly model these homogenizing processes? That seems more informative about the mechanism, and it would also serve significantly better as a null model, since the parameters would be able to be related to in vitro or in vivo measurements of the rates of slippage, gene conversion, etc.

In the end, I find the paper in its current state somewhat difficult to review in more detail, because I have a hard time understanding some of the more technical aspects of the manuscript while so confused about high-level features of the manuscript. I think that a revision would need to be substantially clarified in the ways I highlighted above.

Response 4: We appreciate your perspective on modeling the homogenizing processes of rRNA gene arrays.

We employ the WFH model to track the drift effect of the multi-copy gene system. In the context of the Haldane model, the term K is often referred to as reproductive success, but it might be more accurate to interpret it as “transmission rate” in this study. As stated in the caption of Figure 1D, two new mutations can have very large differences in individual output (K) when transmitted to the next generation through homogenization process.

Regarding why we did not explicitly model different mechanisms of homogenization, previous elegant models of multigene families have involved mechanisms like unequal crossing over(Smith 1974a; Ohta 1976; Smith 1976) or gene conversion (Nagylaki 1983; Ohta 1985) for concerted evolution, or using conversion to approximate the joint effect of conversion and crossing over (Ohta and Dover 1984). However, even when simplifying the gene conversion mechanism, modeling remains challenging due to controversial assumptions, such as uniform homogenization rate across all gene members (Dover 1982; Ohta and Dover 1984). No models can fully capture the extreme complexity of factors, while these unbiased mechanisms are all genetic drift forces that contribute to changes in mutant transmission. Therefore, we opted for a more simplified and collective approach using V*(K) to see the overall strength of genetic drift.

We have discussed the reason for using V*(K) to collectively represent the homogenization effect in Discussion. As stated in our manuscript:

“There have been many rigorous analyses that confront the homogenizing mechanisms directly. These studies (Smith 1974b; Ohta 1976; Dover 1982; Nagylaki 1983; Ohta and Dover 1983) modeled gene conversion and unequal cross-over head on. Unfortunately, on top of the complexities of such models, the key parameter values are rarely obtainable. In the branching process, all these complexities are wrapped into V*(K) for formulating the evolutionary rate. In such a formulation, the collective strength of these various forces may indeed be measurable, as shown in this study.”

Reviewer #2 (Public Review):

Summary:

Multi-copy gene systems are expected to evolve slower than single-copy gene systems because it takes longer for genetic variants to fix in the large number of gene copies in the entire population. Paradoxically, their evolution is often observed to be surprisingly fast. To explain this paradox, the authors hypothesize that the rapid evolution of multi-copy gene systems arises from stronger genetic drift driven by homogenizing forces within individuals, such as gene conversion, unequal crossover, and replication slippage. They formulate this idea by combining the advantages of two classic population genetic models -- adding the V(k) term (which is the variance in reproductive success) in the Haldane model to the Wright-Fisher model. Using this model, the authors derived the strength of genetic drift (i.e., reciprocal of the effective population size, Ne) for the multi-copy gene system and compared it to that of the single-copy system. The theory was then applied to empirical genetic polymorphism and divergence data in rodents and great apes, relying on comparison between rRNA genes and genome-wide patterns (which mostly are single-copy genes). Based on this analysis, the authors concluded that neutral genetic drift could explain the rRNA diversity and evolution patterns in mice but not in humans and chimpanzees, pointing to a positive selection of rRNA variants in great apes.

Strengths:

Overall, the new WFH model is an interesting idea. It is intuitive, efficient, and versatile in various scenarios, including the multi-copy gene system and other cases discussed in the companion paper by Ruan et al.

Weaknesses:

Despite being intuitive at a high level, the model is a little unclear, as several terms in the main text were not clearly defined and connections between model parameters and biological mechanisms are missing. Most importantly, the data analysis of rRNA genes is extremely over-simplified and does not adequately consider biological and technical factors that are not discussed in the model. Even if these factors are ignored, the authors' interpretation of several observations is unconvincing, as alternative scenarios can lead to similar patterns. Consequently, the conclusions regarding rRNA genes are poorly supported. Overall, I think this paper shines more in the model than the data analysis, and the modeling part would be better presented as a section of the companion theory paper rather than a stand-alone paper. My specific concerns are outlined below.

Response 5: We appreciate the reviewer’s feedback and recognize the need for clearer definitions of key terms. We have made revisions to ensure that each term is properly defined upon its first use.

Regarding the model’s simplicity, as in the Response4, our intention was to create a framework that captures the essence of how mutant copies spread by chance within a population, relying on the variance in transmission rates for each copy (V(K)). By doing so, we aimed to incorporate the various homogenization mechanisms that do not affect single-copy genes, highlighting the substantially stronger genetic drift observed in multi-copy systems compared to single-copy genes. We believe that simplifying the model was necessary to make it more accessible and practical for real-world data analysis and provides a useful approximation that can be applied broadly. It is clearly an underestimate the actual rate as some forces with canceling effects might not have been accounted for.

(1) Unclear definition of terms

Many of the terms in the model or the main text were not clearly defined the first time they occurred, which hindered understanding of the model and observations reported. To name a few:

(i) In Eq(1), although C* is defined as the "effective copy number", it is unclear what it means in an empirical sense. For example, Ne could be interpreted as "an ideal WF population with this size would have the same level of genetic diversity as the population of interest" or "the reciprocal of strength of allele frequency change in a unit of time". A few factors were provided that could affect C*, but specifically, how do these factors impact C*? For example, does increased replication slippage increase or decrease C*? How about gene conversion or unequal cross-over? If we don't even have a qualitative understanding of how these processes influence C*, it is very hard to make interpretations based on inferred C*. How to interpret the claim on lines 240-241 (If the homogenization is powerful enough, rRNA genes would have C*<1)? Please also clarify what C* would be, in a single-copy gene system in diploid species.

Response 6: We apology for the confusion caused by the lack of clear definitions in the initial manuscript. We recognize that this has led to misunderstandings regarding the concept we presented. Our aim was to demonstrate the concerted evolution in multi-copy gene systems, involving two levels of “effective copy number” relative to single-copy genes: first, homogenization within populations then divergence between species. We used C* and Ne* to try to designated the two levels driven by the same homogenization force, which complicated the evolutionary pattern.

To address these issues, we have simplified the model and revised the abstract to prevent any misunderstandings:

“On average, rDNAs have C ~ 150 - 300 copies per haploid in humans. While a neutral mutation of a single-copy gene would take 4_N_ (N being the population size) generations to become fixed, the time should be 4_NC* generations for rRNA genes where 1<< C* (C* being the effective copy number; C* < C or C* > C would depend on the drift strength). However, the observed fixation time in mouse and human is < 4_N, implying the paradox of C* < 1. Genetic drift that encompasses all random neutral evolutionary forces appears as much as 100 times stronger for rRNA genes as for single-copy genes, thus reducing C* to < 1.”

Thus, it should be clear that the fixation time as well as the level of polymorphism represent the empirical measures of C*.We have also revised the relevant paragraph in the text to define C* and V*(K) and removed Eq. 2 for clarity:

“Below, we compare the strength of genetic drift in rRNA genes vs. that of single-copy genes using the Haldane model (Ruan, et al. 2024). We shall use * to designate the equivalent symbols for rRNA genes; for example, E(K) vs. E*(K). Both are set to 1, such that the total number of copies in the long run remains constant.

For simplicity, we let V(K) = 1 for single-copy genes. (If we permit V(K) ≠ 1, the analyses will involve the ratio of V*(K) and V(K) to reach the same conclusion but with unnecessary complexities.) For rRNA genes,  V*(K) ≥ 1 may generally be true because K for rDNA mutations are affected by a host of homogenization factors including replication slippage, unequal cross-over, gene conversion and other related mechanisms not operating on single copy genes. Hence,

where C is the average number of rRNA genes in an individual and V*(K) reflects the homogenization process on rRNA genes (Fig. 1D). Thus,

C* = C/V*(K)

represents the effective copy number of rRNA genes in the population, determining the level of genetic diversity relative to single-copy genes. Since C is in the hundreds and V*(K) is expected to be > 1, the relationship of 1 << C* ≤ C is hypothesized. Fig. 1D is a simple illustration that the homogenizing process may enhance V*(K) substantially over the WF model.

In short, genetic drift of rRNA genes would be equivalent to single copy genes in a population of size NC* (or N*). Since C* >> 1 is hypothesized, genetic drift for rRNA genes is expected to be slower than for single copy genes.”

(ii) In Eq(1), what exactly is V*(K)? Variance in reproductive success across all gene copies in the population? What factors affect V*(K)? For the same population, what is the possible range of V*(K)/V(K)? Is it somewhat bounded because of biological constraints? Are V*(K) and C*(K) independent parameters, or does one affect the other, or are both affected by an overlapping set of factors?

Response 7: - In Eq(1), what exactly is V*(K)?  In Eq(1), V*(K) refers to the variance in the number of progeny to whom the gene copy of interest is transmitted (K) over a specific time interval. When considering evolutionary divergence between species, V*(K) may correspond to the divergence time.

- What factors affect V*(K)? For the same population, what is the possible range of V*(K)/V(K)? Is it somewhat bounded because of biological constraints?  “V*(K) for rRNA genes is likely to be much larger than V(K) for single-copy genes, because K for rRNA mutations may be affected by a host of homogenization factors including replication slippage, unequal cross-over, gene conversion and other related mechanisms not operating on single-copy genes. For simplicity, we let V(K) = 1 (as in a WF population) and V*(K) ≥ 1.” Thus, the V*(K)/V(K) = V*(K) can potentially reach values in the hundreds, and may even exceed C, resulting in C*(= C/V*(K)) values less than 1. Biological constraints that could limit this variance include the minimum copy number within individuals, sequence constraints in functional regions, and the susceptibility of chromosomes with large arrays to intrachromosomal crossover (which may lead to a reduction in copy number)(Eickbush and Eickbush 2007), potentially reducing the variability of K.

- Are V*(K) and C*(K) independent parameters, or does one affect the other, or are both affected by an overlapping set of factors?  There is no C*(K), the C* is defined as follows in the text:

“C* = C/V*(K) represents the effective copy number of rRNA genes, reflecting the level of genetic diversity relative to single-copy genes. Since C is in the hundreds and V*(K) is expected to be > 1, the relationship of 1 << C* ≤ C is hypothesized.” The factors influencing V*(K) directly affect C* due to this relationship.

(iii) In the multi-copy gene system, how is fixation defined? A variant found at the same position in all copies of the rRNA genes in the entire population?

Response 8: We appreciate the reviewer's suggestion and have now provided a clear definition of fixation in the context of multi-copy genes within the manuscript.

“For rDNA mutations, fixation must occur in two stages – fixation within individuals and among individuals in the population. (Note that a new mutation can be fixed via homogenization, thus making rRNA gene copies in an individual a pseudo-population.)”

The evolutionary dynamics of multi-copy genes differ from those of single-copy (Mendelian) genes, which mutate, segregate and evolve independently in the population. Fixation in multi-copy genes, such as rRNA genes, is influenced by their ability to transfer genetic information among their copies through nonreciprocal exchange mechanisms, like gene conversion and unequal crossover (Ohta and Dover 1984). These processes can cause fluctuations in the number of mutant copies within an individual's lifetime and facilitate the spread of a mutant allele across all copies even in non-homologous chromosomes. Over time, this can result in the mutant allele replacing all preexisting alleles throughout the population, leading to fixation (Ohta 1976) meaning that the same variant will eventually be present at the corresponding position in all copies of the rRNA genes across the entire population. Without such homogenization processes, fixation would be unlikely to be obtained in multi-copy genes.

(iv) Lines 199-201, HI, Hs, and HT are not defined in the context of a multi-copy gene system. What are the empirical estimators?

Response 9: We appreciate the reviewer's comment and would like to clarify the definitions and empirical estimators for within the context of a multi-copy gene system in the text:

“A standard measure of genetic drift is the level of heterozygosity (H). At the mutation-selection equilibrium

where μ is the mutation rate of the entire gene and Ne is the effective population size. In this study, Ne = N for single-copy gene and Ne = C*N for rRNA genes. The empirical measure of nucleotide diversity H is given by

where L is the gene length (for each copy of rRNA gene, L ~ 43kb) and pi is the variant frequency at the i-th site.

We calculate H of rRNA genes at three levels – within-individual, within-species and then, within total samples (HI, HS and HT, respectively). HS and HT are standard population genetic measures (Hartl, et al. 1997; Crow and Kimura 2009). In calculating HS, all sequences in the species are used, regardless of the source individuals. A similar procedure is applied to HT. The HI statistic is adopted for multi-copy gene systems for measuring within-individual polymorphism. Note that copies within each individual are treated as a pseudo-population (see Fig. 1 and text above). With multiple individuals, HI is averaged over them.”

(v) Line 392-393, f and g are not clearly defined. What does "the proportion of AT-to-GC conversion" mean? What are the numerator and denominator of the fraction, respectively?

Response 10: We appreciate the reviewer's comment and have revised the relevant text for clarity as well as improved the specific calculation methods for f and g in the Methods section.

“We first designate the proportion of AT-to-GC conversion as f and the reciprocal, GC-to-AT, as g. Specifically, f represents the proportion of fixed mutations where an A or T nucleotide has been converted to a G or C nucleotide (see Methods). Given f ≠ g, this bias is true at the site level.”

Methods:

“Specifically, f represents the proportion of fixed mutations where an A or T nucleotide has been converted to a G or C nucleotide. The numerator for f is the number of fixed mutations from A-to-G, T-to-C, T-to-G, or A-to-C. The denominator is the total number of A or T sites in the rDNA sequence of the specie lineage.

Similarly, g is defined as the proportion of fixed mutations where a G or C nucleotide has been converted to an A or T nucleotide. The numerator for g is the number of fixed mutations from G-to-A, C-to-T, C-to-A, or G-to-T. The denominator is the total number of G or C sites in the rDNA sequence of the specie lineage.

The consensus rDNA sequences for the species lineage were generated by Samtools consensus (Danecek, et al. 2021) from the bam file after alignment. The following command was used:

‘samtools consensus -@ 20 -a -d 10 --show-ins no --show-del yes input_sorted.bam output.fa’.”

(2) Technical concerns with rRNA gene data quality

Given the highly repetitive nature and rapid evolution of rRNA genes, myriads of things could go wrong with read alignment and variant calling, raising great concerns regarding the data quality. The data source and methods used for calling variants were insufficiently described at places, further exacerbating the concern.

(i) What are the accession numbers or sample IDs of the high-coverage WGS data of humans, chimpanzees, and gorillas from NCBI? How many individuals are in each species? These details are necessary to ensure reproducibility and correct interpretation of the results.

Response 11: We apologize for not including the specific details of the sample information in the main text. All accession numbers and sample IDs for the WGS data used in this study, including mice, humans, chimpanzee, and gorilla, are already listed in Supplementary Tables S4-S5. We have revised the table captions and referenced them at the appropriate points in the Methods to ensure clarity.

“The genome sequences of human (n = 8), chimpanzee (n = 1) and gorilla (n = 1) were sourced from National Center for Biotechnology Information (NCBI) (Supplementary Table 4). … Genomic sequences of mice (n = 13) were sourced from the Wellcome Sanger Institute’s Mouse Genome Project (MGP) (Keane, et al. 2011).

The concern regarding the number of individuals needed to support the results will be addressed in Response 13.

(ii) Sequencing reads from great apes and mice were mapped against the human and mouse rDNA reference sequences, respectively (lines 485-486). Given the rapid evolution of rRNA genes, even individuals within the same species differ in copy number and sequences of these genes. Alignment to a single reference genome would likely lead to incorrect and even failed alignment for some reads, resulting in genotyping errors. Differences in rDNA sequence, copy number, and structure are even greater between species, potentially leading to higher error rates in the called variants. Yet the authors provided no justification for the practice of aligning reads from multiple species to a single reference genome nor evidence that misalignment and incorrect variant calling are not major concerns for the downstream analysis.

Response 12: While the copy number of rDNA varies in each individuals, the sequence identity among copies is typically very high (median identity of 98.7% (Nurk, et al. 2022)). Therefore, all rRNA genes were aligned against to the species-specific reference sequences, where the consensus nucleotide nearly accounts for >90% of the gene copies in the population. In minimize genotyping errors, our analysis focused exclusively on single nucleotide variants (SNVs) with only two alleles, discarding other mutation types.

Regarding sequence divergence between species, which may have greater sequence variations, we excluded unmapped regions with high-quality reads coverage below 10. In calculation of substitution rate, we accounted for the mapping length (L), as shown in the column 3 in Table 3-5.

We appreciate the reviewer’s comments and have provide details in the Methods.

(vi) It is unclear how variant frequency within an individual was defined conceptually or computed from data (lines 499-501). The population-level variant frequency was calculated by averaging across individuals, but why was the averaging not weighted by the copy number of rRNA genes each individual carries? How many individuals are sampled for each species? Are the sample sizes sufficient to provide an accurate estimate of population frequencies?

Response 13: Each individual was considered as a psedo-population of rRNA genes, varaint frequency within an individual was the proportions of mutant allele in this psedo-population. The calculation of varaint frequency is based on the number of supported reads of each individual.

The reason for calculating population-level variant frequency by averaging across individuals is relevant in the calculation of FIS and FST. In calculating FST, the standard practice is to weigh each population equally. So, when we show FST in humans, we do not consider whether there are more Africans, Caucasians or Asians. There is a reason for not weighing them even though the population sizes could be orders of magnitude different, say, in the comparison between an ethnic minority and the main population. In the case of FIS, the issue is moot. Although copy number may range from 150 to 400 per haploid, most people have 300 – 500 copies with two haploids.

As for the concern regarding the number the individuals needed to support of the results:

Considering the nature of multi-copy genes, where gene members undergo continuous exchanges at a much slower rate compared to the rapid rate of random distribution of chromosomes at each generation of sexual reproduction, even a few variant copies that arise during an individual's lifetime would disperse into the gene pool in the next generation (Ohta and Dover 1984). Thus, there is minimal difference between individuals. Our analysis is also aligns with this theory, particularly in human population (FIS = 0.059), where each individual carries the majority of the population's genetic diversity. Therefore, even a single chimpanzee or gorilla individual caries sufficient diversity with its hundreds of gene copies to calculate divergence with humans.

(vii) Fixed variants are operationally defined as those with a frequency>0.8 in one species. What is the justification for this choice of threshold? Without knowing the exact sample size of the various species, it's difficult to assess whether this threshold is appropriate.

Response 14: First, the mutation frequency distribution is strongly bimodal (see Figure below) with a peak at zero and the other at 1. This high frequency peak starts to rise slowly at 0.8, similar to FST distribution in Figure 4C. That is why we use it as the cutoff although we would get similar results at the cutoff of 0.90 (see Table below). Second, the sample size for the calculation of mutant frequency is based on the number of reads which is usually in the tens of thousands. Third, it does not matter if the mutation frequency calculation is based on one individuals or multiple individuals because 95% of the genetic diversity of the population is captured by the gene pool within each individual.

Author response image 1.

Author response table 1.

The A/T to G/C and G/C to A/T changes in apes and mouse.

New mutants with a frequency >0.9 within an individual are considered as (nearly) fixed, except for humans, where the frequency was averaged over 8 individuals in the Table 2.

The X-squared values for each species are as follows: 58.303 for human, 7.9292 for chimpanzee, and 0.85385 for M. m. domesticus.

(viii) It is not explained exactly how FIS, FST, and divergence levels of rRNA genes were calculated from variant frequency at individual and species levels. Formulae need to be provided to explain the computation.

Response 15: After we clearly defined the HI, HS, and HT in Response9, understanding FIS and F_ST_ becomes straightforward.

“Given the three levels of heterozygosity, there are two levels of differentiation. First, FIS is the differentiation among individuals within the species, defined by

FIS = [HS - HI]/HS  

FIS is hence the proportion of genetic diversity in the species that is found only between individuals. We will later show FIS ~ 0.05 in human rDNA (Table 2), meaning 95% of rDNA diversity is found within individuals.

Second, FST is the differentiation between species within the total species complex, defined as

FST = [HT – HS]/HT 

FST is the proportion of genetic diversity in the total data that is found only between species.”

(3) Complete ignorance of the difference in mutation rate difference between rRNA genes and genome-wide average

Nearly all data analysis in this paper relied on comparison between rRNA genes with the rest (presumably single-copy part) of the genome. However, mutation rate, a key parameter determining the diversity and divergence levels, was completely ignored in the comparison. It is well known that mutation rate differs tremendously along the genome, with both fine and large-scale variation. If the mutation rate of rRNA genes differs substantially from the genome average, it would invalidate almost all of the analysis results. Yet no discussion or justification was provided.

Response 16: We appreciate the reviewer's observation regarding the potential impact of varying mutation rates across the genome. To address this concern, we compared the long-term substitution rates on rDNA and single-copy genes between human and rhesus macaque, which diverged approximately 25 million years ago. Our analysis (see Table S1 below) indicates that the substitution rate in rDNA is actually slower than the genome-wide average. This finding suggests that rRNA genes do not experience a higher mutation rate compared to single-copy genes, as stated in the text:

“Note that Eq. (3) shows that the mutation rate, m, determines the long-term evolutionary rate, l. Since we will compare the l values between rRNA and single-copy genes, we have to compare their mutation rates first by analyzing their long-term evolution. As shown in Table S1, l falls in the range of 50-60 (differences per Kb) for single copy genes and 40 – 70 for the non-functional parts of rRNA genes. The data thus suggest that rRNA and single-copy genes are comparable in mutation rate. Differences between their l values will have to be explained by other means.”

However, given the divergence time (Td) being equal to or smaller than Tf, even if the mutation rate per nucleotide is substantially higher in rRNA genes, these variants would not become fixed after the divergence of humans and chimpanzees without the help of strong homogenization forces. Thus, the presence of divergence sites (Table 5) still supports the conclusion that rRNA genes undergo much stronger genetic drift compared to single-copy genes.

Related to mutation rate: given the hypermutability of CpG sites, it is surprising that the evolution/fixation rate of rRNA estimated with or without CpG sites is so close (2.24% vs 2.27%). Given the 10 - 20-fold higher mutation rate at CpG sites in the human genome, and 2% CpG density (which is probably an under-estimate for rDNA), we expect the former to be at least 20% higher than the latter.

Response 17: While it is true that CpG sites exhibit a 10-20-fold higher mutation rate, the close evolution/fixation rates of rDNA with and without CpG sites (2.24% vs 2.27%) may be attributed to the fact that fixation rates during short-term evolutionary processes are less influenced by mutation rates alone. As observed in the Human-Macaque comparison in the table above, the substitution rate of rDNA in non-functional regions with CpG sites is 4.18%, while it is 3.35% without CpG sites, aligning with your expectation of 25% higher rates where CpG sites are involved.

This discrepancy between the expected and observed fixation rates may be due to strong homogenization forces, which can rapidly fix or eliminate variants, thereby reducing the overall impact of higher mutation rates at CpG sites on the observed fixation rate. This suggests that the homogenization mechanisms play a more dominant role in the fixation process over short evolutionary timescales, mitigating the expected increase in fixation rates due to CpG hypermutability.

Among the weaknesses above, concern (1) can be addressed with clarification, but concerns (2) and (3) invalidate almost all findings from the data analysis and cannot be easily alleviated with a complete revamp work.

Recommendations for the authors:

Reviewing Editor Comments:

Both reviewers found the manuscript confusing and raised serious concerns. They pointed out a lack of engagement with previous literature on modeling and the presence of ill-defined terms within the model, which obscure understanding. They also noted a significant disconnection between the modeling approach and the biological processes involved. Additionally, the data analysis was deemed problematic due to the failure to consider essential biological and technical factors. One reviewer suggested that the modeling component would be more suitable as a section of the companion theory paper rather than a standalone paper. Please see their individual reviews for their overall assessment.

Reviewer #2 (Recommendations For The Authors):

Beyond my major concerns, I have numerous questions about the interpretation of various findings:

Lines 62-63: Please explain under what circumstance Ne=N/V(K) is biologically nonsensical and why.

Response 18: “Biologically non-sensical” is the term used in (Chen, et al. 2017). We now used the term “biologically untenable” but the message is the same. How does one get V(K) ≠ E(K) in the WF sampling? It is untenable under the WF structure. Kimura may be the first one to introduce V(K) ≠ E(K) into the WF model and subsequent papers use the same sort of modifications that are mathematically valid but biologically dubious. As explained extensively in the companion paper, the modifications add complexities but do not give the WF models powers to explain the paradoxes.

Lines 231-234: The claim about a lower molecular evolution rate (lambda) is inaccurate - under neutrality, the molecular evolution rate is always the same as the mutation rate. It is true that when the species divergence Td is not much greater than fixation time Tf, the observed number of fixed differences would be substantially smaller than 2*mu*Td, but the lower divergence level does not mean that the molecular evolution is slower. In other words, in calculating the divergence level, it is the time term that needs to be adjusted rather than the molecular evolution rate.

Response 19: Thanks, we agree that the original wording was not accurate. It is indeed the substitution rate rather than the molecular evolution rate that is affected when species divergence time Td is not much greater than the fixation time Tf. We have revised the relevant text in the manuscript to correct this and ensure clarity.

Lines 277-279: Hs for rRNA is 5.2x fold than the genome average. This could be roughly translated as Ne*/Ne=5.2. According to Eq 2: (1/Ne*)/(1/Ne)= Vh/C*, it can be drived that mean Ne*/Ne=C*/Vh. Then why do the authors conclude "C*=N*/N~5.2" in line 278? Wouldn't it mean that C*/Vh is roughly 5.2?

Response 20: We apologize for the confusion. To prevent misunderstandings, we have revised Equation 1 and deleted Equation 2 from the manuscript. Please refer to the Response6 for further details.

Lines 291-292: What does "a major role of stage I evolution" mean? How does it lead to lower FIS?

Response 21: We apologize for the lack of clarity in our original description, and we have revised the relevant content to make them more directly.

“In this study, we focus on multi-copy gene systems, where the evolution takes place in two stages: both within (stage I) and between individuals (stage II).”

“FIS for rDNA among 8 human individuals is 0.059 (Table 2), much smaller than 0.142 in M. m. domesticus mice, indicating minimal genetic differences across human individuals and high level of genetic identity in rDNAs between homologous chromosomes among human population. … Correlation of polymorphic sites in IGS region is shown in Supplementary Fig. 1. The results suggest that the genetic drift due to the sampling of chromosomes during sexual reproduction (e.g., segregation and assortment) is augmented substantially by the effects of homogenization process within individual. Like those in mice, the pattern indicates that intra-species polymorphism is mainly preserved within individuals.”

Line 297-300: why does the concentration at very allele frequency indicate rapid homogenization across copies? Suppose there is no inter-copy homogenization, and each copy evolves independently, wouldn't we still expect the SFS to be strongly skewed towards rare variants? It is completely unclear how homogenization processes are expected to affect the SFS.

Response 22: We appreciate the reviewer’s insightful comments and apologize for any confusion in our original explanation. To clarify:

If there is no inter-copy homogenization and each copy evolves independently, it would effectively result in an equivalent population size that is C times larger than that of single-copy genes. However, given the copies are distributed on five chromosomes, if the copies within a chromosome were fully linked, there would be no fixation at any sites. Considering the data presented in Table 4, where the substitution rate in rDNA is higher than in single-copy genes, this suggests that additional forces must be acting to homogenize the copies, even across non-homologous chromosomes.

Regarding the specific data presented in the Figure 3, the allele frequency spectrum is based on human polymorphism sites and is a folded spectrum, as the ancestral state of the alleles was not determined. High levels of homogenization would typically push variant mutations toward the extremes of the SFS, leading to fewer intermediate-frequency alleles and reduced heterozygosity. The statement that "allele frequency spectrum is highly concentrated at very low frequency within individuals" was intended to emphasize the localized distribution of variants and the high identity at each site. However, we recognize that it does not accurately reflect the role of homogenization and this conclusion cannot be directly inferred from the figure as presented. Therefore, we have removed the sentence in the text.

The evidence of gBGC in rRNA genes in great apes does not help explain the observed accelerated evolution of rDNA relative to the rest of the genome. Evidence of gBGC has been clearly demonstrated in a variety of species, including mice. It affects not only rRNA genes but also most parts of the genome, particularly regions with high recombination rates. In addition, gBGC increases the fixation probability of W>S mutations but suppresses the fixation of S>W mutations, so it is not obvious how gBGC will increase or decrease the molecular evolution rate overall.

Response 23: We have thoroughly rewritten the last section of Results. The earlier writing has misplaced the emphasis, raising many questions (as stated above). To answer them, we would have to present a new set of equations thus adding unnecessary complexities to the paper. Here is the streamlined and more logical flow of the new section.

First, Tables 4 and 5 have shown the accelerated evolution of the rRNA genes. We have now shown that rRNA genes do not have higher mutation rates. Below is copied from the revised text:

“We now consider the evolution of rRNA genes between species by analyzing the rate of fixation (or near fixation) of mutations. Polymorphic variants are filtered out in the calculation. Note that Eq. (3) shows that the mutation rate, m, determines the long-term evolutionary rate, l. Since we will compare the l values between rRNA and single-copy genes, we have to compare their mutation rates first by analyzing their long-term evolution. As shown in Table S1 l falls in the range of 50-60 (differences per Kb) for single copy genes and 40 – 70 for the non-functional parts of rRNA genes. The data thus suggest that rRNA and single-copy genes are comparable in mutation rate. Differences between their l values will have to be explained by other means.”

Second, we have shown that the accelerated evolution in mice is likely due to genetic drift, resulting in faster fixation of neutral variants. We also show that this is unlikely to be true in humans and chimpanzees; hence selection is the only possible explanation. The section below is copied from the revised text. It shows the different patterns of gene conversions between mice and apes, in agreement with the results of Tables 4 and 5. In essence, it shows that the GC ratio in apes is shifting to a new equilibrium, which is equivalent to a new adaptive peak. Selection is driving the rDNA genes to move to the new adaptive peak.

Revision - “Thus, the much accelerated evolution of rRNA genes between humans and chimpanzees cannot be entirely attributed to genetic drift. In the next and last section, we will test if selection is operating on rRNA genes by examining the pattern of gene conversion. 

3) Positive selection for rRNA mutations in apes, but not in mice – Evidence from gene conversion patterns

For gene conversion, we examine the patterns of AT-to-GC vs. GC-to-AT changes. While it has been reported that gene conversion would favor AT-to-GC over GC-to-AT conversion (Jeffreys and Neumann 2002; Meunier and Duret 2004) at the site level, we are interested at the gene level by summing up all conversions across sites. We designate the proportion of AT-to-GC conversion as f and the reciprocal, GC-to-AT, as g. Both f and g represent the proportion of fixed mutations between species (see Methods). So defined, f and g are influenced by the molecular mechanisms as well as natural selection. The latter may favor a higher or lower GC ratio at the genic level between species. As the selective pressure is distributed over the length of the gene, each site may experience rather weak pressure.

Let p be the proportion of AT sites and q be the proportion of GC sites in the gene. The flux of AT-to-GC would be pf and the flux in reverse, GC-to-AT, would be qg. At equilibrium, pf = qg. Given f and g, the ratio of p and q would eventually reach p/q \= g/f. We now determine if the fluxes are in equilibrium (pf =qg). If they are not, the genic GC ratio is likely under selection and is moving to a different equilibrium.

In these genic analyses, we first analyze the human lineage (Brown and Jiricny 1989; Galtier and Duret 2007). Using chimpanzees and gorillas as the outgroups, we identified the derived variants that became nearly fixed in humans with frequency > 0.8 (Table 6). The chi-square test shows that the GC variants had a significantly higher fixation probability compared to AT. In addition, this pattern is also found in chimpanzees (p < 0.001). In M. m. domesticus (Table 6), the chi-square test reveals no difference in the fixation probability between GC and AT (p = 0.957). Further details can be found in Supplementary Figure 2. Overall, a higher fixation probability of the GC variants is found in human and chimpanzee, whereas this bias is not observed in mice.

Tables 6-7 here

Based on Table 6, we could calculate the value of p, q, f and g (see Table 7). Shown in the last row of Table 7, the (pf)/(qg) ratio is much larger than 1 in both the human and chimpanzee lineages. Notably, the ratio in mouse is not significantly different from 1. Combining Tables 4 and 7, we conclude that the slight acceleration of fixation in mice can be accounted for by genetic drift, due to gene conversion among rRNA gene copies. In contrast, the different fluxes corroborate the interpretations of Table 5 that selection is operating in both humans and chimpanzees.”

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Author Response

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

Responses to the reviewers

We thank the editor and reviewers for their insightful feedback and valuable suggestions on our revised manuscript. In this reply, we provided further clarifications and made changes accordingly. Reviewers’ comments are in bold, and our responses are immediately below. Changes in the main text are presented in italics, accompanied by the specific line numbers in the revised manuscript where these changes can be found. Below, we respond to each reviewer’s comments in turn.

Reviewer #1 (Public Review):

Ps observed 24 objects and were asked which afforded particular actions (14 action types). Affordances for each object were represented by a 14-item vector, values reflecting the percentage of Ps who agreed on a particular action being afforded by the object. An affordance similarity matrix was generated which reflected similarity in affordances between pairs of objects. Two clusters emerged, reflecting correlations between affordance ratings in objects smaller than body size and larger than body size. These clusters did not correlate themselves. There was a trough in similarity ratings between objects ~105 cm and ~130 cm, arguably reflecting the body size boundary. The authors subsequently provide some evidence that this clear demarcation is not simply an incidental reflection of body size, but likely causally related. This evidence comes in the flavour of requiring Ps to imagine themselves as small as a cat or as large as an elephant and showing a predicted shift in the affordance boundary. The manuscript further demonstrates that ChatGPT (theoretically interesting because it's trained on language alone without sensorimotor information; trained now on words rather than images) showed a similar boundary.

The authors also conducted a small MRI study task where Ps decide whether a probe action was affordable (graspable?) and created a congruency factor according to the answer (yes/no). There was an effect of congruency in posterior fusiform and superior parietal lobule for objects within body size range, but not outside. No effects in LOC or M1.

The major strength of this manuscript in my opinion is the methodological novelty. I felt the correlation matrices were a clever method for demonstrating these demarcations, the imagination manipulation was also exciting, and the ChatGPT analysis provided excellent food for thought. These findings are important for our understanding of the interactions between action and perception, and hence for researchers from a range of domains of cognitive neuroscience.

The major element that limits conclusions is that an MRI study with 12 P in this context can really only provide pilot data. Certainly the effects are not strong enough for 12 P to generate much confidence. The others of my concerns have been addressed in the revision.

Reviewer #1 (Recommendations For The Authors):

I think that the authors need to mention in the abstract that the MRI study constitutes a small pilot.

Response: We appreciate the reviewer’s positive evaluation and constructive suggestions. In response to the concern about the limited number of participants in the fMRI study, we fully acknowledge the implications this has on the generalizability and robustness of our findings related to the congruency effect. To clarity, we have explicitly stated its preliminary nature of the MRI study in the abstract [line 22]: “A subsequent fMRI experiment offered preliminary evidence of affordance processing exclusively for objects within the body size range, but not for those beyond.”

Reviewer #2 (Public Review):

Summary

In this work, the authors seek to test a version of an old idea, which is that our perception of the world and our understanding of the objects in it are deeply influenced by the nature of our bodies and the kinds of behaviours and actions that those objects afford. The studies presented here muster three kinds of evidence for a discontinuity in the encoding of objects, with a mental "border" between objects roughly of human body scale or smaller, which tend to relate to similar kinds of actions that are yet distinct from the kinds of actions implied by human-or-larger scale objects. This is demonstrated through observers' judgments of the kinds of actions different objects afford; through similar questioning of AI large-language models (LLMs); and through a neuroimaging study examining how brain regions implicated in object understanding make distinctions between kinds of objects at human and larger-than-human scales.

Strengths 

The authors address questions of longstanding interest in the cognitive neurosciences -- namely how we encode and interact with the many diverse kinds of objects we see and use in daily life. A key strength of the work lies in the application of multiple approaches. Examining the correlations among kinds of objects, with respect to their suitability for different action kinds, is novel, as are the complementary tests of judgments made by LLMs. The authors include a clever manipulation in which participants are asked to judge action-object pairs, having first adopted the imagined size of either a cat or an elephant, showing that the discontinuity in similarity judgments effectively moved to a new boundary closer to the imagined scale than the veridical human scale. The dynamic nature of the discontinuity hints that action affordances may be computed dynamically, "on the fly", during actual action behaviours with objects in the real world.

Weaknesses 

A limitation of the tests of LLMs may be that it is not always known what kinds of training material was used to build these models, leading to a possible "black box" problem. Further, presuming that those models are largely trained on previous human-written material, it may not necessarily be theoretically telling that the "judgments" of these models about action-object pairs shows human-like discontinuities. Indeed, verbal descriptions of actions are very likely to mainly refer to typical human behaviour, and so the finding that these models demonstrate an affordance discontinuity may simply reflect those statistics, rather than providing independent evidence for affordance boundaries.

The relatively small sample size of the brain imaging experiment, and some design features (such as the task participants performed, and the relatively narrow range of objects tested) provide some limits on the extent to which it can be taken as support for the authors' claims.

Response: We thank the reviewer for the positive evaluation and the constructive comments. We agree that how LLMs work is a “black box”, and thus it is speculative to assume them to possess any human-like ability, because, as the reviewer pointed out, “these models demonstrate an affordance discontinuity may simply reflect those statistics.” Indeed, our manuscript has expressed a similar idea [line 338]: “We speculated that ChatGPT models may have formed the affordance boundary through a human prism ingrained within its linguistic training corpus.” That is, our intention was not to suggest that such information could replace sensorimotor-based interaction or achieve human-level capability, but rather to highlight that embodied interaction is necessary. Additionally, the scope of the present study does not extend to elucidating the mechanisms behind LLMs’ resemblance of affordance boundary, whether through statistical learning or actual comprehension. To clarify this point, in the revised manuscript, we have clarified that the mechanisms underlying the observed affordance boundary in LLMs may be different from human cognitive processes, and advocated future studies to explore this possibility [line 415]: “Nevertheless, caution should be taken when interpreting the capability of LLMs like ChatGPT, which are often considered “black boxes.” That is, our observation indicates that certain sensorimotor information is embedded within human language materials presumably through linguistic statistics, but it is not sufficient to assert that LLMs have developed a human-like ability to represent affordances. Furthermore, such information alone may be insufficient for LLMs to mimic the characteristics of the affordance perception in biological intelligence. Future studies are needed to elucidate such limitation.”

Regarding the concern about the models’ results not “providing independent evidence for affordance boundaries”, our objective in employing LLMs was to explore if an affordance boundary could emerge from conceptual knowledge without direct sensorimotor experience, rather than to validate the existence of the affordance boundary per se.

As for the concern about the limitations imposed by the small sample size and certain design features of our brain imaging experiment, please see our reply to Reviewer #1.

Reviewer #3 (Public Review):

Summary:

Feng et al. test the hypothesis that human body size constrains the perception of object affordances, whereby only objects that are smaller than the body size will be perceived as useful and manipulable parts of the environment, whereas larger objects will be perceived as "less interesting components."

To test this idea, the study employs a multi-method approach consisting of three parts:

In the first part, human observers classify a set of 24 objects that vary systematically in size (e.g., ball, piano, airplane) based on 14 different affordances (e.g., sit, throw, grasp). Based on the average agreement of ratings across participants, the authors compute the similarity of affordance profiles between all object pairs. They report evidence for two homogenous object clusters that are separated based on their size with the boundary between clusters roughly coinciding with the average human body size. In follow-up experiments, the authors show that this boundary is larger/smaller in separate groups of participants who are instructed to imagine themselves as an elephant/cat.

In the second part, the authors ask different large language models (LLMs) to provide ratings for the same set of objects and affordances and conduct equivalent analyses on the obtained data. Some, but not all, of the models produce patterns of ratings that appear to show similar boundary effects, though less pronounced and at a different boundary size than in humans.

In the third part, the authors conduct an fMRI experiment. Human observers are presented with four different objects of different sizes and asked if these objects afford a small set of specific actions. Affordances are either congruent or incongruent with objects. Contrasting brain activity on incongruent trials against brain activity on congruent trials yields significant effects in regions within the ventral and dorsal visual stream, but only for small objects and not for large objects.

The authors interpret their findings as support for their hypothesis that human body size constrains object perception. They further conclude that this effect is cognitively penetrable, and only partly relies on sensorimotor interaction with the environment (and partly on linguistic abilities).

Strengths:

The authors examine an interesting and relevant question and articulate a plausible (though somewhat underspecified) hypothesis that certainly seems worth testing. Providing more detailed insights into how object affordances shape perception would be highly desirable. Their method of analyzing similarity ratings between sets of objects seems useful and the multi-method approach is original and interesting.

Weaknesses:

The study presents several shortcomings that clearly weaken the link between the obtained evidence and the drawn conclusions. Below I outline my concerns in no particular order:

(1) It is not entirely clear to me what the authors are proposing and to what extent the conducted work actually speaks to this. For example, in the introduction, the authors write that they seek to test if body size serves not merely as a reference for object manipulation but also "plays a pivotal role in shaping the representation of objects." This motivation seems rather vague motivation and it is not clear to me how it could be falsified.

Overall, the lack of theoretical precision makes it difficult to judge the appropriateness of the approaches and the persuasiveness of the obtained results. I would strongly suggest clarifying the theoretical rationale and explaining in more detail how the chosen experiments allow them to test falsifiable predictions.

(2) The authors used only a very small set of objects and affordances in their study and they do not describe in sufficient detail how these stimuli were selected. This renders the results rather exploratory and clearly limits their potential to discover general principles of human perception. Much larger sets of objects and affordances and explicit data-driven approaches for their selection would provide a more convincing approach and allow the authors to rule out that their results are just a consequence of the selected set of objects and actions.

(3) Relatedly, the authors could be more thorough in ruling out potential alternative explanations. Object size likely correlates with other variables that could shape human similarity judgments and the estimated boundary is quite broad (depending on the method, either between 80 and 150 cm or between 105 to 130 cm). More precise estimates of the boundary and more rigorous tests of alternative explanations would add a lot to strengthen the authors' interpretation.

(4) While I appreciate the manipulation of imagined body size, as a clever way to solidify the link between body size and affordance perception, I find it unfortunate that it is implemented in a between-subjects design, as this clearly leaves open the possibility of pre-existing differences between groups. I certainly disagree with the authors' statement that their findings suggest "a causal link between body size and affordance perception."

(5) The use of LLMs in the current study is not clearly motivated and I find it hard to understand what exactly the authors are trying to test through their inclusion. As it currently stands, I find it hard to discern how the presence of perceptual boundaries in LLMs could constitute evidence for affordance-based perception.

(6) Along the same lines, the fMRI study also provides little evidence to support the authors' claims. The use of congruency effects as a way of probing affordance perception is not well motivated. Importantly (and related to comment 2 above), the very small set of objects and affordances in this experiment heavily complicates any conclusions about object size being the crucial variable determining the occurrence of congruency effects.

Overall, I consider the main conclusions of the paper to be far beyond the reported data. Articulating a clearer theoretical framework with more specific hypotheses as well as conducting more principled analyses on more comprehensive data sets could help the authors obtain stronger tests of their ideas.

Response: We appreciate the insightful inquiries regarding our manuscript. Below, we explained the theoretical motivation and rationale of each part of our experiments.

In response to the reviewer’s insights, we have modified the expression “plays a pivotal role in shaping the representation of objects” in the revised manuscript and have restated the general question of our study in the introduction. Our motivation is on the long-lasting debate over the representation versus direct perception of affordance, specifically examining the “representationalization” of affordance. That is, we tested whether object affordance simply covaried directly with continuous constraints such as object size, a perspective aligned with the representation-free (direct perception) view, or whether affordance became representationalized, adhering to the representation-based view, constrained by body size. Such representationalization would generate a categorization between objects that are affordable and the environment that exceeds affordance.

To test these hypotheses, we first delineated the affordance of various objects. We agree with the reviewer that in this step a broader selection of objects and actions could mitigate the risk of our results being influenced by the specific selection of objects and actions. However, our results are unlikely to be biased, because our selection was guided by two key criteria, rather than being arbitrary. First, the objects were selected from the dataset in Konkle and Oliva's study (2011), which systematically investigated object size’ impact on object recognition, thus providing a well-calibrated range of sizes (i.e., from 14 cm to 7,618 cm) reflective of real-world objects. Second, the selected actions covered a wide range of daily humans-objects/environments interactions, from single-point movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing) based on the kinetics human action video dataset (Kay et al., 2017). Thus, this set of objects and actions is a representative sampling of typical human experiences.

Upon demonstrating a trough in perceived affordance similarity, we recognized the location of the affordance boundary coincidentally fell within the range of human body size. We agree with the reviewer that this observation of the coincidence between body size and the location of boundary alone is not sufficient for a mechanistic explanation, because variables co-varying with object sizes might also generate this coincidence. The identification of a more precise location for the boundary unlikely rules out alternative explanations of this kind. To establish a causal link between body size and the affordance boundary, we opted for a direct manipulation of body sizes through imagination, while keeping all other variables constant across conditions. This approach allowed us to examine whether and how the affordance boundary shifts in response to body size changes.

Regarding the between-subjects design of the imagination experiment, we wish to clarify that this design aimed to prevent carryover effects. Although a within-subjects design indeed is more sensitive in detecting manipulation effects by accounting for subject variability, it risks contamination across conditions. Specifically, transitioning immediately between different imagined body sizes poses a challenge, and sequential participation could induce undesirable response strategies, such as deliberately altering responses to the same objects in different conditions. The between-subjects design, which susceptible to participant variability (e.g., “pre-existing differences between groups” suggested by the reviewer), avoids such contamination. In addition, we employed random assignment of participants to different conditions (cat-size versus elephant-size).

The body imagination experiment provided causal evidence of an embodied discontinuity, suggesting the boundary is tied to the agent’s motor capacity, rather than amodal sources. The LLMs experiment then sought to test a prediction from the embodied theories of cognition: the supramodality of object perception. Especially, we asked whether the embodied discontinuity is supramodally accessible, using LLMs to assess whether affordance perception discretization is supramodally accessible beyond the sensorimotor domain through linguistic understanding. From this perspective, our LLM experiment was employed not to affirm affordance-based perception but to examine and support a prediction by the embodied theories of cognition.

Finally, our preliminary fMRI study aimed to conceptually replicate the perceptual discontinuity and explore it neural correlates using a subset of objects and actions from the behaviour experiments. This approach was chosen to achieve stable neural responses and enhance study power, employing the congruent effect (congruent - incongruent) as a metric for affordance processing (e.g., Kourtis et al., 2018), which reflects facilitated responses when congruent with objects’ affordances (e.g., Ellis & Tucker, 2000). Nevertheless, we recognize the limitation of a relatively small sample sizes, for details please see our reply to the reviewer #1.

In summary, our findings contribute to the discourse on computationalism’s representation concept and influence of these representations, post-discretization, on processes beyond the sensorimotor domain. We hope that these additional explanations and revisions effectively address the concerns raised and demonstrate our commitment to enhancing the quality of our work in light of your valuable feedback. By acknowledging these limitations and directions for future research, we hope to further the discourse on affordance perception and embodied cognition.

References

Ellis, R., & Tucker, M. (2000). Micro‐affordance: The potentiation of components of action by seen objects. British Journal of Psychology, 91(4), 451-471.

Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., ... & Zisserman, A. (2017). The kinetics human action video dataset. arXiv preprint arXiv:1705.06950.

Konkle, T., & Oliva, A. (2011). Canonical visual size for real-world objects. Journal of Experimental Psychology: human perception and performance, 37(1), 23.

Kourtis, D., Vandemaele, P., & Vingerhoets, G. (2018). Concurrent cortical representations of function-and size-related object affordances: an fMRI study. Cognitive, Affective, & Behavioral Neuroscience, 18, 1221-1232.

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

Responses to the reviewers

We deeply appreciate the reviewers’ comments. In response to the concerns raised, we have revised the manuscript accordingly. Below we address each of the reviewers’ comments in turn. Reviewers’ comments are in bold, and our responses are immediately below. Changes in the main text are presented in italics, followed by corresponding page and line numbers in the revised manuscript. We also highlighted tracks of change in the revised manuscript.

Reviewer #1 (Public Review):

(1) The main behavioural work appears well-powered (>500 Ps). This sample reduces to 100 for the imagination study, after removing Ps whose imagined heights fell within the human range (100-200 cm). Why 100-200 cm? 100 cm is pretty short for an adult. Removing 80% of data feels like conclusions from the imagination study should be made with caution.

R1: Sorry for the confusion. We did not remove 80% of the participants; instead, a separate sample of participants was recruited in the imagination experiment. The size of this sample (100 participants) was indeed smaller than the first experiment (528 participants), because the first experiment was set for exploratory purposes and was designed to be over-powered. Besides, inspection of the data of the first sample showed that the affordance pattern became stable after the first 50 participants. We explained this consideration in the revised manuscript:

(p 21, ln 490) “…, another one hundred and thirty-nine participants from the same population were recruited from the same platform. We chose a smaller sample size for the imagination experiment compared to that for the object-action relation judgement task, because inspection of the data of the first sample showed that the affordance pattern became stable after the first 50 participants.”

The average adult human height ranges from 140-170 cm for women and 150180 cm for men (NCD-RisC, 2016). Accordingly, the criterion of 100-200 cm covered this range and was set to ensure that participants unambiguously imagined a body schema different from that of human, as the tallest domestic cat below 100 cm according to the Guinness World Records and an elephant above 200 cm according to Crawley et al. (2017). We clarified these considerations in the revised manuscript:

(p 21, ln 494) “To maximize the validity of the manipulation, data from participants whose imagined height fell within the average human size range (100cm - 200cm) were excluded from further analysis. Consequently, 100 participants (49 males, aged from 17 to 39 years, mean age = 23.2 years) remained in the analysis. This exclusion criterion was broader than the standard adult human height range of 140cm to 180cm (NCD-RisC, 2016). This approach ensured that our analysis focused on participants who unambiguously imagined a body schema different from humans, yet within the known height range of cats and elephants.”

In addition, we also reanalysed the data with a more conservative criterion of 140cm to 180cm, and the results remained.

(2) There are only 12 Ps in the MRI study, which I think should mean the null effects are not interpreted. I would not interpret these data as demonstrating a difference between SPL and LOC/M1, but rather that some analyses happened to fall over the significance threshold and others did not.

R2: We would like to clarify that the null hypothesis of this fMRI study is the lack of two-way interaction between object size and object-action congruency, which was rejected by the observed significant interaction. That is, the interpretation of the present study did not rely on accepting any null effect.

Having said this, we admit that the fMRI experiment is exploratory and the sample size is small (12 participants), which might lead to low power in estimating the affordance effect. In the revision, we acknowledge this issue explicitly:

(p 16, ln 354) “…, supporting the idea that affordance is typically represented only for objects within the body size range. While it is acknowledged that the sample size of the fMRI study was small (12 participants), necessitating cautious interpretation of its results, the observed neural-level affordance discontinuity is notable. That is, qualitative differences in neural activity between objects within the affordance boundary and those beyond replicated our behavioral findings. This convergent evidence reinforced our claim that objects were discretized into two broad categories along the continuous size axis, with affordance only being manifested for objects within the boundary.”

(3) I found the MRI ROI selection and definition a little arbitrary and not really justified, which rendered me even more cautious of the results. Why these particular sensory and motor regions? Why M1 and not PMC or SMA? Why SPL and not other parietal regions? Relatedly, ROIs were defined by thresholding pF and LOC at "around 70%" and SPL and M1 "around 80%", and it is unclear how and why these (different) thresholds were determined.

R3: Our selection of these specific sensory and motor regions was based on prior literature reporting their distinct contribution to affordance perception (e.g., Borghi, 2005; Sakreida et al., 2016). The pFs was chosen as a representative region of the ventral visual stream, involved in object identification and classification, and the SPL was chosen as a representative region of the dorsal visual stream, involved in object perception and manipulation. The primary motor cortex (M1) has also been reported involved in affordance processing (e.g., McDannald et al., 2018), and we chose this region to probe the affordance congruency effect in the motor execution stage of the sense-think-act pathway. We did not choose the premotor cortex (PMC) and the supplementary motor area (SMA) because they were proposedly also involved in processes beyond motor execution (e.g., Hertrich et al., 2016; Kantak et al., 2012), and if any effect was observed, one cannot exclusively attribute the effect to motor execution. As for the parietal regions, our choice of the SPL not IPL/IPS is based on the meta-analysis of affordance processing areas where only the SPL shows consistent activation for both stable and variable affordances (Sakreida et al., 2016). We chose the SPL to capture effects on either type of affordances. In revision, we explained these considerations in the revised manuscript:

(p 14, ln 280) “In addition to the pFs and SPL, we also examined the congruency effect in the lateral occipital cortex (LO), which is involved in object representation (e.g., Grill-Spector et al., 2000; Konkle & Caramazza, 2013) and provides inputs to both the pFs and SPL (Hebart et al., 2018). Meanwhile, the primary motor cortex (M1), which receives inputs from the dorsal stream (Vainio & Ellis, 2020), is involved in affordance processing (e.g., McDannald et al., 2018) and action executions (Binkofski et al., 2002).”

(p 29, ln 684) “We chose the pFs, LO, SPL, and M1 as ROIs based on existing literature highlighting their distinct contributions to affordance perception (Borghi, 2005; Sakreida et al., 2016).”

Regarding ROI thresholding, we apologize for the lack of clarity in reporting the thresholds in the original manuscript. The thresholds were different between ventral regions (from Zhen et al., 2015) and dorsal regions (from Fan et al., 2016) because they are from two different atlases. The former was constructed by probability maps of task-state fMRI activity during localizer contrast with stationary images and the latter by a parcellation of the brain's functional connectivity; therefore, the numerical values in these two atlases are not comparable. To extract ROIs with comparable sizes, we selected a threshold of 55% for the pFs, 90% for the LO, 78% for the SPL, and 94% for the M1 in the original manuscript.

To rule out the possibility that the results were distorted by the specific choice of thresholds, we re-ran the analysis with a threshold 80% for all ROIs (resulting in 456 voxels in the lpFs, 427 voxels in the rpFs, 1667 voxels in the lLO, 999 voxels in the rLO, 661 voxels in the lSPL, 310 voxels in the rSPL, 231 voxels in the lM1, and 327 voxels in the rM1) with the 2-by-2 repeated-measures ANOVA. Our results remained the same qualitatively. A significant interaction between object type and congruency was observed in the pFs (F(1,11) = 24.87, p <.001, 𝜂2=.69) and SPL (F(1,11) = 14.62, p =.003, 𝜂2=.57). The simple effect analysis revealed the congruency effect solely for objects within body size range (pFs: p =.003; SPL: p <.001), not for objects beyond (ps >.30). For the M1 and LO, neither significant main effects (ps >.11) nor interactions were found (ps >.20).

We clarified our choice of thresholds in the methods section in the revised manuscript:

(p 29, ln 686) “Eight ROIs depicted in Fig. 3b were constructed based on the overlap between the whole-brain map activated by both objects within and beyond and corresponding functional atlases (the pFs and LO from Zhen et al., 2015; the SPL and M1 from Fan et al., 2016). To achieve ROIs of similar sizes, we applied varying thresholds to each cortical area: for the pFs and LO, the atlases were thresholded at 55% and 90%, resulting in 266 voxels in the lpFs, 427 in the rpFs, 254 in the lLO and 347 in the rLO; for the SPL and M1, the atlases were thresholded at 78% and 94%, resulting in 661 voxels in the lSPL, 455 in the rSPL, 378 in the lM1, and 449 in the rM1. In the subsequent analysis, homologous areas spanning both cortical hemispheres were merged.”

(4) Discussion and theoretical implications. The authors discuss that the MRI results are consistent with the idea we only represent affordances within body size range. But the interpretation of the behavioural correlation matrices was that there was this similarity also for objects larger than body size, but forming a distinct cluster. I therefore found the interpretation of the MRI data inconsistent with the behavioural findings.

R4: We speculated that the similarity in action perception among objects beyond the body size range may be due to these objects being similarly conceptualized as ‘environment’, in contrast to the objects within the body size range, which are categorized differently, namely as the ‘objects for the animal.’ Accordingly, in cortical regions involved in object processing, objects conceptualized as ‘environment’ unlikely showed the congruency effect, distinct from objects within the body size range. We have explained this point in the revised manuscript:

(p 17, ln 370) “…which resonates the embodied influence on the formation of abstract concepts (e.g., Barsalou, 1999; Lakoff & Johnson, 1980) of objects and environment. Consistently, our fMRI data did not show the congruency effect for objects beyond the body size range, distinct from objects within this range, suggesting a categorization influenced by objects’ relative size to the human body.”

(5) In the discussion, the authors outline how this work is consistent with the idea that conceptual and linguistic knowledge is grounded in sensorimotor systems. But then reference Barsalou. My understanding of Barsalou is the proposition of a connectionist architecture for conceptual representation. I did not think sensorimotor representation was privileged, but rather that all information communicates with all other to constitute a concept.

R5: We are sorry for the confusion. We do not intend to argue that the sensorimotor representation is privileged. Instead, we would like to simply emphasize their engagement in concept. According to our understanding, Barsalou’s Perceptual Symbol Theory proposes that grounded concepts include sensorimotor information, and conceptual knowledge is grounded in the same neural system that supports action (Barsalou, 1999). This is consistent with our proposal that the affordance boundary locked to an animal’s sensorimotor capacity might give rise to a conceptual-ish representation of object-ness specific to the very animal. We have clarified this point in the introduction and discussion on the conceptual knowledge and sensorimotor information:

In the introduction (p 2, ln 59) “…, and the body may serve as a metric that facilitates meaningful engagement with the environment by differentiating objects that are accessible for interactions from those not. Further, grounded cognition theory (see Barsalou, 2008 for a review) suggests that the outputs of such differentiation might transcend sensorimotor processes and integrate into supramodal concepts and language. From this perspective, we proposed two hypotheses...”

In the discussion (p 18, ln 392) “Indeed, it has been proposed that conceptual knowledge is grounded in the same neural system that supports action (Barsalou, 1999; Glenberg et al., 2013; Wilson & Golonka, 2013), thereby suggesting that sensorimotor information, along with other modal inputs, may be embedded in language (e.g., Casasanto, 2011; Glenberg & Gallese, 2012; Stanfield & Zwaan, 2001), as the grounded theory proposed (see Barsalou, 2008 for a review).”

(6) More generally, I believe that the impact and implications of this study would be clearer for the reader if the authors could properly entertain an alternative concerning how objects may be represented. Of course, the authors were going to demonstrate that objects more similar in size afforded more similar actions. It was impossible that Ps would ever have responded that aeroplanes afford grasping and balls afford sitting, for instance. What do the authors now believe about object representation that they did not believe before they conducted the study? Which accounts of object representation are now less likely?

R6: We thank the reviewer for this suggestion. The theoretical motivation of the present study is to explore whether, for continuous action-related physical features (such as object size relative to the agents), affordance perception introduces discontinuity and qualitative dissociation, i.e., to allow the sensorimotor input to be assigned into discrete states/kinds, as representations envisioned by the computationalists; alternatively, whether the activity may directly mirror the input, free from discretization/categorization/abstraction, as proposed by the Replacement proposal of some embodied theories on cognition.

By addressing this debate, we hoped to shed light on the nature of representation in, and resulted from, the vision-for-action processing. Our finding of affordance discontinuity suggests that sensorimotor input undergoes discretization implied in the computationalism idea of representation. Further, not contradictory to the claims of the embodied theories, these representations do shape processes out of the sensorimotor domain, but after discretization.

We have now explained our hypotheses and alternatives explicitly in the revised introduction and discussion:

In the introduction (p 2, ln 45) “However, the question of how object perception is influenced by the relative size of objects in relation to the human body remains open. Specifically, it is unclear whether this relative size simply acts as a continuous variable for locomotion reference, or if it affects differentiating and organizing object representation based on their ensued affordances.”

In the discussion (p 14, ln 295) “One long-lasting debate on affordance centers on the distinction between representational and direct perception of affordance. An outstanding theme shared by many embodied theories of cognition is the replacement hypothesis (e.g., Van Gelder, 1998), which challenges the necessity of representation as posited by computationalism’s cognitive theories (e.g., Fodor, 1975). This hypothesis suggests that input is discretized/categorized and subjected to abstraction or symbolization, creating discrete stand-ins for the input (e.g., representations/states). Such representationalization would lead to a categorization between the affordable (the objects) and those beyond affordance (the environment), in contrast to the perspective offered by embodied theories. The present study probed this ‘representationalization’ of affordance by examining whether affordance perception introduces discontinuity and qualitative dissociation in response to continuous action-related physical features (such as object size relative to the agents), which allows sensorimotor input to be assigned into discrete states/kinds, in line with the representation-based view under the constraints of body size. Alternatively, it assessed whether activity directly mirrors the input, free from discretization/categorization/abstraction, in line with the representation-free view.

First, our study found evidence demonstrating discretization in affordance perception. Then, through the body imagination experiment, we provided causal evidence suggesting that this discretization originates from sensorimotor interactions with objects rather than amodal sources, such as abstract object concepts independent of agent motor capability. Finally, we demonstrated the supramodality of this embodied discontinuity by leveraging the recent advances in AI. We showed that the discretization in affordance perception is supramodally accessible to disembodied agents such as large language models (LLMs), which lack sensorimotor input but can access linguistic materials built upon discretized representations. These results collectively suggest that sensorimotor input undergoes discretization, as implied in the computationalism’s idea of representation. Note that, these results are not contradictory to the claim of the embodied theories, as these representations do shape processes beyond the sensorimotor domain but after discretization.

This observed boundary in affordance perception extends the understanding of the discontinuity in perception in response to the continuity of physical inputs (Harnad, 1987; Young et al., 1997).”

Reviewer #1 (Recommendations For The Authors):

a) I would recommend providing further justification for why 100-200 cm were used as the cut-offs reflecting acceptable imagined body size. Were these decisions preregistered anywhere? If so, please state.

Ra: Please see R1.

b) I would encourage the authors to call the MRI a small pilot study throughout, including in the abstract.

Rb: We completely agree and have indicated the preliminary nature of this study in the revised version:

(p 11, ln 236) “To test this speculation, we ran an fMRI experiment with a small number of participants to preliminarily investigate the neural basis of the affordance boundary in the brain by measuring neural activity in the dorsal and ventral visual streams when participants were instructed to evaluate whether an action was affordable by an object (Fig. 3a).”

c) Please provide much further justification of ROI selection, why these thresholds were chosen, and therefore why they are different across regions.

Rc: Please see R3.

d) Further elucidation in the discussion would help the reader interpret the MRI data, which should always be interpreted also in light of the behavioural findings.

Rd: Please see R4.

e) The authors may wish to outline precisely what they claim concerning the nature of conceptual/linguistic representation. Is sensorimotor information privileged or just part of the distributed representation of concepts?

Re: This is a great point. For details of corresponding revision, please see R5.

f) There are some nods to alternative manners in which we plausibly represent objects (e.g. about what the imagination study tells us) but I think this theoretical progression should be more prominent.

Rf: We thank the reviewer for this suggestion. For details of corresponding revision, please see R6.

Reviewer #2 (Public Review):

(1) A limitation of the tests of LLMs may be that it is not always known what kinds of training material was used to build these models, leading to a possible "black box" problem. Further, presuming that those models are largely trained on previous human-written material, it may not necessarily be theoretically telling that the "judgments" of these models about action-object pairs show human-like discontinuities. Indeed, verbal descriptions of actions are very likely to mainly refer to typical human behaviour, and so the finding that these models demonstrate an affordance discontinuity may simply reflect those statistics, rather than evidence that affordance boundaries can arise independently even without "organism-environment interactions" as the authors claim here.

R1: We agree that how LLMs work is a “black box”, and thus it is speculative to assume them to possess any human-like ability, because, as the reviewer pointed out, “these models demonstrate an affordance discontinuity may simply reflect those statistics.” Indeed, our manuscript has expressed a similar idea: “We speculated that ChatGPT models may have formed the affordance boundary through a human prism ingrained within its linguistic training corpus. (p 16 ln 338)”. That is, we did not intend to claim that such information is sufficient to replace sensorimotor-based interaction, or to restore human-level capability, for which we indeed speculated that embodied interaction is necessary. In the revised manuscript, we have clarified our stand that the mechanism generating the observed affordance boundary in LLMs might be different from that in human cognition, and urged future studies to explore this possibility:

(p 18, ln 413) “…, as well as alignment methods used in fine-tuning the model (Ouyang et al., 2022). Nevertheless, caution should be taken when interpreting the capabilities of LLMs like ChatGPT, which are often considered “black boxes.” That is, our observation indicates that some degree of sensorimotor information is embedded within human language materials presumably through linguistic statistics, but it is not sufficient to assert that LLMs have developed a human-like ability to represent affordances. Furthermore, such information alone may be insufficient for LLMs to mimic the characteristics of the affordance perception in biological intelligence. Future studies are needed to elucidate such limitation.”

Indeed, because of this potential dissociation, our LLM study might bear novel implications for the development of AI agents. We elaborated on them in the revised discussion on LLMs:

(p 19, ln 427) “…, represents a crucial human cognitive achievement that remains elusive for AI systems. Traditional AI (i.e., task-specific AI) has been confined with narrowly defined tasks, with substantial limitations in adaptability and autonomy. Accordingly, these systems have served primarily as tools for humans to achieve specific outcomes, rather than as autonomous agents capable of independently formulating goals and translating them into actionable plans. In recent years, significant efforts have been directed towards evolving traditional AI into more agent-like entities, especially in domains like navigation, object manipulation, and other interactions with the physical world. Despite these advancements, the capabilities of AI still fall behind human-level intelligence. On the other hand, embodied cognition theories suggest that sensorimotor interactions with the environment are foundational for various cognitive domains. From this point of view, endowing AI with human-level abilities in physical agent-environment interactions might provide an unreplaceable missing piece for achieving Artificial General Intelligence (AGI). This development would significantly facilitate AI’s role in robotics, particularly in actions essential for survival and goal accomplishment, a promising direction for the next breakthrough in AI (Gupta et al., 2021; Smith & Gasser, 2005).

However, equipping a disembodied AI with the ability for embodied interaction planning within a specific environment remains a complex challenge. By testing the potential representationalization of action possibilities (affordances) in both humans and LLMs, the present study suggests a new approach to enhancing AI’s interaction ability with the environment. For instance, our finding of supramodal affordance representation may indicate a possible pathway for disembodied LLMs to engage in embodied physical interactions with their surroundings. From an optimistic view, these results suggest that LLM-based agents, if appropriately designed, may leverage affordance representations embedded in language to interact with the physical world. Indeed, by clarifying and aligning such representations with the physical constitutes of LLM-based agents, and even by explicitly constructing an agent-specific object space, we may foster the sensorimotor interaction abilities of LLM-based agents. This progression could lead to achieving animal-level interaction abilities with the world, potentially sparking new developments in the field of embodied cognition theories.”

(2) The authors include a clever manipulation in which participants are asked to judge action-object pairs, having first adopted the imagined size of either a cat or an elephant, showing that the discontinuity in similarity judgments effectively moved to a new boundary closer to the imagined scale than the veridical human scale. The dynamic nature of the discontinuity suggests a different interpretation of the authors' main findings. It may be that action affordance is not a dimension that stably characterises the long-term representation of object kinds, as suggested by the authors' interpretation of their brain findings, for example. Rather these may be computed more dynamically, "on the fly" in response to direct questions (as here) or perhaps during actual action behaviours with objects in the real world.

R2: We thank the reviewer for pointing out the dynamic nature of affordance perception in our study. This feature indeed reinforced our attribution of the boundary into an affordance-based process instead of a conceptual or semantic process, the latter of which would predict the action possibilities being a fixed belief about the objects, instead of being dynamically determined according to the feature of the agent-object dyads. In addition, this dynamic does not contradict with our interpretation of the observed boundary in affordance perception. With this observation, we speculated that continuous input was abstracted or representationalized into discontinued categories, and the boundary between these categories was drawn according to the motor capacity of the agent. The finding of the boundary adapting to manipulation on body schema suggests that the abstraction/representationalization dynamically updates according to the current belief of motor capacity and body schema of the animal. In addition, we agree that future studies are needed to examine the dynamics of the abstraction/representationalization of affordance, probably by investigating the evolvement of affordance representation during ongoing actual interactions with novel objects or manipulated motor capability. These points are now addressed in the revision:

(p 17, ln 380) “Therefore, this finding suggests that the affordance boundary is cognitively penetrable, arguing against the directness of affordance perception (e.g., Gibson, 1979; Greeno, 1994; Prindle et al., 1980) or the exclusive sensorimotor origin of affordances (e.g., Gallagher, 2017; Thompson, 2010; Hutto & Myin, 2012; Chemero, 2013). Further, this finding that the boundary adapted to manipulation on body schema suggests that the abstraction/representationalization may be dynamically updated in response to the current motor capacity and body schema of the agent, suggesting that the affordance-based process is probably determined dynamically by the nature of the agent-object dyads, rather than being a fixed belief about objects. Future studies could explore the dynamics of affordance representationalization, probably by investigating how affordance representations evolve during active interactions with novel objects or under conditions of altered motor capabilities. Finally, our findings also suggest that disembodied conceptual knowledge pertinent to action likely modulates affordance perception.”

Reviewer #2 (Recommendations For The Authors):

a) As described, I think the authors could improve their discussion of the LLM work and consider more deeply possible different interpretations of their findings with those models. Are they really providing an independent data point about how objects may be represented, or instead is this a different, indirect way of asking humans the same questions (given the way in which these models are trained)?

Ra: Please see R1.

b) Some of the decisions behind the design of the fMRI experiment, and some of the logic of its interpretation, could be made clearer. Why those four objects per se? What kinds of confounds, such as familiarity, or the range of possible relevant actions per object, might need to be considered? Is there the possibility that relative performance on the in-scanner behavioural task may be in part responsible for the findings? Why were those specific regions of interest chosen and not others? The authors find that the dorsal and ventral regions make a univariate distinction between congruent and incongruent trials, but only for human-scale objects, but it was not clear from the framework that the authors adopted why that distinction should go in that direction (e.g. congruent > incongruent) nor why there shouldn't also be a distinction for the "beyond" objects? Finally, might some of these brain questions better be approached with an RSA or similar approach, as that would seem to better map onto the behavioural studies?

Rb: We thank the reviewer for the detailed suggestions.

Regarding the fMRI study, we have provided further justification on its rationale in the revised manuscript:

(p 11, ln 231) “The distinct categories of reported affordances demarcated by the boundary imply that the objects on either side of the boundary may be represented differently in the brain. We thus speculated that the observed behavioral discontinuity is likely underpinned by distinct neural activities, which give rise to these discrete ‘representations’ separated by the boundary.”

The objects used in the fMRI study were selected by taking into account the objective of the fMRI study, which was to provide the neural basis for the affordance discontinuity found in behaviour experiments. In other words, the fMRI study is not an exploratory experiment, but a validation experiment. To this end, we deliberately selected a small range of common objects to ensure that participants were sufficiently familiar with them, as confirmed through their oral reports. Furthermore, to ensure a fair comparison between the two categories of objects in terms of action possibility range, we predetermined an equal number of congruent and incongruent actions for each category. This arrangement was intended to eliminate any bias that might arise from different amount of action choices associated with each category. Therefore, the present object and action sets in the fMRI study, which were based on the behavior experiments, are sufficient for its purpose.

Regarding the possibility that the performance of the in-scanner behavioural task may be in part responsible for the findings, we analysed participants’ performance. Not surprisingly, participants demonstrated high consistency and accuracy in their responses:

𝑀𝑒𝑎𝑛𝐶𝑜𝑛𝑔𝑟𝑢𝑒𝑛𝑡_𝑂𝑏𝑗𝑒𝑐𝑡𝑊𝑖𝑡ℎ𝑖𝑛 = 0.991, SD = 0.018;

𝑀𝑒𝑎𝑛𝐼𝑛𝑐𝑜𝑛𝑔𝑟𝑢𝑒𝑛𝑡_𝑂𝑏𝑗𝑒𝑐𝑡𝑊𝑖𝑡ℎ𝑖𝑛 = 0.996, SD = 0.007;

𝑀𝑒𝑎𝑛𝐶𝑜𝑛𝑔𝑟𝑢𝑒𝑛𝑡_𝑂𝑏𝑗𝑒𝑐𝑡𝐵𝑒𝑦𝑜𝑛𝑑 = 0.996, SD = 0.004;

𝑀𝑒𝑎𝑛𝐼𝑛𝑐𝑜𝑛𝑔𝑟𝑢𝑒𝑛𝑡𝑂𝑏𝑗𝑒𝑐𝑡𝐵𝑒𝑦𝑜𝑛𝑑 = 0.998, SD = 0.002

in all conditions, suggesting constant active engagement with the task. Thus, the inscanner behaviour unlikely resulted in the lack of congruency effect for the ‘beyond’ objects observed in the brain.

Regarding the selection of ROIs, our decision to focus on these specific sensory and motor regions was based on existing literature highlighting their distinct contribution to affordance perception (Borghi, 2005; Sakreida et al., 2016). The pFs was chosen for its role in object identification and classification, while the SPL was chosen for its involvement in object manipulation. Additionally, the primary motor cortex (M1) is known to be engaged in affordance processing (e.g., McDannald et al., 2018), which was included to investigate the affordance congruency effect during the motor execution stage of the sense-think-act pathway. These considerations are detailed in the revised manuscript:

(p 14, ln 280) “In addition to the pFs and SPL, we also examined the congruency effect in the lateral occipital cortex (LO), which is involved in object representation (e.g., Grill-Spector et al., 2000; Konkle & Caramazza, 2013) and provides inputs to both the pFs and SPL (Hebart et al., 2018). Meanwhile, the primary motor cortex (M1), which receives inputs from the dorsal stream (Vainio & Ellis, 2020), is involved in affordance processing (e.g., McDannald et al., 2018) and action executions (Binkofski et al., 2002).”

(p 29, ln 684) “We chose the pFs, LO, SPL, and M1 as ROIs based on existing literature highlighting their distinct contributions to affordance perception (Borghi, 2005; Sakreida et al., 2016).”

Regarding the congruency effect, in our study, we followed the established fMRI research paradigm of employing the congruent effect as a measure of affordance processing (e.g., Kourtis et al., 2018), and the rationale behind the directionality of the distinction in our framework (congruent > incongruent) is grounded in the concept of affordance, in which the mere perception of a graspable object facilitates motor responses that are congruent with certain qualities of the object (e.g., Ellis & Tucker, 2000). From the interaction of congruency by object type, we observed only congruency effect for objects within rather than objects beyond. We speculate that the objects beyond the affordance boundary is generally beyond the motor capacities of the very animal, being too large for the animal to manipulate, thus no congruency effect was found. We have added these clarifications in the revised manuscript:

(p 11, ln 244) “The congruency effect, derived from the contrast of Congruent versus Incongruent conditions, is a well-established measure of affordance processing (e.g., Kourtis et al., 2018).”

(p 16, ln 340) “In contrast, objects larger than that range typically surpass the animal’s motor capabilities, rendering them too cumbersome for effective manipulation. Consequently, these larger objects are less likely to be considered as typical targets for manipulation by the animal, as opposed to the smaller objects. That is, they are perceived not as the “objects” in the animal’s eye, but as part of the background environment, due to their impracticality for direct interactions.”

Regarding the RSA analysis, we agree with the reviewer that RSA may offer a more direct comparison with similarities among objects. However, our primary objective in this fMRI study was to explore the neural basis of the affordance boundary observed in the behavioural study, rather than explaining the similarities in neural responses between different objects. For this reason, we did not conduct RSA analysis.

c) Page 4 Re statistical evaluation of the discontinuity in judgments, the authors might consider a Bayesian approach, which would be stronger than using "all ps > 0.05" to argue that within-boundary similarities are consistent and high.

Rc: We thank the reviewer for the suggestion on the Bayesian approach for significance tests, which has been now added in the revised manuscript:

In the results (p 4, ln 105) “This trough suggested an affordance boundary between size rank 4 and 5, while affordance similarities between neighboring ranks remained high (rs > 0.45) and did not significantly differ from each other (ps > 0.05, all 𝐵𝐹10 < 10) on either side of the boundary (Fig. 1d, left panel, green lines).”

In the methods (p 25, ln 597) “Pearson and Filon’s (1898) Z, implemented in R package “cocor” (Diedenhofen & Musch, 2015) was used to evaluate the significance of these similarities (alpha level = .05, one-tail test). For significance tests, Bayesian statistical analyses were conducted using the web version of the “bayesplay” R package (Colling, 2021). Specifically, the data (likelihood) model was specified as a normal distribution, where the correlation coefficients were transformed to Fisher’s z. The null hypothesis was specified as a standard normal distribution centred at zero. Conversely, the alternative hypothesis was specified as a normal distribution centred at 2. Bayes factors (BF10) were calculated and interpreted using the classification scheme suggested by Wagenmakers et al. (2011), wherein a Bayes factor greater than 10 is considered strong evidence for accepting H1 over H0.”

d) Page 4 One question I had about the big objects is whether their internal similarity and dissimilarity to smaller objects, might largely arise if most of the answers about actions for those larger objects are just "no"? This depends on the set of possible actions that were considered: the authors chose 14 from a previous study but did not describe these further or consider possible strengths/limitations of this selection. This is a very important point that needs addressing - to what extent are these findings "fragile" in that they relate only to that specific selection of 14 action kinds?

Rd: The action judgements for objects beyond body size were not mostly “no”; in fact, there was no significant difference between average action possibilities related to objects beyond (25%) and within (26%). Rather, the dissimilarity between objects within and those beyond likely arose from the difference in most-plausible action set they related. For example, the top three actions related to objects within are “grasp”, “hold” and “throw”, while those related to objects beyond are “sit”, “lift” and “stand”, as stated in our original manuscript: “A further analysis on the affordances separated by the boundary revealed that objects within human body size range were primarily subjected to hand-related actions such as grasping, holding and throwing. These affordances typically involve object manipulation with humans’ effectors. In contrast, objects beyond the size range of human body predominantly afforded actions such as sitting and standing, which typically require locomotion or posture change of the whole body around or within the objects (p 11 ln 229)”.

Regarding the validity of action selection, the selection of the objects and affordances in this study was guided by two key criteria. First, the objects were selected from the dataset published in Konkle and Oliva's study (2011), which systematically investigates the effect of object size on object recognition. Therefore, the range of object sizes, from 14 cm to 7,618 cm, is well-calibrated and represents a typical array of object sizes found in the real world. Second, the actions were selected to cover a wide range of daily humans-objects/environments interactions, from singlepoint movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing), based on the kinetics human action video dataset (Kay et al., 2017). Thus, this set of objects and actions is a sufficiently representative of typic human experiences. In revision, we have clarified these two criteria in the methods section:

(p 22, ln 517) “The full list of objects, their diagonal size, and size rankings were provided in Supplementary Table S6. The objects were selected from the dataset in Konkle and Oliva’s study (2011) to cover typic object sizes in the world (ranging from 14 cm to 7,618 cm), and actions related to these objects were selected to span a spectrum of daily humans-objects/environments interactions, from single-point movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing), based on the Kinetics Human Action Video Dataset (Kay et al., 2017).”

Having said this, we agree with reviewer that a larger set of objects and actions will facilitate finer localization of the representational discontinuity, which can be addressed in future studies

(p 16, ln 344): “…, due to their impracticality for direct interactions. Future studies should incorporate a broader range of objects and a more comprehensive set of affordances for finer delineation of the representational discontinuity between objects and the environment.”

e) Page 12 "no region showed the congruency effect for objects beyond the body size" in a whole brain analysis. What about a similar analysis for the humanscale objects? We must also keep in mind that with N=12 there may be relatively little power to detect such effects at the random-effects level, so this null finding may not be very informative.

Re: We thank the reviewer for this advice. The whole brain analysis on the congruency effect for human-scale objects (objects within) has now been included in the supplementary materials (please see Author response figure 1d (New Supplementary Fig. S4d) and Author response table 1 (New Supplementary Table S5) below).

Author response image 1.

Significant brain activations of different contrasts in the whole-brain level analysis. a, the effect of object type, positive values (warm color) indicated higher activation for objects within than objects beyond and negative values (cold color) indicated the opposite. b, the effect of congruency, positive values indicated higher activation in congruent than incongruent condition. c, the effect of interaction between object type and congruency, positive values indicated the larger congruency effect for objects within than beyond. d, the congruency effect for objects within. All contrasts were corrected with cluster-level correction at p < .05. The detailed cluster-level results for each contrast map can be found in Supplementary Table S2 to S5.

Author response table 1.

Cortical regions showing significant congruency effect (congruent versus incongruent) for objects within, whole-brain analysis (R = right hemisphere, L = left hemisphere; Z > 2.3, p = 0.05, cluster corrected)

Regarding the power of the fMRI study, we would like to clarify that, the critical test of this fMRI study is the two-way interaction of congruency effect by object size instead of the (null) congruency effect for the object beyond. Having said this, we agree that the sample size is small which might lead to lack of power in the fMRI study. In the revision we have now acknowledged this issue explicitly:

(p 16, ln 354) “…supporting the idea that affordance is typically represented only for objects within the body size range. While it is acknowledged that the sample size of the fMRI study was small (12 participants), necessitating cautious interpretation of its results, the observed neural-level affordance discontinuity is notable. That is, qualitative differences in neural activity between objects within the affordance boundary and those beyond replicated our behavior findings. This convergent evidence reinforced our claim that objects were discretized into two broad categories along the continuous size axis, with affordance only being manifested for objects within the boundary.”

f) Page 14 [the fMRI findings] "suggest that affordance perception likely requires perceptual processing and is not necessarily reflected in motor execution". This seems a large leap to make from a relatively basic experiment that tests only a small set of (arbitrarily chosen) objects and actions. It's important to keep in mind too that none of the studies here actually asked participants to interact with objects; that objects were shown as 2D images; and that the differences between real-world sizes of objects were greatly condensed by the way they are scaled for presentation on a computer screen (and such scaling is probably greater for the larger-than-human objects).

Rf: The action-congruency judgement task is widely used in the studies of affordance processing (e.g., Kourtis et al., 2018; Peelen & Caramazza, 2012), so does the practice of not including actual interaction with the objects and using 2D instead of 3D objects (e.g., Peelen & Caramazza, 2012; Matić et al., 2020). However, we are aware that alternative practice exists in the field and we agree that it would be interesting for future studies to test whether actual interactions and 3D objects presentation may bring any change on the affordance boundary observed in our study.

Our inference “affordance perception likely requires perceptual processing and is not necessarily reflected in motor execution” was based on the fMRI finding that the congruency effect only in cortical regions proposedly engaged in perceptual processing, but not in the M1 which is associated with motor execution. This significant two-way interaction pointed to a possibility that affordance processing may not necessarily manifest in motor execution.

We acknowledge the scaling issue inherent in all laboratory experiments, but we doubt that it significantly influenced our results. In fact, it is a common practice in studies on object size to present objects of different physical sizes as constantly sized images on a screen (e.g., Konkle & Oliva, 2012; Huang et al., 2022). Moreover, scaling does not change the smoothness of object sizes, whereas the affordance boundary represents a singularity point that disrupts this smoothness. Finally, regarding the limited variety of objects and actions, please see Rd.

g) Page 15 Why are larger objects "less interesting"? They have important implications for navigation, for example?

Rg: We are sorry for the confusion. Our intention was to express that objects beyond the affordance boundary are generally beyond motor capacities of the animal in question. As such, compared to smaller objects within the environment, these larger objects may not typically be considered as potential targets for manipulation. We have now corrected the wording in the revised text:

(p 16, ln 340) “In contrast, objects larger than that range typically surpass the animal’s motor capabilities, rendering them too cumbersome for effective manipulation. Consequently, these larger objects are less likely to be considered as typical targets for manipulation by the animal, as opposed to smaller objects in the environment. That is, they are perceived not as the “objects” in the animal’s eye, but as part of the background environment, due to their impracticality for direct interactions.”

h) Page 15 At several places I wondered whether the authors were arguing against a straw man. E.g. "existing psychological studies...define objects in a disembodied manner..." but no citations are given on this point, nor do the authors describe previous theoretical positions that would make a strong counter-claim to the one advocated here.

Rh: We are sorry for not presenting our argument clearly. Previous studies often define the object space based on object features alone, such as absolute size or function, without reference to the knowledge and the abilities of the agent (e.g., de Beeck et al., 2008; Konkle & Oliva, 2011). This perspective overlooks the importance of the features of the animal-object pairs. Gibson (1979) highlighted that an object’s affordance, which includes all action possibilities it offers to an animal, is determined by the object’s size relative to the animal’s size, rather than its real-world size. Under this embodied view, we argue that the object space is better defined by the features of the agent-object system, and this is the primary assumption and motivation of the present study. We have now clarified this point and added the references in the revision:

(p 2, ln 35) “A contemporary interpretation of this statement is the embodied theory of cognition (e.g., Chemero, 2013; Gallagher, 2017; Gibbs, 2005; Wilson, 2002; Varela et al., 2017), which, diverging from the belief that size and shape are inherent object features (e.g., de Beeck et al., 2008; Konkle & Oliva, 2011), posits that human body scale (e.g., size) constrains the perception of objects and the generation of motor responses.”

(p 17, ln 365) “Existing psychological studies, especially in the field of vision, define objects in a disembodied manner, primarily relying on their physical properties such as shape (e.g., de Beeck et al., 2008) and absolute size (e.g., Konkle & Oliva, 2011).”

Reviewer #3 (Public Review):

(1) Even after several readings, it is not entirely clear to me what the authors are proposing and to what extent the conducted work actually speaks to this. In the introduction, the authors write that they seek to test if body size serves not merely as a reference for object manipulation but also "plays a pivotal role in shaping the representation of objects." This motivation seems rather vague motivation and it is not clear to me how it could be falsified.

Similarly, in the discussion, the authors write that large objects do not receive "proper affordance representation," and are "not the range of objects with which the animal is intrinsically inclined to interact, but probably considered a less interesting component of the environment." This statement seems similarly vague and completely beyond the collected data, which did not assess object discriminability or motivational values.

Overall, the lack of theoretical precision makes it difficult to judge the appropriateness of the approaches and the persuasiveness of the obtained results. This is partly due to the fact that the authors do not spell out all of their theoretical assumptions in the introduction but insert new "speculations" to motivate the corresponding parts of the results section. I would strongly suggest clarifying the theoretical rationale and explaining in more detail how the chosen experiments allow them to test falsifiable predictions.

R1: We are sorry for the confusion about the theoretical motivation and rationale. Our motivation is on the long-lasting debate regarding the representation versus direct perception of affordance. That is, we tested whether object affordance would simply covary with its continuous constraints such as object size, in line with the representation-free view, or, whether affordance would be ‘representationalized’, in line with the representation-based view, under the constrain of body size. In revision, we have clarified the motivation and its relation to our approach:

In the introduction (p 2, ln 45): “However, the question of how object perception is influenced by the relative size of objects in relation to the human body remains open. Specifically, it is unclear whether this relative size simply acts as a continuous variable for locomotion reference, or if it affects differentiating and organizing object representations based on their ensued affordances.”

In the discussion (p 14, ln 295): “One long-lasting debate on affordance centers on the distinction between representational and direct perception of affordance. An outstanding theme shared by many embodied theories of cognition is the replacement hypothesis (e.g., Van Gelder, 1998), which challenges the necessity of representation as posited by computationalism’s cognitive theories (e.g., Fodor, 1975). This hypothesis suggests that input is discretized/categorized and subjected to abstraction or symbolization, creating discrete stand-ins for the input (e.g., representations/states). Such representationalization would lead to a categorization between the affordable (the objects) and those beyond affordance (the environment). Accordingly, computational theories propose the emergence of affordance perception, in contrast to the perspective offered by embodied theories. The present study probed this ‘representationalization’ of affordance by examining whether affordance perception introduces discontinuity and qualitative dissociation in response to continuous action-related physical features (such as object size relative to the agents), which allows sensorimotor input to be assigned into discrete states/kinds, in line with the representation-based view under the constraints of body size. Alternatively, it assessed whether activity directly mirrors the input, free from discretization/categorization/abstraction, in line with the representation-free view.

First, our study found evidence demonstrating discretization in affordance perception. Then, through the body imagination experiment, we provided causal evidence suggesting that this discretization originates from sensorimotor interactions with objects rather than amodal sources, such as abstract object concepts independent of agent motor capability. Finally, we demonstrated the supramodality of this embodied discontinuity by leveraging the recent advances in AI. We showed that the discretization in affordance perception is supramodally accessible to disembodied agents such as large language models (LLMs), which lack sensorimotor input but can access linguistic materials built upon discretized representations. These results collectively suggest that sensorimotor input undergoes discretization, as implied in the computationalism’s idea of representation. Note that, these results are not contradictory to the claim of the embodied theories, as these representations do shape processes beyond the sensorimotor domain but after discretization.

The observed boundary in affordance perception extends the understanding of the discontinuity in perception in response to the continuity of physical inputs (Harnad, 1987; Young et al., 1997).”

We are also sorry for the confusion about the expression “proper affordance representation”. We intended to express that the neural responses to objects beyond the boundary in the whole brain failed to reflect affordance congruency, and therefore did not show evidence of affordance processing. We have clarified this expression in the revised manuscript:

(p 12, ln 265) “Taken together, the affordance boundary not only separated the objects into two categories based on their relative size to human body, but also delineated the range of objects that evoked neural representations associated with affordance processing.”

Finally, we agree with the reviewer that the expressions, such as “not…inclined to interact” and “probably considered a less interesting component of the environment”, may be misleading. Rather, we intended to express that the objects beyond the affordance boundary is generally beyond the motor capacities of the very animal, being too large for the very animal to manipulated, as comparing to the smaller objects in the environment, may not be a typical target object for manipulation for the animal. We have revised these expressions in the manuscript and clarified their speculative nature:

(p 16, ln 340) “In contrast, objects larger than that range typically surpass the animal’s motor capabilities, rendering them too cumbersome for effective manipulation. Consequently, these larger objects are less likely to be considered as typical targets for manipulation by the animal, as opposed to the smaller objects. That is, they are perceived not as the “objects” in the animal’s eye, but as part of the background environment, due to their impracticality for direct interactions.”

(2) The authors used only a very small set of objects and affordances in their study and they do not describe in sufficient detail how these stimuli were selected. This renders the results rather exploratory and clearly limits their potential to discover general principles of human perception. Much larger sets of objects and affordances and explicit data-driven approaches for their selection would provide a far more convincing approach and allow the authors to rule out that their results are just a consequence of the selected set of objects and actions.

R2: The selection of the objects and affordances in this study was guided by two key criteria. First, the objects were selected from the dataset published in Konkle and Oliva's study (2011), which systematically investigates the effect of object size on object recognition. Therefore, the range of object sizes, from 14 cm to 7,618 cm, is well-calibrated and represents a typical array of object sizes found in the real world. Second, the actions were selected to cover a wide range of daily humans objects/environments interactions, from single-point movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing), based on the kinetics human action video dataset (Kay et al., 2017). Thus, this set of objects and actions is a sufficiently representative of typic human experiences. In revision, we have clarified these two criteria in the methods section:

(p 22, ln 517) “The full list of objects, their diagonal sizes, and size rankings were provided in Supplementary Table S6. The objects were selected from the dataset in Konkle and Oliva’s study (2011) to cover typic object sizes in the world (ranging from 14 cm to 7,618 cm), and actions related to these objects were selected to span a spectrum of daily humans-objects/environments interactions, from single-point movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing), based on the Kinetics Human Action Video Dataset (Kay et al., 2017).”

Having said this, we agree with reviewer that a larger set of objects and actions will facilitate finer localization of the representational discontinuity, which can be addressed in future studies

(p 16, ln 344): “…, due to their impracticality for direct interactions. Future studies should incorporate a broader range of objects and a more comprehensive set of affordances for finer delineation of the representational discontinuity between objects and the environment.”

(3) Relatedly, the authors could be more thorough in ruling out potential alternative explanations. Object size likely correlates with other variables that could shape human similarity judgments and the estimated boundary is quite broad (depending on the method, either between 80 and 150 cm or between 105 to 130 cm). More precise estimates of the boundary and more rigorous tests of alternative explanations would add a lot to strengthen the authors' interpretation.

R3: We agree with the reviewer that correlation analyses alone cannot rule out alternative explanations, as any variable co-varying with object sizes might also affect affordance perception. Therefore, our study experimentally manipulated the imagined body sizes, while keeping other variable constant across conditions. This approach provided evidence of a causal connection between body size and affordance perception, effectively ruling out alternative explanations. In revision, the rationale of experimentally manipulation of imagined body sizes has been clarified

(p 7, ln 152): “One may argue that the location of the affordance boundary coincidentally fell within the range of human body size, rather than being directly influenced by it. To rule out this possibility, we directly manipulated participants’ body schema, referring to an experiential and dynamic functioning of the living body within its environment (Merleau-Ponty & Smith, 1962). This allowed us to examine whether the affordance boundary would shift in response to changes in the imagined body size. This experimental approach was able to establish a causal link between body size and affordance boundary, as other potential factors remained constant. Specifically, we instructed a new group of participants to imagine themselves as small as a cat (typical diagonal size: 77cm, size rank 4, referred to as the “cat condition”), and another new group to envision themselves as large as an elephant (typical diagonal size: 577 cm, size rank 7, referred to as the “elephant condition”) throughout the task (Fig. 2a).”

Meanwhile, with correlational analysis, precise location of the boundary cannot help ruling out alternative explanation. However, we agree that future studies are needed to incorporate a broader range of objects and a more comprehensive set of affordances. For details, please see R2.

(4) Even though the division of the set of objects into two homogenous clusters appears defensible, based on visual inspection of the results, the authors should consider using more formal analysis to justify their interpretation of the data. A variety of metrics exist for cluster analysis (e.g., variation of information, silhouette values) and solutions are typically justified by convergent evidence across different metrics. I would recommend the authors consider using a more formal approach to their cluster definition using some of those metrics.

R4: We thank the reviewer for the suggestion. We performed three analyses on this point, all of which consistently indicated the division of objects into two distinct groups along the object size axis.

First, a hierarchical clustering analysis of the heatmaps revealed a two-maincluster structure, which is now detailed in the revised methods section (p 25, ln 589) “A hierarchical clustering analysis was performed, employing the seaborn clustermap method with Euclidean distance and Complete linkage (Waskom, 2021).”

Second, the similarity in affordances between neighbouring size ranks revealed the same two-main-cluster structure. In this analysis, each object was assigned a realworld size rank, and then Pearson’s correlation was calculated as the affordance similarity index for each pair of neighbouring size ranks to assess how similar the perceived affordances were between these ranks. Our results showed a clear trough in affordance similarity, with the lowest point approaching zero, while affordance similarities between neighbouring ranks on either side of the boundary remained high, confirming the observation that objects formed two groups based on affordance similarity.

Finally, we analysed silhouette values for this clustering analysis, where 𝑎𝑖 represents the mean intra-cluster distance, and 𝑏𝑖 represents the mean nearest-cluster distance for each data point i. The silhouette coefficient is calculated as (Rousseeuw, 1987):

The silhouette analysis revealed that the maximum silhouette value coefficient corresponded to a cluster number of two, further confirming the two-cluster structure (please see Author response table 2 below).

Author response table 2.

The silhouette values of a k-means clustering when k (number of clusters) = 2 to 10

(5) While I appreciate the manipulation of imagined body size, as a way to solidify the link between body size and affordance perception, I find it unfortunate that this is implemented in a between-subjects design, as this clearly leaves open the possibility of pre-existing differences between groups. I certainly disagree with the authors' statement that their findings suggest "a causal link between body size and affordance perception."

R5: The between-subjects design in the imagination experiment was employed to prevent contamination between conditions. Specifically, after imagining oneself as a particular size, it can be challenging to immediately transition to envisioning a different body size. In addition, participating sequentially participate in two conditions that only differ in imagined body sizes may lead to undesirable response strategies, such as deliberately altering responses to the same objects in the different conditions. The reason of employing the between-subjects design is now clarified in the revised text (p 7, ln 161): “A between-subject design was adopted to minimize contamination between conditions. This manipulation was effective, as evidenced by the participants’ reported imagined heights in the cat condition being 42 cm (SD = 25.6) and 450 cm (SD = 426.8) in the elephant condition on average, respectively, when debriefed at the end of the task.”

Further, to address the concern that “pre-existing differences between groups” would generate this very result, we adhered to standard protocols such as random assignment of participants to different conditions (cat-size versus elephant-size). Moreover, experimentally manipulating one variable (i.e., body schema) to observe its effect on another variable (i.e., affordance boundary) is the standard method for establishing causal relationships between variables. We could not think of other better ways for this objective.

(6) The use of LLMs in the current study is not clearly motivated and I find it hard to understand what exactly the authors are trying to test through their inclusion. As noted above, I think that the authors should discuss the putative roles of conceptual knowledge, language, and sensorimotor experience already in the introduction to avoid ambiguity about the derived predictions and the chosen methodology. As it currently stands, I find it hard to discern how the presence of perceptual boundaries in LLMs could constitute evidence for affordance-based perception.

R6: The motivation of LLMs is to test the supramodality of this embodied discontinuity found in behavioral experiments: whether this discontinuity is accessible beyond the sensorimotor domain. To do this, we leveraged the recent advance in AI and tested whether the discretization observed in affordance perception is supramodally accessible to disembodied agents which lack access to sensorimotor input but only have access to the linguistic materials built upon discretized representations, such as large language models (LLM). The theoretical motivation and rationale regarding the LLM study are now included in the introduction and discussion:

In the introduction (p 2, ln 59) “…, and the body may serve as a metric that facilitates meaningful engagement with the environment by differentiating objects that are accessible for interactions from those not. Further, grounded cognition theory (see Barsalou, 2008 for a review) suggests that the outputs of such differentiation might transcend sensorimotor processes and integrate into supramodal concepts and language. From this perspective, we proposed two hypotheses...”

In the introduction (p 3, ln 70) “Notably, the affordance boundary varied in response to the imagined body sizes and showed supramodality. It could also be attained solely through language, as evidenced by the large language model (LLM), ChatGPT (OpenAI, 2022).”

For details in the discussion, please see R1.

(7) Along the same lines, the fMRI study also provides very limited evidence to support the authors' claims. The use of congruency effects as a way of probing affordance perception is not well motivated. What exactly can we infer from the fact a region may be more active when an object is paired with an activity that the object doesn't afford? The claim that "only the affordances of objects within the range of body size were represented in the brain" certainly seems far beyond the data.

R7: In our study, we followed the established fMRI research paradigm of employing the congruent effect as a measure of affordance processing (e.g., Kourtis et al., 2018). The choice of this paradigm has now been clarified in the revised manuscript (p 11, ln 244): “The congruency effect, derived from the contrast of Congruent versus Incongruent conditions, is a well-established measure of affordance processing (e.g., Kourtis et al., 2018).”

The statement that “only the affordances of objects within the range of body size were represented in the brain” is based on the observed interaction of congruency by object size. In the revised text, we have weakened this statement to better align with the direct implications of the interaction effect (p 1 ln 22): “A subsequent fMRI experiment revealed evidence of affordance processing exclusively for objects within the body size range, but not for those beyond. This suggests that only objects capable of being manipulated are the objects capable of offering affordance in the eyes of an organism.”

(8) Importantly (related to my comments under 2) above), the very small set of objects and affordances in this experiment heavily complicates any conclusions about object size being the crucial variable determining the occurrence of congruency effects.

R8: The objective of the fMRI study was to provide the neural basis for the affordance discontinuity found in behaviour experiments. In other words, the fMRI study is not an exploratory experiment, and therefore, the present object and action sets, which are based on the behaviour experiments, are sufficient.

(9) I would also suggest providing a more comprehensive illustration of the results (including the effects of CONGRUENCY, OBJECT SIZE, and their interaction at the whole-brain level).

R9: We agree and in revision, we have now included these analyses in the supplementary material (p 30, ln 711): “For the whole-brain analyses on the congruency effect, the object size effect, and their interaction, see Supplementary Fig. S4 and Table S2 to S5.” Please see Author response image 2 (New Supplementary Fig. S4) and Author responses tables 3 to 5 (New Supplementary Table S2 to S4) below.

Author response image 2.

Significant brain activations of different contrasts in the whole-brain level analysis. a, the effect of object type, positive values (warm color) indicated higher activation for objects within than objects beyond and negative values (cold color) indicated the opposite. b, the effect of congruency, positive values indicated higher activation in congruent than incongruent condition. c, the effect of interaction between object type and congruency, positive values indicated the larger congruency effect for objects within than beyond. d, the congruency effect for objects within. All contrasts were corrected with cluster-level correction at p < .05. The detailed cluster-level results for each contrast map can be found in Supplementary Table S2 to S5.

Author response table 3.

Cortical regions reaching significance in the contrasts of (A) objects within versus object beyond and (B) objects beyond versus objects within, whole-brain analysis (R = right hemisphere, L = left hemisphere; Z > 2.3, p = 0.05, cluster corrected).

Author response table 4.

Cortical regions reaching significance in contrasts of (A) congruent versus incongruent and (B) incongruent versus congruent, whole-brain analysis (R = right hemisphere, L = left hemisphere; Z > 2.3, p = 0.05, cluster corrected).

Author response table 5.

Review Table 5 (New Supplementary Table S4). Cortical regions showing significant interaction between object type and congruency, whole-brain analysis (OW = Objects within, OB = Objects beyond; R = right hemisphere, L = left hemisphere; Z > 2.3, p = 0.05, cluster corrected)

Reviewer #3 (Recommendations For The Authors):

a. >a) Clarify all theoretical assumptions already within the introduction and specify how the predictions are tested (and how they could be falsified).

Ra: Please see R1.

b. >b) Explain how the chosen experimental approach relates to the theoretical questions under investigation (e.g., it is not clear to me how affordance similarity ratings can inform inference about which part of the environment is perceived as more or less manipulable).

Rb: We thank the reviewer for the suggestion, and the theoretical motivation and rationale are now clarified. For details, please see R1.

c. >c) Include a much larger set of objects and affordances in the behavioural experiments (that is more generalizable and also permits a more precise estimation of the boundary), and use a more rigorous methodology to justify a particular cluster solution.

Rc: Please see R2 for the limited variance of objects and actions, and R4 for more analyses on the boundary.

d. >d) Clearly motivate what the use of LLMs can contribute to the study of affordance perception.

Rd: Please see R6.

e) Clearly motivate why congruency effects are thought to index "affordance representation in the brain" Re: Please see R7.

e) Include a much larger set of objects and affordances in the fMRI study.

Re: Please see R7.

f) Consider toning down the main conclusions based on the limitations outlined above.

Rf: We have toned down the main conclusions accordingly.

We are profoundly grateful for the insightful comments and suggestions provided by the three reviewers, which have greatly improved the quality of this manuscript.   References

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Author Response

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

We sincerely thank the reviewers for their in-depth consideration of our manuscript and their helpful reviews. Their efforts have made the paper much better. We have responded to each point. The previously provided public responses have been updated they are included after the private response for convenience.

Reviewer #1 (Recommendations For The Authors):

1. In general, the manuscript will benefit from copy editing and proof reading. Some obvious edits;

2. Page 6 line 140. Do the authors mean Cholera toxin B?

Response: We corrected this error and went through the entire paper carefully correcting for grammar and increased clarity.

• Page 8 line 173. Methylbetacyclodextrin is misspelled.

Response: Yes, corrected.

• Figure 4c is missing representative traces for electrophysiology data.

• Figure 4. Please check labeling ordering in figure legend as it does not match the panels in the figure.

Thank you for the correction and we apologize for the confusion in figure 4. We uploaded an incomplete figure legend, and the old panel ‘e’ was not from an experiment that was still in the figure. It was removed and the figure legends are now corrected.

• Please mention the statistical analysis used in all figure legends.

Response: Thank you for pointing out this omission, statistics have been added.

• Although the schematics in each figure helps guide readers, they are very inconsistent and sometimes confusing. For example, in Figure 5 the gating model is far-reaching without conclusive evidence, whereas in Figure 6 it is over simplified and unclear what the image is truly representing (granted that the downstream signaling mechanism and channel is not known).

Response: Figure 5d is the summary figure for the entire paper. We have made this clearer in the figure legend and we deleted the title above the figure that gave the appearance that the panel relates to swell only. It is the proposed model based on what we show in the paper and what is known about the activation mechanism of TREK-1.

Figure 6 is supposed to be simple. It is to help the reader understand that when PA is low mechanical sensitivity is high. Without the graphic, previous reviewers got confused about threshold going down and mechanosensitivity going up and how the levels of PA relate. Low PA= high sensitivity. We’ve added a downstream effector to the right side of the panel to avoid any biased to a putative downstream channel effector. The purpose of the experiment is to show PLD has a mechanosensitive phenotype in vivo.

Reviewer #2 (Recommendations For The Authors):

This manuscript outlines some really interesting findings demonstrating a mechanism by which mechanically driven alterations in molecular distributions can influence a) the activity of the PLD2 molecule and subsequently b) the activation of TREK-1 when mechanical inputs are applied to a cell or cell membrane.

The results presented here suggest that this redistribution of molecules represents a modulatory mechanism that alters either the amplitude or the sensitivity of TREK-1 mediated currents evoked by membrane stretch. While the authors do present values for the pressure required to activate 50% of channels (P50), the data presented provides incomplete evidence to conclude a shift in threshold of the currents, given that many of the current traces provided in the supplemental material do not saturate within the stimulus range, thus limiting the application of a Boltzmann fit to determine the P50. I suggest adding additional context to enable readers to better assess the limitations of this use of the Boltzmann fit to generate a P50, or alternately repeating the experiments to apply stimuli up to lytic pressures to saturate the mechanically evoked currents, enabling use of the Boltzmann function to fit the data.

Response: We thank the reviewer for pointing this out. We agree the currents did not reach saturation. Hence the term P50 could be misleading, so we have removed it from the paper. We now say “half maximal” current measured from non-saturating pressures of 0-60 mmHg. We also deleted the xPLD data in supplemental figure 3C since there is insufficient current to realistically estimate a half maximal response.

In my opinion, the conclusions presented in this manuscript would be strengthened by an assessment of the amount of TREK-1 in the plasma membrane pre and post application of shear. While the authors do present imaging data in the supplementary materials, these data are insufficiently precise to comment on expression levels in the membrane. To strengthen this conclusion the authors could conduct cell surface biotinylation assays, as a more sensitive and quantitative measure of membrane localisation of the proteins of interest.

1. Response: as mentioned previously, we do not have an antibody to the extracellular domain. Nonetheless to better address this concern we directly compared the levels of TREK-1, PIP2, and GM1; in xPLD2, mPLD2, enPLD2 with and without shear. The results are in supplemental figure 2. PLD2 is known to increase endocytosis1 and xPLD2 is known to block both agonist induced and constitutive endocytosis of µ-opioid receptor2. The receptor is trapped on the surface. This is true of many proteins including Rho3, ARF4, and ACE21 among others. In agreement with this mechanism, in Figure S2C,G we show that TREK increases with xPLD and the localization can clearly be seen at the plasma membrane just like in all of the other publications with xPLD overexpression. xPLD2 would be expected to inhibit the basal current but we presume the increased expression likely has compensated and there is sufficient PA and PG from other sources to allow for the basal current. It is in this state that we then conduct our ephys and monitor with a millisecond time resolution and see no activation. We are deriving conclusion from a very clear response—Figure 1b shows almost no current, even at 1-10 ms after applying pressure. There is little pressure current when we know the channel is present and capable of conducting ion (Figure 1d red bar). After shear there is a strong decrease in TREK-1 currents on the membrane in the presence of xPLD2. But it is not less than TREK-1 expression with mPLD2. And since mouse PLD2 has the highest basal current and pressure activation current. The amount of TREK-1 present is sufficient to conduct large current. To have almost no detective current would require at least a 10 fold reduction compared to mPLD2 levels before we would lack the sensitivity to see a channel open. Lasty endocytosis typically in on the order of seconds to minutes, no milliseconds.

2. We have shown an addition 2 independent ways that TREK-1 is on the membrane during our stretch experiments. Figure 1d shows the current immediately prior to applying pressure for wt TREK-1. When catalytically dead PLD is present (xPLD2) there is almost normal basal current. The channel is clearly present. And then in figure 1a we show within a millisecond there is no pressure current. As a control we added a functionally dead TREK-1 truncation (xTREK). Compared to xPLD2 there is clearly normal basal current. If this is not strong evidence the channel was available on the surface for mechanical activation please help us understand why. And if you think within 2.1 ms 100% of the channel is gone by endocytosis please provide some evidence that this is possible so we can reconsider.

3. We have TIRF super resolution imaging with ~20 nm x-y resolution and ~ 100nm z resolution and Figure 2b clearly shows the channel on the membrane. When we apply pressure in 1b, the channel is present.

4. Lastly, In our previous studies we showed activation of PLD2 by anesthetics was responsible for all of TREK-1’s anesthetic sensitivity and this was through PLD2 binding to the C-terminus of TREK-15. We showed this was the case by transferring anesthetic sensitivity to an anesthetic insensitive homolog TRAAK. This established conclusively the basic premise of our mechanism. Here we show the same C-terminal region and PLD2 are responsible for the mechanical current observed by TREK-1. TRAAK is already mechanosensitive so the same chimera will not work for our purposes here. But anesthetic activation and mechanical activation are dramatically different stimuli, and the fact that the role of PLD is robustly observed in both should be considered.

The authors discuss that the endogenous levels of TREK-1 and PLD2 are "well correlated: in C2C12 cells, that TREK-1 displayed little pair correlation with GM1 and that a "small amount of TREK-1 trafficked to PIP2". As such, these data suggest that the data outlined for HEK293T cells may be hampered by artefacts arising from overexpression. Can TREK-1 currents be activated by membrane stretch in these cells C2C12 cells and are they negatively impacted by the presence of xPLD2? Answering this question would provide more insight into the proposed mechanism of action of PLD2 outlined by the authors in this manuscript. If no differences are noted, the model would be called into question. It could be that there are additional cell-specific factors that further regulate this process.

Response: The low pair correlation of TREK-1 and GM1 in C2C12 cells was due to insufficient levels of cholesterol in the cell membrane to allow for robust domain formation. In Figure 4b we loaded C2C12 cells with cholesterol using the endogenous cholesterol transport protein apoE and serum (an endogenous source of cholesterol). As can be seen in Fig. 4b, the pair correlation dramatically increased (purple line). This was also true in neuronal cells (N2a) (Fig 4d, purple bar). And shear (3 dynes/cm2) caused the TREK-1 that was in the GM1 domains to leave (red bar) reversing the effect of high cholesterol. This demonstrates our proposed mechanism is working as we expect with endogenously expressed proteins.

There are many channels in C2C12 cells, it would be difficult to isolate TREK-1 currents, which is why we replicated the entire system (ephys and dSTORM) in HEK cells. Note, in figure 4c we also show that adding cholesterol inhibits TREK-1 whole cell currents in HEK293cells.

As mentioned in the public review, the behavioural experiments in D. melanogaster can not solely be attributed to a change in threshold. While there may be a change in the threshold to drive a different behaviour, the writing is insufficiently precise to make clear that conclusions cannot be drawn from these experiments regarding the functional underpinnings of this outcome. Are there changes in resting membrane potential in the mutant flys? Alterations in Nav activity? Without controlling for these alternate explanations it is difficult to see what this last piece of data adds to the manuscript, particularly given the lack of TREK-1 in this organism. At the very least, some editing of the text to more clearly indicate that these data can only be used to draw conclusions on the change in threshold for driving the behaviour not the change in threshold of the actual mechanotransduction event (i.e. conversion of the mechanical stimulus into an electrochemical signal).

Response: We agree; features other than PLDs direct mechanosensitivity are likely contributing. This was shown in figure 6g left side. We have an arrow going to ion channel and to other downstream effectors. We’ve added the putative alteration to downstream effectors to the right side of the panel. This should make it clear that we no more speculate the involvement of a channel than any of the other many potential downstream effectors. As mentioned above, the figure helps the reader coordinate low PA with increased mechanosensitivity. Without the graphic reviewers got confused that PA increased the threshold which corresponds to a decreased sensitivity to pain. Nonetheless we removed our conclusion about fly thresholds from the abstract and made clearer in the main text the lack of mechanism downstream of PLD in flies including endocytosis. Supplemental Figure S2H also helps emphasize this. .

Nav channels are interesting, and since PLD contribute to endocytosis and Nav channels are also regulated by endocytosis there is likely a PLD specific effect using Nav channels. There are many ways PA likely regulates mechanosensitive thresholds, but we feel Nav is beyond the scope of our paper. Someone else will need to do those studies. We have amended a paragraph in the conclusion which clearly states we do not know the specific mechanism at work here with the suggestions for future research to discover the role of lipid and lipid-modifying enzymes in mechanosensitive neurons.

There may be fundamental flaws in how the statistics have been conducted. The methods section indicates that all statistical testing was performed with a Student's t-test. A visual scan of many of the data sets in the figures suggests that they are not normally distributed, thus a parametric test such as a Student's t-test is not valid. The authors should assess if each data set is normally distributed, and if not, a non-parametric statistical test should be applied. I recommend assessing the robustness of the statistical analyses and adjusting as necessary.

Response: We thank the reviewer for pointing this out, indeed there is some asymmetry in Figure 6C-d. The p values with Mann Whitney were slightly improved p=0.016 and p=0.0022 for 6c and 6d respectively. For reference, the students t-test had slightly worse statistics p=0.040 and p=0.0023. The score remained the same 1 and 2 stars respectively.

The references provided for the statement regarding cascade activation of the TRPs are incredibly out of date. While it is clear that TRPV4 can be activated by a second messenger cascade downstream of osmotic swelling of cells, TRPV4 has also been shown to be activated by mechanical inputs at the cell-substrate interface, even when the second messenger cascade is inhibited. Recommend updating the references to reflect more current understanding of channel activation.

Response: We thank the reviewer for pointing this out. We have updated the references and changed the comment to “can be” instead of “are”. The reference is more general to multiple ion channel types including KCNQ4. This should avoid any perceived conflict with the cellsubstrate interface mechanism which we very much agree is a correct mechanism for TRP channels.

Minor comments re text editing etc:

The central messages of the manuscript would benefit from extensive work to increase the precision of the writing of the manuscript and the presentation of data in the figures, such textual changes alone would help address a number of the concerns outlined in this review, by clarifying some ambiguities. There are numerous errors throughout, ranging from grammatical issues, ambiguities with definitions, lack of scale bars in images, lack of labels on graph axes, lack of clarity due to the mode of presentation of sample numbers (it would be far more precise to indicate specific numbers for each sample rather than a range, which is ambiguous and confusing), unnecessary and repeat information in the methods section. Below are some examples but this list is not exhaustive.

Response: Thank you, reviewer # 1 also had many of these concerns. We have gone through the entire paper and improved the precision of the writing of the manuscript. We have also added the missing error bar to Figure 6. And axis labels have been added to the inset images. The redundancy in cell culture methods has been removed. Where a range is small and there are lots of values, the exact number of ‘n’ are graphically displayed in the dot plot for each condition.

Text:

I recommend considering how to discuss the various aspects of channel activation. A convention in the field is to use mechanical activation or mechanical gating to describe that process where the mechanical stimulus is directly coupled to the channel gating mechanism. This would be the case for the activation of TREK-1 by membrane stretch alone. The increase in activation by PLD2 activity then reflects a modulation of the mechanical activation of the channel, because the relevant gating stimulus is PA, rather than force/stretch. The sum of these events could be described as shear-evoked or mechanically-evoked, TREK-1 mediated currents (thus making it clear that the mechanical stimulus initiates the relevant cascade, but the gating stimulus may be other than direct mechanical input.) Given the interesting and compelling data offered in this manuscript regarding the sensitisation of TREK-1 dependent mechanicallyevoked currents by PLD2, an increase in the precision of the language would help convey the central message of this work.

Response; We agree there needs to be convention. We have taken the suggestion of mechanically evoked and we suggest the following definitions:

1. Mechanical activation of PLD2: direct force on the lipids releasing PLD2 from nonactivating lipids.

2. Mechanical activation/gating of TREK1: direct force from lipids from either tension or hydrophobic mismatch that opens the channel.

3. Mechanically evoked: a mechanical event that leads to a downstream effect. The effect is mechanically “evoked”.

4. Spatial patterning/biochemistry: nanoscopic changes in the association of a protein with a nanoscopic lipid cluster or compartment.

An example of where discussion of mechanical activation is ambiguous in the text is found at line 109: "channel could be mechanically activated by a movement from GM1 to PIP2 lipids." In this case, the sentence could be suggesting that the movement between lipids provides the mechanical input that activates the channel, which is not what the data suggest.

Response: Were possible we have replaced “movement” with “spatial patterning” and “association” and “dissociation” from specific lipid compartment. This better reflects the data we have in this paper. However, we do think that a movement mechanically activates the channel, GM1 lipids are thick and PIP2 lipids are thin, so movement between the lipids could activate the channel through direct lipid interaction. We will address this aspect in a future paper.

Inconsistencies with usage:

• TREK1 versus TREK-1

Response: corrected to TREK-1

• mPLD2 versus PLD2

Response: where PLD2 represents mouse this has been corrected.

• K758R versus xPLD2

Response: we replaced K758R in the methods with xPLD2.

• HEK293T versus HEK293t Response: we have changed all instances to read HEK293T.

• Drosophila melanogaster and D. melanogaster used inconsistently and in many places incorrectly

Response: we have read all to read the common name Drosophila.

Line 173: misspelled methylbetacyclodextrin

Response corrected

Line 174: degree symbol missing

Response corrected

Line 287: "the decrease in cholesterol likely evolved to further decrease the palmate order in the palmitate binding site"... no evidence, no support for this statement, falsely attributes intention to evolutionary processes .

Response: we have removed the reference to evolution at the request of the reviewer, it is not necessary. But we do wish to note that to our knowledge, all biological function is scientifically attributed to evolution. The fact that cholesterol decreases in response to shear is evidence alone that the cell evolved to do it.

Line 307: grammatical error

Response: the redundant Lipid removed.

Line 319: overinterpreted - how is the mechanosensitivy of GPCRs explained by this translocation?

Response: all G-alpha subunits of the GPCR complex are palmitoylated. We showed PLD (which has the same lipidation) is mechanically activated. If the palmitate site is disrupted for PLD2, then it is likely disrupted for every G-alpha subunit as well.

Line 582: what is the wild type referred to here?

Response: human full length with a GFP tag.

Methods:

• Sincere apologies if I missed something but I do not recall seeing any experiments using purified TREK-1 or flux assays. These details should be removed from the methods section

Response: Removed.

• There is significant duplication of detail across the methods (three separate instances of electrophysiology details) these could definitely be consolidated.

Response: Duplicates removed.

Figures:

• Figure 2- b box doesn't correspond to inset. Bottom panel should provide overview image for the cell that was assessed with shear. In bottom panel, circle outlines an empty space.

Response: We have widened the box slightly to correspond so the non shear box corresponds to the middle panel. We have also added the picture for the whole cell to Fig S2g and outlined the zoom shown in the bottom panel of Fig 2b as requested. The figure is of the top of a cell. We also added the whole cell image of a second sheared cell.

Author response image 1.

• Figure 3 b+c: inset graph lacking axis labels

Response; the inset y axis is the same as the main axis. We added “pair corr. (5nM)” and a description in the figure legend to make this clearer. The purpose of the inset is to show statistical significance at a single point. The contrast has been maximized but without zooming in points can be difficult to see.

• Figure 5: replicate numbers missing and individual data points lacking in panels b + c, no labels of curve in b + c, insets, unclear what (5 nm) refers to in insets.

Response: Thank you for pointing out these errors. The N values have been added. Similar to figure 3, the inset is a bar graph of the pair correlation data at 5 nm. A better explanation of the data has been added to the figure legend.

• Figure 6: no scale bar, no clear membrane localization evident from images presented, panel g offers virtually nothing in terms of insight

Response: We have added scale bars to figure 6b. Figure 6g is intentionally simplistic, we found that correlating decreased threshold with increased pain was confusing. A previous reviewer claimed our data was inconsistent. The graphic avoids this confusion. We also added negative effects of low PA on downstream effects to the right panel. This helps graphically show we don’t know the downstream effects.

Reviewer #3 (Recommendations For The Authors):

Minor suggestions:

1. line 162, change 'heat' to 'temperature'.

Response: changed.

1. in figure 1, it would be helpful to keep the unit for current density consistent among different panels. 1e is a bit confusing: isn't the point of Figure 1 that most of TREK1 activation is not caused by direct force-sensing?

Response: Yes, the point of figure 1 is to show that in a biological membrane over expressed TREK-1 is a downstream effector of PLD2 mechanosensation which is indirect. We agree the figure legend in the previous version of the paper is very confusing.

There is almost no PLD2 independent current in our over expressed system, which is represented by no ions in the conduction pathway of the channel despite there being tension on the membrane.

Purified TREK-1 is only mechanosensitive in a few select lipids, primarily crude Soy PC. It was always assumed that HEK293 and Cos cells had the correct lipids since over expressed TREK-1 responded to mechanical force in these lipids. But that does not appear to be correct, or at least only a small amount of TREK-1 is in the mechanosensitive lipids. Figure 1e graphically shows this. The arrows indicate tension, but the channel isn’t open with xPLD2 present. We added a few sentences to the discussion to further clarify.

Panels c has different units because the area of the tip was measured whereas in d the resistance of the tip was measured. They are different ways for normalizing for small differences in tip size.

1. line 178, ~45 of what?

Response: Cells were fixed for ~30 sec.

1. line 219 should be Figure 4f?

Response: thank you, yes Figure 4f.

Previous public reviews with minor updates.

Reviewer #1 (Public Review):

Force sensing and gating mechanisms of the mechanically activated ion channels is an area of broad interest in the field of mechanotransduction. These channels perform important biological functions by converting mechanical force into electrical signals. To understand their underlying physiological processes, it is important to determine gating mechanisms, especially those mediated by lipids. The authors in this manuscript describe a mechanism for mechanically induced activation of TREK-1 (TWIK-related K+ channel. They propose that force induced disruption of ganglioside (GM1) and cholesterol causes relocation of TREK-1 associated with phospholipase D2 (PLD2) to 4,5-bisphosphate (PIP2) clusters, where PLD2 catalytic activity produces phosphatidic acid that can activate the channel. To test their hypothesis, they use dSTORM to measure TREK-1 and PLD2 colocalization with either GM1 or PIP2. They find that shear stress decreases TREK-1/PLD2 colocalization with GM1 and relocates to cluster with PIP2. These movements are affected by TREK-1 C-terminal or PLD2 mutations suggesting that the interaction is important for channel re-location. The authors then draw a correlation to cholesterol suggesting that TREK-1 movement is cholesterol dependent. It is important to note that this is not the only method of channel activation and that one not involving PLD2 also exists. Overall, the authors conclude that force is sensed by ordered lipids and PLD2 associates with TREK-1 to selectively gate the channel. Although the proposed mechanism is solid, some concerns remain.

1) Most conclusions in the paper heavily depend on the dSTORM data. But the images provided lack resolution. This makes it difficult for the readers to assess the representative images.

Response: The images were provided are at 300 dpi. Perhaps the reviewer is referring to contrast in Figure 2? We are happy to increase the contrast or resolution.

As a side note, we feel the main conclusion of the paper, mechanical activation of TREK-1 through PLD2, depended primarily on the electrophysiology in Figure 1b-c, not the dSTORM. But both complement each other.

2) The experiments in Figure 6 are a bit puzzling. The entire premise of the paper is to establish gating mechanism of TREK-1 mediated by PLD2; however, the motivation behind using flies, which do not express TREK-1 is puzzling.

Response: The fly experiment shows that PLD mechanosensitivity is more evolutionarily conserved than TREK-1 mechanosensitivity. We have added this observation to the paper.

-Figure 6B, the image is too blown out and looks over saturated. Unclear whether the resolution in subcellular localization is obvious or not.

Response: Figure 6B is a confocal image, it is not dSTORM. There is no dSTORM in Figure 6. We have added the error bars to make this more obvious. For reference, only a few cells would fit in the field of view with dSTORM.

-Figure 6C-D, the differences in activity threshold is 1 or less than 1g. Is this physiologically relevant? How does this compare to other conditions in flies that can affect mechanosensitivity, for example?

Response: Yes, 1g is physiologically relevant. It is almost the force needed to wake a fly from sleep (1.2-3.2g). See ref 33. Murphy Nature Pro. 2017.

3) 70mOsm is a high degree of osmotic stress. How confident are the authors that a cell health is maintained under this condition and b. this does indeed induce membrane stretch? For example, does this stimulation activate TREK-1?

Response: Yes, osmotic swell activates TREK1. This was shown in ref 19 (Patel et al 1998). We agree the 70 mOsm is a high degree of stress. This needs to be stated better in the paper.

Reviewer #2 (Public Review):

This manuscript by Petersen and colleagues investigates the mechanistic underpinnings of activation of the ion channel TREK-1 by mechanical inputs (fluid shear or membrane stretch) applied to cells. Using a combination of super-resolution microticopy, pair correlation analysis and electrophysiology, the authors show that the application of shear to a cell can lead to changes in the distribution of TREK-1 and the enzyme PhospholipaseD2 (PLD2), relative to lipid domains defined by either GM1 or PIP2. The activation of TREK-1 by mechanical stimuli was shown to be sensi>zed by the presence of PLD2, but not a catalytically dead xPLD2 mutant. In addition, the activity of PLD2 is increased when the molecule is more associated with PIP2, rather than GM1 defined lipid domains. The presented data do not exclude direct mechanical activation of TREK-1, rather suggest a modulation of TREK-1 activity, increasing sensitivity to mechanical inputs, through an inherent mechanosensitivity of PLD2 activity. The authors additionally claim that PLD2 can regulate transduction thresholds in vivo using Drosophila melanogaster behavioural assays. However, this section of the manuscript overstates the experimental findings, given that it is unclear how the disruption of PLD2 is leading to behavioural changes, given the lack of a TREK-1 homologue in this organism and the lack of supporting data on molecular function in the relevant cells.

Response: We agree, the downstream effectors of PLD2 mechanosensitivity are not known in the fly. Other anionic lipids have been shown to mediate pain see ref 46 and 47. We do not wish to make any claim beyond PLD2 being an in vivo contributor to a fly’s response to mechanical force. We have removed the speculative conclusions about fly thresholds from the abstract.

That said we do believe we have established a molecular function at the cellular level. We showed PLD is robustly mechanically activated in a cultured fly cell line (BG2-c2) Figure 6a of the manuscript. And our previous publication established mechanosensation of PLD (Petersen et. al. Nature Com 2016) through mechanical disruption of the lipids. At a minimum, the experiments show PLDs mechanosensitivity is evolutionarily better conserved across species than TREK1.

This work will be of interest to the growing community of scientists investigating the myriad mechanisms that can tune mechanical sensitivity of cells, providing valuable insight into the role of functional PLD2 in sensi>zing TREK-1 activation in response to mechanical inputs, in some cellular systems.

The authors convincingly demonstrate that, post application of shear, an alteration in the distribution of TREK-1 and mPLD2 (in HEK293T cells) from being correlated with GM1 defined domains (no shear) to increased correlation with PIP2 defined membrane domains (post shear). These data were generated using super-resolution microticopy to visualise, at sub diffraction resolution, the localisation of labelled protein, compared to labelled lipids. The use of super-resolution imaging enabled the authors to visualise changes in cluster association that would not have been achievable with diffraction limited microticopy. However, the conclusion that this change in association reflects TREK-1 leaving one cluster and moving to another overinterprets these data, as the data were generated from sta>c measurements of fixed cells, rather than dynamic measurements capturing molecular movements.

When assessing molecular distribution of endogenous TREK-1 and PLD2, these molecules are described as "well correlated: in C2C12 cells" however it is challenging to assess what "well correlated" means, precisely in this context. This limitation is compounded by the conclusion that TREK-1 displayed little pair correlation with GM1 and the authors describe a "small amount of TREK-1 trafficked to PIP2". As such, these data may suggest that the findings outlined for HEK293T cells may be influenced by artefacts arising from overexpression.

The changes in TREK-1 sensitivity to mechanical activation could also reflect changes in the amount of TREK-1 in the plasma membrane. The authors suggest that the presence of a leak currently accounts for the presence of TREK-1 in the plasma membrane, however they do not account for whether there are significant changes in the membrane localisation of the channel in the presence of mPLD2 versus xPLD2. The supplementary data provide some images of fluorescently labelled TREK-1 in cells, and the authors state that truncating the c-terminus has no effect on expression at the plasma membrane, however these data provide inadequate support for this conclusion. In addition, the data reporting the P50 should be noted with caution, given the lack of saturation of the current in response to the stimulus range.

Response: We thank the reviewer for his/her concern about expression levels. We did test TREK-1 expression. mPLD decreases TREK-1 expression ~two-fold (see Author response image 2 below). We did not include the mPLD data since TREK-1 was mechanically activated with mPLD. For expression to account for the loss of TREK-1 stretch current (Figure 1b), xPLD would need to block surface expression of TREK-1 prior to stretch. The opposite was true, xPLD2 increased TREK-1 expression (see Figure S2c). Furthermore, we tested the leak current of TREK-1 at 0 mV and 0 mmHg of stretch. Basal leak current was no different with xPLD2 compared to endogenous PLD (Figure 1d; red vs grey bars respectively) suggesting TREK-1 is in the membrane and active when xPLD2 is present. If anything, the magnitude of the effect with xPLD would be larger if the expression levels were equal.

Author response image 2.

TREK expression at the plasma membrane. TREK-1 Fluorescence was measured by GFP at points along the plasma membrane. Over expression of mouse PLD2 (mPLD) decrease the amount of full-length TREK-1 (FL TREK) on the surface more than 2-fold compared to endogenously expressed PLD (enPLD) or truncated TREK (TREKtrunc) which is missing the PLD binding site in the C-terminus. Over expression of mPLD had no effect on TREKtrunc.

Finally, by manipulating PLD2 in D. melanogaster, the authors show changes in behaviour when larvae are exposed to either mechanical or electrical inputs. The depletion of PLD2 is concluded to lead to a reduction in activation thresholds and to suggest an in vivo role for PA lipid signaling in setting thresholds for both mechanosensitivity and pain. However, while the data provided demonstrate convincing changes in behaviour and these changes could be explained by changes in transduction thresholds, these data only provide weak support for this specific conclusion. As the authors note, there is no TREK-1 in D. melanogaster, as such the reported findings could be accounted for by other explanations, not least including potential alterations in the activation threshold of Nav channels required for action potential generation. To conclude that the outcomes were in fact mediated by changes in mechanotransduction, the authors would need to demonstrate changes in receptor potential generation, rather than deriving conclusions from changes in behaviour that could arise from alterations in resting membrane potential, receptor potential generation or the activity of the voltage gated channels required for action potential generation.

Response: We are willing to restrict the conclusion about the fly behavior as the reviewers see fit. We have shown PLD is mechanosensitivity in a fly cell line, and when we knock out PLD from a fly, the animal exhibits a mechanosensation phenotype. We tried to make it clear in the figure and in the text that we have no evidence of a particular mechanism downstream of PLD mechanosensation.

This work provides further evidence of the astounding flexibility of mechanical sensing in cells. By outlining how mechanical activation of TREK-1 can be sensitised by mechanical regulation of PLD2 activity, the authors highlight a mechanism by which TREK-1 sensitivity could be regulated under distinct physiological conditions.

Reviewer #3 (Public Review):

The manuscript "Mechanical activation of TWIK-related potassium channel by nanoscopic movement and second messenger signaling" presents a new mechanism for the activation of TREK-1 channel. The mechanism suggests that TREK1 is activated by phosphatidic acids that are produced via a mechanosensitive motion of PLD2 to PIP2-enriched domains. Overall, I found the topic interesting, but several typos and unclarities reduced the readability of the manuscript. Additionally, I have several major concerns on the interpretation of the results. Therefore, the proposed mechanism is not fully supported by the presented data. Lastly, the mechanism is based on several previous studies from the Hansen lab, however, the novelty of the current manuscript is not clearly stated. For example, in the 2nd result section, the authors stated, "fluid shear causes PLD2 to move from cholesterol dependent GM1 clusters to PIP2 clusters and this activated the enzyme". However, this is also presented as a new finding in section 3 "Mechanism of PLD2 activation by shear."

For PLD2 dependent TREK-1 activation. Overall, I found the results compelling. However, two key results are missing.

1. Does HEK cells have endogenous PLD2? If so, it's hard to claim that the authors can measure PLD2-independent TREK1 activation.

Response: yes, there is endogenous PLD (enPLD). We calculated the relative expression of xPLD2 vs enPLD. xPLD2 is >10x more abundant (Fig. S3d of Pavel et al PNAS 2020, ref 14 of the current manuscript). Hence, as with anesthetic sensitivity, we expect the xPLD to out compete the endogenous PLD, which is what we see. We added the following sentence and reference : “The xPLD2 expression is >10x the endogenous PLD2 (enPLD2) and out computes the TREK-1 binding site for PLD25.”

1. Does the plasma membrane trafficking of TREK1 remain the same under different conditions (PLD2 overexpression, truncation)? From Figure S2, the truncated TREK1 seem to have very poor trafficking. The change of trafficking could significantly contribute to the interpretation of the data in Figure 1.

Response: If the PLD2 binding site is removed (TREK-1trunc), yes, the trafficking to the plasma membrane is unaffected by the expression of xPLD and mPLD (Author response image 2 above). For full length TREK1 (FL-TREK-1), co-expression of mPLD decreases TREK expression (Author response image 2) and coexpression with xPLD increases TREK expression (Figure S2f). This is exactly opposite of what one would expect if surface expression accounted for the change in pressure currents. Hence, we conclude surface expression does not account for loss of TREK-1 mechanosensitivity with xPLD2. A few sentences was added to the discussion. We also performed dSTORM on the TREKtruncated using EGFP. TREK-truncated goes to PIP2 (see figure 2 of 6)

Author response image 3.

To better compare the levels of TREK-1 before and after shear, we added a supplemental figure S2f where the protein was compared simultaneously in all conditions. 15 min of shear significantly decreased TREK-1 except with mPLD2 where the levels before shear were already lowest of all the expression levels tested.

For shear-induced movement of TREK1 between nanodomains. The section is convincing, however I'm not an expert on super-resolution imaging. Also, it would be helpful to clarify whether the shear stress was maintained during fixation. If not, what is the >me gap between reduced shear and the fixed state. lastly, it's unclear why shear flow changes the level of TREK1 and PIP2.

Response: Shear was maintained during the fixing. xPLD2 blocks endocytosis, presumably endocytosis and or release of other lipid modifying enzymes affect the system. The change in TREK-1 levels appears to be directly through an interaction with PLD as TREK trunc is not affected by over expression of xPLD or mPLD.

For the mechanism of PLD2 activation by shear. I found this section not convincing. Therefore, the question of how does PLD2 sense mechanical force on the membrane is not fully addressed. Par>cularly, it's hard to imagine an acute 25% decrease cholesterol level by shear - where did the cholesterol go? Details on the measurements of free cholesterol level is unclear and additional/alternative experiments are needed to prove the reduction in cholesterol by shear.

Response: The question “how does PLD2 sense mechanical force on the membrane” we addressed and published in Nature Comm. In 2016. The title of that paper is “Kinetic disruption of lipid rafts is a mechanosensor for phospholipase D” see ref 13 Petersen et. al. PLD is a soluble protein associated to the membrane through palmitoylation. There is no transmembrane domain, which narrows the possible mechanism of its mechanosensation to disruption.

The Nature Comm. reviewer identified as “an expert in PLD signaling” wrote the following of our data and the proposed mechanism:

“This is a provocative report that identi0ies several unique properties of phospholipase D2 (PLD2). It explains in a novel way some long established observations including that the enzyme is largely regulated by substrate presentation which 0its nicely with the authors model of segregation of the two lipid raft domains (cholesterol ordered vs PIP2 containing). Although PLD has previously been reported to be involved in mechanosensory transduction processes (as cited by the authors) this is the 0irst such report associating the enzyme with this type of signaling... It presents a novel model that is internally consistent with previous literature as well as the data shown in this manuscript. It suggests a new role for PLD2 as a force transduction tied to the physical structure of lipid rafts and uses parallel methods of disrup0on to test the predic0ons of their model.”

Regarding cholesterol. We use a fluorescent cholesterol oxidase assay which we described in the methods. This is an appropriate assay for determining cholesterol levels in a cell which we use routinely. We have published in multiple journals using this method, see references 28, 30, 31. Working out the metabolic fate of cholesterol after sheer is indeed interesting but well beyond the scope of this paper. Furthermore, we indirectly confirmed our finding using dSTORM cluster analysis (Figure 3d-e). The cluster analysis shows a decrease in GM1 cluster size consistent with our previous experiments where we chemically depleted cholesterol and saw a similar decrease in cluster size (see ref 13). All the data are internally consistent, and the cholesterol assay is properly done. We see no reason to reject the data.

Importantly, there is no direct evidence for "shear thinning" of the membrane and the authors should avoid claiming shear thinning in the abstract and summary of the manuscript.

Response: We previously established a kinetic model for PLD2 activation see ref 13 (Petersen et al Nature Comm 2016). In that publication we discussed both entropy and heat as mechanisms of disruption. Here we controlled for heat which narrowed that model to entropy (i.e., shear thinning) (see Figure 3c). We provide an overall justification below. But this is a small refinement of our previous paper, and we prefer not to complicate the current paper. We believe the proper rheological term is shear thinning. The following justification, which is largely adapted from ref 13, could be added to the supplement if the reviewer wishes.

Justification: To establish shear thinning in a biological membrane, we initially used a soluble enzyme that has no transmembrane domain, phospholipase D2 (PLD2). PLD2 is a soluble enzyme and associated with the membrane by palmitate, a saturated 16 carbon lipid attached to the enzyme. In the absence of a transmembrane domain, mechanisms of mechanosensation involving hydrophobic mismatch, tension, midplane bending, and curvature can largely be excluded. Rather the mechanism appears to be a change in fluidity (i.e., kinetic in nature). GM1 domains are ordered, and the palmate forms van der Waals bonds with the GM1 lipids. The bonds must be broken for PLD to no longer associate with GM1 lipids. We established this in our 2016 paper, ref 13. In that paper we called it a kinetic effect, however we did not experimentally distinguish enthalpy (heat) vs. entropy (order). Heat is Newtonian and entropy (i.e., shear thinning) is non-Newtonian. In the current study we paid closer attention to the heat and ruled it out (see Figure 3c and methods). We could propose a mechanism based on kinetic disruption, but we know the disruption is not due to melting of the lipids (enthalpy), which leaves shear thinning (entropy) as the plausible mechanism.

The authors should also be aware that hypotonic shock is a very dirty assay for stretching the cell membrane. Ouen, there is only a transient increase in membrane tension, accompanied by many biochemical changes in the cells (including acidification, changes of concentration etc). Therefore, I would not consider this as definitive proof that PLD2 can be activated by stretching membrane.

Response: Comment noted. We trust the reviewer is correct. In 1998 osmotic shock was used to activate the channel. We only intended to show that the system is consistent with previous electrophysiologic experiments.

References cited:

1 Du G, Huang P, Liang BT, Frohman MA. Phospholipase D2 localizes to the plasma membrane and regulates angiotensin II receptor endocytosis. Mol Biol Cell 2004;15:1024–30. htps://doi.org/10.1091/mbc.E03-09-0673.

2 Koch T, Wu DF, Yang LQ, Brandenburg LO, Höllt V. Role of phospholipase D2 in the agonist-induced and constistutive endocytosis of G-protein coupled receptors. J Neurochem 2006;97:365–72. htps://doi.org/10.1111/j.1471-4159.2006.03736.x.

3 Wheeler DS, Underhill SM, Stolz DB, Murdoch GH, Thiels E, Romero G, et al. Amphetamine activates Rho GTPase signaling to mediate dopamine transporter internalization and acute behavioral effects of amphetamine. Proc Natl Acad Sci U S A 2015;112:E7138–47. htps://doi.org/10.1073/pnas.1511670112.

4 Rankovic M, Jacob L, Rankovic V, Brandenburg L-OO, Schröder H, Höllt V, et al. ADP-ribosylation factor 6 regulates mu-opioid receptor trafficking and signaling via activation of phospholipase D2. Cell Signal 2009;21:1784–93. htps://doi.org/10.1016/j.cellsig.2009.07.014.

5 Pavel MA, Petersen EN, Wang H, Lerner RA, Hansen SB. Studies on the mechanism of general anesthesia. Proc Natl Acad Sci U S A 2020;117:13757–66. htps://doi.org/10.1073/pnas.2004259117.

6 Call IM, Bois JL, Hansen SB. Super-resolution imaging of potassium channels with genetically encoded EGFP. BioRxiv 2023. htps://doi.org/10.1101/2023.10.13.561998.

Author response:

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

Public reviews:

Reviewer #1:

This work by Leclercq and colleagues performed metabolomics on biospecimens collected from 96 patients diagnosed with several types of alcohol use disorders (AUD). The authors discovered strong alterations in circulating glycerophospholipids, bile acids, and some gut microbe-derived metabolites in AUD patients compared to controls. An exciting part of this work is that metabolomics was also performed in frontal cortex of post-mortem brains and cerebrospinal fluid of heavy alcohol users, and some of the same metabolites were seen to be altered in the central nervous system. This is an important study that will form the basis for hypothesis generation around diet-microbe-host interactions in alcohol use disorder. The work is done in a highly rigorous manner, and the rigorously collected human samples are a clear strength of this work. Overall, many new insights may be gained by this work, and it is poised to have a high impact on the field.

Strengths:

(1) The rigorously collected patient-derived samples.

(2) There is high rigor in the metabolomics investigation.

(3) Statistical analyses are well-described and strong.

(4) An evident strength is the careful control of taking blood samples at the same time of the day to avoid alterations in meal- and circadian-related fluctuations in metabolites.

Weaknesses:

(1) Some validation in animal models of ethanol exposure compared to pair-fed controls would help strengthen causal relationships between metabolites and alterations in the CNS.

(2) The classification of "heavy alcohol users" based on autopsy reports may not be that accurate.

(3) The fact that most people with alcohol use disorder choose to drink over eating food, there needs to be some more discussion around how dietary intake (secondary to heavy drinking) most likely has a significant impact on the metabolome.<br />

We thank this reviewer for his/her encouraging comments and for highlighting the fact that this study is important in the field to generate hypotheses around diet-microbe-host interactions in alcohol use disorder.

Concerning weakness #1: Regarding the validation in animal models of ethanol exposure, we were very careful in our discussion to avoid pretending that the study allowed to test causality of the factors. This was certainly not the objective of the present study. The testing of causality would indeed probably necessitate animal models but these models could only test the effects of one single metabolite at a time and could not at the same time capture the complexity of the changes occurring in AUD patients. The testing of metabolites would be a totally different topic. Hence, we do not feel comfortable in conducting rodent experiments for several reasons. First, AUD is a very complex pathology with physiological and psychological/psychiatric alterations that are obviously difficult to reproduce in animal models. Secondly, as mentioned by the reviewer, AUD pathology spontaneously leads to nutritional deficits, including significant reductions in carbohydrates, lipids, proteins and fiber intakes. We have recently published a paper in which we carefully conducted detailed dietary anamneses and described the changes in food habits in AUD patients (Amadieu et al., 2021). As explained below, some blood metabolites that are significantly correlated with depression, anxiety and craving belong to the xanthine family and are namely theobromine, theophylline, and paraxanthine, which derived from metabolism of coffee, tea or chocolate (which are not part of the normal diet of mice or rats).Therefore, conducting an experiment in animal model of ethanol exposure compared to pair-fed controls will omit the important impact of nutrition in blood metabolomics and consequently won’t mimic the human AUD pathology. In addition, if we take into consideration the European Directive 2010/63/EU (on the protection of animals used for scientific purposes) which aims at Reducing (Refining, Replacing) the number of animals used in experiment, it is extremely difficult to justify, at the ethical point of view, the need to reproduce human results in an animal model that won’t be able to mimic the nutritional, physiological and psychological alterations of alcohol use disorder.

Concerning weakness #2: The classification of subjects to the group who have a history of heavy alcohol use was not solely on autopsy record, but was also based on medical history i.e. diagnosis of alcohol-related diseases: ICD-10 codes F10.X, G31.2, G62.1, G72.1, I42.6, K70.0-K70.4, K70.9, and K86.0, or signs of heavy alcohol use in the clinical or laboratory findings, e.g., increased levels of gamma-glutamyl transferase, mean corpuscular volume, carbohydrate-deficient transferrin, as stated in the methods section of the manuscript. In Finland, the medical records from the whole life of the subjects are available. We consider that getting diagnosis of alcohol-related disease is clear sign of history of heavy alcohol use.

Concerning weakness#3:  As explained above, we do agree with the reviewer that AUD is not only “drinking alcohol” but is also associated with reduction in food intake that obviously influenced the metabolomics data presented in this current study.  We have therefore added some data, which have not been published before, in the results section that refer to key nutrients modified by alcohol intake and we refer to those data and their link with metabolomics in the discussion section:

Results section page 8, Line 153-155. This sentence has been added:

“The changes in metabolites belonging to the xanthine family during alcohol withdrawal could be explained by the changes in dietary intake of coffee, tea and chocolate (see Fig S5).”

Discussion section: Page 11, Line 235-240.

“Interestingly, the caffeine metabolites belonging to the xanthine family such as paraxanthine, theophylline and theobromine that were decreased at baseline in AUD patients compared to controls, increased significantly during alcohol withdrawal to reach the levels of healthy controls. Changes in dietary intake of coffee, tea and chocolate during alcohol withdrawal could explain these results”.

In the conclusion, Page 16, Line 354-356, we clearly stated that: “LC-MS metabolomics plasma analysis allowed for the identification of metabolites that were clearly linked to alcohol consumption, and reflected changes in metabolism, alterations of nutritional status, and gut microbial dysbiosis associated with alcohol intake”

Reference:

Amadieu C, Leclercq S, Coste V, Thijssen V, Neyrinck AM, Bindels LB, Cani PD, Piessevaux H, Stärkel P, Timary P de, Delzenne NM. 2021. Dietary fiber deficiency as a component of malnutrition associated with psychological alterations in alcohol use disorder. Clinical Nutrition 40:2673–2682. doi:10.1016/j.clnu.2021.03.029

Leclercq S, Cani PD, Neyrinck AM, Stärkel P, Jamar F, Mikolajczak M, Delzenne NM, de Timary P. 2012. Role of intestinal permeability and inflammation in the biological and behavioral control of alcohol-dependent subjects. Brain Behav Immun 26:911–918. doi:10.1016/j.bbi.2012.04.001

Leclercq S, De Saeger C, Delzenne N, de Timary P, Stärkel P. 2014a. Role of inflammatory pathways, blood mononuclear cells, and gut-derived bacterial products in alcohol dependence. Biol Psychiatry 76:725–733. doi:10.1016/j.biopsych.2014.02.003

Leclercq S, Matamoros S, Cani PD, Neyrinck AM, Jamar F, Stärkel P, Windey K, Tremaroli V, Bäckhed F, Verbeke K, de Timary P, Delzenne NM. 2014b. Intestinal permeability, gut-bacterial dysbiosis, and behavioral markers of alcohol-dependence severity. Proc Natl Acad Sci U S A 111:E4485–E4493. doi:10.1073/pnas.1415174111

Voutilainen T, Kärkkäinen O. 2019. Changes in the Human Metabolome Associated With Alcohol Use: A Review. Alcohol and Alcoholism 54:225–234. doi:10.1093/alcalc/agz030

Public Reviewer #2:

The authors carried out the current studies with the justification that the biochemical mechanisms that lead to alcohol addiction are incompletely understood. The topic and question addressed here are impactful and indeed deserve further research. To this end, a metabolomics approach toward investigating the metabolic effects of alcohol use disorder and the effect of alcohol withdrawal in AUD subjects is valuable. However, it is primarily descriptive in nature, and these data alone do not meet the stated goal of investigating biochemical mechanisms of alcohol addiction. The current work's most significant limitation is the cross-sectional study design, though inadequate description and citation of the underlying methodological approaches also hampers interest. Most of the data are cross-sectional in the study design, i.e., alcohol use disorder vs controls. However, it is well established that there is a high degree of interpersonal variation with metabolism, and further, there is somewhat high intra-personal variation in metabolism over time. This means that the relatively small cohort of subjects is unlikely to reflect the broader condition of interest (AUD/withdrawal). The authors report a comparison of a later time-point after alcohol withdrawal (T2) vs. the AUD condition. However, without replicative time points from the control subjects it is difficult to assess how much of these changes are due to withdrawal vs the intra-personal variation described above.

We agree with the reviewer. Our goal was not to investigate the biochemical mechanisms of AUD but rather to investigate how metabolomics could contribute to the psychological alterations of AUD. The goals of the study are defined at the end of the introduction (Page 4 – Lines 80-91), as follows:

“The aims of this study are multiple. First, we investigated the impact of severe AUD on the blood metabolome by non-targeted LC-MS metabolomics analysis. Second, we investigated the impact of a short-term alcohol abstinence on the blood metabolome followed by assessing the correlations between the blood metabolome and psychological symptoms developed in AUD patients. Last, we hypothesized that metabolites significantly correlated with depression, anxiety or alcohol craving could potentially have neuroactive properties, and therefore the presence of those neuroactive metabolites was confirmed in the central nervous system using post-mortem analysis of frontal cortex and cerebrospinal fluid of persons with a history of heavy alcohol use. Our data bring new insights on xenobiotics- or microbial-derived neuroactive metabolites, which can represent an interesting strategy to prevent or treat psychiatric disorders such as AUD”.

Due to the fact that the method section describing the study design is located at the end of the manuscript, we have decided to clarify the methodological approach in the first paragraph of the result section in order to show that in fact, we have performed a longitudinal study (which includes the same group of AUD, tested at two time points – at the beginning and at the end of alcohol withdrawal). This is stated as follows:

Results section, Page 6, Line 97-99: “All patients were hospitalized for a 3-week detoxification program, and tested at two timepoints: T1 which represents the first day of alcohol withdrawal, and T2 which represents the last day of the detoxification program”.

We propose to add a figure with a schematic representation of the protocol. We let the editor deciding whether this figure can be added (as supplemental material).

Author response image 1.

Schematic representation of the protocol

We agree with the reviewer that the correlational analysis (between blood metabolites and psychological symptoms) is conducted at one time point (T1) only, which has probably led to the confusion between cross-sectional and longitudinal study. In fact we had a strong motivation to provide correlations at T1, instead of T2. T1, which is at the admission time, is really the moment where we can take into account variability of the psychological scores. Indeed, after 3 weeks of abstinence (T2), the levels of depression, anxiety and alcohol craving decreased significantly ( as shown in other studies from our group (Leclercq et al., 2014b, 2014a, 2012)) and remained pretty low in AUD patients, with a much lower inter-individual variability which makes the correlations less consistent.

We agree with the reviewer that there is a high intra and inter-personal variability in the metabolomics data, that could be due to the differences in previous meals intakes within and between subjects. While AUD subjects have been tested twice (at the beginning and at the end of a 3-week detoxification program), the control subjects have only been tested once. Consequently, we did not take into account the intra-personal variability in the control group. The metabolomics changes observed in AUD patients between T1 and T2 are therefore due to alcohol withdrawal but also to intra-personal variability. This is a limitation of the study that we have now added in the discussion section, Page 16, Lines 354-357  as follows:

“The selection of the control group is always challenging in alcohol research. Here, the healthy subjects were matched for sex, age and BMI but not for smoking status or nutritional intake. Alcohol addiction is a major cause of malnutrition in developed countries and tobacco smoking is more prevalent in alcohol users compared to healthy subjects. These two main confounding factors, although being an integral part of the alcoholic pathology, are known to influence the blood metabolome. Furthermore, another limitation is that the control group was tested only once, while the AUD patients were tested twice (T1 and T2). This means that we do not take into consideration the intra-personal variability of the metabolomics data when interpreting the results of alcohol withdrawal effects”.

The limitation concerning the small sample size is already mentioned in the discussion section, as follows:

“Large studies are usually required in metabolomics to observe small and medium size changes. Here, we included only 96 AUD patients, but they were all well characterized and received standardized therapies (for instance, vitB supplementation) during alcohol withdrawal”.

Overall, there is not enough experimental context to interpret these findings into a biological understanding. For example, while several metabolites are linked with AUD and associated with microbiome or host metabolism based on existing literature, it's unclear from the current study what function these changes have concerning AUD, if any. The authors also argue that alcohol withdrawal shifts the AUD plasma metabolic fingerprint towards healthy controls (line 153). However, this is hard to assess based on the plots provided since the change in the direction of the orange data subset is considers AUD T2 vs T1. In contrast, AUD T2 vs Control would represent the claimed shift. To support these claims, the authors would better support their argument by showing this comparison as well as showing all experimental groups (including control subjects) in their multi-dimensional model (e.g., PCA).

We thank the reviewer for these comments. It is true in this type of discovery-based approach the causality cannot be interpreted nor do we claim so. The aim was to characterize the metabolic alterations in this population, response to withdrawal period and suggest potential candidate metabolites linked to psychological symptoms. Rigorous pre-clinical assays and validation trials in humans are required to prove the causality, if any, of the discussed metabolites.

The original claim on line 153 was poorly constructed and the Figure 2c is meant to visualize the influence of withdrawal on selected metabolites and also show the effect of chronic alcohol intake on the selected metabolites at baseline. The description of the Figure 2c has been modified in result section from line 156 onwards: “Overall, Fig. 2c demonstrates that a number of identified metabolites altered in sAUD patients relative to control are affected by alcohol withdrawal. Apart from 4-pyridoxic acid, cotinine, and heme metabolites bilirubin and biliverdin, the shifts observed in the selected metabolites are generally in the opposite direction as compared to the baseline.”

The authors attempt to extend the significance of their findings by assessing post-mortem brain tissues from AUD subjects; however, the finding that many of the metabolites changed in T2/T1 are also present in AUD brain tissues is interesting; however, not strongly supporting of the authors' claims that these metabolites are markers of AUD (line 173). Concerning the plasma cohort itself, it is unclear how the authors assessed for compliance with alcohol withdrawal or whether the subjects' blood-alcohol levels were independently verified.

We did not claim that the metabolites significantly correlated with the psychological symptoms - and present in central nervous system (frontal cortex or CSF) -  are “markers of AUD”. Line 173 did not refer to this idea, and the terms “markers of AUD” do not appear in the whole manuscript.

Regarding the compliance with alcohol cessation, we did not assess the ethanol blood level. The patients are hospitalized for a 3-week detoxification program, they are not allowed to drink alcohol and are under strict control of the nurses and medical staff of the unit. Consuming alcoholic beverage within the hospitalization unit is a reason for exclusion. However, we carefully monitored the liver function during alcohol withdrawal. For the reviewers’ information, we have added here below, the evolution of liver enzymes (ALT, AST, gGT) during the 3-week detoxification program as indirect markers of alcohol abstinence.

Author response image 2.

Data are described as median ± SEM. AST, Aspartate transaminase; ALT, Alanine transaminase; gGT: gamma glutamyltranspeptidase. ** p<0.01 vs T1, *** p<0.001 vs T1

The second area of concern is the need for more description of the analytical methodology, the lack of metabolite identification validation evidence, and related statistical questions. The authors cite reference #59 regarding the general methodology. However, this reference from their group is a tutorial/review/protocol-focused resource paper, and it is needs to be clarified how specific critical steps were actually applied to the current plasma study samples given the range of descriptions provided in the citations. The authors report a variety of interesting metabolites, including their primary fragment intensities, which are appreciated (Supplementary Table 3), but no MS2 matching scores are provided for level 2 or 3 hits. Further, level 1 hits under their definition are validated by an in-house standard, but no supporting data are provided besides this categorization. Finally, a common risk in such descriptive studies is finding spurious associations, especially considering many factors described in the current work. These include AUD, depression, anxiety, craving, withdrawal, etc. The authors describe the use of BH correction for multiple-hypothesis testing. However, this approach only accounts for the many possible metabolite association tests within each comparison (such as metabolites vs depression). It does not account for the multi-variate comparisons to the many behavior/clinical factors described above. The authors should employ one of several common strategies, such as linear mixed effects models, for these types of multi-variate assessments.

The methodological details related to the sample processing, data acquisition, data pre-processing and metabolite identification have been provided in the supplementary materials and described below. Supplementary table 3 has been amended with characteristic MS2 fragments for both positive and negative ionization modes if data was available. Additionally, all annotations against the in-house library additions have been rechecked, identification levels corrected and EICs for all level 1 identifications are provided in the supplementary material.

As described in the statistical analysis methods, BH correction was employed in the group-wise comparisons to shortlist the altered features for identification. Manual curating was then applied for the significant features and annotated metabolites subjected to correlation analysis. In this discovery-based approach the aim was to discover potential candidates linked with psychological symptoms for subsequent work to evaluate causality. Hence, the application of multi-variate analysis assessing biomarker candidates is not in the scope of this study.

“LC-MS analysis. Plasma sample preparation and LC-MS measurement followed the parameters previously detailed in Klåvus et al (57).  Samples were randomized and thawed on ice before processing. 100 µl of plasma was added to 400 µl of LC-MS grade acetonitrile, mixed by pipetting four time, followed by centrifugation in 700 g for 5 minutes at 4 °C. A quality control sample was prepared by pooling 10 µl of each sample together. Extraction blanks having only cold acetonitrile and devoid of sample were prepared following the same procedure as sample extracts. LC-MS grade acetonitrile, methanol, water, formic acid and ammonium formate (Riedel-de Haën™, Honeywell, Seelze, Germany) were used to prepare mobile phase eluents in reverse phase (Zorbax Eclipse XDBC18, 2.1 × 100 mm, 1.8 μm, Agilent Technologies, Palo Alto, CA, USA) and hydrophilic interaction (Acquity UPLC® BEH Amide 1.7 μm, 2.1 × 100 mm, Waters Corporation, Milford, MA, USA) liquid chromatography separation. In reverse phase separation, the samples were analyzed by Vanquish Flex UHPLC system (Thermo Scientific, Bremen, Germany) coupled to high-resolution mass spectrometry (Q Exactive Focus, Thermo Scientific, Bremen, Germany) in both positive and negative polarity mass range from 120 to 1200, target AGC 1e6 and resolution 70,000 in full scan mode. Data dependent MS/MS data was acquired for both modes with target AGC 8e3 and resolution 17,500, precursor isolation window was 1.5 amu, normalized collision energies were set at 20, 30 and 40 eV and dynamic exclusion at 10.0 seconds. In hydrophobic interaction separation, the samples were analyzed by a 1290 LC system coupled to a 6540 UHD accurate mass Q-ToF spectrometer (Agilent Technologies, Waldbronn, Karlsruhe, Germany) using electrospray ionization (ESI, Jet Stream) in both positive and negative polarity with mass range from 50 to 1600 and scan rate of 1.67 Hz in full scan mode. Source settings were as in the protocol. Data dependent MS/MS data was acquired separately using 10, 20 and 40 eV collision energy in subsequent runs. Scan rate was set at 3.31 Hz, precursor isolation width of 1.3 amu and target counts/spectrum of 20,000, maximum of 4 precursor pre-cycle, precursor exclusion after 2 spectra and release after 15.0 seconds. Detectors were calibrated prior sequence and continuous mass axis calibration was performed throughout runs by monitoring reference ions from infusion solution for operating at high accuracy of < 2 ppm. Quality control samples were injected in the beginning of the analysis to equilibrate the system and after every 12 samples for quality assurance and drift correction in all modes. All data were acquired in centroid mode by either MassHunter Acquisition B.05.01 (Agilent Technologies) or in profile mode by Xcalibur 4.1 (Thermo Fisher Scientific) softwares.

Metabolomics analysis of TSDS frontal cortex and CSF samples using the same 1290 LC system coupled with a 6540 UHD accurate mass Q-ToF spectrometer has been previously accomplished by Karkkainen et al (10).

Peak picking and data processing. Raw instrumental data (*raw and *.d files) were converted to ABF format using Reifycs Abf Converter (https://www.reifycs.com/AbfConverter). MS-DIAL (Version 4.70) was employed for automated peak picking and alignment with the parameters according to Klåvus et al., 2020 (57) separately for each analytical mode. For the 6540 Q-ToF mass data minimum peak height was set at 8,000 and for the Q Exactive Focus mass data minimum peak height was set at 850,000. Commonly, m/z values up to 1600 and all retention times were considered, for aligning the peaks across samples retention time tolerance was 0.2 min and MS1 tolerance 0.015 Da and the “gap filling by compulsion” was selected. Alignment results across all modes and sample types as peak areas were exported into Microsoft Excel sheets to be used for further data pre-processing.

Pre-processing including drift correction and quality assessment was done using the notame package v.0.2.1 R software version 4.0.3 separately for each mode. Features present in less than 80% of the samples within all groups and with detection rate in less than 70% of the QC samples were flagged. All features were subjected to drift correction where the features were log-transformed and a regularized cubic spline regression line was fitted for each feature against the quality control samples. After drift correction, QC samples were removed and missing values in the non-flagged features were imputed using random forest imputation. Finally, the preprocessed data from each analytical mode was merged into a single data matrix.

Molecular feature characteristics (exact mass, retention time and MS/MS spectra) were compared against in-house standard library, publicly available databases such as METLIN, HMDB and LIPIDMAPS and published literature. Annotation of metabolites and the level of identification was based on the recommendations given by the Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI) (59): 1 = identified based on a reference standard, 2 = putatively annotated based on physicochemical properties or similarity with public spectral libraries, 3 = putatively annotated to a chemical class and 4 = unknown.”

Reference 59: Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007;3:211–221.

Recommendations for the authors:

Reviewer #1:

(1) There should be more discussion comparing and contrasting the differences between the 2 cohorts (ALCOHOLBIS versus GUT2BRAIN), instead of stressing the similarities.

As indicated in the results section, we have verified that the ALCOHOLBIS cohort and GUT2BRAIN cohort are similar in term of age, gender, smoking habits, drinking habits and severity of psychological symptoms. Those similar features are important to allow the combination of the metabolomics data from the two cohorts, which subsequently allows to have a bigger sample size (n = 96) and more statistical power.

(2) The identification of 97 heavy alcohol users based on hospital codes at autopsy may not be the most rigorous way to define those with AUD. More information is needed on how these 97 were classified as heavy alcohol users.

The classification of subjects to the group who have a history of heavy alcohol use was not based solely on the autopsy records. The classification was also based on medical history, which in Finland is available from the whole life of the subjects, and including diagnoses and laboratory finding. The subjects needed to have a diagnosis of alcohol-related disease, as stated in the methods section of the manuscript. However, since some of the used diagnoses are related to organ damage related to heavy alcohol use, we do not claim that these subjects would all have alcohol dependence. But history of heavy use of alcohol is needed to get organ damage associated with alcohol use. Therefore, we consider that diagnosis of alcohol-related disease is a clear sign of a history of heavy alcohol use.

(3) The fact that the control group mainly died of cardiovascular disease confounds the interpretations around alcohol impact metabolite levels. How much of the metabolomics differences are related to hyperlipidemia or other CVD risk factors in the controls?

There are no healthy controls in post-mortem studies, since all subjects need to die from something to be included to the cohort. The challenge in studying AUD is that they die relatively young. The only other group of individuals who die outside of hospital at the relatively same age as subjects with AUD are those with CVD. Post-mortem autopsies are done in Finland to all who die outside of hospital, and these are the main source of samples for post-mortem sample cohorts. Therefore, there is no other control group to compare AUD subject to in these types of studies.

As for the altered metabolites in the post-mortem sample, the phospholipids observed could be associated with CVD. However, alterations in phospholipids are also commonly associated with alcohol use and AUD (for a review see (Voutilainen and Kärkkäinen, 2019)) and this effect is also seen in the results from the clinical cohorts in this study (Figure 1). Therefore, it cannot be said that these phospholipids finding would be due to selection of the control group.

(4) When examining metabolomics alterations, it is extremely important to understand what people are eating (i.e., providing a substrate). A major confounding issue here is that heavy alcohol users typically choose drinking over eating food. How much of the observed alterations in the plasma metabolome is due to the decreased food intake? Some validation in animal models of ethanol exposure compared to pair-fed controls would help strengthen causal relationships between metabolites and alterations in the circulation and CNS.

Regarding the validation in animal models of ethanol exposure, we were very careful in our discussion to avoid pretending that the study allowed to test causality of the factors. This was certainly not the objective of the present study. The testing of causality would indeed probably necessitate animal models but these models could only test the effects of one single metabolite at a time and could not at the same time capture the complexity of the changes occurring in AUD patients. The testing of metabolites would be a totally different topic. Hence, we do not feel comfortable in conducting rodent experiments for several reasons. First, AUD is a very complex pathology with physiological and psychological/psychiatric alterations that are obviously difficult to reproduce in animal models. Secondly, as mentioned by the reviewer, AUD pathology spontaneously leads to nutritional deficits, including significant reductions in carbohydrates, lipids, proteins and fiber intakes. We have recently published a paper in which we carefully conducted detailed dietary anamneses and described the changes in food habits in AUD patients (Amadieu et al., 2021). As explained below, some blood metabolites that are significantly correlated with depression, anxiety and craving belong to the xanthine family and are namely theobromine, theophylline, and paraxanthine, which derived from metabolism of coffee, tea or chocolate (which are not part of the normal diet of mice or rats).Therefore, conducting an experiment in animal model of ethanol exposure compared to pair-fed controls will omit the important impact of nutrition in blood metabolomics and consequently won’t mimic the human AUD pathology. In addition, if we take into consideration the European Directive 2010/63/EU (on the protection of animals used for scientific purposes) which aims at Reducing (Refining, Replacing) the number of animals used in experiment, it is extremely difficult to justify, at the ethical point of view, the need to reproduce human results in an animal model that won’t be able to mimic the nutritional, physiological and psychological alterations of alcohol use disorder.

As explained above, we do agree with the reviewer that AUD is not only “drinking alcohol” but is also associated with reduction in food intake that obviously influenced the metabolomics data presented in this current study.  We have therefore added some data, which have not been published in the previous version of the manuscript, in the results section that refer to key nutrients modified by alcohol intake and we refer to those data and their link with metabolomics in the discussion section:

Results section page 8, Line 153-155. This sentence has been added:

“The changes in metabolites belonging to the xanthine family during alcohol withdrawal could be explained by the changes in dietary intake of coffee, tea and chocolate (see Fig S5).”

Discussion section: Page 11, Line 234-238.

“Interestingly, the caffeine metabolites belonging to the xanthine family such as paraxanthine, theophylline and theobromine that were decreased at baseline in AUD patients compared to controls, increased significantly during alcohol withdrawal to reach the levels of healthy controls. Changes in dietary intake of coffee, tea and chocolate during alcohol withdrawal could explain these results”.

In the conclusion, Page 16, Line 360-32, we clearly stated that: “LC-MS metabolomics plasma analysis allowed for the identification of metabolites that were clearly linked to alcohol consumption, and reflected changes in metabolism, alterations of nutritional status, and gut microbial dysbiosis associated with alcohol intake”

Reference:

Amadieu C, Leclercq S, Coste V, Thijssen V, Neyrinck AM, Bindels LB, Cani PD, Piessevaux H, Stärkel P, Timary P de, Delzenne NM. 2021. Dietary fiber deficiency as a component of malnutrition associated with psychological alterations in alcohol use disorder. Clinical Nutrition 40:2673–2682. doi:10.1016/j.clnu.2021.03.029

Leclercq S, Cani PD, Neyrinck AM, Stärkel P, Jamar F, Mikolajczak M, Delzenne NM, de Timary P. 2012. Role of intestinal permeability and inflammation in the biological and behavioral control of alcohol-dependent subjects. Brain Behav Immun 26:911–918. doi:10.1016/j.bbi.2012.04.001

Leclercq S, De Saeger C, Delzenne N, de Timary P, Stärkel P. 2014a. Role of inflammatory pathways, blood mononuclear cells, and gut-derived bacterial products in alcohol dependence. Biol Psychiatry 76:725–733. doi:10.1016/j.biopsych.2014.02.003

Leclercq S, Matamoros S, Cani PD, Neyrinck AM, Jamar F, Stärkel P, Windey K, Tremaroli V, Bäckhed F, Verbeke K, de Timary P, Delzenne NM. 2014b. Intestinal permeability, gut-bacterial dysbiosis, and behavioral markers of alcohol-dependence severity. Proc Natl Acad Sci U S A 111:E4485–E4493. doi:10.1073/pnas.1415174111

Voutilainen T, Kärkkäinen O. 2019. Changes in the Human Metabolome Associated With Alcohol Use: A Review. Alcohol and Alcoholism 54:225–234. doi:10.1093/alcalc/agz030

Reviewer #2:

(1) More methodological information about the laboratory processing of samples, instrumentation, and data analysis needs to be provided. Reference 59 needs to be more specific and include important methodological details for this project. Please provide an actual methods section for the mass-spectrometry-based metabolomics.

The reviewer is correct that the methods should be described in detail but due to word limits, the description was moved to a supplementary file. Methodological details are provided in the answer to the final comment in the public reviews section and we kindly refer to that for the methodological details. Reference 57 (Klåvus et al) is a method paper and covers the whole untargeted metabolomics pipeline that is used in our work.

(2) The VIP figures, e.g., Figure 1b and Figure 2b are not very informative and would be better represented in a supplementary table

VIP scores for all annotated metabolites are provided in the supplementary table 3 along with peak data and other values derived from statistical tests. Furthermore, we have removed the VIP value in figures 1 and 2 and we have replaced them by an updated Volcano plot to represent also the VIP values in addition to the q and Cohen’s d values.

(3) The findings on odd-chain lyso-lipids are interesting, and while these have been reported biologically, odd-chain lipids are uncommon and should be validated with authentic standards as available (please provide an XIC of the level 1 peak and standard if possible, e.g., LPC 17:0) or at least a supplementary figure on manual inspection of the negative mode MS2 spectrum showing the putative fatty acid chain fragment. The current assignments are based on positive mode lipid class fragments and accurate mass.

We thank the reviewer for pointing this out and it is correct that the negative MS2 spectrum is essential for lipid identification. Although the current assignments show only positive fragments for many lipids, the fatty acid chain, if reported, has been confirmed from negative mode MS2 spectrum. The supplementary table 3 with peak information has been augmented with fragment information from both negative and positive ionizations if available. Also, reference and experimental MS2 spectra have been provided as separate supplemental file for level 1 identifications, including the odd-chain lyso-lipids LPC 15:0 and 17:0.

(4) Please provide some supplementary information (MS1/MS2 if available) on the untargeted features of interest (up and down-regulated) from Figure 1C, especially the 5 encircled features. If any manual annotation of these features was attempted, please include a brief description in the results/discussion.

All statistically significant features with MS2 data have been subjected to manual annotation and database searches using at least METLIN, HMDB and LipidMaps. Additionally, if the manual inspection failed to provide any identification, in silico fragmentation software MS-FINDER was used to calculate candidate molecular formula. The features were labeled as unknown if all efforts were unsuccessful. The peak characteristics of the key unknowns in Figure 1b have also been included in the supplemental table.

A note of the manual inspection has been included in the result section line 129: “The top-ranked metabolites in Fig. 1b remained unknown regardless of manual curation.”

Reviewer #3:

I think this is an interesting paper with a very solid methodology and an abundance of results. I am not an expert on metabolomics, and I have some very interesting hours here, trying (but sometimes failing) to grasp this paper's content. This paper also needs to be closely read by a reviewer who knows the metabolomics field and can give feedback on the meaning of the results. I have focused purely on the AUD clinical side as this is where I may contribute. My main concern is conceptualizing the aims and what authors want to investigate. As far as I understand, this is a study of the relationship between alcohol use and the metabolome, and in this respect, I think there are some issues.

Just take the abstract that talks about (in the first sentence) alcohol use disorder ("AUD") - a term that generally sometimes refers to harmful use of alcohol and alcohol addiction and sometimes to all F10-diagnosis (and thus an inaccurate term), then the following sentence talks about what leads to alcohol addiction (not dependence) - and this in a mechanistic direction and in the last part of the second sentence talks about metabolomics being able to decipher metabolic events related to AUD. So, even in the first two sentences, it is confusing - is this about correlates, mechanisms, prevention, or treatment? The inaccuracy of terms continues in sentence 4. We have "chronic alcohol abuse" (?) and "severe alcohol use disorder (AUD)" (abbreviated for the second time). Later, only "alcohol abuse" is used and the abstract ends with something about these findings being interesting in "the management of [...] AUD". All this illustrates that there is a large mixture of concepts - what aspect of alcohol use or abuse are you looking at? Moreover, of intention: is it to find correlates, explanations, or targets for interventions? Without clarity in this respect, one can get lost in what all these interesting measures mean - how we should interpret them. This comment is made only for the abstract. However, but it is equally valid and important for the introduction and discussion parts of the ms, where additional terms and formulations are introduced: "heavy alcohol use" (lines 86-7) and "prevent or treat psychiatric disorders such as AUD" (lines 90-1). This is then reflected in the discussion where the authors claim that what they have found is related to "chronic alcohol abuse" (line 188), "heavy alcohol drinkers" (line 191), and "AUD patients" (lines 199 and 202 and further on).  

We thank the reviewer for this useful comment and we apologize for the confusion. We agree that it is important to use the correct terms and definitions. All patients included in this study were diagnosed as severe AUD (for more information on the diagnosis, see answer to the comments related to DSM-IV and DSM5). This manuscript is consequently related to severe AUD and other terms like “alcohol abuse, “alcohol addiction” are therefore not appropriate. In the revised version of the manuscript, we have used severe AUD or the abbreviation sAUD. The figure and legends have been changed accordingly.

In the first paragraph of the results section, ALCOHOLBIS and GUT2BRAIN are compared. It says they are similar on many measures, including craving, but different on some measures, again including craving. It is difficult to grasp this even if the authors try to explain (lines 101-2). This sentence also introduces some discussion in the results section by saying something normative about their finding and relating this to other research (references 12, 13, and 14).

We would like to apologize for the confusion related to first paragraph of the results section. We have indeed indicated that, while the ALCOHOLBIS cohort and the GUT2BRAIN cohort are highly similar in term of biological and psychological features, a significant difference does exist in the compulsive component of the craving score. Indeed, the mean score of compulsion is 11 ± 3 in the ALCOHOLBIS cohort and 14  ± 3 in the GUT2BRAIN cohort. In healthy controls, the mean score of compulsion is 1.5 ± 1.5. Despite the statistically significant difference in craving between both cohorts, we do not think that this difference is relevant in our context since both scores (11 and 14) are considered high compared to the control group. In order to simplify the message, we have revised the first paragraph as follows:

“Both groups of patients were similar in terms of age, gender, smoking and drinking habits and presented with high scores of depression, anxiety and alcohol craving at T1 (Table 1). These biological and psychological similarities allow us to combine both cohorts (and consequently increase sample size) and compare them to a group of heathy controls for metabolomics analysis”.

In line 104 the abbreviation PCA is introduced but needs to be explained. Such objections could be made for many of the abbreviations used (sPLS-DA VIP, LPC, CSF, CNS, LPE, etc.), but of course, they may be made more difficult by the unusual way of stacking the different sections.

We thank the reviewer for pointing these out. Most abbreviations are written out in the figure legends or method section but indeed the organization of the different sections makes it less evident. The abbreviations pointed out have been opened in the results section when they are first used.

Furthermore, they say that the severity of AUD was "evaluated by a psychiatrist using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, fourth edition (DSM-IV) (ALCOHOLBIS cohort) or fifth edition (DSM-5)" (GUT2BRAIN cohort): This makes sense for DSM-5 but needs to be explained more for DSM-IV. They also need to say what levels were included.

We thank the reviewer for this very appropriate remark that deserves some explanations.

While the patients of the GUT2BRAIN cohort were enrolled in 2018-2019 where the DSM5 was applicable, the patients from the ALCOHOLBIS cohort were recruited many years before. The protocol related to the ALCOHOLBIS cohort was written before 2013, and approved by ethical committee, where the DSM-IV was the last version of the DSM used at that moment. 

We therefore totally agree with the reviewer that our sentence “the severity of AUD was "evaluated by a psychiatrist using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, fourth edition (DSM-IV) (ALCOHOLBIS cohort) or fifth edition (DSM-5)" (GUT2BRAIN cohort)” is not correct. Indeed, DSM-IV (before 2013) described two distinct disorders, alcohol abuse and alcohol dependence, while the DSM-5 integrates the two DSM-IV disorders into a single disorder called alcohol use disorder with mild (2 or 3 symptoms), moderate (4 or 5 symptoms) and severe (6 or more symptoms) sub-classifications.

In this present study, we have enrolled patients that received the diagnosis of alcohol dependence (DSM-IV criteria) or severe alcohol use disorder (DSM5 criteria).

We have changed the paragraph related to this issue into this new one:

“The severity of AUD was evaluated by a psychiatrist using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, fourth edition (DSM-IV) (Alcoholbis cohort) or fifth edition (DSM-5) (GUT2BRAIN cohort). Patients evaluated with the DSM-IV received the diagnosis of “alcohol dependence”, while the patients evaluated with the DSM-5 received the diagnosis of “severe alcohol use disorder” (6 or more criteria). To simplify, we used the term “sAUD” (for severe alcohol use disorder) that includes both diagnosis (sAUD and alcohol dependence)”.

I am unsure about the shared first co-authorship and the shared last co-authorship request, but I leave this up to the editors and the journal policies. Also, the order of the different parts may be correct (the M+M placed last) but is unusual for many journals. This is also up to the journal to decide.

As mentioned in the guidelines to authors, the method section should be included at the end of the manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, James Lee, Lu Bai, and colleagues use a multifaceted approach to investigate the relationship between transcription factor condensate formation, transcription, and 3D gene clustering of the MET regulon in the model organism S. cerevisiae. This study represents a second clear example of inducible transcriptional condensates in budding yeast, as most evidence for transcriptional condensates arises from studies of mammalian systems. In addition, this study links the genomic location of transcriptional condensates to the potency of transcription of a reporter gene regulated by the master transcription factor contained in the condensate. The strength of evidence supporting these two conclusions is strong. Less strong is evidence supporting the claim that Met4-containing condensates mediate the clustering of genes in the MET regulon.

Strengths:

The manuscript is for the most part clearly written, with the overriding model and specific hypothesis being tested clearly explained. Figure legends are particularly well written. An additional strength of the manuscript is that most of the main conclusions are supported by the data. This includes the propensity of Met4 and Met32 to form puncta-like structures under inducing conditions, formation of Met32-containing LLPS-like droplets in vitro (within which Met4 can colocalize), colocalization of Met4-GFP with Met4-target genes under inducing conditions, enhanced transcription of a Met3pr-GFP reporter when targeted within 1.5 - 5 kb of select Met4 target genes, and most impressively, evidence that several MET genes appear to reposition under transcriptionally inducing conditions. The latter is based on a recently reported novel in vivo methylation assay, MTAC, developed by the Bai lab.

Weaknesses:

My principal concern is that the authors fail to show convincing evidence for a key conclusion, highlighted in the title, that nuclear condensates per se drive MET gene clustering. Figure 4E demonstrates that Met4 molecules, not condensates per se, are necessary for fostering distant cis and trans interactions between MET6 and three other Met4 targets under -met inducing conditions. In addition, the paper would be strengthened by discussing a recent study conducted in yeast that comes to many of the same conclusions reported here, including the role of inducible TF condensates in driving 3D genome reorganization (Chowdhary et al, Mol. Cell 2022).

Following the reviewer’s advice, we carried out MTAC with the VP near MET6 in WT Met4 and ΔIDR2.3 strains (results shown below). The conclusions are somewhat ambiguous. For long-distance interactions with MUP1, YKG9, STR3, and MET13, we indeed observe decreased MTAC signals close to background levels in the ΔIDR2.3 strain, which aligns with the model suggesting that Met4 condensation promotes clustering among Met4 targeted genes. However, we also noticed significant decreases in the local MTAC signals (HIS3 and MET6). It is possible that the changes in Met4 condensates alter the chromosomal folding near MET6, thereby affecting the local MTAC signals. Alternatively, LacI-M.CviPI (the methyltransferase) could be induced to a lesser extent in the ΔIDR2.3 strain, leading to a genome-wide decrease in MTAC signals. Due to this ambiguity, we decided not to include the following plot in the main figure.

Author response image 1.

We discussed Hsf1 and added the suggested reference on page 13.

Other concerns:

(1) A central premise of the study is that the inducible formation of condensates underpins the induction of MET gene transcription and MET gene clustering. Yet, Figure 1 suggests (and the authors acknowledge) that puncta-like Met4-containing structures pre-exist in the nuclei of non-induced cells. Thus, the transcription and gene reorganization observed is due to a relatively modest increase in condensate-like structures. Are we dealing with two different types of Met4 condensates? (For example, different combinations of Met4 with its partners; Mediator- or Pol II-lacking vs. Mediator- or Pol II-containing; etc.?) At the very least, a comment to this effect is necessary.

Although Met4 can form smaller puncta in the +met condition (Figure 1A), it cannot be recruited to its target genes due to the absence of its sequence-specific binding partners, Met31 and Met32 (these two factors are actively degraded in the +met condition). Consistently, in the +met condition, Met4 shows extremely low genome-wide ChIP signals (Figure 3C). Therefore, these Met4 puncta in +met do not have organize the 3D genome or have gene regulatory functions. This discussion is added on page 12.

(2) Using an in vitro assay, the authors demonstrate that Met4 colocalizes with Met32 LLPS droplets (Figure 2F). Is the same true in vivo - that is, is Met32 required for Met4 condensation? This could be readily tested using auxin-induced degradation of Met32. Along similar lines, the claim that Met32 is required for MET gene clustering (line 250) requires auxin-induced degradation of this protein.

As the reviewer pointed out above, cells in the +met condition also show small Met4 puncta. In this condition, Met32 is essentially undetectable (Met31 level is even lower and remains undetectable even in the -met conditions). Therefore, Met4 does not strictly require the presence of Met32 in vivo (may require other factors or modifications). Met4 does not have DNA-binding activity, and therefore it cannot target and organize chromosomes on its own. Although we did not do the Met32 degradation experiment, we measured the 3D genome conformation in +met and showed that there are no detectable interactions among Met4 target genes.

(3) The authors use a single time point during -met induction (2 h) to evaluate TF clustering, transcription (mRNA abundance), and 3D restructuring. It would be informative to perform a kinetic analysis since such an analysis could reveal whether TF clustering precedes transcriptional induction or MET gene repositioning. Do the latter two phenomena occur concurrently or does one precede the other?

We appreciate the reviewer’s insightful question. It is indeed intriguing to consider whether TF clustering precedes transcriptional induction and MET gene clustering. However, as mentioned on page 12 of our manuscript, this experiment poses significant challenges. The low intensities of the Met4 and Met32 signals necessitate high excitation for imaging, which also makes them prone to photo-bleaching. Consequently, we have been unable to measure the dynamics of Met4 and Met32 puncta in vivo, let alone co-image them with DNA/RNA. Undertaking this experiment will require considerable effort, which we plan to pursue in the future.

(4) Based on the MTAC assay, MET13 does not appear to engage in trans interactions with other Met4 targets, whereas MET6 does (Figures 4C and 4E). Does this difference stem from the greater occupancy of Met4 at MET6 vs. MET13, greater association of another Met co-factor with the chromatin of MET6 vs. MET13, or something else?

We were also surprised by this result, given that MET13 emerged as one of the strongest transcriptional hotspots in our previous screen. It also exhibits one of the highest Met4 ChIP signals and is closely associated with the nuclear pore complex. Our earlier findings indicate that DNA dynamics near the VP significantly influence the MTAC signal; specifically, a VP with constrained motion is less effective at methylating interacting sites (Li et al., 2024). Therefore, it is plausible that MET13 is associated with a large Met4 condensate, which constrains the motion of nearby chromatin and diminishes MTAC efficiency.

Reviewer #2 (Public Review):

Summary:

This manuscript combines live yeast cell imaging and other genomic approaches to study how transcription factor (TF) condensates might help organize and enhance the transcription of the target genes in the methionine starvation response pathway. The authors show that the TFs in this response can form phase-separated condensates through their intrinsically disordered regions (IDRs), and mediate the spatial clustering of the related endogenous genes as well as reporter inserted near the endogenous target loci.

Strengths:

This work uses rigorous experimental approaches, such as imaging of endogenously labeled TFs, determining expression and clustering of endogenous target genes, and reporter integration near the endogenous target loci. The importance of TFs is shown by rapid degradation. Single-cell data are combined with genomic sequencing-based assays. Control loci engineered in the same way are usually included. Some of these controls are very helpful in showing the pathway-specific effect of the TF condensates in enhancing transcription.

Weaknesses:

Perhaps the biggest weakness of this work is that the role of IDR and phase separation in mediating the target gene clustering is unclear. This is an important question. TF IDRs may have many functions including mediating phase separation and binding to other transcriptional molecules (not limited to proteins and may even include RNAs). The effect of IDR deletion on reduced Fano number in cells could come from reduced binding with other molecules. This should be tested on phase separation of the purified protein after IDR deletion. Also, the authors have not shown IDR deletion affects the clustering of the target genes, so IDR deletion may affect the binding of other molecules (not the general transcription machinery) that are specifically important for target gene transcription. If the self-association of the IDR is the main driving force of the clustering and target gene transcription enhancement, can one replace this IDR with totally unrelated IDRs that have been shown to mediate phase separation in non-transcription systems and still see the gene clustering and transcription enhancement effects? This work has all the setup to test this hypothesis.

We thank the reviewer for raising this point, and we tried more in vitro and in vivo experiments with Met4 IDR deletions. See the answer to Reviewer 1 for the in vivo 3D mapping experiment.

We purified Met4-ΔIDR2 with an MBP tag, but its low yield made labeling and conducting thorough experiments challenging. At concentrations above ~10 μM, the protein tends to aggregate, while at lower concentrations, it remains diffusive in solution and does not form condensates. When we mixed purified Met4-ΔIDR2 with Met32, we observed reduced partitioning inside Met32 condensates compared to the full-length Met4. As the reviewer noted, this diminished interaction may contribute to the decreased puncta formation observed in vivo. This result is added to the manuscript on page 11 and supplementary figure 5.

The Met4 protein was tagged with MBP but Met 32 was not. MBP tag is well known to enhance protein solubility and prevent phase separation. This made the comparison of their in vitro phase behavior very different and led the authors to think that maybe Met32 is the scaffold in the co-condensates. If MBP was necessary to increase yield and solubility during expression and purification, it should be cleaved (a protease cleavage site should be engineered) to allow phase separation in vitro.

Following the reviewer’s advice, we purified Met4-TEV-MBP so that the MBP can be cleaved off. Unfortunately, concentrated Met4-TEV-MBP needs to be stored at high salt (400mM) to be soluble. When exchanged into a suitable buffer for TEV cleavage (≤200 mM NaCl), nearly all soluble protein aggregates. Attempts to digest the protein in storage buffer results in observable aggregation before significant cleavage (see below).  

Author response image 2.

Are ATG36 and LDS2 also supposed to be induced by -met? This should be explained clearly. The signals are high at -met.

Genomic loci ATG36 and LDS2 were chosen as controls because they are not bound by Met TFs (ChIP-seq tracks) and their expressions are not induced by -met (RNA-seq data). This information is added to the manuscript on page 9. When MET3pr-GFP reporter is inserted into these loci, GFP is induced by -met (because it is driven by the MET3 promoter), but the induction level is less than the same reporter inserted into the transcriptional hotspot like MET13 and MET6 (Figure 6E, also see Du et al., Plos Genetics, 2017).

ChIP-seq data:

Author response image 3.

RNA-seq counts:

Author response table 1.

Figure 6B, the Met4-GFP seems to form condensates at all three loci without a very obvious difference, though 6C shows a difference. 6C is from only one picture each. The authors should probably quantify the signals from a large number of randomly selected pictures (cells) and do statistics.

If we understand this comment correctly, the reviewer is referring to the fact that all three loci in Figure 6B appear to show a peak in GFP intensity. This pattern emerges because these images are averaged among many cells (number of cells analyzed in 6B has been added to the Figure legends). GFP intensities near the center will always be higher because peripheral pixels are more likely to fall outside the nuclei boundaries, where Met4 signals are absent (same as in Figure 3F). Importantly, MET6 locus shows higher intensity near the center in comparison to PUT1 and ATG36, indicating its co-localization with Met4 condensates.

Reviewer #3 (Public Review):

Summary:

In this study, the authors probe the connections between clustering of the Met4/32 transcription factors (TFs), clustering of their regulatory targets, and transcriptional regulation. While there is an increasing number of studies on TF clustering in vitro and in vivo, there is an important need to probe whether clustering plays a functional role in gene expression. Another important question is whether TF clustering leads to the clustering of relevant gene targets in vivo. Here the authors provide several lines of evidence to make a compelling case that Met4/32 and their target genes cluster and that this leads to an increase in transcription of these genes in the induced state. First, they found that, in the induced state, Met4/32 forms co-localized puncta in vivo. This is supported by in vitro studies showing that these TFs can form condensates in vitro with Med32 being the driver of these condensates. They found that two target genes, MET6 and MET13 have a higher probability of being co-localized with Met4 puncta compared with non-target loci. Using a targeted DNA methylation assay, they found that MET13 and MET6 show Met4-dependent long-range interactions with other Met4-regulated loci, consistent with the clustering of at least some target genes under induced conditions. Finally, by inserting a Met4-regulated reporter gene at variable distances from MET6, they provide evidence that insertion near this gene is a modest hotspot for activity.

Weaknesses:

(1) Please provide more information on the assay for puncta formation (Figure 1). It's unclear to me from the description provided how this assay was able to quantitate the number of puncta in cells.

Due to the variation in puncta size and intensity (as illustrated in Figure 1A), counting the number of puncta would be highly subjective with arbitrary cutoffs. Therefore, we chose to calculate the CV and Fano values instead, which are unbiased measures. Proteins that form puncta will exhibit greater pixel-to-pixel variations in GFP intensity, resulting in higher CV and Fano values.

(2) How does the number of puncta in cells correspond with the number of Met-regulated genes? What are the implications of this calculation?

As previously mentioned, defining the exact number of Met4 puncta is challenging. The number of puncta does not necessarily have one-to-one correspondence to the number of Met4 target genes. Some puncta may not be associated with chromosomes, while others may interact with multiple genes.

(3) A control for chromosomal insertion of the Met-regulated reporter was a GAL4 promoter derivative reporter. However, this control promoter seems 5-10 fold more active than the Met-regulated promoter (Figure 6). It's possible that the high activity from the control promoter overcomes some other limiting step such that chromosomal location isn't important. It would be ideal if the authors used a promoter with comparable activity to the Met-reporter as a control.

We agree with the reviewer that it will be better to use another promoter with comparable activity. Indeed, this was our rationale for selecting the attenuated GAL1 promoter over the WT version; however, it still exhibited substantially higher activity than the MET3pr. Unfortunately, we do not have a promoter from a different pathway that is calibrated to match the activity level of MET3pr. Nonetheless, MET17pr has much higher activity (~3 fold) than MET3pr, and we observed similar degree of stimulus from the hotspot in comparison to the control locus for both promoters (1.5-2-fold increase in GFP expression) (Figure 6E & F). This suggests that the observed effects are more likely to depend on the activation pathway and TF identity rather than the promoter strength.

(4) It seems like transcription from a very large number of genes is altered in the Met4 IDR mutant (Figure 7F). Why is this and could this variability affect the conclusions from this experiment?

We agree with the reviewer that ΔIDR 2.3 truncation affects the expression of 2711 (P-adj <0.05) genes (1339 up,1372 down). We suspect that this is due to the decreased expression of Met4 target genes, leading to altered levels of methionine and other sulfur-containing metabolites. Such changes would have a global impact on gene expression. Importantly, despite the similar number of genes that show up vs down regulation in the ΔIDR 2.3 strain, almost all Met4 targets showed decreased expression (Fig 7F). This supports the model where Met4 condensates lead to increased expression in its target genes.

Recommendations for the authors:

Reviewer #1 (Recommendations for The Authors):

(1) The introduction contains multiple miscitations. Rather than gene clustering, most of the studies and reviews cited (e.g., lines 35-39) report interactions between genomic loci (E-E, E-P, and P-P). There are other claims not supported by the papers cited. Moreover, the authors lump together original research papers and reviews within a given group without distinguishing which is which.

We thank the reviewer for pointing this out. We reorganized the references in the introduction.

(2) One option to address the concern regarding the lack of evidence that nuclear condensates per se drive MET gene clustering is to test the impact of Met4 ΔIDR2.3 on MTAC signals.

We carried out the suggested experiment. See answer above (Reviewer #1, Question #1).

(3) Authors claim that there are significant differences between values depicted in Figures 1B and 3G. Statistical tests are necessary to show this.

Significance values were calculated in comparison to free GFP using two-tailed Student’s t-test in 1B,1C, and 3G. The corresponding figure legends are updated.

(4) How are the data in Figures 3F, G, and 6B, C generated? This is unclear from the information provided in the Figure legends and Materials and Methods.

For each cell, we projected the highest mCherry and GFP intensity at each pixel for all z positions onto a 2D plane (MIP). The MIP images were aligned with the mCherry dot at the center and averaged among all cells. To calculate the GFP intensities like in Figure 3G and 6C, a single line was drawn across the center and the GFP profile was analyzed by ImageJ. We now describe this in the corresponding figure legends, and the Materials and Methods are also updated.

(5) Typos/ unclear writing: lines 24, 58, 79, 82, 84, 96, 117, 121, 131, 142, 147, 161 (terminus, not "terminal"), 250, 325, 349, 761 (was, not "are"). For several of these: "condense" is not "condensate"; for many others: inappropriate use of "the". Supplementary Figure 1 legend: not "a single nuclei" instead "a single nucleus".

We thank the reviewer for pointing this out. We tried our best to correct grammatical errors.

(6) Define GAL1Spr (Figure 6F).

The GAL1S promoter is an attenuated GAL1 promoter that lacks two out of the four Gal4 binding site. The original paper is now cited in the manuscript on page 10.  

(7) Figure 7B, C: there appears to be an inconsistency between the image and bar graph value for ΔIDR3.

The Fano values calculated in 7C are averaged among a population of cells (we added the cell numbers to the legend), while the image in 7B is an example of an individual nucleus. There is some cell-to-cell variability in how the Met4 appears. To be more representative, we chose a different image for ΔIDR3.

(8) Supplementary Tables: use descriptive titles for file names.

This is corrected.

Reviewer #2 (Recommendations For The Authors):

Minor:

Figure 4F is not cited in the text, and the color legend seems wrong for targeted and control.

Figure 4F is now cited in the text. The labels were corrected.

Author response:

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

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

The investigators in this study analyzed the dataset assembly from 540 Salmonella isolates, and those from 45 recent isolates from Zhejiang University of China. The analysis and comparison of the resistome and mobilome of these isolates identified a significantly higher rate of cross-region dissemination compared to localized propagation. This study highlights the key role of the resistome in driving the transition and evolutionary 

Thank you for summarizing our work. According to your comments, we carefully considered and responded to them and made corresponding revisions to the text. Additionally, to fully contextualize the background knowledge and clarify the major points in this study, we add some references.

Upon further review of our initial manuscript, we realized that the original submission did not strictly follow the lineage order proposed by Zhou et al. (Natl Sci Rev. 2023 Sep 2;10(10):nwad228). To avoid confusion and keep the uniform knowledge in the typing system, we have adjusted the lineage nomenclature along the revised manuscript to reflect the corrected order as follows:

Author response table 1.

To ensure consistency with previous studies, we have revised the nomenclature for the different lineages of bvSP.

Strengths: 

The isolates included in this study were from 16 countries in the past century (1920 to 2023). While the study uses S. Gallinarun as the prototype, the conclusion from this work will likely apply to other Salmonella serotypes and other pathogens. 

Thanks for the constructive comments and the positive reception of the manuscript.

Weaknesses: 

While the isolates came from 16 countries, most strains in this study were originally from China. 

We appreciate the reviewer's observation regarding the sampling distribution of isolates in this study. We acknowledge that while the isolates were collected from 15 different countries, with a significant proportion originated from China (Author response image 1). This focus is due to several reasons:

Author response image 1.

Geographic distribution of 580 S. Gallinarum. Different colors indicate the countries of origin for the 580 S. Gallinarum strains in the dataset. Darker shades represent higher numbers of strains.

(1) As once a globally prevalent pathogen across the 20th century, S. Gallinarum was listed by the World Organization for Animal Health (WOAH) due to its economic importance. After 30 years of implementation of the National Poultry Improvement Plan in the US, it was almost eradicated in high-income countries, and interestingly, it became an endemic pathogen with sporadic outbreaks in most low- or middle-income countries like China and Brazil. Given the vast expanse of China's land area and the country's economic factors, implementing the same measures remains challenging.  

(2) S. Gallinarum is an avian-specific pathogen, particularly affecting chickens, and its distribution is closely linked to chicken meat production in different countries. There are more frequent reports of fowl typhoid in some high chicken-producing developing countries. Data from the United States Department of Agriculture (USDA) on annual chicken meat production for 2023/2024 show that the global distribution of S. Gallinarum aligns closely with the overall chicken meat production of these countries (https://fas.usda.gov/data/production/commodity/0115000).

Author response image 2.

The United States Department of Agriculture (USDA) data on annual chicken meat production for 2023/2024 across different countries globally.

(3) Our primary objective was to investigate the localized resistome adaptation of S. Gallinarum in regions. Being a region with significant disease burden, China has reported numerous outbreaks (Sci Data. 2022 Aug 13;9(1):495; Sci Data. 2024 Feb 27;11(1):244) and a high AMR prevalence of this serovar (Natl Sci Rev. 2023 Sep 2;10(10):nwad228; mSystems. 2023 Dec 21;8(6):e0088323), making it an excellent example for understanding localized resistance mechanisms.

(4) As China is the primary country of origin for the strains in this study, it is necessary to ensure that the strains from China are consistent with the local geographic characteristics of the country. Therefore, we conducted a correlation analysis between the number of strains from different provinces in China and the total GDP/population size of those provinces (Author response image 3). The results show that most points fall within the 95% confidence interval of the regression line. Although some points exhibit relative unbalance in the number of S. Gallinarum strains, most data points for these regions have a small sample size (n < 15). Overall, we found that the prevalence of S. Gallinarum in different regions of China is consistent with the overall nationwide trend.

Author response image 3.

Correlation analysis between the number of S. Gallinarum collected from different provinces in China and the total GDP/population size. The figure depicts a series of points representing individual provinces. The x-axis indicates the number of S. Gallinarum included in the dataset, while the y-axis displays the values for total GDP and total population size, respectively.

Nevertheless, a search of nearly a decade of literature on PubMed and a summary of the S. Gallinarum genome available on public databases indicate that the dataset used is the most complete. Furthermore, focusing on a specific region within China allowed us to conduct a detailed and thorough analysis. However, we highly agree that expanding the study to include more isolates from other countries would enhance the generalizability of our findings, and we are actively collecting additional S. Gallinarum genome data. In the revised manuscript, we have further emphasized the limitations as follow:

Lines 427-429: “However, the current study has some limitations. Firstly, despite assembling the most comprehensive WGS database for S. Gallinarum from public and laboratory sources, there are still biases in the examined collection. The majority (438/580) of S. Gallinarum samples were collected from China, possibly since the WGS is a technology that only became widely available in the 21st century. This makes it impractical to sequence it on a large scale in the 20th century, when S. Gallinarum caused a global pandemic. So, we suspect that human intervention in the development of this epidemic is the main driving force behind the fact that most of the strains in the data set originated in China. In our future work, we aim to actively gather more data to minimize potential biases within our dataset, thereby improving the robustness and generalizability of our findings.”

Reviewer #2 (Public review): 

Summary: 

The authors sequence 45 new samples of S. Gallinarum, a commensal Salmonella found in chickens, which can sometimes cause disease. They combine these sequences with around 500 from public databases, determine the population structure of the pathogen, and coarse relationships of lineages with geography. The authors further investigate known anti-microbial genes found in these genomes, how they associate with each other, whether they have been horizontally transferred, and date the emergence of clades. 

Thank you for your constructive suggestions, which are valuable and highly beneficial for improving our paper. According to your comments, we carefully considered and responded to them and made corresponding revisions to the text. Furthermore, to fully contextualize the background knowledge and clarify the major points in this study, we add some references to support our findings and policy implications.

Upon further review of our initial manuscript, we realized that the original submission did not strictly follow the lineage order proposed by Zhou et al. (Natl Sci Rev. 2023 Sep 2;10(10):nwad228). To avoid confusion in the typing system, we have adjusted the lineage nomenclature in the revised manuscript to reflect the corrected order (see Author response table 1).

Strengths: 

(1) It doesn't seem that much is known about this serovar, so publicly available new sequences from a high-burden region are a valuable addition to the literature. 

(2) Combining these sequences with publicly available sequences is a good way to better contextualise any findings. 

Thank you so much for your thorough review and constructive comments on the manuscript.

Weaknesses: 

There are many issues with the genomic analysis that undermine the conclusions, the major ones I identified being: 

(1) Recombination removal using gubbins was not presented fully anywhere. In this diversity of species, it is usually impossible to remove recombination in this way. A phylogeny with genetic scale and the gubbins results is needed. Critically, results on timing the emergence (fig2) depend on this, and cannot be trusted given the data presented. 

We sincerely thank you for pointing out this issue. In the original manuscript, we aimed to present different lineages of S. Gallinarum within a single phylogenetic tree constructed using BEAST. However, in the revised manuscript, we have addressed this issue by applying the approach recommended by Gubbins to remove recombination events for each lineage defined by FastBAPs. Additionally, to better illustrate the removal of recombination regions in the genome, we have included a figure generated by Gubbins (New Supplementary Figure 12). 

Our results indicate that recombination events are relatively infrequent in Lineage 1, followed by Lineage 3, but occur more frequently in Lineage 2. In the revised manuscript, we have included additional descriptions in the Methods section to clarify this analysis. We hope these modifications adequately address the reviewer’s concerns and enhance the trustworthiness of our findings.

(2) The use of BEAST was also only briefly presented, but is the basis of a major conclusion of the paper. Plot S3 (root-to-tip regression) is unconvincing as a basis of this data fitting a molecular clock model. We would need more information on this analysis, including convergence and credible intervals. 

Thank you very much for raising this issue. We decided to reconduct separate BEAST analyses for each lineage, accurately presenting the evolutionary scale based on the abovementioned improvements. The implementation of individual lineage for BEAST analysis was conducted based on the following steps:

(1) Using R51 as the reference, a reference-mapped multiple core-genome SNP sequence alignment was created, and recombination regions were detected and removed as described above.

(2) TreeTime was used to assess the temporal structure by performing a regression analysis of the root-to-tip branch distances within the maximum likelihood tree, considering the sampling date as a variable (New Supplementary Figures 6). However, the root-to-tip regression analysis presented in New Supplementary Figures 6 was not intended as a basis for selecting the best molecular clock model; its purpose was to clean the dataset with appropriate measurements.

(3) To determine the optimal model for running BEAST, we tested a total of six combinations in the initial phase of our study. These combinations included the strict clock, relaxed lognormal clock, and three population models (Bayesian SkyGrid, Bayesian Skyline, and Constant Size). Before conducting the complete BEAST analysis, we evaluated each combination using a Markov Chain Monte Carlo (MCMC) analysis with a total chain length of 100 million and sampling every 10,000 iterations. We then summarized the results using NSLogAnalyser and determined the optimal model based on the marginal likelihood value for each combination. The results indicated that the model incorporating the Bayesian Skyline and the relaxed lognormal clock yielded the highest marginal likelihood value in our sample. Then, we proceeded to perform a timecalibrated Bayesian phylogenetic inference analysis for each lineage. The following settings were configured: the "GTR" substitution model, “4 gamma categories”, the "Relaxed Clock Log Normal" model, the "Coalescent Bayesian Skyline" tree prior, and an MCMC chain length of 100 million, with sampling every 10,000 iterations.

(4) Convergence was assessed using Tracer, with all parameter effective sampling sizes (ESS) exceeding 200. Maximum clade credibility trees were generated using TreeAnnotator. Finally, key divergence time points (with 95% credible intervals) were estimated, and the tree was visualized using FigTree. 

For the key lineages, L2b and L3b (carrying the resistome, posing antimicrobial resistance (AMR) risks, and exhibiting intercontinental transmission events), we have redrawn Figure 2 based on the updated BEAST analysis results (New Figure 2). For L1, L2a, and L3c, we have added supplementary figures to provide a more detailed visualization of their respective BEAST analysis outcomes (New Supplementary Figures 3-5). The revised BEAST analysis indicates that the origin of L3b in China can be traced back to as early as 1683 (95% CI: 1608 to 1839). In contrast, the earliest possible origin of L2b in China dates back to 1880 (95% CI: 1838 to 1902). This indicates that the previous manuscript's assumption that L2b is an older lineage compared to L3b may be inaccurate. 

Furthermore, In the revised manuscript, we specifically estimated the time points for the first intercontinental transmission events for the two major lineages, L2b and L3b. Our results indicate that L2b, likely underwent two major intercontinental transmission events. The first occurred around 1893 (95% CI: 1870 to 1918), with transmission from China to South America. The second major transmission event occurred in 1923 (95% CI: 1907 to 1940), involving the spread from South America to Europe. In contrast, the transmission pattern of L3b appears relatively more straightforward. Our findings show that L3b, an S. Gallinarum lineage originating in China, only underwent one intercontinental transmission event from China to Europe, likely occurring around 1790 (95% CI: 1661 to 1890) (New Supplementary Figure 7). Based on the more critical BEAST analysis for each lineage, we have revised the corresponding conclusions in the manuscript. We believe that the updated BEAST analysis, performed using a more accurate recombination removal approach, significantly enhances the rigor and credibility of our findings.

(3) Using a distance of 100 SNPs for a transmission is completely arbitrary. This would at least need to be justified in terms of the evolutionary rate and serial interval. 

Using single nucleotide polymorphism (SNP) distance to trace pathogen transmission is a common approach (J Infect Dis. 2015 Apr 1;211(7):1154-63) and in our previous studies (hLife 2024; 2(5):246-256. mLife 2024; 3(1):156-160.). When the SNP distance within a cluster falls below a set threshold, the strains in that cluster are considered to have a potential direct transmission link. It is generally accepted that the lower the threshold, the more stringent the screening process becomes. However, there is little agreement in the literature regarding what such a threshold should be, and the appropriate SNP cut-off for inferring transmission likely depends critically on the context (Mol Biol Evol. 2019 Mar 1;36(3):587-603).

In this study, we compared various thresholds (SNPs = 5, 10, 20, 25, 30, 35, 40, 50, 100) to ensure clustering in an appropriate manner. First, we summarized the tracing results under each threshold (Author response image 4), which demonstrated that, regardless of the threshold used, all strains associated with transmission events originated from the same location (New Figure 3a).

Author response image 4.

Clustering results of 45 newly isolated S. Gallinarum strains using different SNP thresholds of 5, 10, 15, 20, 25, 28, 30, 50, and 100 SNPs. The nine subplots represent the clustering results under each threshold. Each point corresponds to an individual strain, and lines connect strains with potential transmission relationships.

In response to your comments regarding the evolutionary rate, we estimated the overall evolutionary rate of the S. Gallinarum using BEAST. We applied the methodology described by Arthur W. Pightling et al. (Front Microbiol. 2022 Jun 16; 13:797997). The numbers of SNPs per year were determined by multiplying the evolutionary rates estimated with BEAST by the number of core SNP sites identified in the alignments. We hypothesize that a slower evolutionary rate in bacteria typically requires a lower SNP threshold when tracing transmission events using SNP distance analysis. Pightling et al.'s previous research found an average evolutionary rate of 1.97 SNPs per year (95% HPD, 0.48 to 4.61) across 22 different Salmonella serotypes. Our updated BEAST estimation for the evolutionary rate of S. Gallinarum suggests it is approximately 0.74 SNPs per year (95% HPD, 0.42 to 1.06). Based on these findings, and our previous experience with similar studies (mBio. 2023 Oct 31;14(5):e0133323.), we set a threshold of 5 SNPs in the revised manuscript.

Then, we adopted the newly established SNP distance threshold (n=5) to update Figure 3a and New Supplementary Figure 8. The heatmap on the far right of New Figure 3a illustrates the SNP distances among 45 newly isolated S. Gallinarum strains from two locations in Zhejiang Province (Taishun and Yueqing). New Supplementary Figure 8 simulates potential transmission events between the bvSP strains isolated from Zhejiang Province (n=95) and those from China with available provincial information (n=435). These analyses collectively demonstrate the localized transmission pattern of bvSP within China. Our analysis using the newly established SNP threshold indicates that the 45 strains isolated from Taishun and Yueqing exhibit a highly localized transmission pattern, with pairs of strains exhibiting potential transmission events below the set threshold occurring exclusively within a single location. Subsequently, we conducted the SNP distance-based tracing analysis for the 95 strains from Zhejiang Province and those from China with available provincial information (n=435) (New Supplementary Figure 8, New Supplementary Table S8). Under the SNP distance threshold (n=5), we identified a total of 91 potential transmission events, all of which occurred exclusively within Zhejiang Province. No inter-provincial transmission events were detected. Based on these findings, we revised the methods and conclusions in the manuscript accordingly. We believe that the updated version well addresses your concerns.

Nevertheless, the final revised and updated results do not change the conclusions presented in our original manuscript. Instead, applying a more stringent SNP distance threshold allows us to provide solid evidence supporting the localized transmission pattern of S. Gallinarum in China. 

(4) The HGT definition is non-standard, and phylogeny (vertical inheritance) is not controlled for.  

The cited method: 

'In this study, potentially recently transferred ARGs were defined as those with perfect identity (more than 99% nucleotide identity and 100% coverage) in distinct plasmids in distinct host bacteria using BLASTn (E-value {less than or equal to}10−5)' 

This clearly does not apply here, as the application of distinct hosts and plasmids cannot be used. Subsequent analysis using this method is likely invalid, and some of it (e.g. Figure 6c) is statistically very poor. 

Thank you for raising this important question. In our study, Horizontal Gene Transfer (HGT) is defined as the transfer of genetic information between different organisms, a process that facilitates the spread of antibiotic resistance genes (ARGs) among bacteria. This definition of HGT is consistent with that used in previous studies (Evol Med Public Health. 2015; 2015(1):193–194; ISME J. 2024 Jan 8;18(1):wrad032). In Salmonella, the transfer of antimicrobial resistance genes via HGT is not solely dependent on plasmids; other mobile genetic elements (MGEs), such as transposons, integrons, and prophages, also play significant roles. This has also  been documented in our previous work (mSystems. 2023 Dec 21;8(6):e0088323). Given the involvement of various MGEs in the horizontal transfer of ARGs, we propose that the criteria for evaluating horizontal transfer via plasmids can also be applied to ARGs mediated by other MGEs.

In this study, we adopted stricter criteria than those used by Xiaolong Wang et al. Specifically, we defined two ARGs as identical only if they exhibited 100% nucleotide identity and 100% coverage. To address concerns regarding the potential influence of vertical inheritance in our analysis, we have made the following improvements. In the revised manuscript, we provide a more detailed table that includes the co-localization analysis of each ARG with mobile genetic elements (New Supplementary Table 9). For prophages and plasmids, we required that ARGs be located directly within these elements. In contrast, for transposons and integrons, we considered ARGs to be associated if they were located within a 5 kb region upstream or downstream of these elements (Nucleic Acids Res. 2022 Jul 5;50(W1):W768-W773). 

In the revised manuscript, we first categorized a total of 621 ARGs carried by 436 bvSP isolates collected in China according to the aforementioned criteria and found that 415 ARGs were located on MGEs. After excluding the ARGs not associated with MGEs, we recalculated the overall HGT frequency of 10 types of ARGs in China, the horizontal ARGs transfer frequency in three key regions, and the horizontal ARGs transfer frequency within a single region (New Supplementary Table 7). Based on the results, we updated relevant sections of the manuscript and remade Figure 6. The updated manuscript describes the results of this section as follows:

“Horizontal transfer of resistome occurs widely in localized bvSP

Horizontal transfer of the resistome facilitates the acquisition of AMR among bacteria, which may record the distinct acquisition event in the bacterial genome. To compare these events in a geographic manner, we further investigated the HGT frequency of each ARG carried by bvSP isolated from China and explored the HGT frequency of resistome between three defined regions. Potentially horizontally transferred ARGs were defined as those with perfect identity (100% identity and 100% coverage) and were located on MGEs across different strains (Fig. 6a). We first categorized a total of 621 ARGs carried by 436 bvSP isolates collected in China and found that 415 ARGs were located on MGEs. After excluding the ARGs not associated with MGEs, our findings reveal that horizontal gene transfer of ARGs is widespread among Chinese bvSP isolates, with an overall transfer rate of 92%. Specifically, 50% of the ARGs exhibited an HGT frequency of 100%, indicating that these ARGs might underwent extensive frequent horizontal transfer events (Fig. 6b). It is noteworthy that certain resistance genes, such as tet(A), aph(3'')-Ib, and aph(6)-Id, appear to be less susceptible to horizontal transfer.

However, different regions generally exhibited a considerable difference in resistome HGT frequency. Overall, bvSP from the southern areas in China showed the highest HGT frequency (HGT frequency=95%). The HGT frequencies for bvSP within the eastern and northern regions of China are lower, at 92% and 91%, respectively (Fig. 6c). For specifical ARG type, we found tet(A) is more prone to horizontal transfer in the southern region, and this proportion was considerably lower in the eastern region. Interestingly, certain ARGs such as aph(6)-Id, undergo horizontal transfer only within the eastern and northern regions of China (Fig. 6d). Notably, as a localized transmission pathogen, resistome carried by bvSP exhibited a dynamic potential among inter-regional and local demographic transmission, especially from northern region to southern region (HGT frequency=93%) (Fig. 6e, Supplementary Table 7).”

We also modified the current version of the pipeline used to calculat the HGT frequency of resistance genes. In the revised pipeline, users are required to provide a file specifying the locations of mobilome on the genome before formally calculating the HGT frequency of the target ARGs. The specific code and data used in the calculation have been uploaded to https://github.com/tjiaa/Cal_HGT_Frequency.

However, we also acknowledge that the current in silico method has some limitations. This approach heavily relies heavily on prior information in existing resistome/mobilome databases. Additionally, the characteristics of second-generation sequencing data make it challenging to locate gene positions precisely. Using complete genome assemblies might be a crucial approach to address this issue effectively. In the revised manuscript, we have also provided a more detailed explanation of the implications of the current pipeline.

Regarding your second concern, "some of it (e.g., Figure 6c) is statistically very poor," the horizontal ARG transfer frequency calculation for each region was based on the proportion of horizontal transfer events of ARGs in that region to the total possible transfer events. As a result, we are unable to calculate the statistical significance between the two regions. Our aim with this approach is to provide a rough estimate of the extent of horizontal ARG transfer within the S. Gallinarum population in each region. In future studies, we will refine our conclusions by developing a broader range of evaluation methods to ensure more comprehensive assessment and validation.

(5) Associations between lineages, resistome, mobilome, etc do not control for the effect of genetic background/phylogeny. So e.g. the claim 'the resistome also demonstrated a lineage-preferential distribution' is not well-supported. 

Thank you for your comments. We acknowledge that the associations between lineages and the mobilome/resistome may be influenced by the genetic background or phylogeny of the strains. For instance, our conclusion regarding the lineage-preferential distribution of the resistome was primarily based on New Figure 4a, where L3 is clearly shown to carry the most ARGs. Furthermore, we observed that L3b tends to harbor bla_{_TEM-1B}, _sul2, and tet(A) more frequently than other lineages. However, we recognize that this evidence is insufficient to support a definitive conclusion of “demonstrated a lineage-preferential distribution”. Therefore, we have re-examined the current manuscript and described these findings as a potential association between the mobilome/resistome and lineages.

(6) The invasiveness index is not well described, and the difference in means is not biologically convincing as although it appears significant, it is very small. 

Thank you for pointing this out. For the invasiveness index mentioned in the manuscript, we used the method described in previous studies. (PLoS Genet. 2018 May 8;14(5), Nat Microbiol. 2021 Mar;6(3):327-338). Specifically, Salmonella’s ability to cause intestinal or extraintestinal infections in hosts is related to the degree of genome degradation. We evaluated the potential for extraintestinal infection by 45 newly isolated S. Gallinarum strains (L2b and L3b) using a model that quantitatively assesses genome degradation. We analyzed samples using the 196 top predictor genes, employing a machine-learning approach that utilizes a random forest classifier and delta-bitscore functional variant-calling. This method evaluated the invasiveness of S. Gallinarum towards the host, and the distribution of invasiveness index values for each region was statistically tested using unpaired t-test. The code used for calculating the invasiveness index is available at https://github.com/Gardner-BinfLab/invasive_salmonella. In the revised manuscript, we added a more detailed description of the invasiveness index calculation in the Methods section as follows:

Lines 592-603: “Specifically, Salmonella’s ability to cause intestinal or extraintestinal infections in hosts is related to the degree of genome degradation. We evaluated the potential for extraintestinal infection by 45 newly isolated S. Gallinarum strains (L2b and L3b) using a model that quantitatively assesses genome degradation. We analyzed each sample using the 196 top predictor genes for measuring the invasiveness of S. Gallinarum, employing a machine-learning approach that utilizes a random forest classifier and deltabitscore functional variant-calling. This method evaluated the invasiveness of S. Gallinarum towards the host, and the distribution of invasiveness index values for each region was statistically tested using unpaired t-test. The code used for calculating the invasiveness index is available at: https://github.com/Gardner-BinfLab/invasive_salmonella.”

Regarding the second question, 'the difference in means is not biologically convincing as although it appears significant, it is very small,' we believe that this difference is biologically meaningful. In our previous work, we infected chicken embryos with different lineages of S. Gallinarum (Natl Sci Rev. 2023 Sep 2;10(10):nwad228). The virulence of thirteen strains of Salmonella Gallinarum, comprising five from lineage L2b and eight from lineage L3b, was evaluated in 16-day-old SPF chicken embryos through inoculation into the allantoic cavity. Controls included embryos that inoculated with phosphate-buffered saline (PBS). The embryos were incubated in a thermostatic incubator maintained at 37.5°C with a relative humidity ranging from 50% to 60%. Prior to inoculation, the viability of the embryos was assessed by examining the integrity of their venous system and their movements; any dead embryos were excluded from the study. Overnight cultures resuspended in PBS at a concentration of 1000 CFU per 100 μL were administered to the embryos. Mortality was recorded daily for a period of five days, concluding upon the hatching of the chicks. 

It is generally accepted that strains with higher invasive capabilities are more likely to cause chicken embryo mortality. Our experimental results showed that the L2b, which exhibits higher invasiveness, with a slightly higher to cause chicken embryo death (Author response image 5). 

Author response image 5.

The survival curves of chicken embryos infected with bvSP isolates from S. Gallinarum L2b and S. Gallinarum L3b. Inoculation with Phosphate Buffer Saline (PBS) were considered controls. 

(7) 'In more detail, both the resistome and mobilome exhibited a steady decline until the 1980s, followed by a consistent increase from the 1980s to the 2010s. However, after the 2010s, a subsequent decrease was identified.' 

Where is the data/plot to support this? Is it a significant change? Is this due to sampling or phylogenetics? 

Thank you for highlighting these critical points. The description in this statement is based on New Supplementary Figure 11. On the right side of New Supplementary Figure 11, we presented the average number of Antimicrobial Resistance Genes (ARGs) and Mobile Genetic Elements (MGEs) carried by S. Gallinarum isolates from different years, and we described the overall trend across these years. However, we realized that this statement might overinterpret the data. Given that this sentence does not impact our emphasis on the overall increasing trends observed in the resistome and mobilome, as well as their potential association, we decided to remove it in the revised manuscript.

The revised paragraph would read as follows:

Lines 261-268: “Variations in regional antimicrobial use may result in uneven pressure for selecting AMR. The mobilome is considered the primary reservoir for spreading resistome, and a consistent trend between the resistome and the mobilome has been observed across different lineages, from L1-L3c. We observed an overall gradual rise in the resistome quantity carried by bvSP across various lineages, correlating with the total mobilome content (S11 Fig). Furthermore, we investigated the interplay between particular mobile elements and resistome types in bvSP.”

(8) It is not clear what the burden of disease this pathogen causes in the population, or how significant it is to agricultural policy. The article claims to 'provide valuable insights for targeted policy interventions.', but no such interventions are described. 

Thank you for your constructive suggestions. Salmonella Gallinarum is an avian-specific pathogen that induces fowl typhoid, a severe systemic disease characterized by high mortality rates in chickens, thereby posing a significant threat to the poultry industry, particularly in developing countries (Rev Sci Tech. 2000 Aug;19(2):40524). In our previous research, we conducted a comprehensive meta-analysis of 201 publications encompassing over 900 million samples to investigate the global impact of S. Gallinarum (Sci Data. 2022 Aug 13;9(1):495). Our findings estimated that the global prevalence of S. Gallinarum is 8.54% (with a 95% confidence interval of 8.43% to 8.65%), with notable regional variations in incidence rates.

Our previously analysis focused on the prevalence of S. Gallinarum (including biovars SP and SG) across six continents. The results revealed that all continents, except Oceania, exhibited positive prevalences of S. Gallinarum. Asia had the highest prevalence at 17.31%, closely followed by Europe at 16.03%. In Asia, the prevalence of biovar SP was higher than that of biovar SG, whereas in Europe, biovar SG was observed to be approximately two hundred times more prevalent than biovar SP. In South America, the prevalence of S. Gallinarum was higher than that of biovar SP, at 10.06% and 13.20% respectively. Conversely, the prevalence of S. Gallinarum was relatively lower in North America (4.45%) compared to Africa (1.10%) (Author response image 6).

Given the significant economic losses caused by S. Gallinarum to the poultry industry and the potential risk of escalating antimicrobial resistance, more targeted policy interventions are urgently needed. Further elaboration on this implication is provided in the revised “Discussion” section as follows:

Lines 401-416: “In summary, the findings of this study highlight that S. Gallinarum remains a significant concern in developing countries, particularly in China. Compared to other regions, S. Gallinarum in China poses a notably higher risk of AMR, necessitating the development of additional therapies, i.e. vaccine, probiotics, bacteriophage therapy in response to the government's policy aimed at reducing antimicrobial use ( J Infect Dev Ctries. 2014 Feb 13;8(2):129-36). Furthermore, given the dynamic nature of S. Gallinarum risks across different regions, it is crucial to prioritize continuous monitoring in key areas, particularly in China's southern regions where the extensive poultry farming is located. Lastly, from a One-Health perspective, controlling AMR in S. Gallinarum should not solely focus on local farming environments, with improved overall welfare on poultry and farming style. The breeding pyramid of industrialized poultry production should be targeted on the top, with enhanced and accurate detection techniques (mSphere. 2024 Jul 30;9(7):e0036224). More importantly, comprehensive efforts should be made to reduce antimicrobial usage overall and mitigate potential AMR transmission from environmental sources or other hosts (Vaccines (Basel). 2024 Sep 18;12(9):1067; Vaccines (Basel). 2023 Apr 18;11(4):865; Front Immunol. 2022 Aug 11:13:973224).”

Author response image 6.

A comparison of the global prevalence of S. gallinarum across continents.

(9) The abstract mentions stepwise evolution as a main aim, but no results refer to this. 

Thank you for raising this issue. In the revised manuscript, we have changed “stepwise evolution” to simply “evolution” to ensure a more accurate and precise description.

(10) The authors attribute changes in population dynamics to normalisation in China-EU relations and hen fever. However, even if the date is correct, this is not a strongly supported causal claim, as many other reasons are also possible (for example other industrial processes which may have changed during this period). 

Thank you for raising this critical issue. In the revised manuscript, we conducted a more stringent BEAST analysis for each lineage, as described earlier. This led to some changes in the inferred evolutionary timelines. Consequently, we have removed the corresponding statement from the “Results” section. Instead, we now only provide a discussion of historical events, supported by literature, that could have facilitated the intercontinental spread of L2b and L3b in the “Discussion” section. We believe these revisions have made the manuscript more rigorous and precise.

Lines 332-342: “_The biovar types of _S. Gallinarum have been well-defined as bvSP, bvSG, and bvSD historically ( J Vet Med B Infect Dis Vet Public Health. 2005 Jun;52(5):2148). Among these, bvSP can be further subdivided into five lineages (L1, L2a, L2b, L3b, and L3c) using hierarchical Bayesian analysis. Different sublineages exhibited preferential geographic distribution, with L2b and L3b of bvSP being predominant global lineage types with a high risk of AMR. The historical geographical transmission was verified using a spatiotemporal Bayesian framework. The result shows that L3b was initially spread from China to Europe in the 18^th-19^th century, which may be associated with the European hen fever event in the mid-19th century (Burnham GP. 1855. The history of the hen fever: a humorous record). L2b, on the other hand, appears to have spread to Europe via South America, potentially contributing to the prevalence of bvSP in the United States.”  

(11) No acknowledgment of potential undersampling outside of China is made, for example, 'Notably, all bvSP isolates from Asia were exclusively found in China, which can be manually divided into three distinct regions (southern, eastern, and northern).'.

Perhaps we just haven't looked in other places?

We appreciate the reviewer's observation regarding the sampling distribution of isolates in this study. We acknowledge that while the isolates were collected from 15 different countries with, a significant proportion originated from China (Author response image 1). This focus is due to several reasons:

(1) As once a globally prevalent pathogen across the 20th century, S. Gallinarum was listed by the World Organization for Animal Health (WOAH) due to its economic importance. After 30 years of implementation the National Poultry Improvement Plan in the US, it was almost eradicated in high-income countries, and interestingly, it became an endemic pathogen with sporadic outbreaks in most low- or middle-income countries like China and Brazil. Given the vast expanse of China's land area and the country's economic factors, implementing the same measures remains a challenging endeavour. 

(2) S. Gallinarum is an avian-specific pathogen, particularly affecting chickens, and its distribution is closely linked to chicken meat production in different countries. In some high chicken-producing developing countries, such as China and Brazil, there are more frequent reports of fowl typhoid. Data from the United States Department of Agriculture (USDA) on annual chicken meat production for 2023/2024 show that the global distribution of S. Gallinarum aligns closely with the overall chicken meat production of these countries (https://fas.usda.gov/data/production/commodity/0115000).  

(3) Our primary objective was to investigate the localized resistome adaptation of S. Gallinarum in regions. Being a region with significant disease burden, China has reported numerous outbreaks (Sci Data. 2022 Aug 13;9(1):495; Sci Data. 2024 Feb 27;11(1):244) and a high AMR prevalence of this serovar (Natl Sci Rev. 2023 Sep 2;10(10):nwad228; mSystems. 2023 Dec 21;8(6):e0088323), making it an excellent example for understanding localized resistance mechanisms. 

Nevertheless, a search of nearly a decade of literature on PubMed and a summary of the S. Gallinarum genome available on public databases indicate that the dataset used is the most complete. Furthermore, focusing on a specific region within China allowed us to conduct a detailed and thorough analysis. However, we highly agree that expanding the study to include more isolates from other countries would enhance the generalizability of our findings, and we are actively collecting additional S. Gallinarum genome data. In the revised manuscript, we modified this sentence to indicate that this phenomenon is only observed in the current dataset, thereby avoiding an overly absolute statement:

Lines 131-135: “For the bvSP strains from Asia included in our dataset, we found that all originated from China. To further investigate the distribution of bvSP across different regions in China, we categorized them into three distinct regions: southern, eastern, and northern (Supplementary Table 3)”.

(12) Many of the conclusions are highly speculative and not supported by the data. 

Thank you for your comment. We have carefully revised the manuscript to address your concerns. We hope that the changes made in the revised version meet your expectations and provide a clearer and more accurate interpretation of our findings.

(13) The figures are not always the best presentation of the data: 

a. Stacked bar plots in Figure 1 are hard to interpret, the total numbers need to be shown.

Panel C conveys little information. 

b. Figure 4B: stacked bars are hard to read and do not show totals. 

c. Figure 5 has no obvious interpretation or significance. 

Thank you for your comments. We have revised the figures to improve the clarity and presentation of the data.

In summary, the quality of analysis is poor and likely flawed (although there is not always enough information on methods present to confidently assess this or provide recommendations for how it might be improved). So, the stated conclusions are not supported. 

Thank you for your valuable feedback. We have carefully revised the manuscript to address your concerns. We hope that the updated figures and tables, and new data in the revised version meet your expectations and provide more appropriate interpretation of our findings.

Recommendations for the authors:  

Reviewer #1 (Recommendations for the authors): 

This reviewer enjoyed reading this well-written manuscript. The authors are encouraged to address the following comments and revise the manuscript accordingly. 

(1) Title: The authors use avian-restrict Salmonella to refer to Salmonella Gallinarum. Please consider using Salmonella Gallinarum in the title. Also, your analysis relates to resistome and mobilome. Would it make sense to add mobilome in the manuscript? 

Thank you for your guidance. In the revised manuscript, we have changed the title to “Avian-specific Salmonella enterica Serovar Gallinarum transition to endemicity is accompanied by localized resistome and mobilome interaction”. We believe that this revised title more accurately reflects the content of our study.

(2) Abstract: This study uses 45 isolates from your labs. However, you failed to include these 45 isolates in the Abstract. Also, please clarify the sources of these isolates (from dead chickens, or dead chicken embryos? You wrote in two different ways in this manuscript). Also, I am not entirely convinced how the results from these 45 isolates will support the overall conclusion of this work. 

Thank you for your thorough review and constructive comments on the manuscript. In the revised version, we have added a description of 45 newly isolated S. Gallinarum strains in the Abstract to provide readers with a clearer understanding of the dataset used in this study.

Lines 36-41: “Using the most comprehensive whole-genome sequencing dataset of Salmonella enterica serovar Gallinarum (S. Gallinarum) collected from 16 countries, including 45 newly recovered samples from two related local regions, we established the relationship among avian-specific pathogen genetic profiles and localization patterns.”

Furthermore, the newly isolated S. Gallinarum strains were obtained from dead chicken embryos. We think your second concern may arise from the following description in the manuscript: “All 734 samples of dead chicken embryos were collected from Taishun and Yueqing in Zhejiang Province, China. After the thorough autopsy, the liver, intestines, and spleen were extracted and added separately into 2 mL centrifuge tubes containing 1 mL PBS. The organs were then homogenized by grinding.” In fact, all the collected dead chicken embryos were aged 19 to 20 days. At this developmental stage, collecting the liver, intestines, and spleen for isolation and cultivation of S. Gallinarum is possible. To avoid any confusion, we have included a more detailed description of the dead chicken embryos in the revised manuscript as follows:

Lines 447-451: “All 734 samples of dead chicken embryos aged 19 to 20 days were collected from Taishun and Yueqing in Zhejiang Province, China. After a thorough autopsy, the liver, intestines, and spleen were extracted and added separately into 2 mL centrifuge tubes containing 1 mL PBS. The organs were then homogenized by grinding.”

Regarding your concern about the statement, “I am not entirely convinced how the results from these 45 isolates will support the overall conclusion of this work,” we would like to clarify the significance of these new isolates. Our research first identified distinct characteristics in the 45 newly isolated S. Gallinarum strains from Taishun and Yueqing, Zhejiang Province. Specifically, we found that most of the strains from Yueqing belonged to sequence type ST92, whereas the majority from Taishun were ST3717. Additionally, there were significant differences between these geographically close strains in terms of SNP distance and predicted invasion capabilities. These findings suggest that S. Gallinarum may exhibit localized transmission patterns, which forms the basis of the scientific question and hypothesis we originally aimed to address. Furthermore, in our previous work, we collected 325 S. Gallinarum strains. By incorporating the newly isolated 45 strains, we aim to provide a more comprehensive view of the population diversity, transmission pattern and potential risk of S. Gallinarum. We will continue to endeavour to understand the global genomic and population diversity in this field.

Finally, we revised the sentences that could potentially raise concerns for readers: 

Lines 175-177: “To investigate the dissemination pattern of bvSP in China, we obtained forty-five newly isolated bvSP from 734 samples (6.1% overall isolation rate) collected from diseased chickens at two farms in Yueqing and Taishun, Zhejiang Province.”  >  “To investigate the dissemination pattern of bvSP, we obtained forty-five newly isolated bvSP from 734 samples (6.1% overall isolation rate) collected from diseased chickens at two farms in Yueqing and Taishun, Zhejiang Province.”

(3) The manuscript uses nomenclature and classification into different sublineages. Did the authors establish the approaches for defining these sublineages in this group or did you follow the accepted standards? 

Thank you very much for raising this important issue. The biovar types of Salmonella Gallinarum have historically been well-defined as S. Gallinarum biovar

Pullorum (bvSP), S. Gallinarum biovar Gallinarum (bvSG), and S. Gallinarum biovar Duisburg (bvSD) (J Vet Med B Infect Dis Vet Public Health. 2005 Jun;52(5):214-8). However, there seems to be no widespread consensus on the population nomenclature for the key biovar bvSP. In a previous study, Zhou et al. classified bvSP into six lineages:

L1, L2a, L2b, L3a, L3b, and L3c (Natl Sci Rev. 2023 Sep 2;10(10):nwad228). However, our more comprehensive analysis of S. Gallinarum using a larger dataset and hierarchical Bayesian clustering revealed that L3a, previously considered a distinct lineage, is actually a sublineage of L3c. Upon further review of our initial manuscript, we realized that the original submission did not strictly follow the lineage order proposed by Zhou et al. To avoid confusion in the typing system, we have adjusted the lineage nomenclature in the revised manuscript to reflect the corrected order (see Author response table 1).

(4) This reviewer is convinced with the analysis approaches and conclusion of this work.

In the meantime, the authors are encouraged to discuss the application of the conclusion of this study: a) can the data be somehow used in the prediction model? b) would the conclusion from S. Gallinarum have generalized application values for other pathogens. 

Thank you for your constructive comments on the manuscript. 

a) can the data be somehow used in the prediction model?

We believe that genomic data can be effectively used for constructing prediction models; however, the success of such models largely depends on the specific traits being predicted. In this study, we utilized a random forest prediction model based on 196 top genes (PLoS Genet. 2018 May 8;14(5)) to predict the invasiveness of 45 newly isolated strains. In relation to the antimicrobial resistance (AMR) issue discussed in this paper, we also conducted relevant analyses. For instance, we explored the use of image-based models to predict whether a genome is resistant to specific antibiotics (Comput Struct Biotechnol J. 2023 Dec 29:23:559-565). We are confident that the incorporation of newly generated data will facilitate the development of future predictive models, and we plan to pursue further research in this area.

b) would the conclusion from S. Gallinarum have generalized application values for other pathogens.

This might be explained from two perspectives. First, the key role of the mobilome in facilitating the spread of the resistome, as emphasized in this study, has also been confirmed in research on other pathogens (mBio. 2024 Oct 16;15(10):e0242824). Thus, we believe that the pipeline we developed to assess the horizontal transfer frequency of different resistance genes across regions applies to various pathogens. On the other hand, due to distinct evolutionary histories, different pathogens exhibit varying levels of adaptation to their environments. In this study, we found that S. Gallinarum tends to spread highly localized; however, this conclusion may not necessarily hold for other pathogens.

Reviewer #2 (Recommendations for the authors): 

The authors would need to: 

(1) Address my concerns about genomic analyses listed in the public review. 

Thank you for your valuable feedback. We have carefully reviewed your concerns and made the necessary revisions to address the points raised about genomic analyses in the public review. We sincerely hope that these modifications meet your expectations and provide more robust analysis. We appreciate your thoughtful input and remain open to further suggestions to improve the manuscript.

(2) Add more detail on the genomic methods and their outputs, as suggested above. 

We have added further details to clarify the methodologies and outputs as mentioned above. Specifically, we expanded the description of the data processing, and the bioinformatic tools used for analysis. To ensure clarity, we also included an expanded discussion of the key outputs, highlighting their implications. We hope these revisions meet your expectations.

(3) Critically rewrite their introduction to make it clear what problem they are trying to address. 

Thank you for your guidance. In the revised manuscript, we have made the necessary modifications to the Introduction section to more clearly articulate the problem we aim to address.

(4) Critically rewrite their conclusions so they are supported by the data they present, and make it clear when claims are more speculative. 

Thank you for your guidance. In the revised manuscript, we have made the recommended modifications to the relevant sections of the conclusion as outlined above.

More minor issues I identified: 

(1) Typo in the title 'avian-restrict'. 

Done.

Line 1: “Avian-specific Salmonella enterica Serovar Gallinarum transition to endemicity is accompanied by localized resistome and mobilome interaction.”

(2) 'By utilizing the pipeline we developed' -- a pipeline has not been introduced at this point. 

In the revised manuscript, we have removed this section from the 'Abstract'.

Lines 46-48: “Notably, the mobilome-resistome combination among distinct lineages exhibits a geographical-specific manner, further supporting a localized endemic mobilome-driven process.”

(3) 'has more than 90% serovars' -- doesn't make sense. 

Revised.

Lines 82-83: “Salmonella, a pathogen with distinct geographical characteristics, has more than 90% of its serovars frequently categorized as geo-serotypes.”

(4) 'horrific mortality rates that remain a disproportionate burden'. 

Revised.

Lines 83-87: “Among the thousands of geo-serotypes, Salmonella enterica Serovar Gallinarum (S. Gallinarum) is an avian-specific pathogen that causes severe mortality, with particularly detrimental effects on the poultry industry in low- and middle-income countries.”

(5) What is the rate, what is a comparison, how is it disproportionate? 

Thank you for your valuable feedback. It is challenging to accurately estimate the specific prevalence of S. Gallinarum, particularly due to the lack of comprehensive data in many countries. Numerous cases likely go unreported. However, S. Gallinarum is more commonly detected in low- and middle-income countries. Here, we provide three evidence supporting this observation. First, in our previous research, we conducted a comprehensive meta-analysis of 201 studies, involving over 900 million samples, to evaluate the global impact of S. Gallinarum (Sci Data. 2022 Aug 13;9(1):495). The estimated prevalence in 17 countries showed that Bangladesh had the highest rate (25.75%) of S. Gallinarum infections. However, for biovar Pullorum (bvSP), Argentina (20.69%) and China (18.18%) reported the highest prevalence rates. Second, previous studies have also reported that S. Gallinarum predominantly occurs in low- and middleincome countries (Vet Microbiol. 2019 Jan:228:165-172; BMC Microbiol. 2024 Oct 18;24(1):414). Finally, S. Gallinarum was once a globally prevalent pathogen in the 20th century. Following the implementation of eradication programs in most high-income countries, it was listed by the World Organization for Animal Health and subsequently became an endemic pathogen with sporadic outbreaks. However, similar eradication efforts are challenging to implement in low- and middle-income countries, leading to a disproportionately higher incidence of S. Gallinarum in these regions.

In the revised manuscript, we have rephrased this sentence to enhance its accuracy:

Lines 83-87: “Among the thousands of geo-serotypes, Salmonella enterica serovar Gallinarum (S. Gallinarum) is an avian-specific pathogen that causes severe mortality, with particularly detrimental effects on the poultry industry in low- and middle-income countries.”

(6) 'we collected the most comprehensive set of 580 S. Gallinarum isolates', -> 'we collected the most comprehensive set S. Gallinarum isolates, consisting of 580 genomes'. 

Revised.

Lines 97-100: “To fill the gaps in understanding the evolution of S. Gallinarum under regional-associated AMR pressures and its adaptation to endemicity, we collected the most comprehensive set S. Gallinarum isolates, consisting of 580 genomes, spanning the period from 1920 to 2023.” 

(7) Sequence reads are not available, and use a non-standard database. The eLife policy states: 'Sequence reads and assembly must be included for reference genomes, while novel short sequences, including epitopes, functional domains, genetic markers and haplotypes should be deposited, together with surrounding sequences, into Genbank, DNA Data Bank of Japan (DDBJ), or EMBL Nucleotide Sequence Database (ENA). DNA and RNA sequencing data should be deposited in NCBI Trace Archive or NCBI Sequence Read Archive (SRA).' So the sequences assemblies and reads should ideally be mirrored appropriately. 

Thank you for your valuable suggestion regarding submitting the genome data for the newly isolated 45 S. Gallinarum strains. The genome data have been deposited in the NCBI Sequence Read Archive (SRA) under two BioProjects. The “SRA Accession number” for each strain have been added to New Supplementary Table 1. We believe this will ensure that the data are more readily accessible to a broader audience of researchers for download and analysis. We have revised the corresponding paragraph in the manuscript as follows:

Lines 606-608: “For the newly isolated 45 strains of Salmonella Gallinarum, genome data have been deposited in NCBI Sequence Read Archive (SRA) database. The “SRA Accession” for each strain are listed in Supplementary Table 1.”

(8) You should state at the start of the results which data is public, and how much is newly sequenced. 

Revised.

Lines 109-112: “To understand the global geographic distribution and genetic relationships of S. Gallinarum, we assembled the most comprehensive S. Gallinarum WGS dataset (n=580), comprising 535 publicly available genomes and 45 newly sequenced genomes.”

Author response:

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

Reviewer #1 (Public Review):

Summary:  

This paper investigates the relationship between ocular drift - eye movements long thought to be random - and visual acuity. This is a fundamental issue for how vision works. The work uses adaptive optics retinal imaging to monitor eye movements and where a target object is in the cone photoreceptor array. The surprising result is that ocular drift is systematic - causing the object to move to the center of the cone mosaic over the course of each perceptual trial. The tools used to reach this conclusion are state-of-the-art and the evidence presented is convincing.

Strengths  

P1.1. The central question of the paper is interesting, as far as I know, it has not been answered in past work, and the approaches employed in this work are appropriate and provide clear answers.

P1.2. The central finding - that ocular drift is not a completely random process - is important and has a broad impact on how we think about the relationship between eye movements and visual perception.

P1.3. The presentation is quite nice: the figures clearly illustrate key points and have a nice mix of primary and analyzed data, and the writing (with one important exception) is generally clear.

Thank you for your positive feedback.

Weaknesses

P1.4. The handling of the Nyquist limit is confusing throughout the paper and could be improved. It is not clear (at least to me) how the Nyquist limit applies to the specific task considered. I think of the Nyquist limit as saying that spatial frequencies above a certain cutoff set by the cone spacing are being aliased and cannot be disambiguated from the structure at a lower spatial frequency. In other words, there is a limit to the spatial frequency content that can be uniquely represented by discrete cone sampling locations. Acuity beyond that limit is certainly possible with a stationary image - e.g. a line will set up a distribution of responses in the cones that it covers, and without noise, an arbitrarily small displacement of the line would change the distribution of cone responses in a way that could be resolved. This is an important point because it relates to whether some kind of active sampling or movement of the detectors is needed to explain the spatial resolution results in the paper. This issue comes up in the introduction, results, and discussion. It arises in particular in the two Discussion paragraphs starting on line 343.

We thank you for pointing out a possible confusion for readers. Overall, we contrast our results to the static Nyquist limit because it is generally regarded as the upper limit of resolution acuity. We updated our text in a few places, especially the Discussion, and added a reference to make our use of the Nyquist limit clearer.

We agree with the reviewer of how the Nyquist limit is interpreted within the context of visual structure. If visual structure is under-sampled, it is not lost, but creates new, interfered visual structure at lower spatial frequency. For regular patterns like gratings, interference patterns may emerge akin to Moire patterns, which have been shown to occur in the human eye, and which form is based on the arrangement and regularity of the photoreceptor mosaic (Williams, 1985). We note however that the successful resolution of the lower frequency pattern does not necessarily carry the same structural information, specifically, orientation, and the aliased structure might indeed mask the original stimulus. Please compare Figure 1f where we show individual static snapshots of such aliased patterns, especially visible when the optotypes are small (towards the lower right of the figure). We note that theoretical work predicts that with prior knowledge about the stimulus, even such static images might be possible to de-alias (Ruderman & Bialek, 1992). We added this to our manuscript.   

We think the reviewer’s following point about the resolution of a line position, is only partially connected to the first, however. In our manuscript we note in the Introduction that resolution of the relative position of visual objects is a so called hyperacuity phenomenon. The fact that it occurs in humans and other animals demonstrates that visual brains have come up with neuronal mechanisms to determine relative stimulus position with sub-Nyquist resolution. The exact mechanism is however not fully clear. One solution is that relative cone signal intensities could be harnessed, similar as is employed technically, e.g. in a quadrant-cell detector. Its positional precision is much higher than the individual cell’s size (or Nyquist limit), predominantly determined by the detector’s sensitivity and to a lesser degree its size. On the other hand, such detector, being hyperacute with object location, would not have the same resolution as, for instance, letter-E orientation discrimination. 

Note that in all the above occasions, a static image-sensor-relationship is assumed. In our paper, we were aiming to convey, like others did before, that a moving stimulus may give rise to sub-Nyquist structural resolution, beyond what is already known for positional acuity and hence, classical hyperacuity. 

Based on the data shown in this manuscript and other experimental data currently collected in the lab, it seems to us that eye movements are indeed the crucial point in achieving sub-Nyquist resolution. For example, ultra-short presentation durations, allowing virtually no retinal slip, push thresholds close to the Nyquist limit and above. Furthermore, with AOSLO stimulation, it is possible to stabilize a stimulus on the retina, which would be a useful tool studying this hypothesis. Our current level of stabilization is however not accurate enough to completely mitigate retinal image motion in the foveola, where cells are smallest, and transients could occur. From what we observe and other studies that looked at resolution thresholds at more peripheral retinal locations, we would predict that foveolar resolution of a perfectly stabilized stimulus would be indeed limited by the Nyquist limit of the receptor mosaic.

P1.5. One question that came up as I read the paper was whether the eye movement parameters depend on the size of the E. In other words, to what extent is ocular drift tuned to specific behavioral tasks?

This is an interesting question. Yet, the experimental data collected for the current manuscript does not contain enough dispersion in target size to give a definitive answer, unfortunately. A larger range of stimulus sizes and especially a similar number of trials per size would be required. Nonetheless, when individual trials were re-grouped to percentiles of all stimulus sizes (scaled for each eye individually), we found that drift length and directionality was not significantly different between any percentile group of stimulus sizes (Wilcoxon sign rank test, p > 0.12, see also Figure R1). Our experimental trials started with a stimulus demanding visual acuity of 20/16 (logMAR = -0.1), therefore all presented stimulus sizes were rather close to threshold. The high visual demand in this AO resolution task might bring the oculomotor system to a limit, where ocular drift length can’t be decreased further. However, with the limitation due to the small range of stimulus sizes, further investigations would be needed. Given this and that this topic is also ongoing research in our lab where also more complex dynamics of FEM patterns are considered, we refrain from showing this analysis in the current manuscript.  

Author response image 1.

Drift length does not depend on stimulus sizes close to threshold. All experimental trials were sorted by stimulus size and then grouped into percentiles for each participant (left). Additionally, 10 % of trials with stimulus sizes just above or below threshold are shown for comparison (right). For each group, median drift lengths (z-scored) are shown as box and whiskers plot. Drift length was not significantly different across groups.  

Reviewer #2 (Public Review):

Summary:

In this work, Witten et al. assess visual acuity, cone density, and fixational behavior in the central foveal region in a large number of subjects.

This work elegantly presents a number of important findings, and I can see this becoming a landmark work in the field. First, it shows that acuity is determined by the cone mosaic, hence, subjects characterized by higher cone densities show higher acuity in diffraction-limited settings. Second, it shows that humans can achieve higher visual resolution than what is dictated by cone sampling, suggesting that this is likely the result of fixational drift, which constantly moves the stimuli over the cone mosaic. Third, the study reports a correlation between the amplitude of fixational motion and acuity, namely, subjects with smaller drifts have higher acuities and higher cone density. Fourth, it is shown that humans tend to move the fixated object toward the region of higher cone density in the retina, lending further support to the idea that drift is not a random process, but is likely controlled. This is a beautiful and unique work that furthers our understanding of the visuomotor system and the interplay of anatomy, oculomotor behavior, and visual acuity.

Strengths:

P2.1. The work is rigorously conducted, it uses state-of-the-art technology to record fixational eye movements while imaging the central fovea at high resolution and examines exactly where the viewed stimulus falls on individuals' foveal cone mosaic with respect to different anatomical landmarks in this region. The figures are clear and nicely packaged. It is important to emphasize that this study is a real tour-de-force in which the authors collected a massive amount of data on 20 subjects. This is particularly remarkable considering how challenging it is to run psychophysics experiments using this sophisticated technology. Most of the studies using psychophysics with AO are, indeed, limited to a few subjects. Therefore, this work shows a unique set of data, filling a gap in the literature.

Thank you, we are very grateful for your positive feedback.

Weaknesses:

P2.2. No major weakness was noted, but data analysis could be further improved by examining drift instantaneous direction rather than start-point-end-point direction, and by adding a statistical quantification of the difference in direction tuning between the three anatomical landmarks considered.

Thank you for these two suggestions. We now show the development of directionality with time (after the first frame, 33 ms as well as 165 ms, 330 ms and 462 ms), and performed a Rayleigh test for non-uniformity of circular data. Please also see our response to comment R2.4.

Briefly, directional tuning was already visible at 33 ms after stimulus onset and continuously increases with longer analysis duration. Directionality is thus not pronounced at shorter analysis windows. These results have been added to the text and figures (Figure 4 - figure supplement 1).

The statistical tests showed that circular sample directionality was not uniformly distributed for all three retinal locations. The circular average was between -10 and 10 ° in all cases and the variance was decreasing with increasing time (from 48.5 ° to 34.3 ° for CDC, 49.6 ° to 38.6 ° for PRL and 53.9 ° to 43.4 for PCD location, between frame 2 and 15). As we have discussed in the paper, we would expect all three locations to come out as significant, given their vicinity to the CDC (which is systematic in the case of PRL, and random in the case of PCD, see also comment R2.2).        

Reviewer #3 (Public Review):

Summary:

The manuscript by Witten et al., titled "Sub-cone visual resolution by active, adaptive sampling in the human foveola," aims to investigate the link between acuity thresholds (and hyperacuity) and retinal sampling. Specifically, using in vivo foveal cone-resolved imaging and simultaneous microscopic photostimulation, the researchers examined visual acuity thresholds in 16 volunteers and correlated them with each individual's retinal sampling capacity and the characteristics of ocular drift.

First, the authors found that although visual acuity was highly correlated with the individual spatial arrangement of cones, for all participants, visual resolution exceeded the Nyquist sampling limit - a well-known phenomenon in the literature called hyperacuity.

Thus, the researchers hypothesized that this increase in acuity, which could not be explained in terms of spatial encoding mechanisms, might result from exploiting the spatiotemporal characteristics of visual input, which is continuously modulated over time by eye movements even during so-called fixations (e.g., ocular drift).

Authors reported a correlation between subjects, between acuity threshold and drift amplitude, suggesting that the visual system benefits from transforming spatial input into a spatiotemporal flow. Finally, they showed that drift, contrary to the traditional view of it as random involuntary movement, appears to exhibit directionality: drift tends to move stimuli to higher cone density areas, therefore enhancing visual resolution.

Strengths:

P3.1. The work is of broad interest, the methods are clear, and the results are solid.

Thank you.

Weaknesses:

P3.2. Literature (1/2): The authors do not appear to be aware of an important paper published in 2023 by Lin et al. (https://doi.org/10.1016/j.cub.2023.03.026), which nicely demonstrates that (i) ocular drifts are under cognitive influence, and (ii) specific task knowledge influences the dominant orientation of these ocular drifts even in the absence of visual information. The results of this article are particularly relevant and should be discussed in light of the findings of the current experiment.

Thank you for pointing to this important work which we were aware of. It simply slipped through during writing. It is now discussed in lines 390-393. 

P3.3. Literature (2/2): The hypothesis that hyperacuity is attributable to ocular movements has been proposed by other authors and should be cited and discussed (e.g., https://doi.org/10.3389/fncom.2012.00089, https://doi.org/10.10

Thank you for pointing us towards these works which we have now added to the Discussion section. We would like to stress however, that we see a distinction between classical hyperacuity phenomena (Vernier, stereo, centering, etc.) as a form of positional acuity, and orientation discrimination.  

P3.4. Drift Dynamic Characterization: The drift is primarily characterized as the "concatenated vector sum of all frame-wise motion vectors within the 500 ms stimulus duration.". To better compare with other studies investigating the link between drift dynamics and visual acuity (e.g., Clark et al., 2022), it would be interesting to analyze the drift-diffusion constant, which might be the parameter most capable of describing the dynamic characteristics of drift.

During our analysis, we have computed the diffusion coefficient (D) and it showed qualitatively similar results to the drift length (see figures below). We decided to not show these results, because we are convinced that D is indeed not the most capable parameter to describe the typical drift characteristic seen here. The diffusion coefficient is computed as the slope of the mean square displacement (MSD). In our view, there are two main issues with applying this metric to our data, one conceptual, one factual:

(1) Computation of a diffusion coefficient is based upon the assumption that the underlying movement is similar to a random walk process. From a historical perspective, where drift has been regarded as more random, this makes sense. We also agree that D can serve as a valuable metric, depending on the individual research question. In our data, however, we clearly show that drift is not random, and a metric quantifying randomness is thus ill-defined. 

(2) We often observed out- and in-type motion traces, i.e. where the eye somewhat backtracks from where it started. Traces in this case are equally long (and fast) as other motion will be with a singular direction, but D would in this case be much smaller, as the MSD first increases and then decreases. In reality, the same number of cones would have been traversed as with the larger D of straight outward movement, albeit not unique cones. For our current analyses, the drift length captures this relationship better.

Author response image 2.

Diffusion coefficient (D) and the relation to visual acuity (see Figure 3 e-g for comparison to drift length). a, D was strongly correlated between fellow eyes. b, Cone density and D were not significantly correlated. c, The median D had a moderate correlation with visual acuity thresholds in dominant as well as non-dominant eyes. Dominant eyes are indicated by filled, nondominant eyes by open markers.

We would like to put forward that, in general, better metrics are needed, especially in respect to the visual signals arising from the moving eye. We are actively looking into this in follow-up work, and we hope that the current manuscript might spark also others to come up with new ways of characterizing the fine movements of the eye during fixation.

P3.5. Possible inconsistencies: Binocular differences are not expected based on the hypothesis; the authors may speculate a bit more about this. Additionally, the fact that hyperacuity does not occur with longer infrared wavelengths but the drift dynamics do not vary between the two conditions is interesting and should be discussed more thoroughly.

Binocularity: the differences in performance between fellow eyes is rather subtle, and we do not have a firm grip on differences other than the cone mosaic and fixational motor behavior between the two eyes. We would rather not speculate beyond what we already do, namely that some factor related to the development of ocular dominance is at play. What we do show with our data is that cone density and drift patterns seem to have no part in it.  

Effect of wavelength: even with the longer 840 nm wavelength, most eyes resolve below the Nyquist limit, with a general increase in thresholds (getting worse) compared to 788 nm. As we wrote in the manuscript, we assume that the increased image blur and reduced cone contrast introduced by the longer wavelength are key to why there is an overall reduction in acuity. No changes were made to the manuscript. As a more general remark, we would not consider the sub-Nyquist performances seen in our data to be a hyperacuity, although technically it is. The reason is that hyperacuity is usually associated with stimuli that require resolving positional shifts, and not orientation. There is a log unit of difference between thresholds in these tasks.  

P3.6. As a Suggestion: can the authors predict the accuracy of individual participants in single trials just by looking at the drift dynamics?

That’s a very interesting point that we indeed currently look at in another project. As a comment, we can add that by purely looking at the drift dynamics in the current data, we could not predict the accuracy (percent correct) of the participant. When comparing drift length or diffusion coefficients between trials with correct or false response, we do not observe a significant difference. Also, when adding an anatomical correlate and compare between trials where sampling density increases or decreases, there is no significant trend. We think that it is a more complex interplay between all the influencing factors that can perhaps be met by a model considering all drift dynamics, photoreceptor geometry and stimulus characteristics.   

No changes were made to the manuscript.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

As you will see, the reviewers were quite enthusiastic about your work, but have a few issues for your consideration. We hope that this is helpful. We'll consider any revisions in composing a final eLife assessment.

Reviewer #1 (Recommendations For The Authors):

R1.1:  Discussion of myopia. Myopia takes a fair bit of space in the Discussion, but the paper does not include any subjects that are sufficiently myopic to test the predictions. I would suggest reducing the amount of space devoted to this issue, and instead making the prediction that myopia may help with resolution quickly. The introduction (lines 54-56) left me expecting a test of this hypothesis, and I think similarly that issue could be left out of the introduction.

We have removed this part from the Introduction and shortened the Discussion.  

R1.2: Line 118: define CDC here.

Thank you for pointing this out, it is now defined at this location.  

R1.3: Line 159-162: suggest breaking this sentence into two. This sentence also serves as a transition to the next section, but the wording suggests it is a result that is shown in the prior section. Suggest rewording to make the transition part clear. Maybe something like "Hence the spatial arrangement of cones only partially ... . Next we show that ocular motion and the associated ... are another important factor."

Text was changed as suggested.  

R1.4.: Figure 3: The retina images are a bit hard to see - suggest making them larger to take an entire row. As a reader, I also was wondering about the temporal progression of the drift trajectories and the relation to the CDC. Since you get to that in Figure 4, you could clarify in the text that you are starting by analyzing distance traveled and will return to the issue of directed trajectories.

Visibility was probably an issue during the initial submission and review process where images were produced at lower resolution. The original figures are of sufficient resolution to fully appreciate the underlying cone mosaic and will later be able to zoom in the online publication.  

We added a mention of the order of analysis in the Results section (LL 163-165)

R1.5: Line 176: define "sum of piecewise drift amplitude" (e.g. refer to Figure where it is defined).

We refer to this metric now as the drift length (as pointed out rightfully so by reviewer #2), and added its definition at this location.   

R1.6: Lines 205-208: suggest clarifying this sentence is a transition to the next section. As for the earlier sentence mentioned above, this sounds like a result rather than a transition to an issue you will consider next.

This sentence was changed to make the transition clearer. 

R1.7: Line 225: suggest starting a new paragraph here.

Done as suggested

Reviewer #2 (Recommendations For The Authors):

I don't have any major concerns, mostly suggestions and minor comments.

R2.1: (1) The authors use piecewise amplitude as a measure of the amount of retinal motion introduced by ocular drift. However, to me, this sounds like what is normally referred to as the path length of a trace rather than its amplitude. I would suggest using the term length rather than amplitude, as amplitude is normally considered the distance between the starting and the ending point of a trace.

This was changed as suggested throughout the manuscript. 

R2.2: (2) It would be useful to elaborate more on the difference between CDC and PCD, I know the authors do this in other publications, but to the naïve reader, it comes a bit as a surprise that drift directionality is toward the CDC but less so toward the PCD. Is the difference between these metrics simply related to the fact that defining the PCD location is more susceptible to errors, especially if image quality is not optimal? If indeed the PCD is the point of peak cone density, assuming no errors or variability in the estimation of this point, shouldn't we expect drift moving stimuli toward this point, as the CDC will be characterized by a slightly lower density? I.e., is the absence of a PCD directionality trend as strong as the trend seen for the CDC simply the result of variability and error in the estimate of the PCD or it is primarily due to the distribution of cone density not being symmetrical around the PCD?

Thank you for this comment. We already refer in the Methods section to the respective papers where this difference is analyzed in more detail, and shortly discuss it here.

To briefly answer the reviewer’s final question: PCD location is too variable, and ought to be avoided as a retinal landmark. While we believe there is value in reporting the PCD as a metric of maximum density, it has been shown recently (Reiniger et al., 2021; Warr et al., 2024; Wynne et al., 2022) and is visible in our own (partly unpublished) data, that its location will change with changing one or more of these factors: cone density metric, window size or cone quantity selected, cone annotation quality, image quality (e.g. across days), individual grader, annotation software, and likely more. Each of these factors alone can change the PCD location quite drastically, all while of course, the retina does not change. The CDC on the other hand, given its low-pass filtering nature, is immune to the aforementioned changes within a much wider range and will thus reflect the anatomical and, shown here, functional center of vision, better. However, there will always be individual eyes where PCD location and the CDC are close, and thus researchers might be inclined to also use the PCD as a landmark. We strongly advise against this. In a way, the PCD is a non-sense location while its dimension, density, can be a valuable metric, as density does not vary that much (see e.g. data on CDC density and PCD density reported in this manuscript).  

Below we append a direct comparison of PCD vs CDC location stability when only one of the mentioned factors are changed. Sixteen retinas imaged on two different days were annotated and analyzed by the same grader with the same approach, and the difference in both locations are shown.  

Author response image 3.

Reproducibility of CDC and PCD location in comparison. Two retinal mosaics which were recorded at two different timepoints, maximum 1 year apart from each other, were compared for 16 eyes. The retinal mosaics were carefully aligned. The retinal locations for CDC and PCD that were computed for the first timepoint were used as the spatial anchor (coordinate center), the locations plotted here as red circles (CDC) and gray diamonds (PCD) represent the deviations that were measured at the second timepoint for both metrics.  

R2.3.: I don't see a statistical comparison between the drift angle tuning for CDC, PRL, and PCD. The distributions in Figure 4F look very similar and all with a relatively wide std. It would be useful to mark the mean of the distributions and report statistical tests. What are the data shown in this figure, single subjects, all subjects pooled together, average across subjects? Please specify in the caption.

We added a Rayleigh test to test each distribution for nun-uniformity and Kolmogorov-Smirnov tests to compare the distributions towards the different landmarks.  We added the missing specifications to the figure caption of Figure 4 – figure supplement 1. 

R2.4: I would suggest also calculating drift direction based on the average instantaneous drift velocity, similarly to what is done with amplitude. From Figure 3B it is clear that some drifts are more curved than others. For curved drifts with small amplitudes the start-point- end-point (SE) direction is not very meaningful and it is not a good representation of the overall directionality of the segment. Some drifts also seem to be monotonic and then change direction (eg. the last three examples from participant 10). In this case, the SE direction is likely quite different from the average instantaneous direction. I suspect that if direction is calculated this way it may show the trend of drifting toward the CDC more clearly.

In response to this and a comment of reviewer #1, we add a calculation of initial  drift direction (and for increasing duration) and show it in Figure 4 – figure supplement 1. By doing so, we hope to capture initial directionality, irrespective of whether later parts in the path change direction. We find that directionality increases with increasing presentation duration. 

R2.5: I find the discussion point on myopia a bit confusing. Considering that this is a rather tangential point and there are only two myopic participants, I would suggest either removing it from the discussion or explaining it more clearly.

We changed this section, also in response to comment R1.1.

R2.6: I would suggest adding to the discussion more elaboration on how these results may relate to acuity in normal conditions (in the presence of optical aberrations). For example, will this relationship between sampling cone density and visual acuity also hold natural viewing conditions?

We added only a half sentence to the first paragraph of the discussion. We are hesitant to extend this because there is very likely a non-straightforward relationship between acuity in normal and fully corrected conditions. We would predict that, if each eye were given the same type and magnitude of aberrations (similar to what we achieved by removing them), cone density will be the most prominent factor of acuity differences. Given that individual aberrations can vary substantially between eyes, this effect will be diluted, up to the point where aberrations will be the most important factor to acuity. As an example, under natural viewing conditions, pupil size will dominantly modulate the magnitude of aberrations.

R2.7: Line 398 - the point on the superdiffusive nature of drift comes out of the blue and it is unclear. What is it meant by "superdiffusive"?

We simply wanted to express that some drift properties seem to be adaptable while others aren’t. The text was changed at this location to remove this seemingly unmotivated term. 

R2.8: Although it is true that drift has been assumed to be a random motion, there has been mounting evidence, especially in recent years, showing a degree of control and knowledge about ocular drift (eg. Poletti et al, 2015, JN; Lin et al, 2023, Current Biology).

We agree, of course. We mention this fact several times in the paper and adjusted some sentences to prevent misunderstandings. The mentioned papers are now cited in the Discussion. 

R2.9: Reference 23 is out of context and should be removed as it deals with the control of fine spatial attention in the foveola rather than microsaccades or drift.

We removed this reference. 

R2.10: Minor point: Figures appear to be low resolution in the pdf.

This seemed to have been an issue with the submission process. All figures will be available in high resolution in the final online version. 

R2.11: Figure S3, it would be useful to mark the CDC at the center with a different color maybe shaded so it can be visible also on the plot on the left.

We changed the color and added a small amount of transparency to the PRL markers to make the CDC marker more visible. 

R2.12: Figure S2, it would be useful to show the same graphs with respect to the PCD and PRL and maybe highlight the subjects who showed the largest (or smallest) distance between PRL and CDC).

Please find new Figure 4 supplement 1, which contains this information in the group histograms. Also, Figure 4 supplement 2 is now ordered by the distance PRL-CDC (while the participant naming is kept as maximum acuity exhibited. In this way, it should be possible to infer the information of whether PRL-CDC distance plays a role. For us it does not seem to be crucial. Rather, stimulus onset and drift length were related, which is captured in Figure 4g. 

R2.13: There is a typo in Line 410.

We could not find a typo in this line, nor in the ones above and below. “Interindividual” was written on purpose, maybe “intraindividual” was expected? No changes were made to the text. 

References

Reiniger, J. L., Domdei, N., Holz, F. G., & Harmening, W. M. (2021). Human gaze is systematically offset from the center of cone topography. Current Biology, 31(18), 4188–4193. https://doi.org/10.1016/j.cub.2021.07.005

Ruderman, D. L., & Bialek, W. (1992). Seeing Beyond the Nyquist Limit. Neural Computation, 4(5), 682–690. https://doi.org/10.1162/neco.1992.4.5.682

Warr, E., Grieshop, J., Cooper, R. F., & Carroll, J. (2024). The effect of sampling window size on topographical maps of foveal cone density. Frontiers in Ophthalmology, 4, 1348950. https://doi.org/10.3389/fopht.2024.1348950

Williams, D. R. (1985). Aliasing in human foveal vision. Vision Research, 25(2), 195–205. https://doi.org/10.1016/0042-6989(85)90113-0

Wynne, N., Cava, J. A., Gaffney, M., Heitkotter, H., Scheidt, A., Reiniger, J. L., Grieshop, J., Yang, K., Harmening, W. M., Cooper, R. F., & Carroll, J. (2022). Intergrader agreement of foveal cone topography measured using adaptive optics scanning light ophthalmoscopy. Biomedical Optics Express, 13(8), 4445–4454. https://doi.org/10.1364/boe.460821

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The manuscript by Mäkelä et al. presents compelling experimental evidence that the amount of chromosomal DNA can become limiting for the total rate of mRNA transcription and consequently protein production in the model bacterium Escherichia coli. Specifically, the authors demonstrate that upon inhibition of DNA replication the single-cell growth rate continuously decreases, in direct proportion to the concentration of active ribosomes, as measured indirectly by single-particle tracking. The decrease of ribosomal activity with filamentation, in turn, is likely caused by a decrease of the concentration of mRNAs, as suggested by an observed plateau of the total number of active RNA polymerases. These observations are compatible with the hypothesis that DNA limits the total rate of transcription and thus translation. The authors also demonstrate that the decrease of RNAp activity is independent of two candidate stress response pathways, the SOS stress response and the stringent response, as well as an anti-sigma factor previously implicated in variations of RNAp activity upon variations of nutrient sources.

Remarkably, the reduction of growth rate is observed soon after the inhibition of DNA replication, suggesting that the amount of DNA in wild-type cells is tuned to provide just as much substrate for RNA polymerase as needed to saturate most ribosomes with mRNAs. While previous studies of bacterial growth have most often focused on ribosomes and metabolic proteins, this study provides important evidence that chromosomal DNA has a previously underestimated important and potentially rate-limiting role for growth. 

Thank you for the excellent summary of our work.

Strengths: 

This article links the growth of single cells to the amount of DNA, the number of active ribosomes and to the number of RNA polymerases, combining quantitative experiments with theory. The correlations observed during depletion of DNA, notably in M9gluCAA medium, are compelling and point towards a limiting role of DNA for transcription and subsequently for protein production soon after reduction of the amount of DNA in the cell. The article also contains a theoretical model of transcription-translation that contains a Michaelis-Menten type dependency of transcription on DNA availability and is fit to the data. While the model fits well with the continuous reduction of relative growth rate in rich medium (M9gluCAA), the behavior in minimal media without casamino acids is a bit less clear (see comments below). 

At a technical level, single-cell growth experiments and single-particle tracking experiments are well described, suggesting that different diffusive states of molecules represent different states of RNAp/ribosome activities, which reflect the reduction of growth. However, I still have a few points about the interpretation of the data and the measured fractions of active ribosomes (see below). 

Apart from correlations in DNA-deplete cells, the article also investigates the role of candidate stress response pathways for reduced transcription, demonstrating that neither the SOS nor the stringent response are responsible for the reduced rate of growth. Equally, the anti-sigma factor Rsd recently described for its role in controlling RNA polymerase activity in nutrient-poor growth media, seems also not involved according to mass-spec data. While other (unknown) pathways might still be involved in reducing the number of active RNA polymerases, the proposed hypothesis of the DNA substrate itself being limiting for the total rate of transcription is appealing. 

Finally, the authors confirm the reduction of growth in the distant Caulobacter crescentus, which lacks overlapping rounds of replication and could thus have shown a different dependency on DNA concentration. 

Weaknesses: 

There are a range of points that should be clarified or addressed, either by additional experiments/analyses or by explanations or clear disclaimers. 

First, the continuous reduction of growth rate upon arrest of DNA replication initiation observed in rich growth medium (M9gluCAA) is not equally observed in poor media. Instead, the relative growth rate is immediately/quickly reduced by about 10-20% and then maintained for long times, as if the arrest of replication initiation had an immediate effect but would then not lead to saturation of the DNA substrate. In particular, the long plateau of a constant relative growth rate in M9ala is difficult to reconcile with the model fit in Fig 4S2. Is it possible that DNA is not limiting in poor media (at least not for the cell sizes studied here) while replication arrest still elicits a reduction of growth rate in a different way? Might this have something to do with the naturally much higher oscillations of DNA concentration in minimal medium?

The reviewer is correct that there are interesting differences between nutrient-rich and -poor conditions. They were originally noted in the discussion, but we understand how our original presentation made it confusing. We reorganized the text and figures to better explain our results and interpretations. In the revised manuscript, the data related to the poor media are now presented separately (new Figure 6) from the data related to the rich medium (Figures 1-3).  The total RNAP activity (abundance x active fraction) is significantly reduced in poor media (Figure 6A-B) similarly to rich medium (Figure 3H). Thus, DNA is limiting for transcription across conditions. However, the total ribosome activity in poor media (Figure 6C-D) and thus the growth rate (Figure 6EF) was less affected in comparison to rich media (Figure 2H and 1C). Our interpretation of these results is that while DNA is limiting for transcription in all tested nutrient conditions (as shown by the total active RNAP data), post-transcriptional buffering activities compensate for the reduction in transcription in poor media, thereby maintaining a better scaling of growth rates under DNA limitation. 

The authors argue that DNA becomes limiting in the range of physiological cell sizes, in particular for M9glCAA (Fig. 1BC). It would be helpful to know by how much (fold-change) the DNA concentration is reduced below wild-type (or multi-N) levels at t=0 in Fig 1B and how DNA concentration decays with time or cell area, to get a sense by how many-fold DNA is essentially 'overexpressed/overprovided' in wild-type cells. 

We now provide crude estimates in the Discussion section. The revised text reads: “Crude estimations suggest that ≤ 40% DNA dilution is sufficient to negatively affect transcription (total RNAP activity) in M9glyCAAT, whereas the same effect was observed after less than 10% dilution in nutrient-poor media (M9gly or M9ala) (see Materials and Methods).” We obtained these numbers based on calculations and estimates described in the Materials and Methods section and Appendix 1 (Appendix 1 – Table 1).

Fig. 2: The distribution of diffusion coefficients of RpsB is fit to Gaussians on the log scale. Is this based on a model or on previous work or simply an empirical fit to the data? An exact analytical model for the distribution of diffusion constants can be found in the tool anaDDA by Vink, ..., Hohlbein Biophys J 2020. Alternatively, distributions of displacements are expressed analytically in other tools (e.g., in SpotOn). 

We use an empirical fit of Gaussian mixture model (GMM) of three states to the data and extract the fractions of molecules in each state. This avoids making too many assumptions on the underlying processes, e.g. a Markovian system with Brownian diffusion. The model in anaDDA (Vink et al.) is currently limited to two-transitioning states with a maximal step number of 8 steps per track for a computationally efficient solution (longer tracks are truncated). Using a short subset of the trajectories is less accurate than using the entire trajectory and because of this, we consider full tracks with at least 9 displacements. Meanwhile, Spot-On supports a three-state model but it is still based on a semi-analytical model with a pre-calculated library of parameters created by fitting of simulated data. Neither of these models considers the effect of cell confinement, which plays a major role in single-molecule diffusion in small-sized cells such as bacteria. For these reasons, we opted to use an empirical fit to the data. We note that the fractions of active ribosomes in WT cells, which we extracted from these diffusion measurements, are consistent with the range of estimates obtained by others using similar or different approaches (Forchhammer and Lindhal 1971; Mohapatra and Weisshaar, 2018; Sanamrad et al., 2014). 

The estimated fraction of active ribosomes in wild-type cells shows a very strong reduction with decreasing growth rate (down from 75% to 30%), twice as strong as measured in bulk experiments (Dai et al Nat Microbiology 2016; decrease from 90% to 60% for the same growth rate range) and probably incompatible with measurements of growth rate, ribosome concentrations, and almost constant translation elongation rate in this regime of growth rates. Might the different diffusive fractions of RpsB not represent active/inactive ribosomes? See also the problem of quantification above. The authors should explain and compare their results to previous work. 

We agree that our measured range is somewhat larger than the estimated range from Dai et al, 2016. However, they use different media, strains, and growth conditions. We also note that Dai et al did not make actual measurements of the active ribosome fraction. Instead, they calculate the “active ribosome equivalent” based on a model that includes growth rate, protein synthesis rate, RNA/protein abundance, and the total number of amino acids in all proteins in the cell. Importantly, our measurements show the same overall trend (a ~30% decrease) as Dai et al, 2016. Furthermore, our results are within the range of previous experimental estimates from ribosome profiling (Forchhammer and Lindhal 1971) or single-ribosome tracking (Mohapatra and Weisshaar, 2018; Sanamrad et al., 2014). We clarified this point in the revised manuscript. 

To measure the reduction of mRNA transcripts in the cell, the authors rely on the fluorescent dye SYTO RNAselect. They argue that 70% of the dye signal represents mRNA. The argument is based on the previously observed reduction of the total signal by 70% upon treatment with rifampicin, an RNA polymerase inhibitor (Bakshi et al 2014). The idea here is presumably that mRNA should undergo rapid degradation upon rif treatment while rRNA or tRNA are stable. However, work from Hamouche et al. RNA (2021) 27:946 demonstrates that rifampicin treatment also leads to a rapid degradation of rRNA. Furthermore, the timescale of fluorescent-signal decay in the paper by Bakshi et al. (half life about 10min) is not compatible with the previously reported rapid decay of mRNA (24min) but rather compatible with the slower, still somewhat rapid, decay of rRNA reported by Hamouche et al.. A bulk method to measure total mRNA as in the cited Balakrishnan et al. (Science 2022) would thus be a preferred method to quantify mRNA. Alternatively, the authors could also test whether the mass contribution of total RNA remains constant, which would suggest that rRNA decay does not contribute to signal loss. However, since rRNA dominates total RNA, this measurement requires high accuracy. The authors might thus tone down their conclusions on mRNA concentration changes while still highlighting the compelling data on RNAp diffusion. 

Thank you for bringing the Hamouche et al 2021 paper to our attention. To address this potential issue, we have performed fluorescence in situ hybridization (FISH) microscopy using a 16S rRNA probe (EUB338) to quantify rRNA concentration in 1N cells. We found that the rRNA signal only slightly decreases with cell size (i.e., genome dilution) compared to the RNASelect signal (e.g., a ~5% decrease for rRNA signal vs. 50% for RNASelect for a cell size range of 4 to 10 µm2). We have revised the text and added a figure to include the new rRNA FISH data (Figure 4). In addition, as a control, we validated our rRNA FISH method by comparing the intracellular concentration of 16S rRNA in poor vs. rich media (new Figure 4 – Figure supplement 3).

The proteomics experiments are a great addition to the single-cell studies, and the correlations between distance from ori and protein abundance is compelling. However, I was missing a different test, the authors might have already done but not put in the manuscript: If DNA is indeed limiting the initiation of transcription, genes that are already highly transcribed in non-perturbed conditions might saturate fastest upon replication inhibition, while genes rarely transcribed should have no problem to accommodate additional RNA polymerases. One might thus want to test, whether the (unperturbed) transcription initiation rate is a predictor of changes in protein composition. This is just a suggestion the authors may also ignore, but since it is an easy analysis, I chose to mention it here. 

We did not find any correlation when we examined the potential relation between RNA slopes and mRNA abundance (from our first CRISPRi oriC time point) or the transcription initiation rate (from Balakrishnan et al., 2022, PMID: 36480614) across genes. These new plots are presented in Figure 7 – Figure supplement 2B. In contrast, we found a small but significant correlation between RNA slopes and mRNA decay rates (from Balakrishnan et al., 2022, PMID: 36480614), specifically for genes with short mRNA lifetimes (new Figure 7F). This effect is consistent with our model prediction (Figure 5 – Figure supplement 2). 

Related to the proteomics, in l. 380 the authors write that the reduced expression close to the ori might reflect a gene-dosage compensatory mechanism. I don't understand this argument. Can the authors add a sentence to explain their hypothesis? 

We apologize for the confusion. While performing additional analyses for the revisions, we realized that while the proteins encoded by genes close to oriC tend to display subscaling behavior, this is not true at the mRNA level (new Figure 7 – Figure supplement 3B). In light of this result, we no longer have a hypothesis for the observed negative correlation at the protein level (originally Figure 5D, now Figure 7 – Figure supplement 3A). The text was revised accordingly.  

In Fig. 1E the authors show evidence that growth rate increases with cell length/area. While this is not a main point of the paper it might be cited by others in the future. There are two possible artifacts that could influence this experiment: a) segmentation: an overestimation of the physical length of the cell based on phase-contrast images (e.g., 200 nm would cause a 10% error in the relative rate of 2 um cells, but not of longer cells). b) timedependent changes of growth rate, e.g., due to change from liquid to solid or other perturbations. To test for the latter, one could measure growth rate as a function of time, restricting the analysis to short or long cells, or measuring growth rate for short/long cells at selected time points. For the former, I recommend comparison of phase-contrast segmentation with FM4-64-stained cell boundaries.

As the reviewer notes, the small increase in relative growth was just a minor observation that does not affect our story whether it is biologically meaningful or the result of a technical artefact. But we agree with the reviewer that others might cite it in future works and thus should be interpreted with caution.

An artefact associated with time-dependent changes (e.g. changing from liquid cultures to more solid agarose pads) is unlikely for two reasons. 1. We show that varying the time that cells spend on agarose pads relative to liquid cultures does not affect the cell size-dependent growth rate results (Figure 1 – supplement 5A). 2. We show that the growth rate is stable from the beginning of the time-lapse with no transient effects upon cell placement on agarose pads for imaging (Figure 1 – supplement 1). These results were described in the Methods section where they could easily be missed. We revised the text to discuss these controls more prominently in the Results section.

As for cell segmentation, we have run simulations and agree with the reviewer that a small overestimation of cell area (which is possible with any cell segmentation methods including ours) could lead to a small increase in relative growth with increasing cell areas (new Figure 1 – Figure supplement 3). Since the finding is not important to our story, we simply revised the text and added the simulation results to alert the readers to the possibility that the observation may be due to a small cell segmentation bias.

Reviewer #2 (Public Review): 

In this work, the authors uncovered the effects of DNA dilution on E. coli, including a decrease in growth rate and a significant change in proteome composition. The authors demonstrated that the decline in growth rate is due to the reduction of active ribosomes and active RNA polymerases because of the limited DNA copy numbers. They further showed that the change in the DNA-to-volume ratio leads to concentration changes in almost 60% of proteins, and these changes mainly stem from the change in the mRNA levels. 

Thank you for the support and accurate summary!

Reviewer #3 (Public Review): 

Summary: 

Mäkelä et al. here investigate genome concentration as a limiting factor on growth.

Previous work has identified key roles for transcription (RNA polymerase) and translation (ribosomes) as limiting factors on growth, which enable an exponential increase in cell mass. While a potential limiting role of genome concentration under certain conditions has been explored theoretically, Mäkelä et al. here present direct evidence that when replication is inhibited, genome concentration emerges as a limiting factor. 

Strengths: 

A major strength of this paper is the diligent and compelling combination of experiment and modeling used to address this core question. The use of origin- and ftsZ-targeted CRISPRi is a very nice approach that enables dissection of the specific effects of limiting genome dosage in the context of a growing cytoplasm. While it might be expected that genome concentration eventually becomes a limiting factor, what is surprising and novel here is that this happens very rapidly, with growth transitioning even for cells within the normal length distribution for E. coli. Fundamentally, it demonstrates the fine balance of bacterial physiology, where the concentration of the genome itself (at least under rapid growth conditions) is no higher than it needs to be. 

Thank you!

Weaknesses: 

One limitation of the study is that genome concentration is largely treated as a single commodity. While this facilitates their modeling approach, one would expect that the growth phenotypes observed arise due to copy number limitation in a relatively small number of rate-limiting genes. The authors do report shifts in the composition of both the proteome and the transcriptome in response to replication inhibition, but while they report a positional effect of distance from the replication origin (reflecting loss of high-copy, origin-proximal genes), other factors shaping compositional shifts and their functional effects on growth are not extensively explored. This is particularly true for ribosomal RNA itself, which the authors assume to grow proportionately with protein. More generally, understanding which genes exert the greatest copy number-dependent influence on growth may aid both efforts to enhance (biotechnology) and inhibit (infection) bacterial growth. 

We agree but feel that identifying the specific limiting genes is beyond the scope of the study. This said, we carried out additional experiments and analyses to address the reviewer’s comment and identify potential contributing factors and limiting gene candidates. First, we examined the intracellular concentration of 16S ribosomal RNA (rRNA) by rRNA FISH microscopy and found that it decays much slower than the bulk of mRNAs as measured using RNASelect staining (new Figure 4 and Figure 4 – Figure supplements 1 and 3). We found that the rRNA signal is far more stable in 1N cells than the RNASelect signal, the former decreasing by only ~5% versus ~50% for the later in response to the same range of genome dilution (Figure 4C).  Second,  we carried out new correlation analyses between our proteomic/transcriptomic datasets and published genome-wide datasets that report various variables under unperturbed conditions (e.g., mRNA abundance, mRNA degradation rates, fitness cost, transcription initiation rates, essentiality for viability); see new Figure 7E-G and Figure 7 – Figure supplement 2. In the process, we found that genes essential for viability tend, on average, to display superscaling behavior (Figure 7G). This suggests that cells have evolved mechanisms that prioritize expression of essential genes over nonessential ones during DNA-limited growth. Furthermore, this analysis identified a small number of essential genes that display strong negative RNA slopes (Figure 7C, Datasets 1 and 2), indicating that the concentration of their mRNA decreases rapidly relative to the rest of the transcriptome upon genome dilution. These essential genes with strong subscaling behavior are candidates for being growth-limiting. 

The text and figures were revised to include these new results.

Overall, this study provides a fundamental contribution to bacterial physiology by illuminating the relationship between DNA, mRNA, and protein in determining growth rate. While coarse-grained, the work invites exciting questions about how the composition of major cellular components is fine-tuned to a cell's needs and which specific gene products mediate this connection. This work has implications not only for biotechnology, as the authors discuss, but potentially also for our understanding of how DNA-targeted antibiotics limit bacterial growth. 

Thank you!

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors): 

Below are my comments. 

(1) I noticed that a paper by Li et al. on biorxiv has found similar results as this work ("Scaling between DNA and cell size governs bacterial growth homeostasis and resource allocation," https://doi.org/10.1101/2021.11.12.468234), including the linear growth of E. coli when the DNA concentration is low. This relevant reference was not cited or discussed in the current manuscript. 

We agree that authors should cite and discuss relevant peer-reviewed literature. But broadly speaking, we feel that extending this responsibility to all preprints (and by extension any online material) that have not been reviewed is a bit dangerous. It would effectively legitimize unreviewed claims and risk their propagation in future publications. We think that while imperfect, the peer-reviewing process still plays an important role. 

Regarding the specific 2021 preprint that the reviewer pointed out, we think that the presented growth rate data are quite noisy and that the experiments lack a critical control (multi-N cells), making interpretation difficult. Their report that plasmid-borne expression is enhanced when DNA is severely diluted is certainly interesting and makes sense in light of our measurements that the activities, but not the concentrations, of RNA polymerases and ribosomes are reduced in 1N cells. However, we do not know why this preprint has not yet been published since 2021. There could be many possible reasons for this. Therefore, we feel that it is safer to limit our discussion to peer-reviewed literature.

(2) I think the kinetic Model B in the Appendix has been studied in previous works, such as Klump & Hwa, PNAS 2008, https://doi.org/10.1073/pnas.0804953105

Indeed, Klumpp & Hwa 2008 modeled the kinetics of RNA polymerase and promoter association prior to our study. But there is a difference between their model and ours. Their model is based on Michaelis Menten-type (MM) functions in which the RNAP is analogous to the “substrate” and the promoter to the “enzyme” in the MM equation. In contrast, our model uses functions based on the law of mass action (instead of MMtype of function). We have revised the text, included the Klumpp & Hwa 2008 reference, and revised the Materials & Methods section to clarify these points. 

(3) On lines 284-285, if I understand correctly, the fractions of active RNAPs and active ribosomes are relative to the total protein number. It would be helpful if the authors could mention this explicitly to avoid confusion. 

The fractions of active RNAPs and active ribosomes are expressed as the percentage of the total RNAPs and ribosomes. We have revised the text to be more explicit. Thank you.

(4) On line 835, I am not sure what the bulk transcription/translation rate means. I guess it is the maximum transcription/translation rate if all RNAPs/ribosomes are working according to Eq. (1,2). It would be helpful if the authors could explain the meaning of r_1 and r_2 more explicitly. 

Our apology for the lack of clarity. We have added the following equations:

(5) Regarding the changes in protein concentrations due to genome dilution, a recent theoretical paper showed that it may come from the heterogeneity in promoter strengths (Wang & Lin, Nature Communications 2021). 

In the Wang and Lin model, the heterogeneity in promoter strength predicts that the “mRNA production rate equivalent”, which is the mRNA abundance multiplied by the mRNA decay rate, will correlate the RNA slopes. However, we found these two variables to be uncorrelated (see below, The Spearman correlation coefficient ρ was 0.02 with a p-value of 0.24, indicating non-significance (NS).

Author response image 1.

The mRNA production rate equivalent (mRNA abundance at the first time point after CRISPRi oriC induction multiplied by the mRNA degradation rate measured by Balakrishnan et al., 2022, PMID: 36480614, expressed in transcript counts per minute) does not correlate (Spearman correlation’s p-value = 0.24) with the RNA slope in 1N-rich cells.  Data from 2570 genes are shown (grey markers, Gaussian kernel density estimation - KDE), and their binned statistics (mean +/- SEM, ~280 genes per bin, orange markers). 

In addition, we found no significant correlation between RNA slopes and mRNA abundance or transcription initiation rate. These plots are now included in Figure 7E and Figure 7 –Figure supplement 2B. Thus, the promoter strength does not appear to be a predictor of the RNA (and protein) scaling behavior under DNA limitation. 

Reviewer #3 (Recommendations For The Authors): 

One general area that could be developed further is analysis of changes in the proteome/transcriptome composition, given that there may be specific clues here as to the phenotypic effects of genome concentration limitation. Specifically: 

• In Figure 5D, the authors demonstrate an effect of origin distance on sensitivity to replication inhibition, presumably as a copy number effect. However, the authors note that the effect was only slight and postulated a compensatory mechanism. Due to the stability of proteins, one should expect relatively small effects - even if synthesis of a protein stopped completely, its concentration would only decrease twofold with a doubling of cell area (slope = -1, if I'm interpreting things correctly). It would be helpful to display the same information shown in Figure 5D at the mRNA level, since I would anticipate that higher mRNA turnover rates mean that effects on transcription rate should be felt more rapidly. 

We thank the reviewer for this suggestion. To our surprise, we found that there is no correlation between gene location relative to the origin and RNA slope across genes. This suggests that the observed correlation between gene location and protein slopes does not occur at the mRNA level. Given that we do not have an explanation for the underlying mechanism, we decided to present these data (the original data in Figure 5D and the new data for the RNA slope) in a supplementary figure (Figure 7 – Figure supplement 3).

• Related to this, did the authors see any other general trends? For example, do highly expressed genes hit saturation faster, making them more sensitive to limited genome concentration? 

We found that the RNA slopes do not correlate with mRNA abundance or transcription initiation rates. However, they do correlate with mRNA decay. That is, short-lived mRNAs tend to have negative RNA slopes. The new analyses have been added as Figure 7E-F and Figure 7 – Figure supplement 2B. The text has been revised to incorporate this information. 

• Presumably loss of growth is primarily driven by a subset of genes whose copy number becomes limiting. Previously, it has been reported that there is a wide variety among "essential" genes in their expression-fitness relationship - i.e. how much of a reduction in expression you need before growth is reduced (e.g. PMID 33080209). It would be interesting to explore the shifts in proteome/transcriptome composition to see whether any genes particularly affected by restricted genome concentration are also especially sensitive to reduced expression - overlap in these datasets may reveal which genes drive the loss of growth. 

This is a very interesting idea – thank you! We did not find a correlation between the protein/RNA slope and the relative gene fitness as previously calculated (PMID 33080209), as shown below.

Author response image 2.

The relative fitness of each gene (data by Hawkins et al., 2020, PMID: 33080209, median fitness from the highest sgRNA activity bin) plotted versus the gene-specific RNA and protein slopes that we measured in 1Nrich cells after CRISPRi oriC induction. More than 260 essential genes are shown (262 RNA slopes and 270 protein slopes, grey markers), and their binned statistics (mean +/- SEM, 43-45 essential genes per bin, orange markers). The spearman correlations (ρ) with p-values above 10-3 are considered not significant (NS). In our analyses, we only considered correlations significant if they have a Spearman correlation p-value below 10-10.

However, while doing this suggested analysis, we noticed that the essential genes that were included in the forementioned study have RNA slopes above zero on average. This led us to compare the RNA slope distributions of essential genes relative to all genes (now included in Figure 7G). We found that they tend to display superscaling behavior (positive RNA slopes), suggesting the existence of regulatory mechanisms that prioritize the expression of essential genes over less important ones when genome concentration becomes limiting for growth.  The text has been revised to include this new information.

Other suggestions: 

• In Figure 3 the authors report that total RNAP concentration increases with increasing cytoplasmic volume. This is in itself an interesting finding as it may imply a compensatory mechanism - can the authors offer an explanation for this? 

We do not have a straightforward explanation. But we agree that it is very interesting and should be investigated in future studies given that this superscaling behavior is common among essential genes. 

• The explanation of the modeling within the main text could be improved. Specifically, equations 1 and 2, as well as a discussion of models A and B (lines 290-301), do not explicitly relate DNA concentration to downstream effects. The authors provide the key information in Appendix 1, but for a general reader, it would be helpful to provide some intuition within the main text about how genome concentration influences transcription rate (i.e. via 𝛼RNAP).  

We apologize for the lack of clarity. We have added information that hopefully improves clarity.

Author Response

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

eLife assessment

This article presents important results describing how the gathering, integration, and broadcasting of information in the brain changes when consciousness is lost either through anesthesia or injury. They provide convincing evidence to support their conclusions, although the paper relies on a single analysis tool (partial information decomposition) and could benefit from a clearer explication of its conceptual basis, methodology, and results. The work will be of interest to both neuroscientists and clinicians interested in fundamental and clinical aspects of consciousness.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this paper, Luppi et al., apply the recently developed integrated information decomposition to the question how the architecture of information processing changes when consciousness is lost. They explore fMRI data from two different populations: healthy volunteers undergoing reversible anesthesia, as well as from patients who have long-term disorders of consciousness. They show that, in both populations, synergistic integration of information is disrupted in common ways. These results are interpreted in the context of the SAPHIRE model (recently proposed by this same group), that describes information processing in the brain as being composed of several distinct steps: 1) gatekeeping (where gateway regions introduce sensory information to the global synergistic workspace where 2) it is integrated or "processed" before 3) by broadcast back to to the brain.

I think that this paper is an excellent addition to the literature on information theory in neuroscience, and consciousness science specifically. The writing is clear, the figures are informative, and the authors do a good job of engaging with existing literature. While I do have some questions about the interpretations of the various information-theoretic measures, all in all, I think this is a significant piece of science that I am glad to see added to the literature.

One specific question I have is that I am still a little unsure about what "synergy" really is in this context. From the methods, it is defined as that part of the joint mutual information that is greater than the maximum marginal mutual information. While this is a perfectly fine mathematical measure, it is not clear to me what that means for a squishy organ like the brain. What should these results mean to a neuro-biologist or clinician?

Right now the discussion is very high level, equating synergy to "information processing" or "integrated information", but it might be helpful for readers not steeped in multivariate information theory to have some kind of toy model that gets worked out in detail. On page 15, the logical XOR is presented in the context of the single-target PID, but 1) the XOR is discrete, while the data analyzed here are continuous BOLD signals w/ Gaussian assumptions and 2) the XOR gate is a single-target system, while the power of the Phi-ID approach is the multi-target generality. Is there a Gaussian analog of the single-target XOR gate that could be presented? Or some multi-target, Gaussian toy model with enough synergy to be interesting? I think this would go a long way to making this work more accessible to the kind of interdisciplinary readership that this kind of article with inevitably attract.

We appreciate this observation. We now clarify that:

“redundancy between two units occurs when their future spontaneous evolution is predicted equally well by the past of either unit. Synergy instead occurs when considering the two units together increases the mutual information between the units’ past and their future – suggesting that the future of each is shaped by its interactions with the other. At the microscale (e.g., for spiking neurons) this phenomenon has been suggested as reflecting “information modification” 36,40,47. Synergy can also be viewed as reflecting the joint contribution of parts of the system to the whole, that is not driven by common input48.”

In the Methods, we have also added the following example to provide additional intuition about synergy in the case of continuous rather than discrete variables:

“As another example for the case of Gaussian variables (as employed here), consider a 2-node coupled autoregressive process with two parameters: a noise correlation c and a coupling parameter a. As c increases, the system is flooded by “common noise”, making the system increasingly redundant because the common noise “swamps” the signal of each node. As a increases, each node has a stronger influence both on the other and on the system as a whole, and we expect synergy to increase. Therefore, synergy reflects the joint contribution of parts of the system to the whole that is not driven by common noise. This has been demonstrated through computational modelling (Mediano et al 2019 Entropy).”

See below for the relevant parts of Figures 1 and 2 from Mediano et al (2019 Entropy), where Psi refers to the total synergy in the system.

Author response image 1.

Strengths

The authors have a very strong collection of datasets with which to explore their topic of interest. By comparing fMRI scans from patients with disorders of consciousness, healthy resting state, and various stages of propofol anesthesia, the authors have a very robust sample of the various ways consciousness can be perturbed, or lost. Consequently, it is difficult to imagine that the observed effects are merely a quirk of some biophysical effect of propofol specifically, or a particular consequence of long-term brain injury, but do in fact reflect some global property related to consciousness. The data and analyses themselves are well-described, have been previously validated, and are generally strong. I have no reason to doubt the technical validity of the presented results.

The discussion and interpretation of these results is also very nice, bringing together ideas from the two leading neurocognitive theories of consciousness (Global Workspace and Integrated Information Theory) in a way that feels natural. The SAPHIRE model seems plausible and amenable to future research. The authors discuss this in the paper, but I think that future work on less radical interventions (e.g. movie watching, cognitive tasks, etc) could be very helpful in refining the SAPHIRE approach.

Finally, the analogy between the PID terms and the information provided by each eye redundantly, uniquely, and synergistically is superb. I will definitely be referencing this intuition pump in future discussions of multivariate information sharing.

We are very grateful for these positive comments, and for the feedback on our eye metaphor.

Weaknesses

I have some concerns about the way "information processing" is used in this study. The data analyzed, fMRI BOLD data is extremely coarse, both in spatial and temporal terms. I am not sure I am convinced that this is the natural scale at which to talk about information "processing" or "integration" in the brain. In contrast to measures like sample entropy or Lempel-Ziv complexity (which just describe the statistics of BOLD activity), synergy and Phi are presented here as quasi-causal measures: as if they "cause" or "represent" phenomenological consciousness. While the theoretical arguments linking integration to consciousness are compelling, is this is right data set to explore them in? For example, the work by Newman, Beggs, and Sherril (nee Faber), synergy is associated with "computation" performed in individual neurons: the information about the future state of a target neuron that is only accessible when knowing both inputs (analogous to the synergy in computing the sum of two dice). Whether one thinks that this is a good approach neural computation or not, it fits within the commonly accepted causal model of neural spiking activity: neurons receive inputs from multiple upstream neurons, integrate those inputs and change their firing behavior accordingly.

In contrast, here, we are looking at BOLD data, which is a proxy measure for gross-scale regional neural activity, which itself is a coarse-graining of millions of individual neurons to a uni-dimensional spectrum that runs from "inactive to active." It feels as though a lot of inferences are being made from very coarse data.

We appreciate the opportunity to clarify this point. It is not our intention to claim that Phi-R and synergy, as measured at the level of regional BOLD signals, represent a direct cause of consciousness, or are identical to it. Rather, our work is intended to use these measures similarly to the use of sample entropy and LZC for BOLD signals: as theoretically grounded macroscale indicators, whose empirical relationship to consciousness may reveal the relevant underlying phenomena. In other words, while our results do show that BOLD-derived Phi-R tracks the loss and recovery of consciousness, we do not claim that they are the cause of it: only that an empirical relationship exists, which is in line with what we might expect on theoretical grounds. We have now clarified this in the Limitations section of our revised manuscript, as well as revising our language accordingly in the rest of the manuscript.

We also clarify that the meaning of “information processing” that we adopt pertains to “intrinsic” information that is present in the system’s spontaneous dynamics, rather than extrinsic information about a task:

“Information decomposition can be applied to neural data from different scales, from electrophysiology to functional MRI, with or without reference to behaviour 34. When behavioural data are taken into account, information decomposition can shed light on the processing of “extrinsic” information, understood as the translation of sensory signals into behavioural choices across neurons or regions 41,43,45,47. However, information decomposition can also be applied to investigate the “intrinsic” information that is present in the brain’s spontaneous dynamics in the absence of any tasks, in the same vein as resting-state “functional connectivity” and methods from statistical causal inference such as Granger causality 49. In this context, information processing should be understood in terms of the dynamics of information: where and how information is stored, transferred, and modified 34.”

References:

(1) Newman, E. L., Varley, T. F., Parakkattu, V. K., Sherrill, S. P. & Beggs, J. M. Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition. Entropy 24, 930 (2022).

Reviewer #2 (Public Review):

The authors analysed functional MRI recordings of brain activity at rest, using state-of-the-art methods that reveal the diverse ways in which the information can be integrated in the brain. In this way, they found brain areas that act as (synergistic) gateways for the 'global workspace', where conscious access to information or cognition would occur, and brain areas that serve as (redundant) broadcasters from the global workspace to the rest of the brain. The results are compelling and consisting with the already assumed role of several networks and areas within the Global Neuronal Workspace framework. Thus, in a way, this work comes to stress the role of synergy and redundancy as complementary information processing modes, which fulfill different roles in the big context of information integration.

In addition, to prove that the identified high-order interactions are relevant to the phenomenon of consciousness, the same analysis was performed in subjects under anesthesia or with disorders of consciousness (DOC), showing that indeed the loss of consciousness is associated with a deficient integration of information within the gateway regions.

However, there is something confusing in the redundancy and synergy matrices shown in Figure 2. These are pair-wise matrices, where the PID was applied to identify high-order interactions between pairs of brain regions. I understand that synergy and redundancy are assessed in the way the brain areas integrate information in time, but it is still a little contradictory to speak about high-order in pairs of areas. When talking about a "synergistic core", one expects that all or most of the areas belonging to that core are simultaneously involved in some (synergistic) information processing, and I do not see this being assessed with the currently presented methodology. Similarly, if redundancy is assessed only in pairs of areas, it may be due to simple correlations between them, so it is not a high-order interaction. Perhaps it is a matter of language, or about the expectations that the word 'synergy' evokes, so a clarification about this issue is needed. Moreover, as the rest of the work is based on these 'pair-wise' redundancy and synergy matrices, it becomes a significative issue.

We are grateful for the opportunity to clarify this point. We should highlight that PhiID is in fact assessing four variables: the past of region X, the past of region B, the future of region X, and the future of region Y. Since X and Y each feature both in the past and in the future, we can re-conceptualise the PhiID outputs as reflecting the temporal evolution of how X and Y jointly convey information: the persistent redundancy that we consider corresponds to information that is always present in both X and Y; whereas the persistent synergy is information that X and Y always convey synergistically. In contrast, information transfer would correspond to the phenomenon whereby information was conveyed by one variable in the past, and by the other in the future (see Luppi et al., 2024 TICS; and Mediano et al., 2021 arXiv for more thorough discussions on this point). We have now added this clarification in our Introduction and Results, as well as adding the new Figure 2 to clarify the meaning of PhiID terms.

We would also like to clarify that all the edges that we identify as significantly changing are indeed simultaneously involved in the difference between consciousness and unconsciousness. This is because the Network-Based Statistic differs from other ways of identifying edges that are significantly different between two groups or conditions, because it does not consider edges in isolation, but only as part of a single connected component.

Reviewer #3 (Public Review):

The work proposes a model of neural information processing based on a 'synergistic global workspace,' which processes information in three principal steps: a gatekeeping step (information gathering), an information integration step, and finally, a broadcasting step. The authors determined the synergistic global workspace based on previous work and extended the role of its elements using 100 fMRI recordings of the resting state of healthy participants of the HCP. The authors then applied network analysis and two different measures of information integration to examine changes in reduced states of consciousness (such as anesthesia and after-coma disorders of consciousness). They provided an interpretation of the results in terms of the proposed model of brain information processing, which could be helpful to be implemented in other states of consciousness and related to perturbative approaches. Overall, I found the manuscript to be well-organized, and the results are interesting and could be informative for a broad range of literature, suggesting interesting new ideas for the field to explore. However, there are some points that the authors could clarify to strengthen the paper. Key points include:

(1) The work strongly relies on the identification of the regions belonging to the synergistic global workspace, which was primarily proposed and computed in a previous paper by the authors. It would be great if this computation could be included in a more explicit way in this manuscript to make it self-contained. Maybe include some table or figure being explicit in the Gradient of redundancy-to-synergy relative importance results and procedure.

We have now added the new Supplementary Figure 1 to clarify how the synergistic workspace is identified, as per Luppi et al (2022 Nature Neuroscience).

(2) It would be beneficial if the authors could provide further explanation regarding the differences in the procedure for selecting the workspace and its role within the proposed architecture. For instance, why does one case uses the strength of the nodes while the other case uses the participation coefficient? It would be interesting to explore what would happen if the workspace was defined directly using the participation coefficient instead of the strength. Additionally, what impact would it have on the procedure if a different selection of modules was used? For example, instead of using the RSN, other criteria, such as modularity algorithms, PCA, Hidden Markov Models, Variational Autoencoders, etc., could be considered. The main point of my question is that, probably, the RSN are quite redundant networks and other methods, as PCA generates independent networks. It would be helpful if the authors could offer some comments on their intuition regarding these points without necessarily requiring additional computations.

We appreciate the opportunity to clarify this point. Our rationale for the procedure used to identify the workspace is to find regions where synergy is especially prominent. This is due to the close mathematical relationship between synergistic information and integration of information (see also Luppi et al., 2024 TICS), which we view as the core function of the global workspace. This identification is based on the strength ranking, as per Luppi et al (2022 Nature Neuroscience), which demonstrated that regions where synergy predominates (i.e., our proposed workspace) are also involved with high-level cognitive functions and anatomically coincide with transmodal association cortices at the confluence of multiple information streams. This is what we should expect of a global workspace, which is why we use the strength of synergistic interactions to identify it, rather than the participation coefficient. Subsequently, to discern broadcasters from gateways within the synergistic workspace, we seek to encapsulate the meaning of a “broadcaster” in information terms. We argue that this corresponds with making the same information available to multiple modules. Sameness of information corresponds to redundancy, and multiplicity of modules can be reflected in the network-theoretic notion of participation coefficient. Thus, a broadcaster is a region in the synergistic workspace (i.e., a region with strong synergistic interactions) that in addition has a high participation coefficient for its redundant interactions.

Pertaining specifically to the use of resting-state networks as modules, indeed our own (Luppi et al., 2022 Nature Neuroscience) and others’ research has shown that each RSN entertains primarily redundant interactions among its constituent regions. This is not surprising, since RSNs are functionally defined: their constituent elements need to process the same information (e.g., pertaining to a visual task in case of the visual network). We used the RSNs as our definition of modules, because they are widely understood to reflect the intrinsic organisation of brain activity into functional units; for example, Smith et al., (2009 PNAS) and Cole et al (2014 Neuron) both showed that RSNs reflect task-related co-activation of regions, whether directly quantified from fMRI in individuals performing multiple tasks, or inferred from meta-analysis of the neuroimaging literature. This is the aspect of a “module” that matters from the global workspace perspective: modules are units with distinct function, and RSNs capture this well. This is therefore why we use the RSNs as modules when defining the participation coefficient: they provide an a-priori division into units with functionally distinct roles.

Nonetheless, we also note that RSN organisation is robustly recovered using many different methods, including seed-based correlation from specific regions-of-interest, or Independent Components Analysis, or community detection on the network of inter-regional correlations - demonstrating that they are not merely a function of the specific method used to identify them. In fact, we show significant correlation between participation coefficient defined in terms of RSNs, and in terms of modules identified in a purely data-driven manner from Louvain consensus clustering (Figure S4).

(3) The authors acknowledged the potential relevance of perturbative approaches in terms of PCI and quantification of consciousness. It would be valuable if the authors could also discuss perturbative approaches in relation to inducing transitions between brain states. In other words, since the authors investigate disorders of consciousness where interventions could provide insights into treatment, as suggested by computational and experimental works, it would be interesting to explore the relationship between the synergistic workspace and its modifications from this perspective as well.

We thank the Reviewer for bringing this up: we now cite several studies that in recent years have applied perturbative approaches to induce transitions between states of consciousness.

“The PCI is used as a means of assessing the brain’s current state, but stimulation protocols can also be adopted to directly induce transitions between states of consciousness. In rodents, carbachol administration to frontal cortex awakens rats from sevoflurane anaesthesia120, and optogenetic stimulation was used to identify a role of central thalamus neurons in controlling transitions between states of responsiveness121,122. Additionally, several studies in non-human primates have now shown that electrical stimulation of the central thalamus can reliably induce awakening from anaesthesia, accompanied by the reversal of electrophysiological and fMRI markers of anaesthesia 123–128. Finally, in human patients suffering from disorders of consciousness, stimulation of intra-laminar central thalamic nuclei was reported to induce behavioural improvement 129, and ultrasonic stimulation 130,131 and deep-brain stimulation are among potential therapies being considered for DOC patients 132,133. It will be of considerable interest to determine whether our corrected measure of integrated information and topography of the synergistic workspace also restored by these causal interventions.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I would appreciate it if the authors could revisit the figures and make sure that:

(1) All fonts are large enough to be readable for people with visual impairments (for ex. the ranges on the colorbars in Fig. 2 are unreadably small).

Thank you: we have increased font sizes.

(2) The colormaps are scaled to show meaningful differences (Fig. 2A)

We have changed the color scale in Figure 2A and 2B.

Also, the authors may want to revisit the references section: some of the papers that were pre-prints at one point have now been published and should be updated.

Thank you: we have updated our references.

Minor comments:

• In Eqs. 2 and 3, the unique information term uses the bar notation ( | ) that is typically indicative of "conditioned on." Perhaps the authors could use a slash notation (e.g. Unq(X ; Z / Y)) to avoid this ambiguity? My understanding of the Unique information is that it is not necessarily "conditioned on", so much as it is "in the context of".

Indeed, the “|” sign of “conditioning” could be misleading; however, the “/” sign could also be misleading, if interpreted as division. Therefore, we have opted for the “\” sign of “set difference”, in Eq 2 and 3, which is conceptually more appropriate in this context.

• The font on the figures is a little bit small - for readers with poor eyes, it might be helpful to increase the wording size.

We have increased font sizes in the figures where relevant.

• I don't quite understand what is happening in Fig. 2A - perhaps it is a colormap issue, but it seems as though it's just a bit white square? It looks like redundancy is broadly correlated with FC (just based on the look of the adjacency matrices), but I have no real sense of what the synergistic matrix looks like, other than "flat."

We have now changed the color scale in Figure 2.

Reviewer #2 (Recommendations For The Authors):

Besides the issues mentioned in the Public review, I have the following suggestions to improve the manuscript:

• At the end of the introduction, a few lines could be added explaining why the study of DOC patients and subjects under anesthesia will be informative in the context of this work.

By comparing functional brain scans from transient anaesthetic-induced unconsciousness and from the persistent unconsciousness of DOC patients, which arises from brain injury, we can search for common brain changes associated with loss of consciousness – thereby disambiguating what is specific to loss of consciousness.

• On page and in general the first part of Results, it is not evident that you are working with functional connectivity. Many times the word 'connection' is used and sometimes I was wondering whether they were structural or functional. Please clarify. Also, the meaning of 'synergistic connection' or 'redundant connection' could be explained in lay terms.

Thank you for bringing this up. We have now replaced the word “connection” with “interaction” to disambiguate this issue, further adding “functional” where appropriate. We have also provided, in the Introduction, an intuitive explanation of what synergy and redundancy mean int he context of spontaneous fMRI signals.

• Figure 2 needs a lot of improvement. The matrix of synergistic interactions looks completely yellow-ish with some vague areas of white. So everything is above 2. What does it mean?? Pretty uninformative. The matrix of redundant connections looks a lot of black, with some red here and there. So everything is below 0.6. Also, what are the meaning and units of the colorbars?.

We agree: we have increased font sizes, added labels, and changed the color scale in Figure 2. We hope that the new version of Figure 2 will be clearer.

• Caption of Figure 2 mentions "... brain regions identified as belonging to the synergistic global workspace". I didn't get it clear how do you define these areas. Are they just the sum of gateways and broadcasters, or is there another criterion?

Regions belonging to the synergistic workspace are indeed the set comprising gateways and broadcasters; they are the regions that are synergy-dominated, as defined in Luppi et al., 2022 Nature Neuroscience. We have now clarified this in the figure caption.

• In the first lines of page 7, it is said that data from DOC and anesthesia was parcellated in 400 + 54 regions. However, it was said in a manner that made me think it was a different parcellation than the other data. Please make it clear that the parcellation is the same (if it is).

We have now clarified that the 400 cortical regions are from the Schaefer atlas, and 54 subcortical regions from the Tian atlas, as for the other analysis. The only other parcellation that we use is the Schaefer-232, for the robustness analysis. This is also reported in the Methods.

• Figure 3: the labels in the colorbars cannot be read, please make them bigger. Also, the colorbars and colorscales should be centered in white, to make it clear that red is positive and blue is negative. O at least maintain consistency across the panels (I can't tell because of the small numbers).

Thank you: we have increased font sizes, added labels, indicated that white refers to zero (so that red is always an increase, and blue is always a decrease), and changed the color scale in Figure 2.

• The legend of Figure 4 is written in a different style, interpreting the figure rather than describing it. Please describe the figure in the caption, in order to let the read know what they are looking at.

We have endeavoured to rewrite the legend of Figure 4 in a style that is more consistent with the other figures.

• In several parts the 'whole-minus-sum' phi measure is mentioned and it is said that it did not decrease during loss of consciousness. However, I did not see any figure about that nor any conspicuous reference to that in Results text. Where is it?

We apologise for the confusion: this is Figure S3A, in the Supplementary. We have now clarified this in the text.

Reviewer #3 (Recommendations For The Authors):

(1) In the same direction, regarding Fig. 2, in my opinion, it does not effectively aid in understanding the selection of regions as more synergistic or redundant. In panels A) and B), the color scales could be improved to better distinguish regions in the matrices (panel A) is saturated at the upper limit, while panel B) is saturated at the lower limit). Additionally, I suggest indicating in the panels what is being measured with the color scales.

Thank you: we have increased font sizes, added labels, and changed the color scale in Figure 2.

(2) When investigating the synergistic core of human consciousness and interpreting the results of changes in information integration measures in terms of the proposed framework, did the authors consider the synergistic workspace computed in HCP data? If the answer is positive, it would be helpful for the authors to be more explicit about it and elaborate on any differences that may be found, as well as the potential impact on interpretation.

This is correct: the synergistic workspace, including gateways and broadcasters, are identified from the Human Connectome Project dataset. We now clarify this in the manuscript.

Minors:

(1) I would suggest improving the readability of figures 2 and 3, considering font size (letters and numbers) and color bars (numbers and indicate what is measured with this scale). In Figure 1, the caption defines steps instead stages that are indicated in the figure.

Thank you: we have increased font sizes, added labels, and replaced steps with “stages” in Figure 1.