Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study reports that IT neurons have biased representations toward low spatial frequency

(SF) and faster decoding of low SFs than high SFs. High SF-preferred neurons, and low SF-preferred neurons to a lesser degree, perform better category decoding than neurons with other profiles (U and inverted U shaped). SF coding also shows more sparseness than category coding in the earlier phase of the response and less sparseness in the later phase. The results are also contrasted with predictions of various DNN models.

Strengths:

The study addressed an important issue on the representations of SF information in a high-level visual area. Data are analyzed with LDA which can effectively reduce the dimensionality of neuronal responses and retain category information.

We would like to express our sincere gratitude for your insightful and constructive comments which greatly contributed to the refinement of the manuscript. We appreciate the time and effort you dedicated to reviewing our work and providing suggestions. We have carefully considered each of your comments and addressed the suggested revisions accordingly.

Weaknesses:

The results are likely compromised by improper stimulus timing and unmatched spatial frequency spectrums of stimuli in different categories.

The authors used a very brief stimulus duration (35ms), which would degrade the visual system's contrast sensitivity to medium and high SF information disproportionately (see Nachmias, JOSAA, 1967). Therefore, IT neurons in the study could have received more degraded medium and high SF inputs compared to low SF inputs, which may be at least partially responsible for higher firing rates to low SF R1 stimuli (Figure 1c) and poorer recall performance with median and high SF R3-R5 stimuli in LDA decoding. The issue may also to some degree explain the delayed onset of recall to higher SF stimuli (Figure 2a), preferred low SF with an earlier T1 onset (Figure 2b), lower firing rate to high SF during T1 (Figure 2c), somewhat increased firing rate to high SF during T2 (because weaker high SF inputs would lead to later onset, Figure 2d).

We appreciate your concern regarding the course-to-fine nature of SF processing in the vision hierarchy and the short exposure time of our paradigm. According to your comment, we repeated the analysis of SF representation with 200ms exposure time as illustrated in Appendix 1 - Figure 4. Our recorded data contains the 200ms version of exposure time for all neurons in the main phase. As can be seen, the results are similar to what we found with 33 ms experiments.

Next, we bring your attention to the following observations:

(1) According to Figure 2d, the average firing rate of IT neurons for HSF could be higher than LSF in the late response phase. Therefore, the amount of HSF input received by the IT neurons is as much as LSF, however, its impact on the IT response is observable in the later phase of the response. Thus, the LSF preference is because of the temporal advantage of the LSF processing rather than contrast sensitivity.

(2) According to Figure 3a, 6% of the neurons are HSF-preferred and their firing rate in HSF is comparable to the LSF firing rate in the LSF-preferred group. This analysis is carried out in the early phase of the response (70-170 ms). While most of the neurons prefer LSF, this observation shows that there is an HSF input that excites a small group of neurons. Furthermore, the highest separability index also belongs to the HSF-preferred profile in the early phase of the response which supports the impact of the HSF part of the input.

(3) Similar LSF-preferred responses are also reported by Chen et al. (2018) (50ms for SC) and Zhang et al. (2023) (3.5 - 4 secs for V2 and V4) for longer duration times.

Our results suggest that the LSF-preferred nature of the IT responses in terms of firing rate and recall, is not due to the weakness or lack of input source (or information) for HSF but rather to the processing nature of the SF in the vision hierarchy.

To address this issue in the manuscript:

Figure Appendix 1 - Figure 4 is added to the manuscript and shows the recall value and onset for R1-R5 with 200ms of exposure time.

We added the following description to the discussion:

“To rule out the degraded contrast sensitivity of the visual system to medium and high SF information because of the brief exposure time, we repeated the analysis with 200ms exposure time as illustrated in Appendix 1 - Figure 4 which indicates the same LSF-preferred results. Furthermore, according to Figure 2, the average firing rate of IT neurons for HSF could be higher than LSF in the late response phase. It indicates that the amount of HSF input received by the IT neurons in the later phase is as much as LSF, however, its impact on the IT response is observable in the later phase of the response. Thus, the LSF preference is because of the temporal advantage of the LSF processing rather than contrast sensitivity. Next, according to Figure 3(a), 6\% of the neurons are HSF-preferred and their firing rate in HSF is comparable to the LSF firing rate in the LSF-preferred group. This analysis is carried out in the early phase of the response (70-170ms). While most of the neurons prefer LSF, this observation shows that there is an HSF input that excites a small group of neurons. Additionally, the highest SI belongs to the HSF-preferred profile in the early phase of the response which supports the impact of the HSF part of the input. Similar LSF-preferred responses are also reported by Chen et. al. (2018) (50ms for SC) and Zhang et. al. (2023) (3.5 - 4 secs for V2 and V4). Therefore, our results show that the LSF-preferred nature of the IT responses in terms of firing rate and recall, is not due to the weakness or lack of input source (or information) for HSF but rather to the processing nature of the SF in the IT cortex.”

Figure 3b shows greater face coding than object coding by high SF and to a lesser degree by low SF neurons. Only the inverted-U-shaped neurons displayed slightly better object coding than face coding. Overall the results give an impression that IT neurons are significantly more capable of coding faces than coding objects, which is inconsistent with the general understanding of the functions of IT neurons. The problem may lie with the selection of stimulus images (Figure 1b). To study SF-related category coding, the images in two categories need to have similar SF spectrums in the Fourier domain. Such efforts are not mentioned in the manuscript, and a look at the images in Figure 1b suggests that such efforts are likely not properly made. The ResNet18 decoding results in Figure 6C, in that IT neurons of different profiles show similar face and object coding, might be closer to reality.

Because of the limited number of stimuli in our experiments, it is hard to discuss the category selectivity, which needs a higher number of stimuli. To overcome the limited number of stimuli in our experiment, we fixed 60% (nine out of 15 stimuli) while varying the remaining stimuli to reduce the selective bias. To check the coding capability of the IT neurons for face and non-face objects, we evaluated the recall of face vs. non-face classification in intact stimuli (similar to classifiers stated in the manuscript). Results show that at the population level, the recall value for objects is 90.45%, and for faces is 92.45%. However, the difference is not significant (p-value=0.44). On the other hand, we note that a large difference in the SI value does not translate directly to the classification accuracy, rather it illustrates the strength of representation.

Regarding the SF spectrums, after matching the luminance and contrast of the images we matched the power of the images concerning SF and category. Powers are calculated using the sum of the absolute value of the Fourier transform of the image. Considering all stimuli, the ANOVA analysis shows that various SF bands have similar power (one-way ANOVA, p-value=0.24). Furthermore, comparing the power of faces and images in all SF bands (including intact) and both unscrambled and scrambled images indicates no significant difference between face and object (p-vale > 0.1). Therefore, the result of Figure 3b suggests that IT employs various SF bands for the recognition of various objects.

Comparing the results of CNNs and IT shows that the CNNs do not capture the complexities of the IT cortex in terms of SF. One of the sources of this difference is because of the behavioral saliency of the face stimulus in the training of the primate visual system.

To address this issue in the manuscript:

The following description is added to the discussion:

“… the decoding performance of category classification (face vs. non-face) in intact stimuli is 94.2%. The recall value for objects vs. scrambled is 90.45%, and for faces vs. scrambled is 92.45% (p-value=0.44), which indicates the high level of generalizability and validity characterizing our results.”

The following description is added to the method section, SF filtering.

“Finally, we equalized the stimulus power in all SF bands (intact, R-R5). The SF power among all conditions (all SF bands, face vs. non-face and unscrambled vs. scrambled) does not vary significantly (p-value > 0.1). SF power is calculated as the sum of the square value of the image coefficients in the Fourier domain.”

Reviewer #2 (Public Review):

Summary:

This paper aimed to examine the spatial frequency selectivity of macaque inferotemporal (IT) neurons and its relation to category selectivity. The authors suggest in the present study that some IT neurons show a sensitivity for the spatial frequency of scrambled images. Their report suggests a shift in preferred spatial frequency during the response, from low to high spatial frequencies. This agrees with a coarse-to-fine processing strategy, which is in line with multiple studies in the early visual cortex. In addition, they report that the selectivity for faces and objects, relative to scrambled stimuli, depends on the spatial frequency tuning of the neurons.

Strengths:

Previous studies using human fMRI and psychophysics studied the contribution of different spatial frequency bands to object recognition, but as pointed out by the authors little is known about the spatial frequency selectivity of single IT neurons. This study addresses this gap and they show that at least some IT neurons show a sensitivity for spatial frequency and

interestingly show a tendency for coarse-to-fine processing.

We extend our sincere appreciation for your thoughtful and constructive feedback on our paper. We are grateful for the time and expertise you invested in reviewing our work. Your detailed suggestions have been instrumental in addressing several key aspects of the paper, contributing to its clarity and scholarly merit. We have carefully considered each of your comments and have made revisions accordingly.

Weaknesses and requested clarifications:

(1) It is unclear whether the effects described in this paper reflect a sensitivity to spatial frequency, i.e. in cycles/ deg (depends on the distance from the observer and changes when rescaling the image), or is a sensitivity to cycles /image, largely independent of image scale. How is it related to the well-documented size tolerance of IT neuron selectivity?

Our stimuli are filtered using cycles/images and knowing the distance of the subject to the monitor, we can calculate the cycles/degrees. To the best of our knowledge, this is also the case for all other SF-related studies. To find the relation of observations to the cycles/image and degree/image, one should keep one of them fixed while changing the other, for example changing the subject's distance to the monitor will change the SF content in terms of cycle/degree. With our current data, we cannot discriminate this effect. To address this issue, we added the following description to the discussion. To address this issue, we added the following description to the discussion:

“Finally, since our experiment maintains a fixed SF content in terms of both cycles per degree and cycles per image, further experiments are needed to discern whether our observations reflect sensitivity to cycles per degree or cycles per image.”

(2) The authors' band-pass filtered phase scrambled images of faces and objects. The original images likely differed in their spatial frequency amplitude spectrum and thus it is unclear whether the differing bands contained the same power for the different scrambled images. If not, this could have contributed to the frequency sensitivity of the neurons.

After equalizing the luminance and contrast of the images, we equilized their power concerning SF and category. The powers were calculated using the sum of the absolute values of the Fourier transform of the images. The results of the ANOVA analysis across all stimuli indicate that various SF bands exhibit similar power (one-way ANOVA, p-value = 0.24). Additionally, a comparison of power between faces and objects in all SF bands (including intact), for both unscrambled and scrambled images, reveals no significant differences (p-value > 0.1). To clarify this point, we have incorporated the following information into the Methods section.

“Finally, we equalized the stimulus power in all SF bands (intact, R-R5). The SF power among all conditions (all SF bands, face vs. non-face and unscrambled vs. scrambled) does not vary significantly (ANOVA, p-value > 0.1).”

(3) How strong were the responses to the phase-scrambled images? Phase-scrambled images are expected to be rather ineffective stimuli for IT neurons. How can one extrapolate the effect of the spatial frequency band observed for ineffective stimuli to that for more effective stimuli, like objects or (for some neurons) faces? A distribution should be provided, of the net responses (in spikes/s) to the scrambled stimuli, and this for the early and late windows.

The sample neuron in Figure 1c is chosen to be a good indicator of the recorded neurons. In the early response phase, the average firing rate to scrambled stimuli is 26.3 spikes/s which is significantly higher than the response in -50 to 50ms which is 23.4. In comparison, the mean response to intact face stimuli is 30.5 spikes/s, while object stimuli elicit an average response of 28.8 spikes/s. Moving to the late phase, T2, the responses to scrambled, face, and object stimuli are 19.5, 19.4, and 22.4 spikes/s, respectively. Moreover, when the classification accuracy for SF exceeds chance levels, it indicates a significant impact of SF bands on the IT response. This raises a direct question about the explicit coding for SF bands in the IT cortex observed for ineffective stimuli and how it relates to complex and effective stimuli, such as faces. To show the strength of neuron responses to the SF bands in scrambled images, We added Appendix 1 - Figure 2 and also added Appendix 1 - Figure 1, according to comment 4, which shows the average and std of the responses to all SF bands. The following description is added to the results section.

“Considering the strength of responses to scrambled stimuli, the average firing rate in response to scrambled stimuli is 26.3 Hz, which is significantly higher than the response observed between -50 and 50 ms, where it is 23.4 Hz (p-value=3x10-5). In comparison, the mean response to intact face stimuli is 30.5 Hz, while non-face stimuli elicit an average response of 28.8 Hz. The distribution of neuron responses for scrambled, face, and non-face in T1 is illustrated in Appendix 1 - Figure 2.

[…]

Moreover, the average firing rates of scrambled, face, and non-face stimuli are 19.5 Hz, 19.4 Hz, and 22.4 Hz, respectively. The distribution of neuron responses is illustrated in Appendix 1 Figure 2.”

(4) The strength of the spatial frequency selectivity is unclear from the presented data. The authors provide the result of a classification analysis, but this is in normalized units so that the reader does not know the classification score in percent correct. Unnormalized data should be provided. Also, it would be informative to provide a summary plot of the spatial frequency selectivity in spikes/s, e.g. by ranking the spatial frequency bands for each neuron based on half of the trials and then plotting the average responses for the obtained ranks for the other half of the trials. Thus, the reader can appreciate the strength of the spatial frequency selectivity, considering trial-to-trial variability. Also, a plot should be provided of the mean response to the stimuli for the two analysis windows of Figure 2c and 2d in spikes/s so one can appreciate the mean response strengths and effect size (see above).

The normalization of the classification result is just obtained by subtracting the chance level, which is 0.2, from the whole values. Therefore the values could still be interpreted in percent as we did in the results section. To make this clear, we removed the “a.u.” from the figure and we added the following description to the results section.

“The accuracy value is normalized by subtracting the chance level (0.2).”

Regarding the selectivity of the neuron, as suggested by your comment, we added a new figure in the appendix section, Appendix 1 - figure 2. This figure shows the strength of SF selectivity, considering trial-to-trial variability. The following description is added to the results section:

“The strength of SF selectivity, considering the trial-to-trial variability is provided in Appendix 1 Figure 2, by ranking the SF bands for each neuron based on half of the trials and then plotting the average responses for the obtained ranks for the other half of the trials.”

The firing rates of Figures 2c and 2d are normalized for better illustration since the variation in firing rates is high across neurons, as can be observed in Figure Appendix 1 - Figure 1. Since we seek trends in the response, the absolute values are not important (since the baseline firing rates of neurons are different), but the values relative to the baseline firing rate determine the trend. To address the mean response and the strength of the SF response, the following description is added to the results section.

“Considering the strength of responses to scrambled stimuli, the average firing rate in response to scrambled stimuli is 26.3 Hz, which is significantly higher than the response observed between -50 and 50 ms, where it is 23.4 Hz (p-value=3x10-5). In comparison, the mean response to intact face stimuli is 30.5 Hz, while non-face stimuli elicit an average response of 28.8 Hz. The distribution of neuron responses for scrambled, face, and non-face in T1 is illustrated in Appendix 1 - Figure 2.

[…]

Moreover, the average firing rates of scrambled, face, and non-face stimuli are 19.5 Hz, 19.4

Hz, and 22.4 Hz, respectively. The distribution of neuron responses is illustrated in Appendix 1 Figure 2.”

Furthermore, we added a figure, Appendix 1 - Figure 3, to illustrate the strength of SF selectivity in our profiles. The following is added to the results section:

“To check the robustness of the profiles, considering the trial-to-trial variability, the strength of SF selectivity in each profile is provided in Appendix 1 - Figure 3, by forming the profile of each neuron based on half of the trials and then plotting the average SF responses with the other

half of the trials.”

(5) It is unclear why such brief stimulus durations were employed. Will the results be similar, in particular the preference for low spatial frequencies, for longer stimulus durations that are more similar to those encountered during natural vision?

Please refer to the first comment of Reviewer 1.

(6) The authors report that the spatial frequency band classification accuracy for the population of neurons is not much higher than that of the best neuron (line 151). How does this relate to the SNC analysis, which appears to suggest that many neurons contribute to the spatial frequency selectivity of the population in a non-redundant fashion? Also, the outcome of the analyses should be provided (such as SNC and decoding (e.g. Figure 1D)) in the original units instead of undefined arbitrary units.

The population accuracy is approximately 5% higher than the best neuron. However, we have no reference to compare the effect size (the value is roughly similar for face vs object while the chance levels are different). However, as stated in Methods, SNC is calculated for two label modes (LSF and HSF) and it can not be directly compared to the best neuron accuracy. Regarding the unit of SNC, it can be interpreted directly to percent by multiplying by a factor of 100. We removed the “a.u.” to prevent misunderstanding and modified the results section for clearance.

“… SNC score for SF (two labels, LSF (R1 and R2) vs. HSF (R4 and R5)) and category … (average SNC for SF=0.51\%±0.02 and category=0.1\%±0.04 …”

(7) To me, the results of the analyses of Figure 3c,d, and Figure 4 appear to disagree. The latter figure shows no correlation between category and spatial frequency classification accuracies while Figure 3c,d shows the opposite.

In Figure 3c,d, following what we observed in Figure 3a,b about the category coding capabilities in the population of neurons based on the profile of the single neurons, we tested a similar idea if the coding capability of single neurons in SF/category could predict the coding capability of population neurons in terms of category/SF. Therefore, both analyses investigate a relation between a characteristic of single neurons and the coding capability of a population of similar neurons. On the other hand, in Figure 4, the idea is to check the characteristics of the coding mechanisms behind SF and category coding. In Figure 4a, we check if there exists any relation between category and SF coding capability within a single neuron activity without the impact of other neurons, to investigate the idea that SF coding may be a byproduct of an object recognition mechanism. In Figure 4b, we investigated the contribution of all neurons in population decision, again to check whether the mechanisms behind the SF and category coding are the same or not. This analysis shows how individual neurons contribute to SF or category coding at the population level. Therefore, the experiments in Figures 3 and 4 are different in the analysis method and what they were designed to investigate and we cannot directly compare the results.

(8) If I understand correctly, the "main" test included scrambled versions of each of the "responsive" images selected based on the preceding test. Each stimulus was presented 15 times (once in each of the 15 blocks). The LDA classifier was trained to predict the 5 spatial frequency band labels and they used 70% of the trials to train the classifier. Were the trained and tested trials stratified with respect to the different scrambled images? Also, LDA assumes a normal distribution. Was this the case, especially because of the mixture of repetitions of the same scrambled stimulus and different scrambled stimuli?

In response to your inquiry regarding the stratification of trials, both the training and testing data were representative of the entire spectrum of scrambled images used in our experiment. To address your concern about the assumption of a normal distribution, especially given the mixture of repetitions of the same scrambled stimulus and different stimuli, our analysis of firing rates reveals a slightly left-skewed normal distribution. While there is a deviation from a perfectly normal distribution, we are confident that this skewness does not compromise the robustness of the LDA classifier.

(9) The LDA classifiers for spatial frequency band (5 labels) and category (2 labels) have different chance and performance levels. Was this taken into account when comparing the SNC between these two classifiers? Details and SNC values should be provided in the original (percent difference) instead of arbitrary units in Figure 5a. Without such details, the results are impossible to evaluate.

For both SNC and CMI calculations in SF, we considered two labels of HSF (R4 and R5) and LSF (R1 and R2). This was mentioned in the Methods section, after equation (5). According to your comment, to make it clear in the results section, we also added this description to the results section.

“… illustrates the SNC score for SF (two labels, LSF (R1 and R2) vs. HSF (R4 and R5)) and category (face vs. non-face) … conditioned on the label, SF (LSF (R1 and R2) vs. HSF (R4 and R5)) or category, to assess the information.”

The value of SNC can also be directly converted to the percent by a factor of 100. To make it clear, we removed “a.u.” from the y-axis.

(10) Recording locations should be described in IT, since the latter is a large region. Did their recordings include the STS? A/P and M/L coordinate ranges of recorded neurons?

We appreciate your suggestion for the recording location. Nevertheless, given the complexities associated with neurophysiological recordings and the limitations imposed by our methodologies, we face challenges in precisely localizing every unit if they are located in STS or not. To address your comment, We added Appendix 1 - Figure 5 which shows the SF and category coding capability of neurons along their recorded locations.

(11) The authors should show in Supplementary Figures the main data for each of the two animals, to ensure the reader that both monkeys showed similar trends.

We added Appendix 2 which shows the consistency of the main results in the two monkeys.

(12) The authors found that the deep nets encoded better the spatial frequency bands than the IT units. However, IT units have trial-to-trial response variability and CNN units do not. Did they consider this when comparing IT and CNN classification performance? Also, the number of features differs between IT and CNN units. To me, comparing IT and CNN classification performances is like comparing apples and oranges.

Deep convolutional neural networks are currently considered the state-of-the-art models of the primate visual pathway. However, as you mentioned and based on our results, they do not yet capture various complexities of the visual ventral stream. Yet studying the similarities and differences between CNN and brain regions, such as the IT cortex, is an active area of research, such as:

a. Kubilius, Jonas, et al. "Brain-like object recognition with high-performing shallow recurrent ANNs." Advances in neural information processing systems 32 (2019).

b. Xu, Yaoda, and Maryam Vaziri-Pashkam. "Limits to visual representational correspondence between convolutional neural networks and the human brain." Nature Communications, 12.1 (2021).

c. Jacob, Georgin, et al. "Qualitative similarities and differences in visual object representations between brains and deep networks." Nature Communications, 12.1 (2021).

Therefore, we believe comparing IT and CNN, despite all of the differences in terms of their characteristics, can help both fields grow faster, especially in introducing brain-inspired networks.

(13) The authors should define the separability index in their paper. Since it is the main index to show a relationship between category and spatial frequency tuning, it should be described in detail. Also, results should be provided in the original units instead of undefined arbitrary units. The tuning profiles in Figure 3A should be in spikes/s. Also, it was unclear to me whether the classification of the neurons into the different tuning profiles was based on an ANOVA assessing per neuron whether the effect of the spatial frequency band was significant (as should be done).

Based on your comment, we added the description of the separability index to the methods section. However, since the separability index is defined as the division of two dispersion matrices, it has no units by nature. The tuning profiles in Figure 3a are normalized for better illustration since the variation in firing rates is high. Since we seek trends in the response, the absolute values are not important. Regarding the SF profile formation, to better present the SF profile assignment, we updated the method section. Furthermore, The strength of responses for scrambled stimuli can be observed in Appendix 1 - Figures 1 and 2.

(14) As mentioned above, the separability analysis is the main one suggesting an association between category and spatial frequency tuning. However, they compute the separability of each category with respect to the scrambled images. Since faces are a rather homogeneous category I expect that IT neurons have on average a higher separability index for faces than for the more heterogeneous category of objects, at least for neurons responsive to faces and/or objects. The higher separability for faces of the two low- and high-pass spatial frequency neurons could reflect stronger overall responses for these two classes of neurons. Was this the case? This is a critical analysis since it is essential to assess whether it is category versus responsiveness that is associated with the spatial frequency tuning. Also, I do not believe that one can make a strong claim about category selectivity when only 6 faces and 3 objects (and 6 other, variable stimuli; 15 stimuli in total) are employed to assess the responses for these categories (see next main comment). This and the above control analysis can affect the main conclusion and title of the paper.

We appreciate your concern regarding category selectivity or responsiveness of the SF profiles. First, we note that we used SI since it overcomes the limitations of the accuracy and recall metrics as they are discrete and can be saturated. Using SI, we cannot directly calculate face vs object with SI, since this index only reports one value for the whole discrimination task. Therefore, we have to calculate the SI for face/object vs scrambled to obtain a value per category. However, as you suggested, it raises the question of whether we assess how well the neural responses distinguish between actual images (faces or objects) and their scrambled versions or if we just assess the responsiveness. Based on Figure 3b, since we have face-selective (LSF and HSF preferred profiles), object-selective (inverse U), and the U profile, where SI is the same for both face and object, we believe the SF profile is associated with the category selectivity, otherwise we would have the same face/object recall in all profiles, as we have in the U shape profile.

To analyze this issue further, we calculated the number of face/object selective neurons in 70-170ms. We found 43 face-selective neurons and 36 object-selective neurons (FDR corrected p-value < 0.05). Therefore, the number of face-selective and object-selective neurons is similar. Next, we check the selectivity of the neurons within each profile. Number of face/object selective neurons is LP=13/3, HP=6/2, IU=3/9, U=14/13, and the remaining belong to the NP group. Results show higher face-selective neurons in LP and HP and a higher number of object-selective neurons in the IU class. The U class contains roughly the same number of face and object-selective neurons. This observation supports the relationship between category selectivity and profiles.

Next, we examined the average neuron response to the face and object in each profile. The difference between the firing rate of the face and object in none of the profiles was significant (Ranksum with a significance level of 0.05). However, the rates are as follows. The average firing rate (spikes/s) of face/object is LP=36.72/28.77, HP=28.55/25.52, IU=21.55/27.25, U=38.48/36.28. While the differences are not significant, they support the relationship between profiles and categories instead of responsiveness.

The following description is added to the results section to cover this point of view.

“To assess whether the SF profiles distinguish category selectivity or merely evaluate the neuron's responsiveness, we quantified the number of face/non-face selective neurons in the 70-170ms time window. Our analysis shows a total of 43 face-selective neurons and 36 non-face-selective neurons (FDR-corrected p-value < 0.05). The results indicate a higher proportion of face-selective neurons in LP and HP, while a greater number of non-face-selective neurons is observed in the IU category (number of face/non-face selective neurons: LP=13/3, HP=6/2, IU=3/9). The U category exhibits a roughly equal distribution of face and non-face-selective neurons (U=14/13). This finding reinforces the connection between category selectivity and the identified profiles. We then analyzed the average neuron response to faces and non-faces within each profile. The difference between the firing rates for faces and non-faces in none of the profiles is significant (face/non-face average firing rate (Hz): LP=36.72/28.77, HP=28.55/25.52, IU=21.55/27.25, U=38.48/36.28, Ranksum with significance level of 0.05). Although the observed differences are not statistically significant, they provide support for the association between profiles and categories rather than mere responsiveness.”

About the low number of stimuli, please check the next comment.

(15) For the category decoding, the authors employed intact, unscrambled stimuli. Were these from the main test? If yes, then I am concerned that this represents a too small number of stimuli to assess category selectivity. Only 9 fixed + 6 variable stimuli = 15 were in the main test. How many faces/ objects on average? Was the number of stimuli per category equated for the classification? When possible use the data of the preceding selectivity test which has many more stimuli to compute the category selectivity.

We used only the main phase recorded data, which contains 15 images in each session. Each image results in 12 stimuli (intact, R1-R5, and phase-scrambled version). Thus, there exists a total of 180 unique stimuli in each session. Increasing the number of images would have increased the recording time. We compensated for this limitation by increasing the diversity of images in each session by picking the most responsive ones from the selectivity phase. On average, 7.54 of the stimuli were face in each session. We added this information to the Methods section. Furthermore, as mentioned in the discussion, for each classification run, the number of samples per category is equalized. We note that we cannot use the selectivity data for analysis, since the SF-related stimuli are filtered in different bands.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

I suggest that the authors double-check their results by performing control experiments with longer stimulus duration and SF-spectrum-matched face and object stimuli.

Thanks for your suggestion, according to your comment, we added Appendix 1 - Figure 3.

In addition, I had a very difficult time understanding the differences between Figure 3c and Figure 4a. Please rewrite the descriptions to clarify.

Thanks for your suggestion, we tried to revise the description of these two figures. The following description is added to the results section for Figure 3c.

“Next, to examine the relation between the SF (category) coding capacity of the single neurons and the category (SF) coding capability of the population level, we calculated the correlation between coding performance at the population level and the coding performance of single neurons within that population (Figure 3 c and d). In other words, we investigated the relation between single and population levels of coding capabilities between SF and category. The SF (or category) coding performance of a sub-population of 20 neurons that have roughly the same single-level coding capability of the category (or SF) is examined.”

Lines 147-148: The text states that 'The maximum accuracy of a single neuron was 19.08% higher than the chance level'. However, in Figure 4, the decoding accuracies of individual neurons for category and SF range were between 49%-90% and 20%-40%, respectively.

Please explain the discrepancies.

The first number is reported according to chance level which is 20%, thus the unnormalized number is 39% which is consistent with the SF accuracy in Figure 4. We added the following description to prevent any misunderstanding.

“… was 19.08\% higher than the chance level (unnormalized accuracy is 49.08\%, neuron \#193, M2).”

Lines 264-265: Should 'the alternative for R3 and R4' be 'the alternative for R4 and R5'?

Thanks for your attention, it's “R4 and R5”. We corrected that mistake.

Lines 551-562: The labels for SF classification are R1-R5. Is it a binary or a multi-classification task?

It’s a multi-label classification. We made it clear in the text.

“… labels were SF bands (R1, R2, ..., R5, multi-label classifier).”

Figure 4b: Neurons in SF/category decoding exhibit both positive and negative weights. However, in the analysis of sparse neuron weights in Equation 6, only the magnitude of the weights is considered. Is the sign of weight considered too?

We used the absolute value of the neuron weight to calculate sparseness. We also corrected Equation 6.

Reviewer #2 (Recommendations For The Authors):

(1) Line 52: what do the authors mean by coordinate processing in object recognition?

To avoid any potential misunderstanding, we used the exact phrase in Saneyoshi and Michimata (2015). It is in fact, coordinate relations processing. Coordinate relations specify the metric information of the relative locations of objects.

(2) About half of the Introduction is a summary of the Results. This can be shortened.

Thanks for your suggestion.

(3) Line 134: Peristimulus time histogram instead of Prestimulus time histogram.

Thanks for your attention. We corrected that.

(4) Line 162: the authors state that R1 is decoded faster than R5, but the reported statistic is only for R1 versus R2.

It was a typo, the p-value is only reported for R1 and R5.

(5) Line 576: which test was used for the asses the statistical significance?

The test is Wilcoxon signed-rank. We added it to the text.

(6) How can one present a 35 ms long stimulus with a 60 Hz frame rate (the stimuli were presented on a 60Hz monitor (line 470))? Please correct.

Thanks for your attention. We corrected that. The time of stimulus presentation is 33ms and the monitor rate is 120Hz.

Author response:

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

These are valuable findings that support a link between low-dimensional brain network organization, patterns of ongoing thought, and trait-level personality factors, making it relevant for researchers in the field of spontaneous cognition, personality, and neuropsychiatry. While this link is not entirely new, the paper brings to bear a rich dataset and a well-conducted study, to approach this question in a novel way. The evidence in support of the findings is convincing.

We thank the reviewers and editors for their time, feedback, and recommendations for improvement. We have revised the manuscript with those recommendations in mind and provide a point-by-point description of the revisions below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors ran an explorative analysis in order to describe how a "tri-partite" brain network model could describe the combination of resting fMRI data and individual characteristics. They utilized previously obtained fMRI data across four scanning runs in 144 individuals. At the end of each run, participants rated their patterns of thinking on 12 statements (short multi-dimensional experience sampling-MDES) using a 0-100% visual analog scale. Also, 71 personality traits were obtained on 21 questionnaires. The authors ran two separate principal component analyses (PCA) to obtain low dimensional summaries of the two individual characteristics (personality traits from questionnaires, and thought patterns from MDES). The dimensionality reduction of the fMRI data was done by means of gradient analysis, which was combined with Neurosynth decoding to visualize the functional axis of the gradients. To test the reliability of thought components across scanning time, intra-class correlation coefficients (ICC) were calculated for the thought patterns, and discriminability indices were calculated for whole gradients. The relationship between individual differences in traits, thoughts, and macro-scale gradients was tested with multivariate regression.

The authors found: a) reliability of thought components across the one hour of scanning, b) Gradient 1 differentiated between visual regions and DMN, Gradient 2 dissociated somatomotor from visual cortices, Gradient 3 differentiated the DMN from the fronto-parietal system, c) the associations between traits/thought patterns and brain gradients revealed significant effects of "introversion" and "specific internal" thought: "Introversion" was associated with variant parcels on the three gradients, with most of parcels belonging to the VAN and then to the DMN; and "Specific internal thought" was associated with variant parcels on the three gradients with most of parcels belonging to the DAN and then the visual. The authors conclude that interactions between attention systems and the DMN are important influences on ongoing thought at rest.

Strengths:

The study's strength lies in its attempt to combine brain activity with individual characteristics using state-of-the-art methodologies.

Weaknesses:

The study protocol in its current form restricts replicability. This is largely due to missing information on the MRI protocol and data preprocessing. The article refers the reader to the work of Mendes et al 2019 which is said to provide this information, but the paper should rather stand alone with all this crucial material mentioned here, as well. Also, effect sizes are provided only for the multiple multivariate regression of the inter-class correlations, which makes it difficult to appreciate the power of the other obtained results. 

Thank you for these comments. We have addressed both issues by adding effect sizes for reported trait and thought related effects within the results table (Table 3, Line 427) and providing more information about the fMRI protocol and preprocessing steps.  (Lines 162- 188)

Reviewer #2 (Public Review):

The authors set out to draw further links between neural patterns observed at "rest" during fMRI, with their related thought content and personality traits. More specifically, they approached this with a "tri-partite network" view in mind, whereby the ventral attention network (VAN), the dorsal attention network (DAN), and the default mode network (DMN) are proposed to play a special role in ongoing conscious thought. They used a gradients approach to determine the low dimensional organisation of these networks. In concert, using PCA they reduced thought patterns captured at four time points during the scan, as well as traits captured from a large battery of questionnaires.

The main findings were that specific thought and trait components were related to variations in the organisation of the tri-partite networks, with respect to cortical gradients.

Strengths of the methods/results: Having a long (1 hr) resting state MRI session, which could be broken down into four separate scanning/sampling components is a strength. Importantly, the authors could show (via intra-class correlation coefficients) the similarity of thoughts and connectivity gradients across the entire session. Not only did this approach increase the richness of the data available to them, it speaks in an interesting way to the stability of these measures. The inclusion of both thought patterns during scanning along with trait-level dispositional factors is most certainly a strength, as many studies will often include either/or of these, rather than trying to reconcile across. Of the two main findings, the finding that detailed self-generated thought was associated with a decoupling of regions of DAN from regions in DMN was particularly compelling, in light of mounting literature from several fields that support this.

Weaknesses of the methods/results: Considering the richness of the thought and personality data, I was a little surprised that only two main findings emerged (i.e., a relationship with trait introversion, and a relationship with the "specific internal" thought pattern). I wondered whether, at least in part and in relation to traits, this might stem from the large and varied set of questionnaires used to discern the traits. These questionnaires mostly comprised personality/mood, but some sampled things that do not fall into that category (e.g., musicality, internet addition, sleep), and some related directly to spontaneous thought properties (e.g., mind wandering, musical imagery). It would be interesting to see what relationships would emerge by being more selective in the traits measured, and in the tools to measure them.

We agree that being more selective in trait measures and measuring tools could lead to more insights into trait – brain relationships. In part the emergence of only two main findings could also be a trade-off of multiple comparison corrections inherent in our current approach (i.e. 400 separate models for all parcels). Furthermore, we have adjusted the text in the discussion in this revision to highlight that more targeted measures of personality (e.g. self-consciousness) could provide a more nuanced view of the relationship between traits and patterns of thought at rest. (Line 532):

“In the future it may also be important to consider measures of traits that could have relationships to both neural activity and or experience at rest (e.g. self-consciousness de Caso et al., 2017, or autistic tendencies, Turnbull et al., 2020a).”  

Taken together, the main findings are interesting enough. However, the real significance of this work, and its impact, lie in the richness of the approach: combing across fMRI, spontaneous thought, and trait-level factors. Triangulating these data has important potential for furthering our understanding of brain-behaviour relationship across different levels of organisation.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Recommendations for improving the writing and presentation.

- Frame the study objectives more clearly. If it's about which theoretical framework best supports the data, you might need to advocate on why the tri-partite approach is a more efficient framework than others. If not, the argument will beg the question: you will find an effect on this model, so you will claim that this is an informative model. For example, if the focus is on these three RSNs and thought reporting, the authors might want to contextualize it historically, like how from two networks (DMN-antagonistic; Vanhaudenhuyse JCognNeurosci 2012; Demertzi et al, NetwNeuroci 2022) we end up to three and why this is a more suitable approach. What about whole-brain connectomic approaches, such as the work by Amico et al? 

We have expanded on the objectives and rationale of the study by editing/ expanding the introduction as follows (Lines 84-87): 

“Traditionally, it was argued that the DMN was thought to have an antagonistic relationship with systems linked to external processing (Fox et al., 2005). However, according to the ‘tri-partite’ network accounts the relationship between the DMN and other brain systems is more nuanced. From this perspective key hubs of the ventral attention network, such as the anterior insula and dorso-lateral prefrontal cortex, help gate access to conscious experience, influencing regardless of the focus of attention. This is hypothesised to occur because the VAN influences interactions between the DAN, which is more important for external mental content (Corbetta and Shulman, 2002), and the DMN which is important when states (including tasks) rely more on internal representations (Smallwood et al., 2021a)..”  (… and Lines 112:125):

“Our current study explored whether this “tri-partite network” view of ongoing conscious thought derived from studies focused on understanding conscious experience, provides a useful organizing framework for understanding the relation between observed brain activity at rest and patterns of cognition/ personality traits. Such analysis is important because at rest there are multiple features of brain activity that can be identified via complex analyses that include regions that show patterns of coactivation (which are traditionally viewed as forming a cohesive network, (Biswal et al., 1995) as well as patterns of anti-correlation with other regions (e.g. Fox et al., 2005). However, it is unclear which of these relationships reflect aspects of cognition or behaviour or are in fact aspects of the functional organization of the cortex (Fox and Raichle, 2007). Consequently, our study builds on foundational work (e.g. Vanhaudenhuyse et al., 2011) in order to better understand which aspects of neural function observed at rest are mostly likely linked to cognition and behaviour. With this aim in mind, we examined links between macro-scale neural activation and both (i) trait descriptions of individuals and (ii) patterns of ongoing thought.”

- As there was no explicit description of the adopted design and the fMRI procedure, I deduced that it was about a within-subject design, 1-hour scanning session, comprised of four runs, each lasting 15 min, can that be correct? In any case, an explicit description of the design and the fMRI procedure eases the reading and replicability. 

Thank you for pointing this out. We have now restructured and edited the text relating to write those details clearly and explain the MDES part of the procedure in the same section. It now reads (Lines 162:167): 

“Resting state fMRI with Multidimensional Experience Sampling (MDES)

The current sample includes one hour of fully pre-processed rs-fMRI data from 144 participants (four scans from 135 participants, and three scans from nine participants whose data were missing or incomplete). The rs-fMRI was performed in four adjacent 15-minute sessions each immediately followed by MDES which retrospectively measured various dimensions of spontaneous thought during the scan.”

- Was there a control to the analysis, such as a gradient which also associated with these characteristics? Anything else?

In our analyses we explore multiple gradients and how they link to traits and thoughts at rest. While there is no explicit control, each analyses provides a constraint on the interpretation of the other analyses. We have added the following text to expand on this point (Line 372): 

“To this end, we performed a multiple multivariate regression with thoughts, traits, and nuisance variables (motion, age and gender) as independent variables, with whole brain functional organisation, as captured by the first three gradients, as dependent variables. In this analytic approach relationships between cognition along one gradient but not along another help identify which relationships between brain systems are mostly likely to relate to the feature of cognition in question (i.e. each gradient acts as a control for the other).”  

- I feel that Table 1 (list of tests) carries less information compared to Supplementary Table 1 (how spontaneous thought was reported and scored). I would suggest swapping them, unless Table 1 further contains which outcome measures per test were used for the analysis.  

Thank you for this suggestion. Table showing the MDES questions has now been moved to the main text (Table 1, Line 194). However, as there is no other description of the questionnaires included in the main text, we have also retained the table listing personality/ trait questionnaires (Table 2, Line 200).

- Ten group-level gradients were calculated out of which three were shown on the basis of previous work. Please, visualize all 10 gradients as complementary material to inform potential future works on how these look.  

Thank you for this suggestion. Supplementary figure 3 now shows all 10 gradients.

- Please provide more information on preprocessing, especially with motion artifacts and how the global signal was processed.  

Thank you for pointing this out. We have now included the following text, summarized from Mendes et al., 2019, to describe the preprocessing in brief (Line 171:188): 

“Motion correction parameters were derived by rigid-body realignment of the timeseries to the first (after discarding the first five volumes) volume with FSL MCFLIRT (Jenkinson et al., 2002). Parameters for distortion correction were calculated by rigidly registering a temporal mean image of this time series to the fieldmap magnitude image using FSL FLIRT (Jenkinson and Smith, 2001) which was then unwarped using FSL FUGUE (Jenkinson et al., 2012). Transformation parameters were derived by coregistering the unwarped temporal mean to the subject’s structural scan using FreeSurfer’s boundary-based registration algorithm (Greve and Fischl, 2009). All three spatial transformations were then combined and applied to each volume of the original time series in a single interpolation step. The time series was residualised against the six motion parameters, their first derivatives, “outliers” identified by Nipype’s rapidart algorithm (https://nipype.readthedocs.io/en/latest/interfaces/ A CompCor (Behzadi et al., 2007) approach was implemented to remove physiological noise from the residual time-series- which included first six principal components from all the voxels identified as white-matter cerebrospinal fluid. The denoised time series were temporally filtered to a frequency range between 0.01 and 0.1 Hz using FSL, mean centered and variance normalized using Nitime (Rokem et al., 2009). Imaging and pre-processing protocols are described in detail in Mendes et al (Mendes et al., 2019).”

- Please, describe the duration of the whole process, and when the questionnaire data were collected.

We apologize for the lack of clarity. “Data” section of the Methods has now been edited to explain this more clearly, it now reads (Line 146:154):

“The dataset used here is part of the MPI-Leipzig Mind-Brain-Body (MPILMBB) database (Mendes et al., 2019). The complete dataset consists of a battery of selfreported personality measures, measures of spontaneous thought, task data, and structural and resting-state functional MRI (one hour, divided into four adjacent 15-min sessions) from participants between 20 and 75 years of age. Data were collected over a period of five days, with the MRI sessions always falling on day 3. The questionnaires were completed by participants before and after this day, using Limesurvey (https://www.limesurvey.org: version 2.00+) at their own convenience and using penand-paper on-site. A detailed description of the participants, measures, and data acquisition protocol has been previously published along with the dataset (Mendes et al., 2019).”

- In light of the discussion about sample sizes and the power of the correlations, can you indicate the effect sizes of the reported results?  

Thank you for pointing this out. Effect sizes have been added to the results table (Table 3, Line 427)

Minor corrections to the text and figures

- Introduction: "Our sample was a cohort....states were explanatory variables": Better move this part to Methods. Ideally, provide the hypotheses here, the ways you wanted to test them, and how you would negate them. What would it mean that you got the hypotheses confirmed? What would the opposite outcome mean? 

We have added the following text before this part to clarify expand on the objective of the study (Lines 112:125): 

“Our current study explored whether this “tri-partite network” view of ongoing conscious thought derived from studies focused on understanding conscious experience, provides a useful organising framework for understanding the relation between observed brain activity at rest and patterns of cognition/ personality traits. Such analysis is important because at rest there are multiple features of brain activity that can be identified via complex analyses that include regions that show patterns of coactivation (which are traditionally viewed as forming a cohesive network, (Biswal et al., 1995) as well as patterns of anti-correlation with other regions (e.g. Fox et al., 2005). However, it is unclear which of these relationships reflect aspects of cognition or behaviour or are in fact aspects of the functional organisation of the cortex (Fox and Raichle, 2007). Consequently, our study builds on foundational work (e.g. Vanhaudenhuyse et al., 2011) in order to better understand which aspects of neural function observed at rest are mostly likely linked to cognition and behaviour. With this aim in mind, we examined links between macro-scale neural activation and both (i) trait descriptions of individuals and (ii) patterns of ongoing thought.”   

We have refrained from listing hypothesis, as the analyses we performed were data driven rather than hypothesis driven to include all possible associations between largescale connectivity patterns and individual state and trail level differences in personality and thought/ experience. We hope that the added text provides more context to understand this rationale.  

- Please, clarify whether "conscious thought" means "reportable. 

Thank you for this suggestion. We have now edited the first reference to thought patterns in the discussions to read “self-reports of ongoing thought”, instead of just “ongoing thought” (Line 432)

- Please, clarify whether "experience" and "thought" are used interchangeably. This is because experience can also be ineffable, beyond thought reporting. 

To clarify this in the context of the current study, we have edited first reference to “ongoing experience” in the introduction to “self-reports of ongoing experience”. (Line 75)

- To ease reading comprehension for each Figure, communicate the main findings first, before describing the figures. 

We believe this lack of clarity is caused by including the figure reference in the heading of the results subsections. We hope this issue is fixed by editing the text in the following manner (Line 381):

“Trait Introversion 

Along the first gradient, a parcel within the right orbitofrontal cortex (within the executive control network, shown in orange) showed more similarity with transmodal regions for individuals high on introversion. Six parcels within the ventral attention network, including anterior insula, operculum and cingulate cortex were closer to the somatomotor end along gradient two (shown in purple). The same regions showed lower scores along the third gradient in participants with higher introversion scores, indicating stronger integration with the default mode network. A parcel within posterior cingulate cortex (control) was also more segregated from the visual end of gradient two in participants with higher introversion scores. Associations between trait “introversion” and brain-wide activity are shown in Figure 4.”

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In "Prediction error determines how memories are organized in the brain: a study of Pavlovian fear 2 extinction in rats", Kennedy et al examine how new information is organized in memory. They tested an idea based on latent theory that suggests that a large prediction error leads to the formation of a new memory, whereas a small prediction error leads to memory updating. They directly tested the prediction by extinguishing fear-conditioned rats with gradual extinction. For their experiment, gradual extinction was carried out by progressively reducing the intensity of shocks that were co-terminated with the CS, until the CS was presented alone. Doing so resulted in diminished spontaneous recovery and reinstatement compared to Standard Extinction. The results are compelling, and have important implications for the field of fear learning and memory as well as translation to anxiety-related disorders.

The authors carried out the Spontaneous Recovery experiment in 2 separate experiments. In one, they found differences between the Gradual and Standard Extinction groups, but in the second, they did not. It seems that their reinstatement test was more robust, and showed significant differences between the Gradual and Standard Extinction groups.

The authors carried out important controls that enable proper contextualization of the findings. They included a "Home" group, in which rats received fear conditioning, but not extinction manipulation. Relative to this group, the Gradual and Standard extinction groups showed a reduction in freezing.

In Experiments 3 and 4, the authors essentially carried out clever controls that served to examine whether shock devaluation (Experiment 4) and reduction in shock intensity (rather than a gradual decrease in shock intensity) (Experiment 3) would also yield a decrease in the return of fear. In line with a latent-cause updating explanation for accounting for the Gradual Extinction, they did not.

In Experiment 5, the authors examined whether a prediction error produced by a change of context might contribute interference to the latent cause updating afforded by the Gradual Extinction. Such a prediction would align with a more flexible interpretation of a latent-cause model, such as those proposed by Redish (2007) and Gershman et al (2017), but not the latent-cause interpretation put forth by the Cochran-Cisler model (2019). Their findings showed that whereas Gradual Extinction carried out in the same context as acquisition resulted in less return of fear than Standard Extinction, it actually yielded a greater degree of return of fear when carried out in a different context, in support of the Redish and Gershman accounts, but not Cochran-Cisler.

Experiment 6 extended the findings from Experiment 5 in a different state-splitting modality: timing. In this experiment, the authors tested whether a shift in temporal context also influenced the gradual extinction effect. They thus carried out the extinction sessions 21 days after conditioning. They found that while Gradual Extinction was indeed effective when carried out one day after fear conditioning, it did not when conducted 21 days later.

The authors next carried out an omnibus analysis which included all the data from their 6 experiments, and found that overall, Gradual Extinction resulted in diminished return of fear relative to Standard Extinction. I thought the omnibus analysis was a great idea and an appropriate way to do their data justice.

Strengths:

Compelling findings. The data support the conclusions. 6 rigorous experiments were conducted which included clever controls. Data include male and female rats. I really liked the omnibus analysis.

We thank the reviewer for their positive comments – they are appreciated.

Weaknesses:

None noted

Reviewer #2 (Public Review):

Summary:

The present article describes a series of experiments examining how a gradual reduction in unconditional stimulus intensity facilitates fear reduction and reduces relapse (spontaneous recovery and reinstatement) relative to a standard extinction procedure. The experiments provide compelling, if somewhat inconsistent, evidence of this effect and couch the results in a scholarly discussion surrounding how mechanisms of prediction error contribute to this effect.

Strengths:

The experiments are theoretically motivated and hypothesis-driven, well-designed, and appropriately conducted and analyzed. The results are clear and appropriately contextualized into the broader relevant literature. Further, the results are compelling and ask fundamental questions regarding how to persistently weaken fear behavior, which has both strong theoretical and real-world implications. I found the 'scrambled' experiment especially important in determining the mechanism through which this reduction in shock intensity persistently weakens fear behavior.

We thank the reviewer for their positive comments – they are appreciated.

Weaknesses:

Overall, I found very few weaknesses in this paper. I think some might view the somewhat inconsistent effects on relapse between experiments to be a substantial weakness, I appreciate the authors directly confronting this and using it as an opportunity to aggregate data to look at general trends. Further, while Experiment 1 only used males, this was corrected in the rest of the experiments and therefore is not a substantial concern.

Reviewer #3 (Public Review):

Summary:

The manuscript examined the role of large versus small prediction errors (PEs) in creating a state-based memory distinction between acquisition and extinction. The premise of the paper is based on theoretical claims and empirical findings that gradual changes between acquisition and extinction would lead to the potential overwriting of the acquisition memory with extinction, resulting in a more durable reduction in conditioned responding (i.e. more durable extinction effect). The paper tests the hypotheses in a series of elegant experiments in which the shock intensity is decreased across extinction sessions before non-reinforced CS presentations are given. Additional manipulations include context change, shock devaluation, and controlling for lower shock intensity exposure. The critical comparison was standard non-reinforced extinction training. The critical tests were done in spontaneous recovery and reinstatement.

Strengths:

The findings are of tremendous importance in understanding how memories can be updated and reveal a well-defined role of PE in this process. It is well-established that PE is critical for learning, so delineating how PE is critical for generating memory states and the role it serves in keeping memories dissociable (or not) is exciting and clever. As such the paper addresses a fundamental question in the field.

The studies test clear and defined predictions derived from simulations of the state-belief model of Cochran & Cisler (2019). The designs are excellent: well-controlled and address the question.

The authors have done an excellent job of explaining the value of the latent state models.

The authors have studied both sexes in the study presented, providing generality across the sexes in their findings. However, depicting the individual data points in the bar graphs and noting which data represent males and which represent females would be of great value.

We thank the reviewer for their positive comments. We have included individual data points in the bar graphs and indicated which represent males and females.

Weaknesses:

(1) While it seems obvious that delivering a lower intensity shock will generate a smaller PE than say no shock, it would have been nice to see data from say a compound testing procedure that confirms this.

It would be great if we could provide independent evidence that shifting from a 0.8 mA shock to a 0.4 mA shock (first session of gradual extinction) produces a smaller prediction error than shifting from a 0.8 mA shock to no shock at all (first session of standard extinction). In theory, this could be assessed using Rescorla’s (2000) compound test procedure. However, application of this procedure requires the use of a within-subject design and latent state theories would not predict the gradual extinction effect in such a design (as all prediction errors generated in such a design would affect the state-splitting process). That is, the between-subject design used to generate the gradual extinction effect is not amenable to application of the compound test procedure; and the within-subject design in which the compound test procedure could be applied is unlikely to generate the gradual extinction effect. Thus, we instead rely on the high degree of similarity between our results and those predicted by Cochran & Cisler (2019) to argue that the gradual extinction protocol produces a series of smaller prediction errors than does the standard extinction protocol: hence the present pattern of results.

(2) The devaluation experiment is quite clever, but it also would be strengthened if there was evidence in the paper that this procedure does indeed lead to shock devaluation.

The aim of Experiment 3 was to determine whether the gradual extinction effect is due to prediction error-based memory updating or shock devaluation. If the effect was due to shock devaluation, the group that received the gradual extinction treatment should have displayed the same low level of spontaneous recovery as the group that only experienced the shock at its lowest (0.1 mA) intensity (i.e., the shock devaluation group). Contrary to this prediction, the results showed that the gradually extinguished group displayed less spontaneous recovery than the shock devaluation group. That is, in this experiment, the slow and progressive reduction in shock intensity was processed differently to the repeated 0.1 mA shock exposures but the results were inconsistent with any shock devaluation effect. Hence, we conclude that the gradual extinction effect does not involve shock devaluation but instead is due to prediction error-based memory updating.

(3) It would have been very exciting to see even more parametric examinations of this idea, like maintaining shock intensity but gradually reducing shock duration, which would have increased the impact of the paper.

We appreciate the reviewer’s point. As each shock was presented for just 0.5 s, we are not confident that rats would detect gradual and progressive changes in its duration in the same way as they can obviously detect gradual and progressive changes in its intensity. We are, however, investigating the effects of gradual extinction in a second order conditioning protocol, which will allow us to examine the full range of parameters that are important for its regulation, including manipulations of stimulus duration. In our second-order conditioning protocol, rats are first exposed to pairings of a 10 s S1 and a 0.5 s foot shock US; and then exposed to pairings of a 30 s S2 and the 10 s S1. Across the latter pairings, rats acquire second-order conditioned fear responses to S2. Importantly, these responses can be extinguished through repeated presentations of the S2 in the absence of its S1-associate; and the duration of the S1 can be progressively and gradually reduced from 10 s to 0 s across the shift to this extinction. These experiments are currently in progress and will eventually represent an extension of the present findings.

(4) Individual data points should be represented in the test figures (see above also).

We have updated the figures to show these data points.

Rescorla, R. A. (2000). Associative changes in excitors and inhibitors differ when they are conditioned in compound. Journal of Experimental Psychology: Animal Behavior Processes, 26(4), 428.

Reviewing Editor (Recommendations For The Authors):

The eLife assessment relates to the present form of the paper. However, following a discussion with the reviewers, the significance of the findings could be bolstered to fundamental if you decided to revise the current manuscript by scaling up the investigation to examine a wider set of parameters and conditions under which error can influence state allocation of memories. One way of doing this, but not limited to this, is suggested in the reviews (e.g. maintaining shock intensity, reducing its duration). Relatedly, a more extensive discussion of the Gershamn et al. (2013) paper would be relevant.

As noted in our response to Reviewer 3, we are currently investigating the effects of gradual extinction in a second order conditioning protocol, which will allow us to examine the full range of parameters that are important for its regulation, including manipulations of stimulus duration. These experiments are in-progress and will eventually represent an extension of the present findings. They are not, however, ready to be included as part of the present study.

We have further referenced the Gershman et al., (2013) paper as well as the related Bouton et al., (2004) paper on the effects of gradually reducing the frequency of the US across extinction. This appears in the fifth paragraph of the Discussion: “The present study adds to a growing body of evidence that manipulations applied across the shift from CS-US pairings to presentations of the CS alone can influence the effectiveness of extinction. For example, Gershman et al., (2013) and Bouton et al., (2004) showed that gradually reducing the proportion of reinforced CS presentations results in less spontaneous recovery and slower reacquisition, respectively; though both studies left open fundamental questions about the basis of their findings (see also Woods & Bouton, 2007).”

Reviewer #1 (Recommendations For The Authors):

I don't have any strong recommendations. I think the paper is really great as is.

One minor suggestion to consider:

The authors carried out the Spontaneous Recovery experiment in 2 separate experiments. In one, they found differences between the Gradual and Standard Extinction groups, but in the second, they did not. This is perhaps not entirely surprising, since their extinction test was conducted 2 weeks post-extinction, and not all rats show spontaneous recovery within that timeframe. The authors mention that the lack of SR might be due to the low level of freezing reported in their test, but since they are showing group mean data, they might consider showing the individual data points to showcase the range of SR freezing as an additional way to make sense of the variability (ie, maybe a few rats that showed very low freezing carried the mean down in the Standard Extinction group, while others showed return of fear).

We agree and have included individual data points for test results in Figures 2D, 2F, 3D, 3H, 4D and 4H. Hence, these figures now reflect both group and individual freezing levels.

Reviewer #2 (Recommendations For The Authors):

Overall, I thought this was an exceptional paper. Aside from the comments listed above which I'm not sure are inherently addressable, the only real changes I would like to see are that individual data points should be depicted in the main testing figures, as is becoming more conventional in the field.

We thank the reviewer for their positive comments. As indicated in our response to the other reviewers, we have added individual data points to the histograms showing test results.

Reviewer #3 (Recommendations For The Authors):

Figures

(1) The test data are presented as bars, but I did wonder if there were differences between the groups from the start of testing or if those emerged across testing (SR vs extinction savings).

We have added two new figures to the supplementary section, Figures 8 and 9. These display the trial-by-trial data from spontaneous recovery and reinstatements tests in each experiment. The data clearly show that the between-group differences in freezing were very stable across the test sessions.

(2) While I understand the importance of presenting the last extinction session, I felt depicting the entire CS session would be more informative. Alternatively, removing this altogether and leaving the information from the extinction session in the supplemental would focus the reader on the key test data.

We appreciate the reviewer’s point. It is important to show that the groups displayed equivalent freezing in the final extinction session prior to testing. Given that the test data are conveniently and best presented in a histogram, we have chosen to present the data from the final extinction session in the same way. The full, trial-by-trial trajectory of freezing across conditioning and extinction, as well as the analyses of these data, are presented in the supplementary A.

(3) I did not find the figures to be very aesthetically pleasing (in part because some panels were unnecessarily large). For example, I found it rather odd that the simulation panels were split in Figure 1. One suggestion of how this figure could look better is to keep the size of panels B, C, and D the same and align them on the same row with the design figure above them. The other option is to have the design figure above the test figure and the two simulation figures above each other and next to the design and test. Also, there are grey lines that appear around the simulation figures on my PDF.

We have updated the figures so that they are consistent across experiments and more aesthetically pleasing. Specifically, we have consistently: 1) inserted the simulations of Cochran & Cisler (2019) next to the design schematic; 2) inserted the extinction and test data beneath the design schematic; and 3) Made the sizing of figures more uniform across Experiments 1-6.

Author response:

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

eLife assessment 

This study presents valuable findings as it shows that sleep rhythm formation and memory capabilities depend on a balanced and rich diet in fly larvae. The evidence supporting the claims of the authors is convincing with rigorous behavioral assays and state-of-the-art genetic manipulations. The work will be of interest to researchers working on sleep and memory. 

Public Reviews: 

Summary: 

This manuscript investigates how energetic demands affect the sleep-wake cycle in Drosophila larvae. L2 stage larvae do not show sleep rhythm and long-term memory (LTM), however, L3 larvae do. The authors manipulate food content to provide insufficient nutrition, which leads to more feeding, no LTM, and no sleep even in older larvae. Similarly, activation of NPF neurons suppresses sleep rhythm. Furthermore, they try to induce a sleep-like state using pharmacology or genetic manipulations in L2 larvae, which can mimic some of the L3 behaviours. A key experimental finding is that activation of DN1a neurons activate the downstream DH44 neurons, as assayed by GCaMP calcium imaging. This occurs only in third instar and not in second instar, in keeping with the development of sleep-wake and feeding separation. The authors also show that glucose metabolic genes are required in Dh44 neurons to develop sleep rhythm and that DH44 neurons respond differently in malnutrition or younger larvae. 

Strengths: 

Previous studies from the same lab have shown the sleep is required for LTM formation in the larvae, and that this requires DN1a and DH44 neurons. The current work builds upon this observation and addresses in more detail when and how this might develop. The authors can show that low quality food exposure and enhanced feeding during larval stage of Drosophila affects the formation of sleep rhythm and long-term memory. This suggests that the development of sleep and LTM are only possible under well fed and balanced nutrition in fly larvae. Non-sleep larvae were fed in low sugar conditions and indeed, the authors also find glucose metabolic genes to be required for a proper sleep rhythm. The paper presents precise genetic manipulations of individual classes of neurons in fly larvae followed by careful behavioural analysis. The authors also combine thermogenetic or peptide bath application experiments with direct calcium imaging of specific neurons. 

Weaknesses: 

The authors tried to induce sleep in younger L2 larvae, however the behavioral results suggest that they were not able to induce proper sleep behaviour as in normal L3 larvae. Thus, they cannot show that sleep during L2 stage would be sufficient to form LTM. 

We agree that the experiments with Gaboxadol feeding in L2 did not perfectly mimic L3 sleep behaviors. However, genetic induction of sleep in L2 was effective in increasing sleep duration and depth similar to that observed in normal L3. As noted below in response to specific reviewer comments, because gaboxadol feeding is standard in the field for adult sleep induction, we prefer to still include this data in the manuscript for transparency. Moreover, the gaboxadol manipulation did cause a significant decrease in arousal threshold compared to control larvae. Together these approaches support the hypothesis that sleeping more/more deeply is not sufficient to promote LTM in L2.

The authors suggest that larval Dh44 neurons may integrate "information about the nutritional environment through the direct sensing of glucose levels to modulate sleep-wake rhythm development". They identify glucose metabolism genes (e.g., Glut1) in the downstream DH44 neurons as being required for the organization of the sleep-wake-feeding rhythm, and that CCHa signaling in DN1a signaling to the DH44 cells via the receptor. However, how this is connected is not well explained. Do the authors think that the nutrient sensing is only occurring in the DH44 neurons and not in DN1a or other neurons? Would not knocking down glucose metabolism in any neuron lead to a functional defect? What is the evidence that Dh44 neurons are specific sensors of nutritional state? For example, do the authors think that e.g. the overexpression of Glut1 in Dh44 neurons, a manipulation that can increase transport of glucose into cells, would rescue the effects of low-sugar food? 

We thank the reviewer for these suggestions and have added the experiment proposed. We found that knockdown of Hex-C in DN1a neurons did not disrupt sleep-wake rhythms (Fig. S4G-I) suggesting that Dh44 neurons are specialized in requiring glucose metabolism to drive sleep-wake rhythms. We have also added further clarification in the text regarding the existing evidence that Dh44 neurons act has nutrient sensors.

Some of the genetic controls seem to be inconsistent suggesting some genetic background effects. In Figure 2B, npf-gal4 flies without the UAS show no significant circadian change in sleep duration, whereas UAS-TrpA flies do. The genetic control data in Figure 2D are also inconsistent. Npf-Gal4 seems to have some effect by itself without the UAS. The same is not seen with R76G11-Gal4. Suppl Fig 2: Naïve OCT and AM preference in L3 expressing various combinations of the transgenes show significant differences. npf-Gal4 alone seems to influence preference. 

The sleep duration and bout number/length data are highly variable. 

All experiments are performed in isogenized background so variability seen in genetic controls likely reflects stochastic nature of behavioral experiments. Indeed, adult sleep data also shows a great deal of variability within the same genetic background (PMID: 29228366). We agree it is an important point, and we attempt to minimize variability as much as possible with backcrossing of flies and tight control of environmental conditions.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Low sugar exposure and activation of NPF neurons might not induce the same behavioral changes. LS exposure does not enhance mouth hook movements, but overall food intake. NPF activation seems to enhance mouth hook movements, but the data for food intake is not shown. This information would be necessary to compare the two different manipulations. 

We thank the reviewer for this suggestion. However, we elected not to perform food intake experiments with the NPF activation experiments. Since we are not directly comparing the low sugar and NPF manipulations to each other, we think that both experiments together support the conclusion that immature food acquisition strategies (whether food intake or feeding rate) limit LTM performance. 

The authors write that the larval feeding assays run for 4 hours, can they explain why that long? Larvae should already have processed food within 4 hours, so that the measurement would not include all eaten food.

We clarified the rationale for doing 4 hour feeding assays in the results section. We did 4 hours on blue dyed food because initial experiments of 1 hour with control L3 at CT1-4 were difficult to interpret. The measurement does not include all of the eaten food in the 4 hours but does reflect more long-term changes in food intake.

Sleep induction with Gaboxadol seems to not really work - sleep duration, bout number and length are not enhanced, and arousal threshold is only slightly lower. Thus, the authors should not use this data as an example for inducing sleep behaviour. 

We agree this approach did not have a large effect in larvae. However, because gaboxadol feeding is standard in the field for adult sleep induction, we prefer to still include this data in the manuscript for transparency. Moreover, the Gaboxadol manipulation did cause a mild (but significant) decrease in arousal threshold compared to control larvae. Gaboxadol feeding also caused a significant decrease in total body weight compared to control larvae indicating that even slightly deeper sleep could be detrimental to younger animals.

Activation of R76G11 with TrpA1 seems to work better for inducing sleep like behaviour. However, the authors describe that they permanently activated neurons. To induce a "normal" sleep pattern, the authors might try to only activate these neurons during the normal enhanced sleep time in L3 (CT13?) and not during the whole day. This might also allow larvae to eat during day time and gain more weight. 

We apologize that this point was not clearer, but we did do acute activation of R76G11(+) neurons, as proposed by the reviewer. We have clarified the text to make this point.

It would be interesting to see how larvae fed with high sucrose and low protein diet would behave in this assay. Do the authors suggest that sugar is most important for the development of sleep behaviour or that it is a combination of sugar and protein that might be required? 

We agree that feeding larvae a high sucrose and low protein diet would be interesting. However, we initially tried a low protein diet and observed significant developmental delays. Therefore, we are concerned that developmental defects on a high sucrose and low protein diet would confound behavioral results. Additionally, the Dh44 manipulations (glucose & GCN2 signaling) suggest that sugar is the most important for the development of sleep behaviors.

Reviewer #3 (Recommendations For The Authors): 

The authors could discuss if the interaction between DN1a clock neurons and Dh44 neurons is mediated synaptic or by volume transmission following the extracellular release of the CCHa1 neuropeptide. They write that "the development of Dh44 neuronal competency to receive clock-driven cues" and that "DN1a clock neurons anatomically and functionally connect to Dh44" but a discussion about volume vs. synaptic signalling would be of interest. 

We thank the reviewer for this suggestion. We revised the discussion to address this point.

line 223 " demonstrating that post-synaptic processes likely". It would be interesting to read a discussion on whether it is known if these are postsynaptic or peptide-mediated volume effects? 

We added additional text to the discussion to address these points.

- The authors may want to include a schematic of the circuit and how its position in the general anatomy of the fly larva. 

We thank the reviewer for this suggestion. We have added a model figure to Fig. S6.

"Dh44 neurons act through glucose metabolic genes" - consider rewording e.g. require glucose metabolic genes 

We revised the text.

- line 45 "Early in development, young animals must obtain enough nutrients to ensure proper growth" - this is too general, many animals do not feed in early life-cycle stages (e.g. lecitotrophic development), consider rewording 

We revised the text to be more specific.

- line 90 "however, L3 at CT1 consume more than L3 at CT12 (Figure S1A)" - typo CT13, also consider rewording to match the structure of the sentence before 'however, L3 consumed more at CT1 than at CT13' 

We revised the text to fix this error.

- Line 111 "and loss of deep sleep" - how is deep sleep defined and measured in the larvae? It is not clear from the data or the text. 

We revised the text to define deep sleep in the results section. We also have a description of how arousal threshold is calculated in the methods.

- In Figure 3B and G the individual data points are not shown 

We did not show individual data points for those graphs because we are plotting the average percentage of 4 biological replicates.

Typo: 

Figure 1 legend "F, n= n=100-172 " 

We revised the text to fix this typo.

Author response:

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The manuscript by Hussain and collaborators aims at deciphering the microtubule-dependent ribbon formation in zebrafish hair cells. By using confocal imaging, pharmacology tools, and zebrafish mutants, the group of Katie Kindt convincingly demonstrated that ribbon, the organelle that concentrates glutamate-filled vesicles at the hair cell synapse, originates from the fusion of precursors that move along the microtubule network. This study goes hand in hand with a complementary paper (Voorn et al.) showing similar results in mouse hair cells. 

Strengths: 

This study clearly tracked the dynamics of the microtubules, and those of the microtubule-associated ribbons and demonstrated fusion ribbon events. In addition, the authors have identified the critical role of kinesin Kif1aa in the fusion events. The results are compelling and the images and movies are magnificent. 

Weaknesses: 

The lack of functional data regarding the role of Kif1aa. Although it is difficult to probe and interpret the behavior of zebrafish after nocodazole treatment, I wonder whether deletion of kif1aa in hair cells may result in a functional deficit that could be easily tested in zebrafish? 

We have examined functional deficits in kif1aa mutants in another paper David et al. 2024. In Submission, preprint available:  

https://www.biorxiv.org/content/10.1101/2024.05.20.595037v1

In addition to playing a role in ribbon fusions, Kif1aa is also responsible for enriching glutamate-filled secretory vesicles at the presynaptic active zone. In kif1aa mutants (and crispants), vesicles are no longer localized to the hair cell base, and there is a reduction in the number of vesicles associated with presynaptic ribbons. Kif1aa mutants also have functional defects including reductions in spontaneous vesicle release and evoked postsynaptic calcium responses. Behaviorally, kif1aa mutants exhibit impaired rheotaxis, indicating defects in the lateral-line system and an inability to accurately detect water flow.  Since our paper focuses on microtubule-associated ribbon movement and dynamics early in hair cell development, we have only discussed the effects of Kif1aa directly related to ribbon dynamics during this time window in this paper. In our revision, we will reference this recently submitted work.

Impact: 

The synaptogenesis in the auditory sensory cell remains still elusive. Here, this study indicates that the formation of the synaptic organelle is a dynamic process involving the fusion of presynaptic elements. This study will undoubtedly boost a new line of research aimed at identifying the specific molecular determinants that target ribbon precursors to the synapse and govern the fusion process. 

Reviewer #2 (Public Review): 

Summary:

In this manuscript, the authors set out to resolve a long-standing mystery in the field of sensory biology - how large, presynaptic bodies called "ribbon synapses" migrate to the basolateral end of hair cells. The ribbon synapse is found in sensory hair cells and photoreceptors, and is a critical structural feature of a readily-releasable pool of glutamate that excites postsynaptic afferent neurons. For decades, we have known these structures exist, but the mechanisms that control how ribbon synapses coalesce at the bottom of hair cells are not well understood. The authors addressed this question by leveraging the highly-tractable zebrafish lateral line neuromast, which exhibits a small number of visible hair cells, easily observed in time-lapse imaging. The approach combined genetics, pharmacological manipulations, high-resolution imaging, and careful quantifications. The manuscript commences with a developmental time course of ribbon synapse development, characterizing both immature and mature ribbon bodies (defined by position in the hair cell, apical vs. basal). Next, the authors show convincing (and frankly mesmerizing) imaging data of plus end-directed microtubule trafficking toward the basal end of the hair cells, and data highlighting the directed motion of ribbon bodies. The authors then use a series of pharmacological and genetic manipulations showing the role of microtubule stability and one particular kinesin (Kif1aa) in the transport and fusion of ribbon bodies, which is presumably a prerequisite for hair cell synaptic transmission. The data suggest that microtubules and their stability are necessary for normal numbers of mature ribbons and that Kif1aa is likely required for fusion events associated with ribbon maturation. Overall, the data provide a new and interesting story on ribbon synapse dynamics. 

Strengths: 

(1) The manuscript offers a comprehensive Introduction and Discussion sections that will inform generalists and specialists. 

(2) The use of Airyscan imaging in living samples to view and measure microtubule and ribbon dynamics in vivo represents a strength. With rigorous quantification and thoughtful analyses, the authors generate datasets often only obtained in cultured cells or more diminutive animal models (e.g., C. elegans). 

(3) The number of biological replicates and the statistical analyses are strong. The combination of pharmacology and genetic manipulations also represents strong rigor. 

(4) One of the most important strengths is that the manuscript and data spur on other questions - namely, do (or how do) ribbon bodies attach to Kinesin proteins? Also, and as noted in the Discussion, do hair cell activity and subsequent intracellular calcium rises facilitate ribbon transport/fusion? 

These are important strengths and we do plan to investigate adaptors and how hair cell activity impacts ribbon fusion and transport in the future!

Weaknesses: 

(1) Neither the data or the Discussion address a direct or indirect link between Kinesins and ribbon bodies. Showing Kif1aa protein in proximity to the ribbon bodies would add strength.

This is a great point, and we are working to create a transgenic line with fluorescently labelled Kif1aa to directly visualize its association with ribbons. At present, we have not obtained a transgenic line, and localization of Kif1aa and ribbons in live hair cells it is beyond the scope of this paper. In our revision we will discuss this caveat.

(2) Neither the data or Discussion address the functional consequences of loss of Kif1aa or ribbon transport. Presumably, both manipulations would reduce afferent excitation.

Excellent point. Please see the response above to Reviewer #1 weaknesses.  

(3) It is unknown whether the drug treatments or genetic manipulations are specific to hair cells, so we can't know for certain whether any phenotypic defects are secondary. 

This is correct and is a caveat of our Kif1aa and drug experiments. However, to mitigate this in the pharmacological experiments, we have done the drug treatments at 3 different timescales: long-term (overnight), short-term (4 hr) and fast (30 min) treatments. The faster experiment done after 30 min drug treatment is where we observe reduced directional motion and fusions. This later experiment should not be affected by any long-term changes or developmental defects that could be caused by the drugs as hair cell development occurs over 8-12 hrs. However, we acknowledge that these treatments and genetic experiments could have secondary phenotypic defects that are not hair-cell specific. In our revision, we will discuss these issues.

Reviewer #3 (Public Review): 

Summary: 

The manuscript uses live imaging to study the role of microtubules in the movement of ribeye aggregates in neuromast hair cells in zebrafish. The main findings are that 

(1) Ribeye aggregates, assumed to be ribbon precursors, move in a directed motion toward the active zone; 

(2) Disruption of microtubules and kif1aa increases the number of ribeye aggregates and decreases the number of mature synapses. 

The evidence for point 2 is compelling, while the evidence for point 1 is less convincing. In particular, the directed motion conclusion is dependent upon fitting of mean squared displacement that can be prone to error and variance to do stochasticity, which is not accounted for in the analysis. Only a small subset of the aggregates meet this criteria and one wonders whether the focus on this subset misses the bigger picture of what is happening with the majority of spots. 

Strengths: 

(1) The effects of Kif1aa removal and nocodozole on ribbon precursor number and size are convincing and novel. 

(2) The live imaging of Ribeye aggregate dynamics provides interesting insight into ribbon formation. The movies showing the fusion of ribeye spots are convincing and the demonstrated effects of nocodozole and kif1aa removal on the frequency of these events is novel. 

(3) The effect of nocodozole and kif1aa removal on precursor fusion is novel and interesting. 

(4) The quality of the data is extremely high and the results are interesting. 

Weaknesses: 

(1) To image ribeye aggregates, the investigators overexpressed Ribeye-a TAGRFP under the control of a MyoVI promoter. While it is understandable why they chose to do the experiments this way, expression is not under the same transcriptional regulation as the native protein, and some caution is warranted in drawing some conclusions. For example, the reduction in the number of puncta with maturity may partially reflect the regulation of the MyoVI promoter with hair cell maturity. Similarly, it is unknown whether overexpression has the potential to saturate binding sites (for example motors), which could influence mobility. 

We agree that overexpression in transgenic lines is a common issue and would have loved to do these experiments with endogenously expressed fluorescent proteins under a native promoter. However, this was not technically possible for us. We originally characterized several transgenic Ribeye lines in the past to ensure they have normal ribbon numbers and size (myo6b:ribb-mcherry, myo6b:riba-tagRFP and myo6b:riba-GFP) - in 2014. Unfortunately, we no longer have the raw data from this analysis. In our revision, we will repeat our immunolabel on myo6b:riba-tagRFP transgenic fish and examine ribbon numbers and size and show what impact (or not) exogenous Ribeye expression has on ribbon formation.

(2) The examples of punctae colocalizing with microtubules look clear (Figures 1 F-G), but the presentation is anecdotal. It would be better and more informative, if quantified. 

We attempted a co-localization study between microtubules and ribbons but decided not to move forward with it due to several issues:

(1)  Hair cells have an extremely crowded environment, especially since the nucleus occupies the majority of the cell. All proteins are pushed together in the small space surrounding the nucleus and hence co-localization is not meaningful because the distances are so small.

(2) We also attempted to segment microtubules in these images and quantify how many ribbons were associated with microtubules, but 3D microtubule segmentation was not accurate in these hair cells due to highly varying filament intensities, and diffuse cytoplasmic tubulin signal.

Therefore, we decided that a better measure of ribbon-microtubule association would be a demonstration that individual ribbons keep their association with microtubules over time (in our time lapses), rather than a co-localization study. We see that ribbons localize to microtubules in all our timelapses, including the examples shown. We observed that if a ribbon dissociates, it is just to switch from one filament to another. We have not observed free-floating ribbons in our study.

(3) It appears that any directed transport may be rare. Simply having an alpha >1 is not sufficient to declare movement to be directed (motor-driven transport typically has an alpha approaching 2). Due to the randomness of a random walk and errors in fits in imperfect data will yield some spread in movement driven by Brownian motion. Many of the tracks in Figure 3H look as though they might be reasonably fit by a straight line (i.e. alpha = 1). 

As we have stated in the paper, we only see a small subset of the ribbon precursors moving directionally. The majority of the ribbons are stationary. We cannot say for sure what is happening with the stationary ribbons, but our hypothesis is that these ribbons eventually exhibit directed motion. This idea is supported by the fact that we have seen ribbons that are stationary begin movement, and ribbons that are moving come to a stop during the acquisition of our timelapses. The ribbons that are stationary may not have enough motors attached, or they may be in a sort of ‘seeding’ phase where the ribeye protein could be condensing on the ribbon. We have discussed the possibility of ribbons being biomolecular condensates in our Discussion.

In our revision we will discuss why ribbon transport does not resemble typical motor-driven transport (also see response to point 4 below). We will also reexamine our MSD data in more detail as suggested by Reviewer 3 and provide distributions of alpha values in our revision.

(4) The "directed motion" shown here does not really resemble motor-driven transport observed in other systems (axonal transport, for example) even in the subset that has been picked out as examples here. While the role of microtubules and kif1aa in synapse maturation is strong, it seems likely that this role may be something non-canonical (which would be interesting). 

One major difference between axonal and ribbon transport is that microtubules are very stable and linear in axonal transport. Therefore, the directed motion observed is ‘canonical’. In hair cells, the microtubules are extremely dynamic, especially towards the hair cell base. Within a single time frame (60-100 s), we see the network changing (moving and branching). This dynamic network adds another layer of complexity onto the motion of the ribbon, as the filament track itself is changing. Therefore, we see a lot of stalling, filament switching, and reversals of ribbon movement in our movies. However, we have demonstrated in our movies as well as using MSD analysis, that a subset of ribbons exhibit directional motion. In our revision we will discuss why directed motion in hair cells does not resemble canonical motor-driven transport in axons.

(5) The effect of acute treatment with nocodozole on microtubules in movie 7 and Figure 6 is not obvious to me and it is clear that whatever effect it has on microtubules is incomplete. 

When using Nocodazole, it is important to optimize the concentration of the drug such that there is minimal cytotoxicity, while still being effective. Microtubules in the apical region of hair cells are very stable and do not respond well to Nocodazole treatment at concentrations that are tolerable to hair cells. While a few stable filaments remain largely at the cell apex, there are almost no filaments at the hair cell base, which is different from the wild-type hair cells. In addition, Nocodazole-treated hair cells have more cytoplasmic YFP-tubulin signal compared to wild type. We will add additional images and quantification in our revision to illustrate these points.

Author response:

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

Reviewer #1 (Public Review):

The model presented by the authors is consistent with the data described. Further testing of this model, for example by mutating the deep cholesterol binding site, would strengthen the model. However, such experiments might be challenging due to the relatively non-specific/hydrophobic nature of the deep cholesterol binding site.

We completely agree that testing of the deep cholesterol-binding site by mutagenesis would be ideal. However, as the reviewer points out, such experiments would be challenging, not only because of the non-specific/hydrophobic nature of the deep cholesterol-binding site but also because we have been purifying AQP0 from natural sources (sheep eyes) and because it would be very difficult to secure the substantial amount of cryo-EM time needed to generate an electron crystallographic structure.

Reviewer #2 (Public Review):

The authors report that the findings generally apply to raft formation in membranes. However, this point is less clear as the lens membrane in which AQP0 resides is rather unique in lipid and protein content and density.

We agree that the lens membrane is quite unique in its lipid and protein content and density, but rafts are also characterized by the same lipids and high protein density. Nonetheless, we do agree that our suggested implications for lipid rafts are speculative and so we emphasize this more in the revised version of the manuscript by writing: “This model is specific for the formation of AQP0 arrays in lens membranes, but we speculate that similar principles may underlie the organization of lipid rafts”.

Reviewer #3 (Public Review):

The authors showed that these adjacent tetramers can withstand a larger lateral detachment force when deep cholesterol molecules are present at the interface compared to scenarios with sphingomyelin (SM) molecules at the interface between two AQP0 tetramers. Authors interpret that result as evidence that deep cholesterol molecules mechanically stabilize the interface of the AQP0 tetramers. This conclusion has minor weaknesses, and the rigor of the lateral detachment simulations could be increased by establishing a reference point for the detachment force needed to separate AQP0 tetramers in a scenario without lipids at the interface between tetramers, and by increasing the number of repeats for the non-equilibrium steered MD simulations. Thermodynamic integration might be a better approach to compute the stabilization energy in the presence of cholesterol compared to the SM case.

In all electron crystallographic structures of AQP0 determined to date, lipids have always been observed sandwiched in between the AQP0 tetramers (see, for example, Gonen et al., Nature, 2005 and Hite et al., EMBO J., 2010). Therefore, considering a scenario without lipids at the interface would be unnatural and the AQP0 array would likely not be stable. Such a scenario would thus not be the most appropriate reference point for the lateral detachment simulations. In our view, comparison of a scenario with the deep cholesterol at the interface versus a scenario without it appeared a more realistic setup to investigate the stabilizing role the deep cholesterol has on the association of AQP0 tetramers. In the Results subsection regarding these simulations, we added the following sentence to further stress the rationale of our experimental setup: “Comparison of these two cases should allow us to assess the effect of the deep-binding Chol3 molecules on the mechanical stability of the associated AQP0 tetramers.”

Concerning the second suggestion of the reviewer of increasing the number of repeats, we doubled the number of simulation replicas: now it is n=20 for each pulling velocity and lipid interface. The trend of higher detachment forces for the interface containing cholesterol prevailed in a statistically significant, robust fashion (see Figure 7 of the revised manuscript and the main text referring to it). In consequence, as the reviewer suggested, extension of the dataset increased the rigor of the lateral detachment simulations. In addition to Figure 7 and the Results section, the Methods section and Table 4 have been updated to reflect the expanded dataset. 

Finally, concerning the usage of thermodynamic integration to compute the stabilization energy, we agree with the reviewer that calculation of the free energy would be better to determine the thermodynamic stabilization imparted by the cholesterol molecules. At an earlier stage of the project, we did indeed consider carrying out this type of simulations, but we decided against it because of the complexity and poor convergence of such calculations. Our choice is also based on a previous attempt in which it proved very challenging to use free energy calculations to assess the binding of lipids to a flippase (see Wang et al. BioRxiv, https://doi.org/10.1101/ 2020.06.24.169771, 2021). We now included this consideration in the revised manuscript by adding the following sentence in the Discussion: “Although we provide solid evidence here that deep cholesterol impart mechanical stabilization, free energy calculations would be required to obtain the full picture of thermodynamic stabilization. Such free energy calculations are challenging for lipids, due to the chemical complexity and poor convergence involved (Wang et al., 2021), and are thus beyond the scope of the current work.”

Reviewer #1 (Recommendations For The Authors):

Reorganizing a few concepts would make the story easier to follow. For example, the analysis of the bilayer thickness seems disjointed. Although Figure 4 shows measurements, it is not clear that the measurements represent bilayer thickness until the last paragraph of page 21 in the discussion, where "Hydrophobic thickness" is first introduced. Moving that first paragraph of page 22 that refers to Fig. 4A to the results would be helpful to understand the figure, and would prepare the reader for this part of the discussion.

In response to the reviewer, we moved the description of the measurements of the hydrophobic thickness to the Results section (Page 12) and adjusted the Discussion to minimize repetition (page 22).

Likewise, Figure 4E shows measurements of something, but it is not clear that these are the dimensions of a protein pocket until well into the discussion.

In response to the reviewer’s comment, we added a sentence both in the Results section [It sits in a pocket between the two adjacent AQP0 tetramers that is wider in the extracellular leaflet than the cytoplasmic leaflet (Figure 4E)] as well as to the caption of Figure 4E [The dotted lines indicate the distance between the two adjacent AQP0 tetramers at the positions of the ring system (~8.5 Å) and the acyl chain (~2.5 Å)].

Figure 2 - a comment for the non-specialists on what this region of the protein is would be helpful context. Is this the pore with part of the NPA motif?

We agree with the referee and added the following sentence to the caption of Figure 2: “A region of the water-conducting pathway close to the NPA (asparagine-proline-alanine), the AQP signature motif, is shown”.

Reviewer #2 (Recommendations For The Authors):

There is only one recommendation: In the results section entitled "Cholesterol positions observed in the electron crystallographic structures are representative of those around single AQP0 tetramers" the authors do not describe their approach. They refer to a reference (AponteSantamaria et al., 2012). The authors state the problem (investigate cholesterol positions), but it would be helpful to the readers if they also described the experimental approach.

We agree with the reviewer and made the following addition to the sentence “we performed MD simulations and calculated time-averaged densities to investigate ...”

Reviewer #3 (Recommendations For The Authors):

Technical comments:

(1) Authors stated: "Equilibration simulations were then performed until bulk membrane properties, such as thickness and deuterium order parameters, became stable and congruent with previous reports such as those by (Doktorova et al., 2020) and others (Figure 5-figure supplement 2 and Figure 5-figure supplement 3)." However, bilayer thickness is not represented in these figures. Additionally, I observed that the area per lipid (APL) appeared to be somewhat variable. This variation was particularly noticeable in systems where SM:CHOL=2:1, which seem to be not fully equilibrated. Is the figure displaying APL data for only one repetition? Could you please include plots for the other repetitions?

We thank the reviewer for pointing this out. We would like to clarify that we used CHARMMGUI to generate one lipid bilayer configuration for each mixture and system size. These configurations (one per system) were extensively simulated to generate stable initial configurations of the lipid bilayers. Figure 5 – supplements 2 and 3 refer to this pre-equilibration step. The final pre-equilibrated configurations were then used in the subsequent multiple equilibrium MD runs that we performed, either with a single cholesterol molecule or with the AQP0 tetramer(s) inserted. We have clarified this procedure in the revised manuscript (see changes in the Methods section for the MD equilibrium simulations).  

Concerning this pre-equilibration step, we have chosen the area per lipid, not thickness, to characterize the equilibration of the pure lipid bilayers. Accordingly, the area per lipid is the quantity shown in Figure 5 – figure supplement 3. We no longer refer to the membrane thickness in the revised manuscript.

Concerning the variability in the area per lipid, we note that the large changes occur within the first few tens of nanoseconds of the pre-equilibration step, after which the area per lipid stabilizes. We would like to also point out that in Figure 5 – figure supplement 3, we chose a logarithmic scale for the time axis to actually make it possible for the reader to see the major changes that occur at the beginning of the pre-equilibration step (which would otherwise be difficult to see). In the particular case of the SM:CHOL=2:1 mixture_,_ the 64 lipids/leaflet system converged to a stable area per lipid value in the last 70 ns and the 244 lipids/leaflet system approached the same value in approximately the last 30 ns. This was a good indication that the large system had also converged. After equilibration of the membranes, a single cholesterol or AQP0 tetramer(s) were inserted and equilibrium simulations were initiated. However, the first 100 ns (or 300 ns in the case of the double tetramer system) were considered as a further equilibration time and were not included in the analysis. This is now explicitly stated in the revised manuscript: “The first 100 ns of each simulation replica (the first 300 ns for the two tetramer simulations) were considered as additional equilibration time and were not included in further analysis.”

(2) Could you clarify the reasoning behind conducting the simulations at 323 K?

We conducted the simulations at 323 K to ensure that the lipid bilayers were in the liquid phase.

SM:CHOL mixtures have been reported to be in the liquid phase above 314 K (Keyvanloo et al. Biophys. J. 114: 1344, 2018). 323 K was thus chosen to be well above this value. Note that this temperature was also chosen in a previous MD simulation study of pure sphyngomyelin bilayers (Niemelä et al. Biophys. J. 87: 2976, 2004). This reasoning, as well as the two references, have been added to the Methods section in the revised manuscript.  

(3) There appears to be a discrepancy in Figure 7. Panel F does not align with the provided caption. 

We apologize for this mistake. The captions for panels E and F were switched. We corrected this mistake.

(4) Likewise, in Figure 8, there is a mismatch between the caption and the figures. Furthermore, in the text, the authors assert, "In the absence of cholesterol, the AQP0 surface is completely covered by sphingomyelin in the hydrophobic region of the membrane and by water outside this region (Figure 8A, left column). As noted before, there are essentially no direct protein-protein interactions between the adjacent tetramers. When cholesterol was present at the interface, it interacted with AQP0 at the center of the membrane and remained mostly in place (Figure 8A, right column)." However, the left column shows cholesterol density. Could you please clarify this inconsistency, especially regarding the absence of cholesterol?

We apologize for this mistake. The panels in Figure 8A showing the AQP0 surfaces in the absence and presence of cholesterol were switched. We corrected this mistake.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript by Estevam et al. reports new insights into the regulation of the receptor tyrosine kinase MET gained from two deep mutational scanning (DMS) datasets. In this paper, the authors use a classic selection system for oncogenic kinase signaling, the murine Ba/F3 cell line, to assess the functional effects of thousands of mutations in the kinase domains of MET in two contexts: (1) fusion of the whole MET intracellular region to the dimerization domain TPR, and (2) the same fusion protein, but with exon 14, which encodes part of the juxtamembrane region of MET, skipped. Critically, exon 14 skipping yields a version of MET that is found in many cancers and has higher signaling activity than the canonical MET isoform. The authors extensively analyze their DMS data to very convincingly show that their selection assay reports on kinase activity, by illustrating that many functionally important structural components of the kinase domain are not tolerant of mutation. Then, they turn their attention to a helical region of the juxtamembrane region (αJM), immediately after exon 14, which is posited to play a regulatory role in MET. Their DMS data illustrate that the strength and mutational tolerance of interactions between αJM and the key αC helix in the kinase domain depends on the presence or absence of exon 14. They also identify residues in the N-lobe of the kinase, such as P1153, which are not conserved across tyrosine kinases but appear to be essential for MET and MET-like kinases. Finally, the authors analyze their DMS data in the context of clinically-observed mutations and drug-resistance mutations.

Overall, this manuscript is exciting because it provides new insights into MET regulation in general, as well as the role of exon 14. It also reveals ways in which the JM region of MET is different from that of many other receptor tyrosinekinases. The exon 14-skipped fusion protein DMS data is somewhat underexplored and could be discussed in greater detail, which would elevate excitement about the work. Furthermore, some of the cell biological validation experiments and the juxtaposition with clinical data are perhaps not assessed/interpreted as clearly they could be. Some constructive suggestions are given below to enhance the impact of the manuscript.

Strengths:

The main strengths of this paper, also summarized above in the summary, are as follows:

(1) The authors very convincingly show that Ba/F3 cells can be coupled with deep mutational scanning to examine MET mutational effects. This is most clearly shown by highlighting how all of the known kinase structure and regulatory elements are highly sensitive to mutations, in accordance with a few other DMS datasets on other kinases.

(2) A highlight of this paper is the juxtaposition of two DMS datasets for two different isoforms of the MET receptor. Very few comparisons like this exist in the literature, and they show how small changes to the overall architecture of a protein can impact its regulation and mutational sensitivity.

(3) Another exciting advance in this manuscript is the deep structural analysis of the MET juxtamembrane region with respect to that of other tyrosine kinases - guided by the striking effect of mutations in the juxtamembrane helical region. The authors illustrate how the JM region of MET differs from that of other tyrosine kinases.

(4) Overall, this manuscript will provide a resource for interpreting clinically relevant MET mutations.

Weaknesses:

(1) The manuscript is front-loaded with extensive analysis of the first DMS dataset, in which exon 14 is present, however, the discussion and analysis of the exon 14-skipped dataset is somewhat limited. In particular, a deeper discussion of the differences between the two datasets is warranted, to lay out the full landscape of mutations that have different functional consequences in the two isoforms. Rather, the authors only focus on differences in the JM region. What are the broader structural effects of exon 14 skipping across the whole kinase domain?

Thank you for your feedback on our manuscript and our analysis of the exon 14 skipped mutational scanning data. The lack of a robust growth differential  between the wild type MET intracellular domain and the exon 14 skipped isoform within the Ba/F3 system suggests that there is not a significant growth advantage related to exon 14 skipping, likely due to the constitutive activation of both constructs by the TPR domain, which also suggests that the assay is potentially less sensitive to nuanced JM-driven effects between these two isoforms, aside from the highly sensitive ⍺JM-helix. We also lose insight on membrane-related interactions imposed on the juxtamembrane that may be important to fully understand the differences between these two isoforms in the cytoplasmically-expressed context. Therefore, we can at most speculate exon 14 skipped related differences between these two datasets.

With these caveats in mind, to further address exon 14 and juxtamembrane-driven differences between these two mutational landscapes, we calculated the absolute score difference between TPR-METΔEx14 and TPR-MET (|METΔEx14 - MET|) and plotted the |ΔScore| in a heatmap. Overall, the two landscapes, as noted in the text, are largely similar with differences emerging mostly for specific mutations. Where we see the largest secondary structural difference continues to be the ⍺JM-helix, where MET is more sensitive to helix-breaking mutations such as proline. Again L1062 has the greatest difference in sensitivity between these two datasets for the ⍺JM-helix, with the introduction of negative charge resulting in loss-of-function for the TPR-MET kinase domain but having a null effect in the TPR-METΔEx14 kinase domain. Other positions with strong differences include the ⍺G and APE motif.

We have incorporated more detailed discussion in text. 

(2) It is unclear if gain-of-function mutations can actually be detected robustly in this specific system. This isn't a problem at face value, as different selection assays have different dynamic ranges. However, the authors don't discuss the statistical significance and reproducibility of gain- vs loss-of-function mutations, and none of the gain-of-function mutations are experimentally validated (some appear to show loss-of-function in their cellular validation assay with full-length MET). The manuscript would benefit from deeper statistical analysis (and discussion in the text) of gain-of-function mutations, as well as further validation of a broad range of activity scores in a functional assay. For the latter point, one option would be to express individual clones from their library in Ba/F3 cells and blot for MET activation loop phosphorylation (which is probably a reasonable proxy for activity/activation).

Thank you for your comment on the statistical interpretations of gain-of-function (GOF) and loss-of-function (LOF) mutations. In this study we classify GOF and LOF based on the following metrics:

(1) The difference between the missense mutation score and the wild type synonymous score for a given position must be smaller than the calculated propagated error, for both IL-3 withdrawal and IL-3 conditions

(2) Missense mutations must be ≥ ±2 standard deviations (SD) from the mean of wild type synonymous mutations

Given that our assay was conducted in a constitutively active kinase in the TPR-fusion context, gain-of-function mutations are expected to not only be rare, but also supersede baseline fitness. Within the IL-3 conditions, we expect that cells are not reliant or “addicted” to MET for growth proliferation. Nevertheless, due to the parallel nature of the screen, we can compare scores for variants in the IL-3 control and IL-3 withdrawal conditions to filter mutations that are solely exhibiting high fitness under selective pressure.

To identify these mutations we 1) calculated the propagation of error between IL-3 and IL-3 withdrawal scores for the same variant 2) calculated the absolute difference between IL-3 and IL-3 withdrawal scores for the same variant 3) filtered variants if the IL-3 withdrawal score was ≥ +2 SDs, the IL-3 score was ≤ 0, and the absolute score difference between IL-3 and withdrawal conditions was larger than the propagated error.

In analyzing mutations within the IL-3 withdrawal conditions, applying our statistical metrics, we find 33 mutations within the MET library, and 30 in the METΔEx14 library, that have a score of ≥ +2 SD and low propagated error. By increasing our boundary to ≥+2.5 SD, we can classify mutations with even higher confidence, identifying 10 mutations within the MET library, and 9 in the METΔEx14 library (Supplemental Data Figure 7).

(3) In light of point 2, above, much of the discussion about clinically-relevant gain-of-function mutations feels a bit stretched - although this section is definitely very interesting in premise. A clearer delineation of gain-of-function, with further statistical support and ideally also some validation, would greatly strengthen the claims in this section.

To address this concern, we have provided additional analysis and details on gain-of-function (GOF) classification in Supplemental Data Figure 5 and the overlap between GOF and clinically associated mutations in Supplemental Data Figure 8. Within our gain-of-function classifications, we pick up on several mutations at positions that have been clinically detected and experimentally validated in previous studies in both libraries (D1228, G1163, L1195), and show that GOF mutations also have low variance.

Reviewer #2 (Public Review):

Summary:

The authors describe a deep mutational scanning (DMS) study of the kinase domain of the c-MET receptor tyrosine kinase. The screen is conducted with a highly activated fusion oncoprotein - Tpr-MET - in which the MET kinase domain is fused to the Tpr dimerization element. The mutagenized region includes the entire kinase domain and an alpha-helix in the juxtamembrane region that is essentially part of the MET kinase domain. The DMS screen is carried out in two contexts, one containing the entire cytoplasmic region of MET, and the other with an "exon 14 deletion" which removes a large portion of the juxtamembrane region (but retains the aforementioned alpha-helix). The work provides a robust and essentially exhaustive catalog of the effect of mutations (within the kinase domain) on the ability of the Tpr-MET fusion oncoproteins to drive IL3-independent growth of Ba/F3 cells. Every residue in the kinase is mutated to every natural amino acid. Given the design of the screen, one would expect it to be a powerful tool for identifying mutations that impair catalytic activity and therefore impair IL3-independent proliferation, but not the right tool for identifying gain-of-function mutations that operate by shifting the kinase from an inactive to active state (because the Tpr-Met fusion construct is already very highly activated). This is borne out by the data, which reveal many many deleterious mutations and few "gain-of-function" mutations (which are of uncertain significance, as discussed below).

Strengths:

The authors take a very scholarly and thorough approach to interpreting the effect of mutations in light of available information for the structure and regulation of MET and other kinases. They examine the effect of mutations in the so-called catalytic (C) and regulatory (R) spines, the interface between the JM alpha-helix and the C-helix, the glycine-rich loop, and other key elements of the kinase, providing a structural rationale for the deleterious effect of mutations. Comparison of the panoply of deleterious mutations in the TPR-met versus TPR- exon14del-MET DMS screens reveals an interesting difference - the exon14 deletion MET is much more tolerant of mutations in the JM alpha-helix/C-helix interface. The reason for this is unclear, however.

Weaknesses:

Because the screens were conducted with highly active Tpr-MET fusions, they have limited power to reveal gain-of-function mutations. Indeed, to the extent that Tpr-MET is as active or even more active than ligand-activated WT MET, one could argue that it is "fully" activated and that any additional gain of fitness would be "super-physiologic". I would expect such mutations to be rare (assuming that they could be detected at all in the Ba/F3 proliferation assay). Consistent with this, the authors note that gain-of-function mutations are rare in their screen (as judged by being more fit than the average of synonymous mutations). In their discussion of cancer-associated mutations, they highlight several "strong GOF variants in the DMS". It is unclear what the authors mean by "strong GOF", indeed it is unclear to this reviewer whether the screen has revealed any true gain of function mutations at all. A few points in this regard:

(1) More active than the average of synonymous mutations (nucleotide changes that have no effect on the sequence of the expressed protein) seems to be an awfully low bar for GOF - by that measure, several synonymous mutations would presumably be classified as GOF.

We completely agree that any mutation above the average synonymous would not be a robust assessment and thus why we statically filtered mutations in our entire analysis. To this point, and that of  Reviewer 1, we have further outlined our statistical definitions. In classifying mutations as GOF or LOF, the following parameters were used:

(1) The difference between the missense mutation score and the wild type synonymous score for a given position must be smaller than the calculated propagated error, for both IL-3 withdrawal and IL-3 conditions

(2) Missense mutations must be ≥ ±2 standard deviations (SD) from the mean of wild type synonymous mutations

Therefore, only variants at the tail-ends of the mutational distribution were assessed, and further filtered based on propagation of error. For this reason, a “strong GOF” mutation as noted in this study is one that improves the fitness of an already active kinase. As pointed out, within our analysis, these are very rare occurrences, and in focusing on cancer-associated mutations we find that the variants that meet these statistical parameters require a larger genetic “leap” in the codon space. Overall, we have also changed our language in reference to GOF mutations in text.

We hope this concern has been addressed in the new Supplemental Data Figures.

(2) In the +IL3 heatmap in supplemental Figure 1A, there is as much or more "blue" indicating GOF as in the -IL3 heatmap, which could suggest that the observed level of gain in fitness is noise, not signal.

We hope this concern has been addressed in the previous responses and new Supplemental Data Figures.

(3) And finally, consistent with this interpretation, in Supplemental Figure 1C, comparing the synonymous and missense panels in the IL3 withdrawal condition suggests that the most active missense mutations (characterized here as strong GOF) are no more active than the most active synonymous mutations.

We hope this concern has been addressed in the previous responses and figures above.

My other major concern with the work as presented is that the authors conflate "activity" and "activation" in discussing the effects of mutations. "Activation" implies a role in regulation - affecting a switch between inactive and active conformations or states - at least in this reviewer's mind. As discussed above, the screen per se does not probe activation, only activity. To the extent that the residues discussed are important for activation/regulation of the kinase, that information is coming from prior structural/functional studies of MET and other kinases, not from the DMS screen conducted here. Of course, it is appropriate and interesting for the authors to consider residues that are known to form important structural/regulatory elements, but they should be careful with the use of activity vs. activation and make it clear to the reader that the screen probes the former. One example - in the abstract, the authors rightly note that their approach has revealed a critical hydrophobic interaction between the JM segment and the C-helix, but then they go on to assert that this points to differences in the regulation of MET and other RTKs. There is no evidence that this is a regulatory interaction, as opposed to simply a structural element present in MET (and indeed the authors' examination of prior crystal structures shows that the interaction is present in both active and inactive states.

Thank you, and we completely agree that the distinction between “activity” and “activation” is important and that we can at most speculate and propose models for effects related to activation from this screen. We have edited the text to reflect these distinctions. In respect to activation and the second point, we believe the screen highlights the ⍺JM-C interface as a critical structural region, which may have a role in regulation based on the paradigm of juxtamembrane regulation in RTKs, the presence of a similar interface in TAM family kinases, the co-movement of the ⍺JM-helix and ⍺C-helix between active and inactive conformations in the structural ensemble, and the observation that within the TPR-METΔEx14 library there is a greater tolerance for mutations at interface positions than TPR-MET. We hope that are follow-up studies that directly probe the ⍺JM-C interface in respect to the entire juxtamembrane to truly say if/ what role this conserved motif plays in regard to MET function. We have changed the language of the text to reflect how these differences contribute to our proposed model, rather than any unintended assertion on direct regulatory effects.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Suggested major points to address:

(1) Although the authors show that several key functional residues in the kinase domain are highly sensitive to mutation, it would be nice if the authors further established a clear connection between kinase activity and enrichment in the Ba/F3 assay. Specifically, it is unclear to what extent there is a correlation between the extent of enrichment/depletion and kinase activity - is a larger activity score necessarily indicative of higher kinase activity? This is partly validated by the P1153L mutation autophosphorylation western blots in Figure 4B, but this correlation is somewhat undermined by the data in 5F. Autophosphorylation data (or phosphorylation data on a direct downstream substrate) for a few mutants would really solidify what the activity score is truly reporting. This might also clarify the extent to which the difference between the two screens can be interpreted, and the extent to which gain-of-function can be interpreted.

The Ba/F3 assay was carefully chosen for its addiction to exogenous IL-3, which serves as a permissive signaling switch. Any mutation that prevents TPR-MET/ΔEx14 from properly functioning is therefore dampening its signaling ability. Nevertheless, it is possible that some mutations with high scores are truly improving activity and others are sustaining activity through more stable interactions than the wild type kinase domain or with downstream signaling partners, which would require careful biochemical dissection outside the scope of this study. To address these points, we now refer to the mutation score simply as “score” rather than “activity score” and further discuss these caveats in text.

(2) Overall, the exon 14-skipped dataset is under-discussed in the paper. The comparison of the two datasets is where most deep insights are likely to be found, and so a more thorough analysis/discussion of this dataset would really elevate the significance of the paper. For example, there appear to be a very large number of mutations that have divergent effects in the two screens (everything along the dashed lines in Figure 5D), but it's unclear where most of these mutations lie on the structure. It would be helpful if the residues with divergent mutational effects between the two screens (Supplementary Figure 5E) were mapped onto a structure of the JM-KD construct.

To address this concern, new analysis has been added to the supplement, showing the score differences between MET and METΔEx14 mutations as a heatmap (Supplemental Data Figure 7A). Within this analysis we further applied our statistical filtering methods and structurally mapped positions with the greatest differential scores to show where divergent effects cluster (Supplemental Data Figure 7D). Consistent with our previous reports, the ⍺JM-helix and ⍺C-helix show the largest cluster of divergent effects, in addition to sites such as the ⍺G and APE motif. Further discussion of these points have been added to the text.

(3) Based on the observations that αJM-αC interactions seem to be less strictly required in the exon 14 mutant, the hypothesis that exon 14 skipping merely removes a Cbl docking site seems largely unsatisfactory. There seems to be more direct structural alterations that could explain this change, but these are not really discussed or speculated on. Related to this, while L1062 mutations are more tolerated, as the authors showed in both the mutational heatmap and the cellular experiments, its binding counterpart L1125 still seems to be somewhat immutable based on the heatmaps. So, more hypothesis/exploration of how exon 14 skipping affects MET KD structure would be a nice addition to the paper.

We agree that loss of the Cbl docking site is an insufficient model to capture the full nature of JM regulation and exon 14 skipping effects, which was a major incentive for this study. The outstanding ⍺JM-⍺C-helix sensitivity also excites us because it points to a potential regions of the JM that potentially is involved in kinase activity through ⍺C-helix interactions, much like the CDK models and other RTK-JM interactions. We observed that the ⍺JM-⍺C helix retain contact, and propose that the ⍺JM-⍺C helix move in unison between active and inactive conformations. However, it is possible that a more complicated mechanism might also exist, where there is a larger degree of maintenance of these contacts in a homodimer. For instance, in Figure 3G, if you compare the ⍺JM-helix conformations, in both RON and AXL there is more distance and a pivot away from the ⍺C-helix. It’s is possible that there are shared mechanisms between the MET and TAM families that could further elucidate exactly how these ⍺JM-helices interact with the kinase domain during the activity transitions and what biophysical role JM truncations play.

(4) The discussion about mutations S1122Q and L1062D is a bit confusing and incomplete. From the DMS data, it appears that L1062D should be mildly gain-of-function for the exon 14 deletion variant and very loss of function for wild-type MET. In the validation HeLa cell experiments L1062D is loss-of-function in both contexts, but a mention of this discrepancy is omitted. Then, when the discordance between DMS and HeLa cell experiments is observed again for S1122Q, it is explicitly called out for activation-loop phosphorylation, but then there is no mention of the fact that HGF stimulation leads to greater pERK levels for S1122Q in the exon 14 deletion context (the opposite of the DMS result). The Erk phosphorylation discrepancy should be mentioned. It is entirely reasonable, as the authors suggest, that there are differences between full-length MET and the TPR fusions, but the enhanced Erk phosphorylation by the S1122Q mutation is surprising (and intriguing!). This section could use some re-analysis/re-writing and further discussion.

Thank you for this comment. As noted L1062D shows slight GOF in METΔEx14 but LOF in MET. The blots show expression of L1062D and S1122Q in the full length receptor in the absence and presence of HGF stimulation. L1062D is loss of function for both contexts only in -HGF conditions, but shows expression in phosphorylated METΔEx14, but not MET. For S1122Q, indeed there is a stronger pERK signal in the METΔEx14, which highlights how probing all regions of phosphorylation (A-loop and C-tail) and many MET-associate pathways (ERK, AKT) may be important to understand in what way these mutations are affect MET phosphorylation and proliferation. We have included this point in the text.

(5) Related to the previous point, one other thing to consider here is that perhaps gain-of-function mutations are simply not detectable in this particular DMS assay. The authors state that GOF and LOF are defined as 2 standard deviations from the mean of the WT-synonymous distribution. How many mutations are actually designated to be GOF based on this criterion? Are those GOF mutations as reproducible as the LOF mutations? It would be worthwhile to separately analyze the variance in activity scores for every loss-of-function mutation and gain-of-function mutation. It seems likely that loss-of-function scores are a lot more reproducible than gain-of-function ones, suggesting that the most apparent gain-of-function signal is just noise in the assay. The few outliers to this point (true gain-of-function mutations) may be some of the ones discussed in Figure 6. If this is true, it would lend confidence to the claims associated with Figure 6.

In analyzing and classifying both GOF and LOF mutations, error was a main filtering parameter. Each fitness score, calculated by Enrich2, is representative of the slope across time points  and biological replicates for the read frequency of the mutation. The associated standard error (SE) reflects the variance for each mutation within the scoring framework (Rubin et al., 2017). Mutations were then further filtered based on low propagated error, calculated by comparing the standard error (SE) of each missense mutation to the SE of the respective wild type synonymous mutation. Therefore, mutations were only classified as GOF or LOF if there was low error, in addition to the other score filters previously described. We have plotted the classified GOF mutations with their respective SE in the newly incorporated Supplemental Data Figure 8C.

(6) In the discussion of panels 6C and 6D, the assertion is that the "clinical, not validated" category has more mutations that are low-fitness outliers than the "clinical, validated" category. From the graphs, it's actually hard to tell if this is the case for two reasons: (1) the way the graphs are normalized, (to the largest value in each histogram), you cannot compare bar heights (and thus number of mutations) between two histograms on the same graph. (2) Just looking at the shapes of the distributions, or considering maybe the mean or median values, it's unclear whether the "validated" and "not validated" populations are actually different from one another.

This is an important indication, and we have added analysis showing the distribution and number of clinically-associated mutations within our libraries without normalization in the main text and in Supplemental Data Figure 8A-B.

(7) This sentence in the last results section is somewhat unclear: "GOF resistance mutations may indicate an effect on the equilibrium of kinase activation, whereas LOF resistance mutations likely affect inhibitor-protein interactions directly." The first part makes sense, but it is not totally obvious how one can infer anything about inhibitor-protein interactions from mutations that are LOF with respect to kinase activity. Related to this, how are LOF mutations selected in the presence of an inhibitor? Is the assumption here that the mutation might totally abrogate inhibitor binding but only slightly impair the kinase? Perhaps this could be explained a bit more.

Here, the idea we wanted to get across is that there are two models  that can explain how a mutation can contribute to resistance: shift the activity equilibrium at baseline or directly impair drug effects and restore baseline activity. Mutations that are labeled resistant and GOF, favor the first model. Mutations that are labeled resistant and LOF, favor the second model. In the presence of an inhibitor, which is in the scope outside of this study, LOF mutations would be sensitive to the inhibitor (ie WT-like and sensitive).

(8) Some additional details of the library preparation and sequencing should be given in the methods section. It appears that the variable region of the library is roughly 275 amino acid residues long, which means >800 bases. How was this sequenced? From the methods, it sounds like all of the variants were pooled into a single library, but then sequencing was done using a 300x300 paired-end Illumina kit, which would not cover the length of the whole variable region. Was the library actually screened in segments as sub-libraries and then separately sequenced? Alternatively, was the whole library screened at once, and then different segments were amplified out for sequencing? If the latter approach is used, this could yield confounding results for counting wild-type variants that have the parent wild-type coding sequence. For example, if you amplify your kinase library in three segments after a single selection on the whole library, and you sequence those three segments separately, you might find a read that appears as wild-type in the part you amplified/sequenced but has a mutation in a region that you did not sequence. If this approach is taken, the counts for the wild-type sequence would be inaccurate, in which case, how is the data normalized with WT as a reference? Regardless of the method used, some more details should be provided in the methods section.

In this study, we used the Nextera XT DNA Library Preparation Kit (Illumina), which uses a tagmentanation approach that randomly fragments our 861 bp amplicon into ~300 bp fragments with a transposase, resulting in a Poisson distribution of fragment sizes. This allows for direct sequencing of all amplicons and libraries with an SP300 paired-end run, which we ran on two lanes of a NovaSeq6000. Samples are demultiplexed  and processed by our analysis pipeline with a lookup table that associates the unique dual index to the specific sample (library, time point, biological replicate, IL-3 condition).

The TPR-MET and TPR-METΔEx14 libraries were prepared in parallel throughout the entire experiment, from cloning to virus generation to transductions, screening, cell harvesting, sequencing prep, and sequencing. In other words, the TPR-MET and TPR-METΔEx14 were transduced into their own, respective batch of cells for each biological replicate, then selected and screened on the same day for each replicate and time point. Each library and condition (time point, biological replicate, IL-3 condition) was prepared in parallel but still an independent sample. At the stage of tagmentation, each sample was arrayed, where each well corresponds to a library, biological replicate, and time point. At the stage of sequencing, samples across the two libraries were normalized to 10mM (library, biological replicate, time point, IL-3 condition) then pooled together and all run on two lanes of the same NovaSeq6000 flow cell.

PCR and sequencing bias was one of the most important parameters for us, which is why we performed tagmentation in parallel and sequenced everything on the same run. We have added extra details to the methods and hope that we have clarified your questions on this matter.

Suggested minor points to address:

(1) TPR (as in TPR-MET fusion) is not defined in the text when it is first mentioned. And it wasn't immediately clear that this is not a membrane-associated domain (Figure 5E makes this way more obvious than Figure 1B does). Perhaps this could be made more explicit in the text or in Figure 1.

We have incorporated a new schematic in Figure 1B to better illustrate the TPR-fusion constructs used within this study. The usage of the TPR-fusion is first mentioned in the introduction, paragraph 4, and revised the main-text to delineate the usage of the TPR-fusion more clearly.

(2) In Figure 2G, it would be helpful if the wild-type amino acid residue was listed underneath the position number in the two graphs (even though those residues are also highlighted in 2H).

Thank you for this recommendation, we have added the wild type amino acid next to the position number in the x-axis label.

(3) For Supplementary Data Figure 2, is it possible to calculate conservation scores at each position using some kind of evolutionary model, rather than relying on visual inspection of the sequence logo? Can one quantitatively assert that the C-spine is less conserved than the R-spine overall, or can this only be said for certain positions? Related to this, in comparing Figure 2G to Supplementary Data Figure 2, it is interesting that there isn't any obvious correspondence between mutational tolerance and conservation within the C-spine. For example, 1165 seems to be the most conserved position in the C-spine, but several substitutions are tolerated at this position, just like 1210, which is one of the least conserved positions in the C-spine. Finally, it's very likely that positions 1165, 1210, 1272, and 1276 co-vary, given that they all pack into the same hydrophobic cluster. This might be why they appear less conserved. These last few points might be worth discussing briefly if the authors want to relate mutational tolerance to evolutionary conservation.

Thank you for this recommendation. To better quantitatively determine C-spine versus R-spine conservation, we performed a multiple sequence alignment of all RTK kinase domain sequences to properly identify corresponding R- and C-spine locations, as previously done in generating the spine logos, then used the bio3D structural bioinformatics package in R to calculate the conservation score of each residue position by amino acid “similarity” with a blosum62 matrix (Supplemental Data Figure 2B). In concordance with the logos, we find that C-spine positions 1092, 1108, 1165 have the highest conservation scores, even compared to some R-spine mutations. We also see across the alignment that indeed, C-spine positions 1165 1210,1211,1212, and 1272, and 1276 co-vary within RTK families. We have revised the text to reflect these points, and more specifically discuss position-level conservation rather than generalizing conservation for the C- and R-spines.

(4) On Page 7 of the merged document, there appear to be some figure labeling errors. In the first and second paragraphs of the "Critical contacts between..." section, Figure 3B is referenced multiple times as a structural alignment/ensemble, but this is a heatmap.

Thank you for catching this! The correct figure panels are now referenced.

(5) In the text describing Figure 3A, it is stated that the structures were aligned to the N-lobe, but the figure legend says that all structures were aligned to alpha-C and alpha-JM.

Thank you - a local alignment to the ⍺JM-helix and ⍺C-helix is correct, the idea here being that if the ⍺JM-helix and ⍺C-helix are linked to an active/inactive conformation like in the case of the insulin receptor, these two clusters could be revealed through the structural ensemble. However, we discovered this was not the case, combined with the DMS sensitivity to mutations at the packing interface leads us to believe that the MET JM has a distinctive regulatory mechanism that relies on this ⍺C-helix interface. We have made this correction to the text.

(6) It would be helpful if the alpha-C and alpha-JM helices in Figure 3D were labeled on the MET structures.

The ⍺C-helix and ⍺JM-helix are now labeled in Figure 3D.

(7) It appears that Figure 4E is never explicitly referenced in the text.

Thank you, Figure 4E is now appropriately referenced in the text.

(8) Throughout the Figure 6 legend, for the histograms, it is stated that "Counts are normalized to the total mutations in each screen dataset." This might not be the correct description of normalization, as this would mean that the sum of all of the bins should equal 1. Rather, the normalization appears to be to the bin with the largest number of mutants in it, which is given a value of 1. This difference is really critical to how one visually inspects the overlaid histograms.

Thank you for this comment. Here, the intention was to aid in the visualization of the distribution of cancer-associated and resistance associated mutations, which is a much smaller population compared to the whole library and becomes easily masked. We originally applied a “stat(ncount)” function in R, which as noted scales the data and sets the peak to 1, which only applied to the clinical and cancer-associated mutations plotted. Now, to better compare distributions, normalization has been removed, instead opting to overlay the distributions of all missense mutations and the subset of clinical mutations directly with their own y-axis scale. This modification has been made throughout Figure 6 panels, hopefully improving interpretability.

Reviewer #2 (Recommendations For The Authors):

A few thoughts/suggestions:

(1) Regarding kinase regulation, the "closing of N- and C-lobe" upon activation is an often mentioned component of activation, and I'm sure is true in many cases, but it is not a general feature of kinase activation.

The text has been updated - we removed the description of N- and C-lobe closure. 

(2) With respect to the inactive state of MEK, the DFG-flipped structure discussed here is almost certainly an inhibitor-induced conformation. Again, DFG-flip is often discussed as a mechanism of kinase regulation, and while in some kinases this might be the case, more often it is a drug-induced or drug-stabilized inactive conformation. The SRC/CDK-like inactive conformation in 2G15 is more likely a physiologically relevant inactive state. (or even better, the ATP-bound inactive state structure 3DKC, which exhibits a somewhat different SRC/CDK-like inactive conformation).

The PDB 3R7O structure was chosen as the main representation because it was the clearest representation of a wild type structure with an aligned R- and C- spine, solvent-exposed, phosphorylated activation loop. Although 3DKC is bound to ATP, this structure is still in an inactive conformation and has stabilizing mutations (Y1234/F, Y1235D) and an atypical alpha helix structure in the activation loop. However, we agree the SRC/CDK-like inactive conformation is an important representation and we have incorporated our structural mapping on 2G15 in the new supplemental figures with further details on statistical analysis and comparison of libraries.

(3) Following the comments above, I would describe the process of activation in a simpler way (in any case, it is peripheral to the work described here). Something along the lines of "phosphorylation on tyrosines XX and XX induces rearrangement of the activation segment and promotes and stabilizes the inward active position of the C-helix." Can go on to mention that this forms the E1127/K1110 salt bridge. (The DFG is already "in" in the SRc/CDK-like inactive state).

We have changed the language to more simply describe activation. Thank you!

(4) Would be great to see DMS with the intact receptor done in a way that could identify mutations that lead to activation in a ligand-independent manner. (but obviously beyond the scope of this paper).

Agreed! This would be an excellent follow up for the future, especially to elucidate juxtamembrane regulation, as the membrane context is likely required.

A typo or two:

Boarded instead of bordered/outlined in legend to Fig. 1.

P11553L in the 2nd line of the 2nd paragraph in that section.

Thank you, we have addressed these typos!

Author response:

eLife assessment

This valuable study uses single-cell transcriptomics to explore the mouse vomeronasal organ and represents an advance that enhances our understanding of neural diversity within this sensory system. Findings suggest a unique endoplasmic reticulum (ER) structure in Gnao1 neurons and allow for the synthesis of a developmental trajectory from stem cells to mature vomeronasal sensory neurons. Convincing methods, data, and analyses broadly support the claims, although experiments supporting the main ER-related claim are incomplete and lack quantification of co-expression and statistics on labeling intensity or coverage. Adding these data would greatly strengthen the conclusions of the paper.

Public Reviews:

Reviewer #1 (Public Review):

Devakinandan and colleagues present a manuscript analyzing single-cell RNA-sequencing data from the mouse vomeronasal organ. The main advances in this manuscript are to identify and verify the differential expression of genes that distinguish apical and basal vomeronasal neurons. The authors also identify the enriched expression of ER-related genes in Gnao1 neurons, which they verify with in situ hybridizations and immunostaining, and also explore via electron microscopy. Finally, the results of this manuscript are presented in an online R shiny app. Overall, these data are a useful resource to the community. I have a few concerns about the manuscript, which I've listed below.

General Concerns:

(1) The authors mention that they were unable to identify the cells in cluster 13. This cluster looks similar to the "secretory VSN" subtype described in a recent preprint from C. Ron Yu's lab (10.1101/2024.02.22.581574). The authors could try comparing or integrating their data with this dataset (or that in Katreddi et al. 2022) to see if this is a common cell type across datasets (or arises from a specific type of cell doublets). In situ hybridizations for some of the marker genes for this cluster could also highlight where in the VNO these cells reside.

Cluster13 (Obp2a+) cells identified in our study have similar gene expression markers to those identified with the “putative secretory” cells in Hills et al. manuscript. At the time this manuscript was available publicly, our publication was already finalized and communicated. We welcome the suggestion to integrate data, which we will attempt and address in our revision.      

(2) I found the UMAPs for the neurons somewhat difficult to interpret. Unlike Katreddi et al. 2022 or Hills et al. 2024, it's tricky to follow the developmental trajectories of the cells in the UMAP space. Perhaps the authors could try re-embedding the data using gene sets that don't include the receptors? It would also be interesting to see if the neuron clusters still cluster by receptor-type even when the receptors are excluded from the gene sets used for clustering. Plots relating the original clusters to the neuronal clusters, or dot plots showing marker gene expression for the neuronal clusters might both be useful. For example, right now it's difficult to interpret clusters like n8-13.

We will represent the UMAPs to make the developmental trajectory clearer. How neuron clusters are affected by the presence or exclusion of receptors is an interesting question that we will address in our revision, along with showing markers of each neuronal cluster, as suggested by the reviewer.  

Reviewer #2 (Public Review):

Summary:

The study focuses on the vomeronasal organ, the peripheral chemosensory organ of the accessory olfactory system, by employing single-cell transcriptomics. The author analyzed the mouse vomeronasal organ, identifying diverse cell types through their unique gene expression patterns. Developmental gene expression analysis revealed that two classes of sensory neurons diverge in their maturation from common progenitors, marked by specific transient and persistent transcription factors. A comparative study between major neuronal subtypes, which differ in their G-protein sensory receptor families and G-protein subunits (Gnai2 and Gnao1, respectively), highlighted a higher expression of endoplasmic reticulum (ER) associated genes in Gnao1 neurons. Moreover, distinct differences in ER content and ultrastructure suggest some intriguing roles of ER in Gnao1-positive vomeronasal neurons. This work is likely to provide useful data for the community and is conceptually novel with the unique role of ER in a subset of vomeronasal neurons. This reviewer has some minor concerns and some suggestions to improve the manuscript.

Strengths:

(1) The study identified diverse cell types based on unique gene expression patterns, using single-cell transcriptomic.

(2) The analysis suggests that two classes of sensory neurons diverge during maturation from common progenitors, characterized by specific transient and persistent transcription factors.

(3) A comparative study highlighted differences in Gnai2- and Gnao1-positive sensory neurons.

(4) Higher expression of endoplasmic reticulum (ER) associated genes in Gnao1 neurons.

(5) Distinct differences in ER content and ultrastructure suggest unique roles of ER in Gnao1-positive vomeronasal neurons.

(6) The research provides conceptually novel on the unique role of ER in a subset of vomeronasal neurons, offering valuable insights to the community.

Weaknesses:

(1) The connection between observations from sc RNA-seq and EM is unclear.

(2) The lack of quantification for the ER phenotype is a concern.

We would like to point out that the connection between scRNA-seq and EM was made in our experiments that investigated the localization of ER proteins via IHC (in Figure 5). The intriguing observation that the levels of a number of ER luminal and membrane proteins were higher in Gnao1 compared to Gnai2 neurons, led us to hypothesize a differential ER content or ultrastructure, which was verified by EM. The quantification of ER phenotype would definitely strengthen our observations, which we will add in our revised manuscript.       

Reviewer #3 (Public Review):

Summary:

In this manuscript, Devakinandan and colleagues have undertaken a thorough characterization of the cell types of the mouse vomeronasal organ, focusing on the vomeronasal sensory neurons (VSNs). VSNs are known to arise from a common pool of progenitors that differentiate into two distinct populations characterized by the expression of either the G protein subunit Gnao1 or Gnai2. Using single-cell RNA sequencing followed by unsupervised clustering of the transcriptome data, the authors identified three Gnai2+ VSN subtypes and a single Gnao1+ VSN type. To study VSN developmental trajectories, Devakinandan and colleagues took advantage of the constant renewal of the neuronal VSN pool, which allowed them to harvest all maturation states. All neurons were re-clustered and a pseudotime analysis was performed. The analysis revealed the emergence of two pools of Gap43+ clusters from a common lineage, which differentiate into many subclusters of mature Gnao1+ and Gnai2+ VSNs. By comparing the transcriptomes of these two pools of immature VSNs, the authors identified a number of differentially expressed transcription factors in addition to known markers. Next, by comparing the transcriptomes of mature Gnao1+ and Gnai2+ VSNs, the authors report the enrichment of ER-related genes in Gnao1+ VSNs. Using electron microscopy, they found that this enrichment was associated with specific ER morphology in Gnao1+ neurons. Finally, the authors characterized chemosensory receptor expression and co-expression (as well as H2-Mv proteins) in mature VSNs, which recapitulated known patterns.

Strengths:

The data presented here provide new and interesting perspectives on the distinguishing features between Gnao1+ and Gnai2+ VSNs. These features include newly identified markers, such as transcription factors, as well as an unsuspected ER-related peculiarity in Gnao1+ neurons, consisting of a hypertrophic ER and an enrichment in ER-related genes. In addition, the authors provide a comprehensive picture of specific co-expression patterns of V2R chemoreceptors and H2-Mv genes.

Importantly, the authors provide a browser (scVNOexplorer) for anyone to explore the data, including gene expression and co-expression, number and proportion of cells, with a variety of graphical tools (violin plots, feature plots, dot plots, ...).

Weaknesses:

The study still requires refined analyses of the data and rigorous quantification to support the main claims.

The method description for filtering and clustering single-cell RNA-sequencing data is incomplete. The Seurat package has many available pipelines for single-cell RNA-seq analysis, with a significant impact on the output data. How did the authors pre-process and normalize the data? Was the pipeline used with default settings? What batch correction method was applied to the data to mitigate possible sampling or technical effects? Moreover, the authors do not describe how cell and gene filtering was performed.

The data in Figure 7-Supplement 3 show that one-sixth of the V1Rs do not express any chemoreceptor, while over a hundred cells express more than one chemoreceptor. Do these cells have unusually high or low numbers of genes or counts? To exclude the possibility of a technical artifact in these observations, the authors should describe how they dealt with putative doublet cells or debris.

Surprisingly, some clusters are characterized by the expression of specific chemoreceptors (VRs). Have these been used for clustering? If so, clustering should be repeated after excluding these receptors.

The identification of the VSN types should be consistent across the different analyses and validated. The data presented in Figure 1 lists four mature VSN types, whereas the re-clustering of neurons presented in Figure 3 leads to a different subdivision. At present, it remains unclear whether these clusters reflect the biology of the system or are due to over-clustering of the data, and therefore correspond to either noise or arbitrary splitting of continua. Clusters should be merged if they do not correspond to discrete categories of cells, and correspondence should be established between the different clustering analyses. To validate the detected clusters as cell types, markers characteristic of each of these populations can be evaluated by ISH or IHC.

There is a lack of quantification of imaging data, which provides little support for the ER-related main claim. Quantification of co-expression and statistics on labeling intensity or coverage would greatly strengthen the conclusions and the title of the paper.

scRNA-seq data analysis methods: We agree with the reviewer and will elaborate on the various criterion, parameters and methods in our revision. As described above, our revised manuscript will include analysis of how inclusion / exclusion of VRs affects cell clusters, as well as quantification of the ER phenotype. We will address the reviewer’s concern of over-clustering.

We think that the cells expressing zero as well as two V1Rs are real and cannot be attributed to debris or doublets for the following reasons:

a) Cells expressing no V1Rs are not necessarily debris because they express other neuronal markers at the same level as cells that express one or two V1Rs. Higher expression threshold values used in our analysis may have somewhat increased the proportion of cells with zero V1Rs. We will modify figure 7-supplement 3c to add another group showing Gnai2 level in cells expressing zero V1Rs.

b) Cells co-expressing V1R genes: We listed the frequency of cells co-expressing V1R gene combinations in Supplementary table - 8. Among 134 cells that express two V1Rs, 44 cells express Vmn1r85+Vmn1r86, 21 express Vmn1r184+Vmn1r185, 13 express Vmn1r56+Vmn1r57, 6 express Vmn1r168+Vmn1r177, and so on. Doublets generally are a random combination of two cells. Here, each specific co-expression combination represents multiple cells and is highly unlikely by random chance. Some of the co-expression combinations were identified earlier and verified experimentally in Lee et al., 2019 and Hills et. al. Furthermore, Figure-7 supplement 3c shows that the level of Gnai2 expression is comparable across cells expressing one or two V1Rs. If the V1R expressing cells are doublets, we expect the level of Gnai2 to be higher, as compared to cells expressing single V1R. We will elaborate on this in our revised manuscript.

Author Response

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

Reviewer #1 (Public Review):

The manuscript by Sejour et al. is testing "translational ramp" model described previously by Tuller et al. in S. cerevisiae. Authors are using bioinformatics and reporter based experimental approaches to test whether "rare codons" in the first 40 codons of the gene coding sequences increase translation efficiency and regulate abundance of translation products in yeast cells. Authors conclude that "translation ramp" model does not have support using a new set of reporters and bioinformatics analyses. The strength of bioinformatic evidence and experimental analyses (even very limited) of the rare codons insertion in the reporter make a compelling case for the authors claims. However the major weakness of the manuscript is that authors do not take into account other models that previously disputed "rare or slow codon" model of Tuller et al. and overstate their own results that are rather limited. This maintains to be the weak part of the manuscript even in the revised form.

We are glad the reviewer thinks our evidence makes “a compelling case for the authors claims”. This was our main aim, and we are satisfied with this.

The reviewer believes the major weakness of the manuscript is that we do not take into account other models and do not (see below) cite numerous other relevant papers. The reviewer made essentially the same criticism at the first review, at which time we looked quite hard for papers generally meeting the reviewer’s description. We found a few, which we incorporated here. Still, we did not find the body of evidence whose existence the reviewer implies. We are citing every study we know to be relevant, though of course we will have inadvertently missed some, given the huge body of literature. After the first round of review, we wrote “the reviewer did not give specific references, and, though we looked, we weren’t always sure which papers the reviewer had in mind.” We hoped the reviewer would provide citations. But only two citations are provided here, both to A. Kochetov, and these don’t seem central to the reviewer’s points.

The studies that authors do not mention argue with "translation ramp" model and show more thorough analyses of translation initiation to elongation transition as well as early elongation "slow down" in ribosome profiling data. Moreover several studies have used bioinformatical analyses to point out the evolution of N-terminal sequences in multiple model organisms including yeast, focusing on either upstream ORFs (uORFs) or already annotated ORFs. The authors did not mention multiple of these studies in their revised manuscript and did not comment on their own results in the context of these previous studies.

Mostly, we do not know to what papers the reviewer is referring. This may be our failing, but it would have helped if the reviewer had cited one of them. There are papers discussing the evolution of N-terminal sequences, but as far as we know, these do not discuss translation speed or codon usage. Of course, we may have missed some papers.

As such the authors approach to data presentation, writing and data discussion makes the manuscript rather biased, focused on criticizing Tuller et al. study and short on discussing multiple other possible reasons for slow translation elongation at the beginning of the protein synthesis. This all together makes the manuscript at the end very limited.

We think the reviewer may be considering our paper as being generally about translation speeds, whereas in our minds, it is not. This difference in views as to what the paper is “about” is perhaps causing friction. To us, it is indeed a limited paper. We are narrowly focused on the finding of Tuller that there is an enrichment of rare, slow codons at the 5’ end of genes, and we have sought an explanation of this particular fact. This is not a paper about rates of translation generally—it is a limited paper about the reason for the 5’ enrichment of rare, slow codons.

To expand on this, the encoded slow 5’ translation due to rare, slow codons (of Tuller et al.) is a small effect (1% to 3%). The possible unencoded slow 5’ translation of unknown mechanism discussed by some other papers (e.g., Weinberg et al. 2016, Shah et al. 2013) is a much larger effect (50% or more). Just from the different magnitudes, it seems likely these are different phenomena. And yet, despite the small size of the encoded effect, it is for some reason this paper by Tuller et al. that has captured the attention of the literature: as we point out below, Tuller et al. has been cited over 900 times. Partly because of the wide and continuing influence of this paper, it is worth specifically and narrowly addressing its findings.

Reviewer #2 (Public Review):

Tuller et al. first made the curious observation, that the first ∼30-50 codons in most organisms are encoded by scarce tRNAs and appear to be translated slower than the rest of the coding sequences (CDS). They speculated that this has evolved to pace ribosomes on CDS and prevent ribosome collisions during elongation - the "Ramp" hypothesis. Various aspects of this hypothesis, both factual and in terms of interpreting the results, have been challenged ever since. Sejour et al. present compelling results confirming the slower translation of the first ~40 codons in S. cerevisiae but providing an alternative explanation for this phenomenon. Specifically, they show that the higher amino acid sequence divergence of N-terminal ends of proteins and accompanying lower purifying selection (perhaps the result of de novo evolution) is sufficient to explain the prevalence of rare slow codons in these regions. These results are an important contribution in understanding how aspects of the evolution of protein coding regions can affect translation efficiency on these sequences and directly challenge the "Ramp" hypothesis proposed by Tuller et al.

I believe the data is presented clearly and the results generally justify the conclusions.

We thank the reviewer for his/her attention to the manuscript, and for his/her comments.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

As mentioned in the public review major weakness of the manuscript is the lack of analyses for confounding effects, overstatements of the results (using single amino acid sequence reporter) and the lack of discussion of previous work that argues against Tuller et al model. In my previous review I mentioned multiple other studies that addressed "slow codons" model in more detail.

No, the reviewer did not cite any specific studies.

While some of these studies are mentioned in the revised manuscript, authors are still rather biased and selective in their discussions. I should also point out that previous studies, that authors fail again to mention, were focused on either translation initiation, initiation to elongation transition or early elongation effects in relation to mRNA sequence, structure, codons as well as amino acid sequence. Also additional studies with bioinformatic analyses of N-terminal conservation and existence of start sites at the beginning of the protein sequences in multiple model organisms were also omitted.

Again, we do not know to what papers the reviewer is referring. But this sounds like a lot. Our paper is aimed at a specific, narrow topic: Why is there an excess of rare, slow codons in the 5’ region of genes? We are not trying to make general statements about all things affecting and affected by translation speed, we are just trying to explain the excess of rare, slow codons.

In general manuscript seems to be too much focused-on discussion of Tuller's paper . . .

Yes, we are focused on the Tuller findings, the excess of rare slow codons in 5’ regions.

. . . and arguing with the model that was already shown by multiple other studies to be limited and not correct.

We find it unsatisfactory that the reviewer states in a public review that there are multiple other studies showing that the Tuller model is not correct, and yet does not cite any of them. Furthermore, for the reviewer to say that Tuller et al. is “not correct” is too sweeping. The core finding of Tuller et al. was the excess of rare, slow codons in the 5’ regions of genes. We confirm this; we believe it is correct; we are not aware of any literature disputing this. Then, Tuller interpreted this as an adaptation to promote translational efficiency. On the interpretation, we disagree with Tuller. But if one is to disagree with this interpretation, one needs an alternative explanation of the fact of the excess rare, slow codons. Providing such an alternative explanation, and doing an experiment to distinguish the explanations, is our contribution. We are not aware of any other paper making our interpretation.

There are of course many papers that discuss various aspects of translation at the 5’ ends of genes, and we do cite quite a few such papers in our manuscript, though certainly not all. But papers of this general kind do not, and cannot, show that Tuller et al. is “not correct”. As far as we know, no paper provides an alternative explanation for the rare slow codons, and no paper does an experiment to modulate translation speed and look at the effect on gene expression. Notably, the slow translation phenomenon associated with the rare codons found by Tuller et al. is a very small effect—a change of about 1% to 3% of translation speed. Some other papers on translation speed are dealing with possible changes in the range of 50% or more. These are presumably some other phenomenon (if indeed they are even real changes in translation speed), and, whether they are true or not, the results and interpretations of Tuller et al. could still be true or not. Of course, if we knew of some previous paper showing the Tuller paper is not correct, we should and would cite it.

To expand on the current view of Tuller in the literature, Tuller et al. has been cited 956 times according to Google Scholar. This makes it an extremely influential paper. After finding Tuller et al. in Entrez Pubmed, one can look under “Cited by” and see the five most recent papers that cite Tuller et al. The five papers given on May 23 2024 were Bharti . . . Ignatova 2024; Uddin 2024; Khandia . . . Choudhary 2024; Love and Nair 2024; and Oelschlaeger 2024. We went through these five most recent papers that cite Tuller et al., and asked, did these authors cite the Tuller results as fully correct, or did they mention any doubts about the results? All five of the papers cited the Tuller results as fully correct, with no mention of any kind of doubt. For instance, Kandia et al. 2024 state “The slow “ramp” present at 5’ end of mRNA forms an optimal and robust means to reduce ribosomal traffic jams, thus minimizing the cost of protein expression40.”, while Oelschlaeger (2024) states “Slow translation ramps have also been described elsewhere and proposed to prevent traffic jams along the mRNA [51,52,53].” Although Uddin (2024) cited Tuller as fully correct, Uddin seemed to think (it is a little unclear) that Tuller found an enrichment of highly-used codons, opposite to the actual finding. The multiple contrary studies mentioned by the reviewer do not seem to have been very influential.

There are papers containing skepticism about the Tuller interpretation, and also papers with results that are difficult to reconcile in a common-sense way with the Tuller interpretation. But skepticism, and a difficulty to reconcile with common sense, are far from a demonstration that a paper is incorrect. Indeed, Tuller et al. may have been published in Cell, and may be so highly cited, exactly because the findings are counter-intuitive, colliding with common sense. Our contribution is to find a common-sense interpretation of the surprising but correct underlying fact of the 5’ enrichment of rare, slow codons.

Having wrote that in the previous review, I have to admit that Sejour et al manuscript in the main text has a minimal amount of novelty with experimental evidence, the conclusions are based on three reporters with and without stalling/collision sequence with the same amino acid sequence and varying codons. Some more novelty is seen in bioinformatic analyses of multiple yeast sequences and sequence conservation at the N-termini of proteins. However, even this part of the manuscript is not discussed fully and with correct comparison to previous studies. Authors, based on my previous comments discuss further experimental shortcomings in their new and "expanded" discussion but the use of a single reporter in this case cannot relate to all differences that may be coming from ORFs seen in complete yeast transcriptome. There are multiple studies that used more reporters with more than one amino-acid and mRNA sequence as well as with similar variation of the rare or common codons. The handwaving argument about the influence of all other mechanisms that can arise from different start sites, RNA structure, peptide interaction with exit channel, peptidyl-tRNA drop-off, eIF3 complex initiation-elongation association, and etc, is just pointing up to a manuscript that is more about bashing up Tuller's model and old paper than trying to make a concise story about their own results and discuss their study in plethora of studies that indicated multiple other models for slow early elongation.

We don’t understand why the reviewer is so grudging.

Discussion of the ribosome's collisions and potential impact of such scenario in the author's manuscript is left completely without citation, even though such work has relevant results to the author's conclusions and Tuller's model.

This is not true. We cite Dao Duc and Song (2018) “The impact of ribosomal interference, codon usage, and exit tunnel interactions on translation elongation rate variation.” PLoS Genet 14, and Tesina, . . . and Green (2020) “Molecular mechanism of translational stalling by inhibitory codon combinations and Poly(A) tracts. EMBO J., which are two excellent papers on this subject. We also cite Gamble et al. (2016), who found the underlying result, but at that time did not attribute it to ribosome collisions.

Previous studies (not cited) for example clearly indicate how the length from stalling sequence to start codon is related to ribosome collisions. Moreover such studies are pointing out differences in initiation vs elongation rates that may impact ribosome collisions and protein expression. Both of these topics would be very valuable in discussions of evolutionary changes in the current yeast ORFs. Not to mention that authors do not really discuss also possibilities for differences in 5'UTRs and uORFs in relation to downstream ORFs sequence and codon composition.

It is not clear to us that such papers are highly relevant to the issue on which we are working.

The argument about whether cycloheximide or not is doing 5' ribosome slowdown (lines 425-443) is just rambling about Weinberg's paper from 2016 without any real conclusion. In this section authors are just throwing down hypothesis that were more clearly explained in Weinberg's manuscript or shown experimentally in studies done after the Weinberg et al. paper was published.

Earlier, the reviewer had the criticism that “The studies that authors do not mention argue with "translation ramp" model and show more thorough analyses of translation initiation to elongation transition as well as early elongation "slow down" in ribosome profiling data.” The main study we know of dealing with these issues like these is that of Weinberg et al. 2016. In our opinion, this is a thoughtful paper on these issues. But now, at this point, the reviewer seems to criticize the fact that we do extensively cite results from Weinberg et al. It is true that there is no ultimate conclusion, but why there is no conclusion is a little bit interesting. Weinberg et al show that even in studies that do not use cycloheximide as the first step in ribosome profiling, there is some left-over high density of ribosomes near 5’ ends. But, all these ribosome profiling experiments do use cycloheximide at a later step in the procedure. Until someone does a ribosome profiling experiment without the use of any cycloheximide at any step, there will be no firm conclusion. This is not our fault—and also not the issue we are writing about. And, the reason this paragraph is in the manuscript at all is that the reviewer (we thought) had asked for something like this in the first review.

At the end, even in the limited novelty of evolutionary arguments about non-existing N-terminal conservation of codons or amino acids they fail to cite and discuss previous work by Kochetov (BioEssays, 2008 and NAR, 2011) which have additional explanation on evolution of N-terminal sequences in yeast, human or Drosophila.

These two papers of Dr. Kochetov’s have some relevance and we now cite them. These are the only papers cited by the reviewer in his/her two reviews.

Probably the reviewer would have preferred a paper on a different subject.

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

Response to Reviewers:

We thank the reviewers for their comments, and their evident close reading of the manuscript. Generally, we agree with the reviewers on the strengths and weaknesses of our manuscript. Our revised manuscript has a more extensive discussion of alternative explanations for initial high ribosome density as seen by ribosome profiling, and which more specifically points out the limitations of our work.

As a preface to specific responses to the reviewers, we will say that we could divide observations of slow initial translation into two categories, which we will call “encoded slow codons”, and “increased ribosome density”. With respect to the first category, Tuller et al. documented initial “encoded slow codons”, that is, there is a statistical excess of rare, slowly-translated codons at the 5’ ends of genes. Although the size of this effect is small, statistical significance is extremely high, and the existence of this enrichment is not in any doubt. At first sight, this appears to be a strong indication of a preference for slow initial translation. In our opinion, our main contribution is to show that there is an alternative explanation for this initial enrichment of rare, slow codons—that they are a spandrel, a consequence of sequence plasticity at the 5’ (and 3’) ends of genes. The reviewers seem to generally agree with this, and we are not aware that any other work has provided an explanation for the 5’ enrichment of rare codons.

The second category of observations pertaining to slow initial translation is “increased ribosome density”. Early ribosome profiling studies used cycloheximide to arrest cell growth, and these studies showed a higher density of ribosomes near the 5’ end of genes than elsewhere. This high initial ribosome density helped motivate the paper of Tuller et al., though their finding of “encoded slow codons” could explain only a very small part of the increased ribosome density. More modern ribosome profiling studies do not use cycloheximide as the first step in arresting translation, and in these studies, the density of ribosomes near the 5’ end of genes is greatly reduced. And yet, there remains, even in the absence of cycloheximide at the first step, a significantly increased density of ribosomes near the 5’ end (e.g., Weinberg et al., 2016). (However, most or all of these studies do use cycloheximide at a later step in the protocol, and the possibility of a cycloheximide artefact is difficult to exclude.) Some of the reviewer’s concerns are that we do not explain the increased 5’ ribosome density seen by ribosome profiling. We agree; but we feel it is not the main point of our manuscript. In revision, we more extensively discuss other work on increased ribosome density, and more explicitly point out the limitations of our manuscript in this regard. We also note, though, that increased ribosome density is not a direct measure of translation speed—it can have other causes.

Specific Responses.

Reviewer 1 was concerned that we did not more fully discuss other work on possible reasons for slow initial translation. We discuss such work more extensively in our revision. However, as far as we know, none of this work proposes a reason for the 5’ enrichment of rare, slow codons, and this is the main point of our paper. Furthermore, it is not completely clear that there is any slow initial translation. The increase in ribosome density seen in flash-freeze ribosome profiling could be an artefact of the use of cycloheximide at the thaw step of the protocols; or it could be a real measure of high ribosome density that occurs for some other reason than slow translation (e.g., ribosomes might have low processivity at the 5’ end).

Reviewer 1 was also concerned about confounding effects in our reporter gene analysis of the effects of different codons on efficiency of translation. We have two comments. First, it is important to remember that although we changed codons in our reporters, we did not change any amino acids. We changed codons only to synonymous codons. Thus at least one of the reviewer’s possible confounding effects—interactions of the nascent peptide chain with the exit channel of the ribosome—does not apply. However, of course, the mRNA nucleotide sequence is altered, and this would cause a change in mRNA structure or abundance, which could matter. We agree this is a limitation to our approach. However, to fully address it, we feel it would be necessary to examine a really large number of quite different sequences, which is beyond the scope of this work. Furthermore, mRNAs with low secondary structure at the 5’ end probably have relatively high rates of initiation, and also relatively high rates of elongation, and it might be quite difficult to disentangle these. But in neither case is there an argument that slow initial translation is efficient. Accurate measurement of mRNA levels would be helpful, but would not disentangle rates of initiation from rates of elongation as causes of changes in expression.

Reviewer 2 was concerned that the conservation scores for the 5’ 40 amino acids, and the 3’ 40 amino acids were similar, but slow translation was only statistically significant for the 5’ 40 amino acids. As we say in the manuscript, we are also puzzled by this. We note that 3’ translation is statistically slow, if one looks over the last 100 amino acids. Our best effort at an explanation is a sort of reverse-Tuller explanation: that in the last 40 amino acids, the new slow codons created by genome plasticity are fairly quickly removed by purifying selection, but that in the first 40 amino acids, for genes that need to be expressed at low levels, purifying selection against slow codons is reduced, because poor translation is actually advantageous for these genes. To expand on this a bit, we feel that the 5000 or so proteins of the proteome have to be expressed in the correct stoichiometric ratios, and that poor translation can be a useful tool to help achieve this. In this explanation, slow translation at the 5’ end is bad for translation (in agreement with our reporter experiments), but can be good for the organism, when it occurs in front of a gene that needs to be expressed poorly. Whereas, in Tuller, slow translation at the 5’ end is good for translation.

Reviewer 2 wondered whether the N-terminal fusion peptide affects GFP fluorescence in our reporter. This specific reporter, with this N-terminus, has been characterized by Dean and Grayhack (2012), and by Gamble et al. (2016), and the idea that a super-folder GFP reporter is not greatly affected by N-terminal fusions is based on the work of Pedelacq (2006). None of these papers show whether this N-terminal fusion might have some effect, but together, they provide good reason to think that any effect would be small. These citations have been added.

Author response:

Reviewer #1 (Public Review):

Abbasi et al. assess in this MEG study the directed connectivity of both cortical and subcortical regions during continuous speech production and perception. The authors observed bidirectional connectivity patterns between speech-related cortical areas as well as subcortical areas in production and perception. Interestingly, they found in speaking low-frequency connectivity from subcortical (the right cerebellum) to cortical (left superior temporal) areas, while connectivity from the cortical to subcortical areas was in the high frequencies. In listening a similar cortico-subcortical connectivity pattern was observed for the low frequencies, but the reversed connectivity in the higher frequencies was absent.

The work by Abbasi and colleagues addresses a relevant, novel topic, namely understanding the brain dynamics between speaking and listening. This is important because traditionally production and perception of speech and language are investigated in a modality-specific manner. To have a more complete understanding of the neurobiology underlying these different speech behaviors, it is key to also understand their similarities and differences. Furthermore, to do so, the authors utilize state-of-the-art directed connectivity analyses on MEG measurements, providing a quite detailed profile of cortical and subcortical interactions for the production and perception of speech. Importantly, and perhaps most interesting in my opinion, is that the authors find evidence for frequency-specific directed connectivity, which is (partially) different between speaking and listening. This could suggest that both speech behaviors rely (to some extent) on similar cortico-cortical and cortico-subcortical networks, but different frequency-specific dynamics.

These elements mentioned above (investigation of both production and perception, both cortico-cortical and cortico-subcortical connectivity is considered, and observing frequency-specific connectivity profiles within and between speech behaviors), make for important novel contributions to the field. Notwithstanding these strengths, I find that they are especially centered on methodology and functional anatomical description, but that precise theoretical contributions for neurobiological and cognitive models of speech are less transparent. This is in part because the study compares speech production and perception in general, but no psychophysical or psycholinguistic manipulations are considered. I also have some critical questions about the design which may pose some confounds in interpreting the data, especially with regard to comparing production and perception.

(1) While the cortico-cortical and cortico-subcortical connectivity profiles highlighted in this study and the depth of the analyses are impressive, what these data mean for models of speech processing remains on the surface. This is in part due, I believe, to the fact that the authors have decided to explore speaking and listening in general, without targeting specific manipulations that help elucidate which aspects of speech processing are relevant for the particular connectivity profiles they have uncovered. For example, the frequency-specific directed connectivity is it driven by low-level psychophysical attributes of the speech or by more cognitive linguistic properties? Does it relate to the monitoring of speech, timing information, and updating of sensory predictions? Without manipulations trying to target one or several of these components, as some of the referenced work has done (e.g., Floegel et al., 2020; Stockert et al., 2021; Todorović et al., 2023), it is difficult to draw concrete conclusions as to which representations and/or processes of speech are reflected by the connectivity profiles. An additional disadvantage of not having manipulations within each speech behavior is that it makes the comparison between listening and speaking harder. That is, speaking and listening have marked input-output differences which likely will dominate any comparison between them. These physically driven differences (or similarities for that matter; see below) can be strongly reduced by instead exploring the same manipulations/variables between speaking and listening. If possible (if not to consider for future work), it may be interesting to score psychophysical (e.g., acoustic properties) or psycholinguistic (e.g., lexical frequency) information of the speech and see whether and how the frequency-specific connectivity profiles are affected by it.

We thank the reviewer for pointing this out. The current study is indeed part of a larger project investigating the role of the internal forward model in speech perception and production. In the original, more comprehensive study, we also included a masked condition where participants produced speech as usual, but their auditory perception was masked. This allowed us to examine how the internal forward model behaves when it doesn't receive the expected sensory consequences of generated speech. However, for the current study, we focused solely on data from the speaking and listening conditions due to its specific research question. We agree that further manipulations would be interesting. However, for this study our focus was on natural speech and we avoided other manipulations (beyond masked speech) so that we can have sufficiently long recording time for the main speaking and listening conditions.

(2) Recent studies comparing the production and perception of language may be relevant to the current study and add some theoretical weight since their data and interpretations for the comparisons between production and perception fit quite well with the observations in the current work. These studies highlight that language processes between production and perception, specifically lexical and phonetic processing (Fairs et al., 2021), and syntactic processing (Giglio et al., 2024), may rely on the same neural representations, but are differentiated in their (temporal) dynamics upon those shared representations. This is relevant because it dispenses with the classical notion in neurobiological models of language where production and perception rely on (partially) dissociable networks (e.g., Price, 2010). Rather those data suggest shared networks where different language behaviors are dissociated in their dynamics. The speech results in this study nicely fit and extend those studies and their theoretical implications.

We thank the reviewer for the suggestion and we will include these references and the points made by the reviewer in our revised manuscript.

(3) The authors align the frequency-selective connectivity between the right cerebellum and left temporal speech areas with recent studies demonstrating a role for the right cerebellum for the internal modelling in speech production and monitoring (e.g., Stockert et al., 2021; Todorović et al., 2023). This link is indeed interesting, but it does seem relevant to point out that at a more specific scale, it does not concern the exact same regions between those studies and the current study. That is, in the current study the frequency-specific connectivity with temporal regions concerns lobule VI in the right cerebellum, while in the referenced work it concerns Crus I/II. The distinction seems relevant since Crus I/II has been linked to the internal modelling of more cognitive behavior, while lobule VI seems more motor-related and/or contextual-related (e.g., D'Mello et al., 2020; Runnqvist et al., 2021; Runnqvist, 2023).

We thank the reviewer for their insightful comment. The reference was intended to provide evidence for the role of the cerebellum in internal modelling in speech. We do not claim that we have the spatial resolution with MEG to reliably spatially resolve specific parts of the cerebellum.

(4) On the methodological side, my main concern is that for the listening condition, the authors have chosen to play back the speech produced by the participants in the production condition. Both the fixed order as well as hearing one's own speech as listening condition may produce confounds in data interpretation, especially with regard to the comparison between speech production and perception. Could order effects impact the observed connectivity profiles, and how would this impact the comparison between speaking and listening? In particular, I am thinking of repetition effects present in the listening condition as well as prediction, which will be much more elevated for the listening condition than the speaking condition. The fact that it also concerns their own voice furthermore adds to the possible predictability confound (e.g., Heinks-Maldonado et al., 2005). In addition, listening to one's speech which just before has been articulated may, potentially strategically even, enhance inner speech and "mouthing" in the participants, hereby thus engaging the production mechanism. Similarly, during production, the participants already hear their own voice (which serves as input in the subsequent listening condition). Taken together, both similarities or differences between speaking and listening connectivity may have been due to or influenced by these order effects, and the fact that the different speech behaviors are to some extent present in both conditions.

This is a valid point raised by the reviewer. By listening to their own previously produced speech, our participants might have anticipated and predicted the sentences easier. However, during designing our experiment, we tried to lower the chance of this anticipation by several steps. First, participants were measured in separate sessions for speech production and perception tasks. There were always several days' intervals between performing these two conditions. Secondly, our questions were mainly about a common/general topic. Consequently, participants may not remember their answers completely.

Importantly, using the same stimulus material for speaking and listening guaranteed that there was no difference in the low-level features of the material for both conditions that could have affected the results of our statistical comparison.

Due to bone conduction, hearing one’s unaltered own speech from a recording may seem foreign and could lead to unwanted emotional reactions e.g. embarrassment, so participants were asked whether they heard their own voice in a recording already (e.g. from a self-recorded voice-message in WhatsApp) which most of them confirmed. Participants were also informed that they were going to hear themselves during the measurement to further reduce unwanted psychophysiological responses.

(5) The ability of the authors to analyze the spatiotemporal dynamics during continuous speech is a potentially important feat of this study, given that one of the reasons that speech production is much less investigated compared to perception concerns motor and movement artifacts due to articulation (e.g., Strijkers et al., 2010). Two questions did spring to mind when reading the authors' articulation artifact correction procedure: If I understood correctly, the approach comes from Abbasi et al. (2021) and is based on signal space projection (SSP) as used for eye movement corrections, which the authors successfully applied to speech production. However, in that study, it concerned the repeated production of three syllables, while here it concerns continuous speech of full words embedded in discourse. The articulation and muscular variance will be much higher in the current study compared to three syllables (or compared to eye movements which produce much more stable movement potentials compared to an entire discourse). Given this, I can imagine that corrections of the signal in the speaking condition were likely substantial and one may wonder (1) how much signal relevant to speech production behavior is lost?; (2) similar corrections are not necessary for perception, so how would this marked difference in signal processing affect the comparability between the modalities?

One of the results of our previous study (Abbasi et al., 2021) was that the artefact correction was not specific to individual syllables but generalised across syllables. Also, the repeated production of syllables was associated with substantial movements of the articulators mimicking those observed during naturalistic speaking. We therefore believe that the artefact rejection is effective during speaking. We also checked this by investigating speech related coherence in brain parcels in spatial proximity to the articulators. In our previous study we also show that the correction method retains neural activity to a very large degree. We are therefore confident that speaking and listening conditions can be compared and that the loss of true signals from correcting the speaking data will be minor.

References:

• Abbasi, O., Steingräber, N., & Gross, J. (2021). Correcting MEG artifacts caused by overt speech. Frontiers in Neuroscience, 15, 682419.

• D'Mello, A. M., Gabrieli, J. D., & Nee, D. E. (2020). Evidence for hierarchical cognitive control in the human cerebellum. Current Biology, 30(10), 1881-1892.

• Fairs, A., Michelas, A., Dufour, S., & Strijkers, K. (2021). The same ultra-rapid parallel brain dynamics underpin the production and perception of speech. Cerebral Cortex Communications, 2(3), tgab040.

• Floegel, M., Fuchs, S., & Kell, C. A. (2020). Differential contributions of the two cerebral hemispheres to temporal and spectral speech feedback control. Nature Communications, 11(1), 2839.

• Giglio, L., Ostarek, M., Sharoh, D., & Hagoort, P. (2024). Diverging neural dynamics for syntactic structure building in naturalistic speaking and listening. Proceedings of the National Academy of Sciences, 121(11), e2310766121.

• Heinks‐Maldonado, T. H., Mathalon, D. H., Gray, M., & Ford, J. M. (2005). Fine‐tuning of auditory cortex during speech production. Psychophysiology, 42(2), 180-190.

• Price, C. J. (2010). The anatomy of language: a review of 100 fMRI studies published in 2009. Annals of the new York Academy of Sciences, 1191(1), 62-88.

• Runnqvist, E., Chanoine, V., Strijkers, K., Pattamadilok, C., Bonnard, M., Nazarian, B., ... & Alario, F. X. (2021). Cerebellar and cortical correlates of internal and external speech error monitoring. Cerebral Cortex Communications, 2(2), tgab038.

• Runnqvist, E. (2023). Self-monitoring: The neurocognitive basis of error monitoring in language production. In Language production (pp. 168-190). Routledge.

• Stockert, A., Schwartze, M., Poeppel, D., Anwander, A., & Kotz, S. A. (2021). Temporo-cerebellar connectivity underlies timing constraints in audition. Elife, 10, e67303.

• Strijkers, K., Costa, A., & Thierry, G. (2010). Tracking lexical access in speech production: electrophysiological correlates of word frequency and cognate effects. Cerebral cortex, 20(4), 912-928.

• Todorović, S., Anton, J. L., Sein, J., Nazarian, B., Chanoine, V., Rauchbauer, B., ... & Runnqvist, E. (2023). Cortico-cerebellar monitoring of speech sequence production. Neurobiology of Language, 1-21.

Reviewer #2 (Public Review):

Summary:

The authors re-analyse MEG data from a speech production and perception study and extend their previous Granger causality analysis to a larger number of cortical-cortical and in particular cortical-subcortical connections. Regions of interest were defined by means of a meta-analysis using Neurosynth.org and connectivity patterns were determined by calculating directed influence asymmetry indices from the Granger causality analysis results for each pair of brain regions. Abbasi et al. report feedforward signals communicated via fast rhythms and feedback signals via slow rhythms below 40 Hz, particularly during speaking. The authors highlight one of these connections between the right cerebellum lobule VI and auditory association area A5, where in addition the connection strength correlates negatively with the strength of speech tracking in the theta band during speaking (significant before multiple comparison correction). Results are interpreted within a framework of active inference by minimising prediction errors.

While I find investigating the role of cortical-subcortical connections in speech production and perception interesting and relevant to the field, I am not yet convinced that the methods employed are fully suitable to this endeavour or that the results provide sufficient evidence to make the strong claim of dissociation of bottom-up and top-down information flow during speaking in distinct frequency bands.

Strengths:

The investigation of electrophysiological cortical-subcortical connections in speech production and perception is interesting and relevant to the field. The authors analyse a valuable dataset, where they spent a considerable amount of effort to correct for speech production-related artefacts. Overall, the manuscript is well-written and clearly structured.

Weaknesses:

The description of the multivariate Granger causality analysis did not allow me to fully grasp how the analysis was performed and I hence struggled to evaluate its appropriateness. Knowing that (1) filtered Granger causality is prone to false positives and (2) recent work demonstrates that significant Granger causality can simply arise from frequency-specific activity being present in the source but not the target area without functional relevance for communication (Schneider et al. 2021) raises doubts about the validity of the results, in particular with respect to their frequency specificity. These doubts are reinforced by what I perceive as an overemphasis on results that support the assumption of specific frequencies for feedforward and top-down connections, while findings not aligning with this hypothesis appear to be underreported. Furthermore, the authors report some main findings that I found difficult to reconcile with the data presented in the figures. Overall, I feel the conclusions with respect to frequency-specific bottom-up and top-down information flow need to be moderated and that some of the reported findings need to be checked and if necessary corrected.

Major points

(1) I think more details on the multivariate GC approach are needed. I found the reference to Schaum et al., 2021 not sufficient to understand what has been done in this paper. Some questions that remained for me are:

(i) Does multivariate here refer to the use of the authors' three components per parcel or to the conditioning on the remaining twelve sources? I think the latter is implied when citing Schaum et al., but I'm not sure this is what was done here?

If it was not: how can we account for spurious results based on indirect effects?

Yes, multivariate refers to the three components.

(ii) Did the authors check whether the GC of the course-target pairs was reliably above the bias level (as Schaum et. al. did for each condition separately)? If not, can they argue why they think that their results would still be valid? Does it make sense to compute DAIs on connections that were below the bias level? Should the data be re-analysed to take this concern into account?

We performed statistics on DAI and believe that this is a valid approach. We argue that random GC effects would not survive our cluster-corrected statistics.

(iii) You may consider citing the paper that introduced the non-parametric GC analysis (which Schaum et al. then went on to apply): Dhamala M, Rangarajan G, Ding M. Analyzing Information Flow in Brain Networks with Nonparametric Granger Causality. Neuroimage. 2008; 41(2):354-362. https://doi.org/10.1016/j.neuroimage.2008.02. 020

Thanks, we will add this reference in the revised version.

(2) GC has been discouraged for filtered data as it gives rise to false positives due to phase distortions and the ineffectiveness of filtering in the information-theoretic setting as reducing the power of a signal does not reduce the information contained in it (Florin et al., 2010; Barnett and Seth, 2011; Weber et al. 2017; Pinzuti et al., 2020 - who also suggest an approach that would circumvent those filter-related issues). With this in mind, I am wondering whether the strong frequency-specific claims in this work still hold.

This must be a misunderstanding. We are aware of the problem with GC on filtered data. But GC was here computed on broadband data and not in individual frequency bands.

(3) I found it difficult to reconcile some statements in the manuscript with the data presented in the figures:

(i) Most notably, the considerable number of feedforward connections from A5 and STS that project to areas further up the hierarchy at slower rhythms (e.g. L-A5 to R-PEF, R-Crus2, L CB6 L-Tha, L-FOP and L-STS to R-PEF, L-FOP, L-TOPJ or R-A5 as well as R-STS both to R-Crus2, L-CB6, L-Th) contradict the authors' main message that 'feedback signals were communicated via slow rhythms below 40 Hz, whereas feedforward signals were communicated via faster rhythms'. I struggled to recognise a principled approach that determined which connections were highlighted and reported and which ones were not.

(ii) "Our analysis also revealed robust connectivity between the right cerebellum and the left parietal cortex, evident in both speaking and listening conditions, with stronger connectivity observed during speaking. Notably, Figure 4 depicts a prominent frequency peak in the alpha band, illustrating the specific frequency range through which information flows from the cerebellum to the parietal areas." There are two peaks discernible in Figure 4, one notably lower than the alpha band (rather theta or even delta), the other at around 30 Hz. Nevertheless, the authors report and discuss a peak in the alpha band.

(iii) In the abstract: "Notably, high-frequency connectivity was absent during the listening condition." and p.9 "In contrast with what we reported for the speaking condition, during listening, there is only a significant connectivity in low frequency to the left temporal area but not a reverse connection in the high frequencies."

While Fig. 4 shows significant connectivity from R-CB6 to A5 in the gamma frequency range for the speaking, but not for the listening condition, interpreting comparisons between two effects without directly comparing them is a common statistical mistake (Makin and Orban de Xivry). The spectrally-resolved connectivity in the two conditions actually look remarkably similar and I would thus refrain from highlighting this statement and indicate clearly that there were no significant differences between the two conditions.

(iv) "This result indicates that in low frequencies, the sensory-motor area and cerebellum predominantly transmit information, while in higher frequencies, they are more involved in receiving it."

I don't think that this statement holds in its generality: L-CB6 and R-3b both show strong output at high frequencies, particularly in the speaking condition. While they seem to transmit information mainly to areas outside A5 and STS these effects are strong and should be discussed.

We appreciate the reviewer's thoughtful comments. We acknowledge that not all connectivity patterns strictly adhere to the initial observation regarding feedback and feedforward communication. It's true that our primary focus was on interactions between brain regions known to be crucial for speech prediction, including auditory, somatosensory, and cerebellar areas. However, we also presented connectivity patterns across other regions to provide a more comprehensive picture of the speech network. We believe this broader perspective can be valuable for future research directions.

Regarding the reviewer's observation about the alpha band peak in Figure 4, we agree that a closer examination reveals the connectivity from right cerebellum to the left parietal is in a wider low frequency range. We will refrain from solely emphasizing the alpha band and acknowledge the potential contribution of lower frequencies to cerebellar-parietal communication.

We also appreciate the reviewer highlighting the need for a more nuanced interpretation of the listening condition connectivity compared to the speaking condition. The reviewer is correct in pointing out that while Figure 4 suggests a high-frequency connectivity from L-A5 to R-CB only in the speaking condition, a direct statistical comparison between conditions might not reveal a significant difference. We will revise the manuscript to clarify this point.

Finally, a closer examination of Figure 3 revealed that the light purple and dark green edges in the speaking condition for R-CB6 and L-3b suggest outgoing connections at low frequencies, while other colored edges indicate information reception at high frequencies. We acknowledge that exceptions to this directional pattern might exist and warrant further investigation in future studies.

(4) "However, definitive conclusions should be drawn with caution given recent studies raising concerns about the notion that top-down and bottom-up signals can only be transmitted via separate frequency channels (Ferro et al., 2021; Schneider et al., 2021; Vinck et al., 2023)."

I appreciate this note of caution and think it would be useful if it were spelled out to the reader why this is the case so that they would be better able to grasp the main concerns here. For example, Schneider et al. make a strong point that we expect to find Granger-causality with a peak in a specific frequency band for areas that are anatomically connected when the sending area shows stronger activity in that band than the receiving one, simply because of the coherence of a signal with its own linear projection onto the other area. The direction of a Granger causal connection would in that case only indicate that one area shows stronger activity than the other in the given frequency band. I am wondering to what degree the reported connectivity pattern can be traced back to regional differences in frequency-specific source strength or to differences in source strength across the two conditions.

This is indeed an important point. That is why we are discussing our results with great caution and specifically point the reader to the relevant literature. We are indeed thinking about a future study where we investigate this connectivity using other connectivity metrics and a detailed consideration of power.

Reviewer #3 (Public Review):

In the current paper, Abbasi et al. aimed to characterize and compare the patterns of functional connectivity across frequency bands (1 Hz - 90 Hz) between regions of a speech network derived from an online meta-analysis tool (Neurosynth.org) during speech production and perception. The authors present evidence for complex neural dynamics from which they highlight directional connectivity from the right cerebellum to left superior temporal areas in lower frequency bands (up to beta) and between the same regions in the opposite direction in the (lower) high gamma range (60-90 Hz). Abbasi et al. interpret their findings within the predictive coding framework, with the cerebellum and other "higher-order" (motor) regions transmitting top-down sensory predictions to "lower-order" (sensory) regions in the lower frequencies and prediction errors flowing in the opposite direction (i.e., bottom-up) from those sensory regions in the gamma band. They also report a negative correlation between the strength of this top-down functional connectivity and the alignment of superior temporal regions to the syllable rate of one's speech.

Strengths:

(1) The comprehensive characterization of functional connectivity during speaking and listening to speech may be valuable as a first step toward understanding the neural dynamics involved.

(2) The inclusion of subcortical regions and connectivity profiles up to 90Hz using MEG is interesting and relatively novel.

(3) The analysis pipeline is generally adequate for the exploratory nature of the work.

Weaknesses:

(1) The work is framed as a test of the predictive coding theory as it applies to speech production and perception, but the methodological approach is not suited to this endeavor.

We agree that we cannot provide definite evidence for predictive coding in speech production and perception and we believe that we do not make that claim in the manuscript. However, our results are largely consistent with what can be expected based on predictive coding theory.

(2) Because of their theoretical framework, the authors readily attribute roles or hierarchy to brain regions (e.g., higher- vs lower-order) and cognitive functions to observed connectivity patterns (e.g., feedforward vs feedback, predictions vs prediction errors) that cannot be determined from the data. Thus, many of the authors' claims are unsupported.

We will revise the manuscript to more clearly differentiate our results (e.g. directed Granger-Causality from A to B) from their interpretation (potentially indicating feedforward or feedback signals).

(3) The authors' theoretical stance seems to influence the presentation of the results, which may inadvertently misrepresent the (otherwise perfectly valid; cf. Abbasi et al., 2023) exploratory nature of the study. Thus, results about specific regions are often highlighted in figures (e.g., Figure 2 top row) and text without clear reasons.

Our connectograms reveal a multitude of results that we hope is interesting to the community. At the same time the wealth of findings poses a problem for describing them. We did not see a better way then to highlight specific connections of interest.

(4) Some of the key findings (e.g., connectivity in opposite directions in distinct frequency bands) feature in a previous publication and are, therefore, interesting but not novel.

We actually see this as a strength of the current manuscript. The computation of connectivity is here extended to a much larger sample of brain areas. It is reassuring to see that the previously reported results generalise to other brain areas.

(5) The quantitative comparison between speech production and perception is interesting but insufficiently motivated.

We thank the reviewer for this comment. We have addressed that in detail in response to the point (1&4) of reviewer 1.

(6) Details about the Neurosynth meta-analysis and subsequent selection of brain regions for the functional connectivity analyses are incomplete. Moreover, the use of the term 'Speech' in Neurosynth seems inappropriate (i.e., includes irrelevant works, yielding questionable results). The approach of using separate meta-analyses for 'Speech production' and 'Speech perception' taken by Abbasi et al. (2023) seems more principled. This approach would result, for example, in the inclusion of brain areas such as M1 and the BG that are relevant for speech production.

We agree that there are inherent limitations in automated meta-analysis tools such as Neurosynth. Papers are used in the meta-analysis that might not be directly relevant. However, Neurosynth has proven its usefulness over many years and has been used in many studies. We also agree that our selection of brain areas is not complete. But Granger Causality analysis of every pair of ROIs leads to complex results and we had to limit our selection of areas.

(7) The results involving subcortical regions are central to the paper, but no steps are taken to address the challenges involved in the analysis of subcortical activity using MEG. Additional methodological detail and analyses would be required to make these results more compelling. For example, it would be important to know what the coverage of the MEG system is, what head model was used for the source localization of cerebellar activity, and if specific preprocessing or additional analyses were performed to ensure that the localized subcortical activity (in particular) is valid.

There is a large body of evidence demonstrating that MEG can record signals from deep brain areas such as thalamus and cerebellum including Attal & Schwarz 2013, Andersen et al, Neuroimage 2020; Piastra et al., 2020; Schnitzler et al., 2009. These and other studies provide evidence that state-of-the-art recording (with multichannel SQUID systems) and analysis is sufficient to allow reconstruction of subcortical areas. However, spatial resolution is clearly reduced for these deep areas. We will add a statement in the revised manuscript to acknowledge this limitation.

(8) The results and methods are often detailed with important omissions (a speech-brain coupling analysis section is missing) and imprecisions (e.g., re: Figure 5; the Connectivity Analysis section is copy-pasted from their previous work), which makes it difficult to understand what is being examined and how. (It is also not good practice to refer the reader to previous publications for basic methodological details, for example, about the experimental paradigm and key analyses.) Conversely, some methodological details are given, e.g., the acquisition of EMG data, without further explanation of how those data were used in the current paper.

We will revise the relevant sections of the manuscript.

(9) The examination of gamma functional connectivity in the 60 - 90 Hz range could be better motivated. Although some citations involving short-range connectivity in these frequencies are given (e.g., within the visual system), a more compelling argument for looking at this frequency range for longer-range connectivity may be required.

Given previous evidence of connectivity in the gamma band we think that it would be a weakness to exclude this frequency band from analysis.

(10) The choice of source localization method (linearly constrained minimum variance) could be explained, particularly given that other methods (e.g. dynamic imaging of coherent sources) were specifically designed and might potentially be a better alternative for the types of analyses performed in the study.

Both LCMV and DICS are beamforming methods. We used LCMV because we wanted used Granger Causality which requires broadband signals. DICS would only provide frequency-specific band-limited signals.

(11) The mGC analysis needs to be more comprehensively detailed for the reader to be able to assess what is being reported and the strength of the evidence. Relatedly, first-level statistics (e.g., via estimation of the noise level) would make the mGC and DAI results more compelling.

We perform group-level cluster-based statistics on mGC while correcting for multiple comparisons across frequency bands and brain parcels and report only significant results. This is an established approach that is routinely used in this type of studies.

(12) Considering the exploratory nature of the study, it is essential for other researchers to continue investigating and validating the results presented in the current manuscript. Thus, it is concerning that data and scripts are not fully and openly available. Data need not be in its raw state to be shared and useful, which circumvents the stated data privacy concerns.

We acknowledge the reviewer's concern regarding the full availability of the dataset. Due to privacy limitations on the collected data, we are unable to share it publicly at this time. However, to promote transparency and enable further exploration, we have provided the script used for data analysis and an example dataset. This example dataset should provide a clear understanding of the data structure and variables used in the analysis. Additionally, we are happy to share the complete dataset upon request from research teams interested in performing in-depth secondary analyses.

Author response:

We would like to thank all reviewers for their time, critical evaluation, recognition, and constructive comments of the manuscript. We will revise the manuscript accordingly. Below are our point-to-point response to the comments.

From Reviewer #1:

“…several previous studies have identified co-expression of vomeronasal receptors by vomeronasal sensory neurons, and the expression of non-vomeronasal receptors, and this was not adequately addressed in the manuscript as presented.”

We plan to add context and citations to the Introduction and Results sections relating to recent studies on the co-expression of vomeronasal receptors and the expression of non-vomeronasal receptors in VSNs.

“The data resulting from the use of the Resolve Biosciences spatial transcriptomics platform are somewhat difficult to interpret, and the methods are somewhat opaque.”

Unfortunately, detailed Molecular Cartography protocols remain proprietary at Resolve Biosciences and were not disclosed. We acknowledge this limitation. Our role in the acquisition and processing of data for this experiment is included in the current Methods section. We will clarify this in the revised manuscript. Additional figures produced by the Molecular Cartography analysis will also be added (See response to Reviewer #2, below) to the supplemental materials to help clarify interpretation of the results.

From Reviewer #2:

“…the authors present a biased report of previously published work, largely including only those results that do not overlap with their own findings, but ignoring results that would question the novelty of the data presented here.”

We had no intention of misleading the readers. In fact, we have discussed discrepancies between our results with other studies. However, we inadvertently left out a critical publication in preparing the manuscript. We plan to add context and citations (where missing) relating to recent studies that use single cell RNA sequencing in the vomeronasal organ, studies relating to the co-expression of vomeronasal receptors, and studies discussing V1R/V2R lineage determination.

“Did the authors perform any cell selectivity, or any directed dissection, to obtain mainly neuronal cells? Previous studies reported a greater proportion of non-neuronal cells. For example, while Katreddi and co-workers (ref 89) found that the most populated clusters are identified as basal cells, macrophages, pericytes, and vascular smooth muscle, Hills Jr. et al. in this work did not report such types of cells. Did the authors check for the expression of marker genes listed in Ref 89 for such cell types?”

For VNO dissections, we removed bones and blood vessels from VNO tissue and only kept the sensory epithelium. This procedure removed vascular smooth muscle cells, pericytes, and other non-neuronal cell types, which explains differences in cell proportions between out study and previous studies. We used a DAPI/Draq5 assay to sort live/nucleated cells for sequencing and no specific markers were used for cell selection. All cells in the experiment were successfully annotated using the cell-type markers shown in Fig. 1B, save for cells from the sVSN cluster, which were novel, and required further analysis to characterize.

“The authors should report the marker genes used for cell annotation.”

Marker genes used for cell annotation are shown in figure 1B. A full list of all marker genes used in the cell annotation process will be provided.

“The authors reported no differences between juvenile and adult samples, and between male and female samples. It is not clear how they evaluate statistically significant differences, which statistical test was used, or what parameters were evaluated.”

The claims made about male/female mice and P14/P56 mice directly pertain to the distribution of clusters and cells in UMAP space as seen in Figure 1 C & D. We have indeed performed differential gene expression analysis for male/female and P14/P56 comparisons using the FindMarkers function from the Seurat R package. Although we have found significant differential expression between male and female, and between P14 and P56 animals, the genes in this list do not appear to be influential for the neuronal lineage and cell type specification or related to cell adhesion molecules, which are the main focuses of this study. Nevertheless, we plan to add these results to the supplemental materials in a revised manuscript.

“‘Based on our transcriptomic analysis, we conclude that neurogenic activity is restricted to the marginal zone.’ This conclusion is quite a strong statement, given that this study was not directed to carefully study neurogenesis distribution, and when neurogenesis in the basal zone has been proposed by other works, as stated by the authors.”

Eighteen slides from whole VNO sections were used in Molecular Cartography analysis, while one representative slide was used to present findings. Across all slides, GBCs, INPs, and iVSNs show a pattern of proximity to the marginal zone (MZ), with GBCs presenting nearest to the MZ and iVSNs presented furthest. We believe that the full scope of our results justifies our claim that neurogenesis is restricted to the MZ. This claim is also supported by the 2021 study by Katreddi & Forni. We will provide additional figures to further support this claim.

“The authors report at least two new types of sensory neurons in the mouse VNO, a finding of huge importance that could have a substantial impact on the field of sensory physiology. However, the evidence for such new cell types is based solely on this transcriptomic dataset and, as such, is quite weak, since many crucial morphological and physiological aspects would be missing to clearly identify them as novel cell types. As stated before, many control and confirmatory experiments, and a careful evaluation of the results presented in this work must be performed to confirm such a novel and interesting discovery. The reported "novel classes of sensory neurons" in this work could represent previously undescribed types of sensory neurons, but also previously reported cells (see below) or simply possible single-cell sequencing artefacts.”

The reviewer is correct that detailed morphological and physiological studies are needed to further understand these cells. This is an opinion we share. Our paper is primarily intended as a resource paper to provide access to a large-scale single-cell RNA-sequenced dataset and discoveries based on the transcriptomic data that can support and inspire ongoing and future experiments in the field. Nonetheless, we are confident that neither of the novel cell clusters are the result of sequencing artefacts. We performed a robust quality-control protocol, including count correction for ambient RNA with the R package, SoupX, multiplet cell detection and removal with the Python module, Scrublet, and a strict 5% mitochondrial gene expression cut-off. Furthermore, the cell clusters in question show no signs of being the result of sequencing artefacts, as they are physically connected in a reasonable orientation to the rest of the neuronal lineage in modular clusters in 2D and 3D UMAP space. The OSN and sVSN (S1H) cell clusters each show distinct and self-consistent expressions of genes. Gene ontology (GO) analysis reveals significant GO term enrichment for both the sVSN (Fig. 2G) and mOSN clusters when compared to mature V1R and V2R VSNs, indicating functional differences. Additional figures for mOSN differential gene expression and gene ontology analysis results will be added to the supplemental figures.

“The authors report the co-expression of V2R and Gnai2 transcripts based on sequencing data. That could dramatically change classical classifications of basal and apical VSNs. However, did the authors find support for this co-expression in spatial molecular imaging experiments?” 

Genes with extremely high expression levels overwhelm signals from other genes, and therefore had to be removed from the experiment. This is a limitation of the Molecular Cartography platform. Unfortunately, Gnai2 was determined to be one of these genes and was not evaluated for this purpose.

“Canonical OSNs: The authors report a cluster of cells expressing neuronal markers and ORs and call them canonical OSN. However, VSNs expressing ORs have already been reported in a detailed study showing their morphology and location inside the sensory epithelium (References 82, 83). Such cells are not canonical OSNs since they do not show ciliary processes, they express TRPC2 channels and do not express Golf. Are the "canonical OSNs" reported in this study and the OR-expressing VSNs (ref 82, 83) different? Which parameters, other than Gnal and Cnga2 expression, support the authors' bold claim that these are "canonical OSNs"? What is the morphology of these neurons? In addition, the mapping of these "canonical OSNs" shown in Figure 2D paints a picture of the negligible expression/role of these cells (see their prediction confidence).” 

We observe OR expression in VSNs in our data; these cells cluster with VSNs. The putative mOSN cluster exhibits its own trajectory, distinct from VSN clusters. These cells express Gnal (Golf), which is not expressed in VSNs expressing ORs, nor in any other cell-type in the data. After performing differential gene expression on the putative mOSN cluster, comparing with V1R and V2R VSNs, independently, GO analysis returned the top significantly enriched GO molecular function, ‘olfactory receptor activity’, and the top significantly enriched cellular component, ‘cilium’. Because we were limited to list of 100 genes in Molecular Cartography probe panel, we have prioritized the detection of canonical VNO cell-types, vomeronasal receptor co-expression, and the putative sVSNs, and were not able to include a robust analysis of the putative OSNs.

“Secretory VSN: The authors report another novel type of sensory neurons in the VNO and call them "secretory VSNs". Here, the authors performed an analysis of differentially expressed genes for neuronal cells (dataset 2) and found several differentially expressed genes in the sVSN cluster. However, it would be interesting to perform a gene expression analysis using the whole dataset including neuronal and non-neuronal cells. Could the authors find any marker gene that unequivocally identifies this new cell type?”

We did not find unequivocal marker genes for sVSNs. We did perform differential analysis of the sVSN cluster with whole VNO data and with the neuronal subset, as well as against specific cell-types. We could not find a single gene that was perfectly exclusive to sVSNs. We used a combinatorial marker-gene approach to predicting sVSNs in the Molecular Cartography data. This required a larger subset of our 100 gene panel to be dedicated to genes for detecting sVSNs.

“When the authors evaluated the distribution of sVSN using the Molecular Cartography technique, they found expression of sVSN in both sensory and non-sensory epithelia. How do the authors explain such unexpected expression of sensory neurons in the non-sensory epithelium?” 

In our scRNA-Seq experiment, blood vessels were removed, limiting the power to distinguish between certain cell types. Because of the limited number of genes that we can probe using Molecular Cartography, the number of genes associated with sVSNs may be present in the non-sensory epithelium. This could lead to the identification of cells that may or may not be identical to the sVSNs in the non-neuronal epithelium. Indeed, further studies will need to be conducted to determine the specificity of these cells.

“The low total genes count and low total reads count, combined with an "expression of marker genes for several cell types" could indicate low-quality beads (contamination) that were not excluded with the initial parameter setting. It looks like cells in this cluster express a bit of everything V1R, V2R, OR, secretory proteins...”

We are confident that the putative sVSN cell cluster is not the result of low-quality cells. We performed a robust quality-control protocol, including count correction for ambient RNA with the R package, SoupX, multiplet cell detection and removal with the Python module, Scrublet, and a strict 5% mitochondrial gene expression cut-off. Furthermore, the cell clusters in question show no signs of being the result of sequencing artefacts, as they are connected in a reasonable orientation to the rest of the neuronal lineage in modular clusters in 2D and 3D UMAP space. The OSN and sVSN cell clusters each show distinct and self-consistent expressions of genes (Fig. S1H). Gene ontology (GO) analysis reveals significant GO term enrichment for both the sVSN (Fig. 2G) and mOSN clusters when compared to mature V1R and V2R VSNs, indicating functional differences. Moreover, while some genes were expressed at a lower level when compared to the canonical VSNs, others were expressed at higher levels, precluding the cause of discrepancy as resulting from an overall loss of gene counts.

“The authors wrote ‘...the transcriptomic landscape that specifies the lineages is not known...’. This statement is not completely true, or at least misleading. There are still many undiscovered aspects of the transcriptomics landscape and lineage determination in VSNs. However, authors cannot ignore previously reported data showing the landscape of neuronal lineages in VSNs (Ref ref 88, 89, 90, 91 and doi.org/10.7554/eLife.77259). Expression of most of the transcription factors reported by this study (Ascl1, Sox2, Neurog1, Neurod1...) were already reported, and for some of them, their role was investigated, during early developmental stages of VSNs (Ref ref 88, 89, 90, 91 and doi.org/10.7554/eLife.77259). In summary, the authors should fully include the findings from previous works (Ref ref 88, 89, 90, 91 and doi.org/10.7554/eLife.77259), clearly state what has been already reported, what is contradictory and what is new when compared with the results from this work.“

This is a difference in opinion about the terminology. Transcriptomic landscape in our paper refers to the genome-wide expression by individual cells, not just individual genes. The reviewer is correct that many of the genetic specifiers have been identified, which we cited and discussed. We consider these studies as providing a “genetic” underpinning, rather than the “transcriptomic landscape” in lineage progression. We will clarify this point in the revised manuscript. 

“…the co-expression of specific V2Rs with specific transcription factors does not imply a direct implication in receptor selection. Directed experiments to evaluate the VR expression dependent on a specific transcription factor must be performed.” 

The reviewer is correct, and we did not claim that the co-expression of specific transcription factors indicate a direct relationship with receptor selection. We agree that further directed experiments are required to investigate this question.

“This study reports that transcription factors, such as Pou2f1, Atf5, Egr1, or c-Fos could be associated with receptor choice in VSNs. However, no further evidence is shown to support this interaction. Based on these purely correlative data, it is rather bold to propose cascade model(s) of lineage consolidation.”

The reviewer is correct. As any transcriptomic study will only be correlative, additional studies will be needed to unequivocally determine the mechanistic link between the transcription factors with receptor choice. Our model provides a base for these studies.

“The authors use spatial molecular imaging to evaluate the co-expression of many chemosensory receptors in single VNO cells. […] However, it is difficult to evaluate and interpret the results due to the lack of cell borders in spatial molecular imaging. The inclusion of cell border delimitation in the reported images (membrane-stained or computer-based) could be tremendously beneficial for the interpretation of the results.”

The most common practice for cell segmentation of spatial transcriptomics data is to determine cell borders based on nuclear staining with expansion. We have tested multiple algorithms based on recent studies, but each has its own caveat. We will clarify this point in the revised manuscript.

“It is surprising that the authors reported a new cell type expressing OR, however, they did not report the expression of ORs in Molecular Cartography technique. Did the authors evaluate the expression of OR using the cartography technique?” 

We were limited to a 100-gene probe panel and only included one OR, the expression was not high enough for us to substantiate any claims.

From Reviewer #3:

“(1) The authors claim that they have identified two new classes of sensory neurons, one being a class of canonical olfactory sensory neurons (OSNs) within the VNO. This classification as canonical OSNs is based on expression data of neurons lacking the V1R or V2R markers but instead expressing ORs and signal transduction molecules, such as Gnal and Cnga2. Since OR-expressing neurons in the VNO have been previously described in many studies, it remains unclear to me why these OR-expressing cells are considered here a "new class of OSNs." Moreover, morphological features, including the presence of cilia, and functional data demonstrating the recognition of chemosignals by these neurons, are still lacking to classify these cells as OSNs akin to those present in the MOE. While these cells do express canonical markers of OSNs, they also appear to express other VSN-typical markers, such as Gnao1 and Gnai2 (Figure 2B), which are less commonly expressed by OSNs in the MOE. Therefore, it would be more precise to characterize this population as atypical VSNs that express ORs, rather than canonical OSNs.”

We observe OR expression in VSNs in our data; these cells cluster with VSNs. The putative mOSN cluster exhibits its own trajectory, distinct from VSN clusters. These cells express Gnal (Golf), which is not expressed in VSNs expressing ORs, nor in any other cell-type in the data. We have performed differential gene expression analysis on the putative mOSN cluster to compare with V1R and V2R VSNs. GO analysis returned the top significantly enriched GO terms include “olfactory receptor activity” and “cilium”., further supporting that these are OSNs Because we were limited to list of 100 genes in Molecular Cartography probe panels, we have prioritized the detection of canonical VNO cell-types, vomeronasal receptor co-expression, and the putative sVSNs, and were not able to include a robust analysis of the putative OSNs. With regard to Gnai2 and Go expression, we have examined our data from the OSNs dissociated from the olfactory epithelium and detected substantial expression of both. This new analysis provides additional support for our claim. We will update the information in a revised manuscript.

“(2) The second new class of sensory neurons identified corresponds to a group of VSNs expressing prototypical VSN markers (including V1Rs, V2Rs, and ORs), but exhibiting lower ribosomal gene expression. Clustering analysis reveals that this cell group is relatively isolated from V1R- and V2R-expressing clusters, particularly those comprising immature VSNs. The question then arises: where do these cells originate? Considering their fewer overall genes and lower total counts compared to mature VSNs, I wonder if these cells might represent regular VSNs in a later developmental stage, i.e., senescent VSNs. While the secretory cell hypothesis is compelling and supported by solid data, it could also align with a late developmental stage scenario. Further data supporting or excluding these hypotheses would aid in understanding the nature of this new cell cluster, with a comparison between juvenile and adult subjects appearing particularly relevant in this context.” 

We wholeheartedly agree with this assessment. Our initial thought was that these were senescent VSNs, but the trajectory analysis did not support this scenario, leading us to propose that these are putative secretive cells. Our analysis also shows that overall, 46% of the putative sVSNs were from the P14 sample and 54% from P56. These cells comprise roughly 6.4% of all P14 cells and 8.5% of P56 cells. In comparison, 28.4% of all cells are mature V1R VSNs at P14, but the percentage rise to 46.7% at P56. The significant presence of sVSNs at P14, and the disproportionate increase when compared with mature VSNs indicate that these are unlikely to be late developmental stage or senescent cells, although we cannot exclude these possibilities. We plan to clarify these points in the revised manuscript.   

We did not include sVSNs in the trajectory inference analysis because of inherent uncertainty about their developmental origins. However, PCA embeddings were the basis of the pseudotime analysis, and those embeddings that do include the sVSN cluster show that it is distributed evenly between the mature V1R and V2R clusters, with all mature clusters equidistant from GBC and INP clusters, indicating that they may indeed originate from the same stem cell populations. We plan to include trajectory analysis based on this assumption in the revised manuscript.

(3) The authors' decision not to segregate the samples according to sex is understandable, especially considering previous bulk transcriptomic and functional studies supporting this approach. However, many of the highly expressed VR genes identified have been implicated in detecting sex-specific pheromones and triggering dimorphic behavior. It would be intriguing to investigate whether this lack of sex differences in VR expression persists at the single-cell level. Regardless of the outcome, understanding the presence or absence of major dimorphic changes would hold broad interest in the chemosensory field, offering insights into the regulation of dimorphic pheromone-induced behavior. Additionally, it could provide further support for proposed mechanisms of VR receptor choice in VSNs. 

The reviewer raised a good point. We did not observe differences between male and female, or between P14 and P56 mice in the distribution of clusters and cells in UMAP space. Indeed, our differential expression analysis has revealed significantly differentially expressed genes in both comparisons. These genes have not been implicated in lineage or cell type determination and we decided not to include the analysis in the current version. In the revised manuscript, we plan to include the results.   

“(4) The expression analysis of VRs and ORs seems to have been restricted to the cell clusters associated with the neuronal lineage. Are VRs/ORs expressed in other cell types, i.e. sustentacular, HBC, or other cells?” 

Sparsely expressed low counts of VR and OR genes were observed in non-neuronal cell-types. When their expression as a percentage of cell-level gene counts is considered, however, the expression is negligible when compared to the neurons. The observed expression may be explained by stochastic base-level expression, or it may be the result of remnant ambient RNA that passed filtering. We will clarify this point in the revision.

Author response:

Thank you for organising the review and providing us with the reviewer's feedback. These comments are very useful, and we would like to express our gratitude to the reviewers for their efforts.

The reviewers all point out a number of related improvements, relating to: 1) describing various processing steps more clearly, in the online documentation but also in the manuscript itself (e.g. for particle picking), 2) describing more clearly what features Ais offers, how these compare to those of other programmes, and how they might be interfaced with in third-party programmes (e.g. the expected format of models), and 3) a degree of subjectivity in discussion of the results presented in the manuscript (e.g. our statement that Pix2pix performed better in some cases than did other architectures).

We will address these points, as well as the various other suggestions, in the upcoming revised manuscript and updates to Ais.

Author response:

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

Reviewer #1 (Public Review):

Summary:

Perampalam et al. describe novel methods for genome-wide CRISPR screening to identify and validate genes essential for HGSOC spheroid viability. In this study, they report that Netrin signaling is essential for maintaining disseminated cancer spheroid survival, wherein overexpression of Netrin pathway genes increases tumor burden in a xenograft model of ovarian cancer. They also show that high netrin expression correlates with poor survival outcomes in ovarian cancer patients. The study provides insights into the biology of netrin signaling in DTC cluster survival and warrants development of therapies to block netrin signaling for treating serous ovarian cancer.

Strengths:

- The study identifies Netrin signaling to be important in disseminated cancer spheroid survival

- A Novel GO-CRISPR methodology was used to find key genes and pathways essential for disseminated cancer cell survival

Thanks for the endorsement of our work and its importance to metastasis in ovarian cancer.

Weaknesses:

- The term dormancy is not fully validated and requires additional confirmation to claim the importance of Netrin signaling in "dormant" cancer survival.

- Findings shown in the study largely relate to cancer dissemination and DTS survival rather than cancer dormancy.

Much of the validation of dormancy and cell cycle arrest in HGSOC spheroids, as well as the culture model, have been published previously and hence was not repeated here.  I think this reviewer will appreciate the updated citations and explanations to better illustrate the state of knowledge.  We have also added new experiments that further emphasize the dormant state of spheroid cells in culture and xenografts, as well as patient derived spheroids used in this study.

Reviewer #1 (Recommendations for Authors):

(1) It is unclear what spheroid/adherent enrichment ratio is and how it ties into genes affecting cell viability. Why is an ER below 1 the criteria for selecting survival genes?

Our screen uses the ‘guide only’ comparison in each culture condition to establish a gene score under that specific condition.  A low adherent score captures genes that are essential under standard culture conditions where cells are proliferating and this can include genes needed for proliferation or other basic functions in cell physiology.  A low spheroid score identifies the genes that are most depleted in suspension when cells are growth arrested and this is an indication of cell death in this condition.  Since gene knock outs are first established in adherent proliferating conditions, essential genes under these conditions will already start to become depleted from the population before suspension culture.  By selecting genes with a ratio of <1 we can identify those that are most relevant to dormant suspension culture conditions.  Ultimately, the lowest enrichment ratio scores represent genes whose loss of function is dispensable in the initial adherent condition, but critical for survival in suspension and this is what we aimed to identify. We’ve updated Figure 1B to illustrate this and we’ve updated the explanation of the enrichment ratio on page 6, lines 144 to 147 of the results.

(2) The WB for phospho-p38 in figure 1A for OVCAR8 line does not show increased phosphorylation in the spheroid relative to the adherent. If anything, phospho-p38 appears to be reduced in the spheroid. Can the authors provide a better western blot?

We’ve updated this blot with a longer exposure, see Figure 1A.  Phosphorylation levels of p38 are essentially unchanged in OVCAR8 cells in suspension culture, although the overall levels of p38 may be slightly reduced in dormant culture conditions.

(3) How did the authors confirm dormancy apart from western blot for phospho-ERK vs phospho-p38? Authors should add EdU/BrdU staining and/or Ki67 staining to confirm dormancy.

Previous publications that appear as citations 7,10, and 33 in the reference list established the growth arrest state of these cells in suspension culture in the past.  This included measuring other known markers of dormancy and quiescence such as p27, p130, and reduced cyclin/cdk activity and 3H-thymidine incorporation. In addition, other associated characteristics of dormancy such as EMT and catabolic metabolism have been demonstrated in these culture conditions (see citation 11 and Rafehi et al. Endocr. Relat. Cancer 23;147-59).  We’ve added these additional citations to our descriptions of dormant spheroid culture to better clarify the status of these cells in our experiments (see page 6, lines 126-28).  To ensure that cells are growth arrested in the experiments shown in this paper, we have updated Figure 1A to include blots of p130 and Ki67 to further emphasize that spheroid cells are not proliferating as the quiescence marker (p130) is high and the proliferative marker (Ki67) is lost in suspension culture.

(4) Can the authors report spheroid volume over time in culture? How was viability measured?

We’ve updated the methods (see page 27, line 574) to better highlight the description of cell survival that answers both of these questions. At the ends of experimental time points in both the screen and viability assays we captured live cells by replating on adherent plasticware. We fixed and stained with crystal violet and photographed plates to illustrate the sizes of spheroids (shown in Fig. 2 Supplement 1E, Fig. 6C, and 7D). We subsequently extracted the dye and quantitated it spectrophotometrically to quantitatively compare biomass of viable cells between experiments irrespective of the relatively random shapes of spheroids. We found reattachment and staining in this manner to match traditional viability assays such as CellTiter-Glo in a previous paper (10). Furthermore, biomass never increases in culture and diminishes gradually over time in culture consistent with the non-proliferative state of these experiments. Double checks of this equivalency of viability and reattached biomass measurments, as well as demonstrating that biomass is lost over time, are shown in Fig. 2 Supplement 1E that compares reattached crystal violet staining measurements with CellTiter-Glo for DYRK1A knock out cells over time in culture. In addition, we include a comparison of crystal violet staining of reattached spheroids with trypan blue dye exclusion in Fig. 5G and H. In both cases reattachment and more direct viability assays demonstrate the same conclusion that Netrin signaling supports viability in dormant culture.

(5) Please show survival significance of Netrin signaling genes in recurrence/relapse free survival to claim importance in cancer dormancy.

See Fig. 7 Supplement 1C where we include the recurrence free survival data. Netrin-1, and -3 high expressors also have a numerically shorter progression free survival but it is not statistically significant. Netrin-1 overexpression alone is also shown and it shows shorter survival with a P-value of 0.0735. Elevated survival of dormant cells in a residual disease state is expected to increase the chance of relapse and shorten this interval. Thus, this data is consistent with our model, but lacks statistical significance. 

There are many alternative ways to interpret what shorter progression free survival, or overall survival, may mean biologically. Since survival of dormant cells is but one of them, we also added new data to experimentally investigate the role of endogenous Netrin signaling in dormant residual disease in Fig. 6 and described on page 12, lines 266-87.  We used xenograft experiments to show OVCAR8 spheroids form and withdraw from the cell cycle equivalently to suspension culture following intraperitoneal injection.  Furthermore, loss of Netrin signaling due to receptor deletions compromises survival during this early window before disseminated lesions form.  This argues that Netrin signaling contributes to survival during this window of dormancy.  In addition, mice engrafted with mutant cells experience prolonged survival when Netrin signaling is blocked.  Together, these experiments further argue that Netrin signaling supports survival in the dormant, non-proliferative phase, and leads to reduced survival of mice.

(6) The authors show IHC staining of patient ascites derived HGSOC spheroids. However, no marker for dormancy is shown in these spheroids. Adding Ki67 staining or phospho-ERK vs phospho-p38 would be necessary to confirm cancer dormancy.

We have added new staining for Ki67 and p130 that compares these markers in HGSOC tumors where Ki67 is high and p130 is low with ascites derived spheroids where staining is the opposite. Importantly, expression of p130 is linked to cellular quiescence and is not found to accumulate in the nucleus of cells that are just transiting through G1.  This confirms that the ascites derived spheroids are dormant.  See Fig. 4A-E and described on page 9, lines 201-7.

(7) Overall, the findings are interesting in the context of cancer dissemination. There is not enough evidence for cancer dormancy and the importance of Netrin signaling in the survival of cancer dormancy. Overexpression of Netrin increases phosphorylation of ERK, leading one to expect an increase in proliferation. This suggests that Netrin breaks cancer cells out of dormancy, into a proliferative state.

We have found that the discovery of Netrin activation of MEK-ERK in growth arrested cells is counterintuitive to many cancer researchers.  However, this axis exists in other paradigms of Netrin signaling in axon outgrowth that are not proliferation related (see citation 26, Forcet et al. Nature 417; 443-7 as an example).  We have added Fig. 5D and descriptions on page 11, lines 244-52 to better clarify that Netrins CAN’T induce cell proliferation through ERK.  Addition of recombinant Netrin-1 can only induce ERK phosphorylation in suspension culture conditions and not in quiescent adherent conditions.  The small magnitude of ERK phosphorylation induced by Netrin-1 in suspension compared to treating adherent, quiescent cells with the same concentration of mitogenic EGF further emphasizes that this is not a proliferative signal.  Lastly, the new xenograft experiment in Fig. 6A-D (described on page 12, lines 266-81 demonstrates the growth arrested context in which Netrin signaling in dormant spheroids leads supports viability.

(8) If authors wish to claim cancer dormancy as the premise of their study, additional confirmatory experiments are required to support their claims. Alternatively, based on the current findings of the study, it would be best to change the premise of the article to Netrin signaling in cancer dissemination and survival of disseminated cancer spheroids rather than cancer dormancy.

I expect that this reviewer will agree that we have added more than sufficient explanations of background work on HGSOC spheroid dormancy from the literature, as well as new experiments that address their questions about dormancy in our experiments.

Reviewer #2 (Public Review):

Summary:

In this article, the authors employed modified CRISPR screens ["guide-only (GO)-CRISPR"] in the attempt to identify the genes which may mediate cancer cell dormancy in the high grade serous ovarian cancer (HGSOC) spheroid culture models. Using this approach, they observed that abrogation of several of the components of the netrin (e.g., DCC, UNC5Hs) and MAPK pathways compromise the survival of non-proliferative ovarian cancer cells. This strategy was complemented by the RNAseq approach which revealed that a number of the components of the netrin pathway are upregulated in non-proliferative ovarian cancer cells and that their overexpression is lost upon disruption of DYRK1A kinase that has been previously demonstrated to play a major role in survival of these cells. Perampalam et al. then employed a battery of cell biology approaches to support the model whereby the Netrin signaling governs the MEK-ERK axis to support survival of non-proliferative ovarian cancer cells. Moreover, the authors show that overexpression of Netrins 1 and 3 bolsters dissemination of ovarian cancer cells in the xenograft mouse model, while also providing evidence that high levels of the aforementioned factors are associated with poor prognosis of HGSOC patients.

Strengths:

Overall it was thought that this study is of potentially broad interest in as much as it provides previously unappreciated insights into the potential molecular underpinnings of cancer cell dormancy, which has been associated with therapy resistance, disease dissemination, and relapse as well as poor prognosis. Notwithstanding the potential limitations of cellular models in mimicking cancer cell dormancy, it was thought that the authors provided sufficient support for their model that netrin signaling drives survival of non-proliferating ovarian cancer cells and their dissemination. Collectively, it was thought that these findings hold a promise to significantly contribute to the understanding of the molecular mechanisms of cancer cell dormancy and in the long term may provide a molecular basis to address this emerging major issue in the clinical practice.

Thanks for the kind words about the importance of our work in the broader challenges of cancer treatment.

Weaknesses:

Several issues were observed regarding methodology and data interpretation. The major concerns were related to the reliability of modelling cancer cell dormancy. To this end, it was relatively hard to appreciate how the employed spheroid model allows to distinguish between dormant and e.g., quiescent or even senescent cells. This was in contrast to solid evidence that netrin signaling stimulates abdominal dissemination of ovarian cancer cells in the mouse xenograft and their survival in organoid culture. Moreover, the role of ERK in mediating the effects of netrin signaling in the context of the survival of non-proliferative ovarian cancer cells was found to be somewhat underdeveloped.

Experiments previously published in citation 7 show that growth arrest in patient ascites derived spheroids is fully reversible and that argued against non-proliferative spheroids being a form of senescence and moved this work into the dormancy field.  We have added extensive new support for our model systems and data to address the counterintuitive aspects of MEK-ERK signaling in survival instead of proliferation. 

Reviewer #1 Recommendations for Authors

(1) A better characterization of the spheroid model may be warranted, including staining for the markers of quiescence and senescence (including combining these markers with staining for the components of the netrin pathway)

See Figure 1A and page 6, lines 126-36 where we have added blots for Ki67 and p130 to better emphasize the arrested proliferative state of cells in our screening conditions.  We have also added these same controls for patient ascites-derived spheroids in Figure 4 and described on page 9, lines 203-7.  One realization from this CRISPR screen, and others in our lab, is that it identifies functionally important aspects of cell physiology and not necessarily ones that are easily explored using commercially available antibodies.  Netrin-1 and -3 staining of patient derived spheroids in Fig. 4, as well as cell line spheroids stained in Fig. 4 Supplement 1 further support the relevance of this pathway in dormant cancer cells because Netrins are expressed in the right place at the right time.  The Netrin-1 stimulation experiments in Fig. 5C were originally carried out to probe HGSOC cells for functionality of Netrin receptors since we couldn’t reliably detected them by blotting or staining with available antibodies.  This demonstrates that this pathway is active in the various HGSOC cell lines we’ve used and specifically, using OVCAR8 cells, we show it is only active in suspension culture conditions.

(2) In figure 1A it appears that total p38 levels are reduced in some cell lines in spheroid vs. adherent culture. The authors should comment on this.

These blots have been updated to be more clear.  Overall p38 levels may be reduced in some cell lines and when compared with activation levels of phosphorylated p38 it suggests the fraction of activated p38 is higher. OVCAR8 cells may be an exception where the overall activity level remains approximately the same.

(3) The authors should perhaps provide a clearer rationale for choosing to focus on the netrin signaling vs. e.g., GPCR signaling, and consider more explicit defining of "primary" vs. "tertiary" categories in Reactome gene set analysis.

We’ve updated Fig. 1E and the text on page7, lines 161-5 to illustrate which gene categories identified in the screen belong to which tiers of Reactome categories. It better visualizes why we have investigated the Axon guidance pathway that includes Netrin because it is a highly specific signaling pathway that scores similarly to the broader and less specific categories at the very top of the list. As an aside, the GPCR signaling and GPCR downstream signaling have proven to be fairly intractable categories.  As best we can tell the GPCR downstream signaling category is full of MAPK family members and likely represents some redundancy with MAPK further down.  

(4) In figure 3A-C, including factors whose expression did not appear to change between adherent and suspension conditions may be warranted as the internal control. Figure 3D-F may benefit from some sort of quantification.

The mRNA expression levels are normalized to GAPDH as an internal control. We have updated this figure and re-plotted it as fold change relative to adherent culture cells with statistical comparisons to indicate which are significantly upregulated in suspension culture.

The IHC experiments are now in Fig. 4D-F and show positive staining for Netrin-1 and -3.  Netrin-3 is easiest to see, while Netrin-1 is trickier because the difference with the no primary antibody control isn’t intensity, but the tint of the DAB stain.  We had to counter stain the patient spheroids with Hematoxylin in order for the slide scanner to find the best focal plane and make image registration between sections possible.  This unfortunately makes the Netrin-1 staining rather subtle.  For cell line spheroids in the Fig. 4, Supplement 1 we didn’t need the slide scanner and show negative controls without counter stain that are much more convincing of Netrin-1 detection and reassure us that our staining detects the intended target.  We’ve updated the labels in Fig. 4 and Fig. 4, Supplement 1 for this to be more intuitive.  Unfortunately, relying on the tint of the DAB stain leaves this as a qualitative experiment.

- In figure 4C-E the authors show that Netrin-1 stimulation induces ERK phosphorylation whereby it is argued that this is a "low-level" stimulation of ERK signaling required for the survival of ovarian cells in the suspension. This is however hard to appreciate, and it was thought that having adherent cells in parallel would be helpful to wage whether this indeed is a "low level" ERK activity. Moreover, the authors should likely include downstream substrates of ERK (e.g., RSKs) as well as p38 in these experiments. The control experiments for the effects of PD184352 on ERK phosphorylation also appear to be warranted. Finally, performing the experiments with PD184352 in the presence of Netrin-1 stimulation would also be advantageous.

We have added a new Netrin-1 stimulation experiment in Fig. 4D (described on page 11, line 244-52) that shows that Netrins can only activate  very low levels of ERK phosphorylation in suspension when proliferation is arrested. Netrin-1 stimulation of quiescent adherent cells where stimulation of proliferation is possible shows that Netrins are unable to activate ERK phosphorylation in this condition.  In contrast, we also stimulate quiescent adherent OVCAR8 cells with an equal concentration of EGF (a known mitogen) to offer high level ERK phosphorylation as a side by side comparison.  I think that this offers clear evidence that Netrin signaling is inconsistent with inducing cell proliferation.  We’ve also updated citations in the introduction to include citation 26 that offers a previously reported paradigm of Netrin-ERK signaling in axon outgrowth that is a non-cancer, non-proliferative context to remind readers that Netrins utilize MEK-ERK differently. 

We highlight Netrin-MEK-ERK signaling as key to survival for a number of reasons.  First, Netrin signaling in this paradigm does not fit the dependence receptor paradigm where loss of Netrin receptors protect against cell death.  Fig. 5B rules this out as receptor loss never offers a survival advantage, but clearly receptor deletions compromise survival in suspension culture.  Second, positive Netrin signaling is known to support survival by inactivating phosphorylation of DAPK1.  We’ve added this experiment as Fig. 5 Supplement 1D and show that loss of Netrin receptors doesn’t reduce DAPK1 phosphorylation in a time course of suspension culture.  Consequently, we conclude this isn’t the survival signal either.  Since MEK and ERK family members scored in our screen, we investigated their role in survival.  We now show two different MEK inhibitors with different inhibitory mechanisms to confirm that MEK inhibition induces cell death. In addition to the previous PD184352 inhibitor in our first submission, we’ve added Trametinib as well and this is shown in Fig. 5G.  Since it is surprising the MEK inhibition can kill instead of just arrest proliferation, we’ve also added another cell death assay in which we show trypan blue dye exclusion as a second look at survival.  This is now Fig. 5H.  Lastly, we include Trametinib inhibition of ERK phosphorylation in these assays in Fig. 5I.  While we leave open what takes place downstream of ERK, our model in Fig. 5J offers a very detailed look at the components upstream.

- Does inhibition of ERK prevent the abdominal spread of ovarian cancer cells? The authors may feel that this is out of the scope of the study, which I would agree with, but then the claims regarding ERK being the major mediator of the effects of netrin signaling should be perhaps slightly toned down.

We agree that loss of function xenograft experiments will enhance our discovery of Netrin’s role in dormancy and metastasis.  We have added a new Fig. 6 that uses xenografts with Netrin receptor deficient OVCAR8 cells (UNC5 4KO).  It demonstrates that two weeks following IP engraftment we can isolate spheroids from abdominal washes and that cells have entered a state of reduced proliferation as determined by lowered Ki67 expression as well as other proliferation inducing genes.  In the case of UNC5 4KO cells, there is significant attrition of these cells as determined by recovering spheroids in adherent culture (Fig.6C) and by Alu PCR to detect human cells in abdominal washes (Fig. 6D).  Lastly, xenografts of UNC5 4KO cells cause much less aggressive disease and significantly extend survival of these mice (Fig. 6E,F).  Not exactly the experiment that the reviewer is asking for, but a clear indication that Netrin signaling supports survival in xenograft model of dormancy.

- Notwithstanding that this could be deduced from figures 6D and F, it would be helpful if the number of mice used in each experimental group is clearly annotated in the corresponding figure legends. Moreover, indicating the precise statistical tests that were used in the figures would be helpful (e.g., specifying whether anova is one-way, two-way, or?)

We have added labels to what is now Fig. 8B to indicate the number of animals used for each genotype of cells.  We have also updated figure legends to include more details of statistical tests used in each instance.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Roy et al. used the previously published deep transfer learning tool, DEGAS, to map disease associations onto single-cell RNA-seq data from bulk expression data. The authors performed independent runs of DEGAS using T2D or obesity status and identified distinct β-cell subpopulations. β-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. Finally, immunostaining using human pancreas sections from healthy and T2D donors validated the heterogeneous expression and depletion of DLK1 in T2D islets.

Strengths:

(1) This meta-analysis of previously published scRNA-seq data using a deep transfer learning tool.

(2) Identification of novel beta cell subclusters.

(3) Identified a relatively innovative role of DLK1 in T2D disease progression.

We thank the reviewer for their constructive critiques and positive feedback. We hope to further apply deep transfer learning tools in future scRNA-seq meta-analyses.

Weaknesses:

(1) There is little overlap of the DE list of bulk RNA-seq analysis in Figure 1D and 1E overlap with the DE list of pseudo-bulk RNA-seq analysis of all cells in Figure S2C.

We thank the reviewer for this insightful thought and plan to perform additional analyses and comparisons to address this comment.

(2) The biological meaning of "beta cells had the lowest scores compared to other cell types" is not clear.

We agree with the reviewer and will amend this statement to clarify in the revised manuscript. In summary, the relatively lower T2D-DEGAS scores for beta cells overall compared to all other cell types (alpha cells, acinar cells, etc) reflects the fact that in T2D, beta cell-specific genes can be downregulated. This is also possibly due to beta cell loss in T2D and would be reflected in bulk islet RNAseq data. This affects the DEGAS model which is reflected in the scores of all cells in the scRNA-seq data (Fig 3A). For this reason, subsetting the beta cells and replotting them on their own (Fig 4B) is an important step to identify relative differences in DEGAS scores between different subsets of beta cells.

(3) The figures and supplemental figures were not cited following the sequence, which makes the manuscript very difficult to read. Some supplemental figures, such as Figures S1C-S1D, S2B-S2E, S3A-S3B, were not cited or mentioned in the text.

We apologize and thank the reviewer for pointing out these errors. All of the annotated errors will be amended in the revised manuscript.

(4) In Figure 7, the current resolution is too low to determine the localization of DLK1.

We will include the original highest-resolution confocal images in our resubmission. We will also improve the color combination to improve visibility of colocalization of DLK1 with Insulin.

Reviewer #2 (Public Review):

Summary:

The manuscript by Gitanjali Roy et al. applies deep transfer learning (DEGAS) to assign patient-level disease attributes (metadata) to single cells of T2D and non-diabetic patients, including obese patients. This led to the identification of a singular cluster of T2D-associated β-cells; and two subpopulations of obese- β-cells derived from either non-diabetic or T2D donors. The objective was to identify novel and established genes implicated in T2D and obesity. Their final goal is to validate their findings at the protein level using immunohistochemistry of pancreas tissue from non-diabetic and T2D organ donors.

Strengths:

This paper is well-written, and the findings are relevant for β-cell heterogeneity in T2D and obesity.

We thank the reviewer for their constructive critiques and positive feedback. We believe this study can improve our understanding β-cell heterogeneity in the context of T2D and obesity.

Weaknesses:

The validation they provide is not sufficiently strong: no DLK1 immunohistochemistry is shown of obese patient-derived sections. Additional presumptive relevant candidates from this transcriptomic analysis should be screened for, at the protein level.

Thank the reviewer for this suggestion. We are planning to perform new immunostaining of DLK1 in human pancreas tissue sections from non-diabetic lean, non-diabetic obese, T2D lean, and T2D obese donors. We also note that Table S6 contains the patient metadata for the pancreas samples we show in the current manuscript. Two of the T2D donors have BMI > 30 (obese). However, the non-diabetic donors have BMI between 26-29. Our new planned studies should address the question of differential DLK1 expression / beta cell heterogeneity in the context of both diabetes and obesity.

Author response:

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

Reviewer 1 (Public Review):

He et al. investigate the requirement and function of Blimp1 (encoded by Prdm1) in murine NK cells and ILC1. Employing a conditional knockout mouse model (Prdm1flox x Ncr1cre), the authors describe impaired abundance and maturation of Prdm1-deficient NK cells and ILC1 in different tissues. Blimp1-deficient NK cells have reduced expression of cytotoxic molecules (Gzmb, Prf1) and, in some instances, Ifng production, and Prdm1flox x Ncr1cre mice show impaired tumor control in experimental metastasis models. Using single-cell RNA sequencing analysis, the authors propose that Prdm1 regulates JunB expression and NK cell maturation. Based on in silico analyses, the authors suggest manifold intercellular communication between NK/ILC1 and macrophages. Without following up on any of these potentially interesting suggestions, the authors conclude their study reiterating that Prdm1 regulates IFNg-production of tumor-infiltrating NK cells and ILC1. Many of the reported functions of Blimp1 in NK cells have previously been identified using a mixed-chimera strategy comparing Prdm1 WT and KO NK cells (Kallies et al., Blood 2011). Here, the authors expand on these findings using a conditional model to delete Prdm1 in NK/ILC1 and single-cell sequencing and provide a more refined analysis of the functions of Blimp1 in these cells. Cell-chat analysis suggests close interactions of Blimp-dependent NK/ILC1 subsets with hepatic macrophages, but these suggestions are not followed up by experiments. Potentially interesting differences in the macrophage compartment of Ncr1-Cre x Prdm1-fl/fl mice are suggested by the scRNA-Seq data but are not validated e.g. by FACS. The study falls short in providing new mechanistic insights. Nevertheless, it is an interesting confirmation of "old" suggestions in a more refined setting, and the provided single-cell mRNA-Seq data represents a potentially valuable resource for the community. There are some control analyses that are required to support the conclusions of the authors, and I have a few suggestions that would help to improve the manuscript.

We sincerely appreciate your careful review and insightful feedback on our manuscript. We have carefully considered your comments and present the results of new experiments conducted in response to your suggestions. Please find the detailed responses below.

Major comments

Comment 1: The authors do not control for the potential effects of Cre expression. Expression of Cre from within the Ncr1 locus (using the mouse model established by Narni-Mancinelli et al.) has significant effects on NK cells and especially ILC1s (reducing their frequency and absolute numbers and altering their functionality. The authors should characterize the Ncr1cre mice used here (developed by Shanghai Model Organism Center) in this regard and should use proper controls (Ncr1Cre+ Prdm1wt/wt as control for Ncr1Cre+ Prdm1fl/fl, instead of WT littermates) for all of their key data, e.g. those depicted in Fig 1FG, 2ADFH, 7D, S2,3,4.

Response 1: This is a very insightful question that has posed a challenge for many researchers, including us, engaged in conditional knockout studies. The expression of Cre and the insertion of loxP sequences both have the potential to influence gene expression. This is because the region where loxP is inserted may contain regulatory sequences for the gene of interest. Ncr1-Cre is a frequently used transgenic mouse model in our laboratory. In our prior research, we also had concerns about the possible impact of Cre on NKp46 expression, which could lead to a decline in NK cell function. Therefore, in our previous studies focused on Smad4 expression in NK cells, we conducted similar experiments. In Figure 6 of our published paper in the Journal of Clinical Investigation (Wang et al., J Clin Invest, 2018), we compared NKp46-iCreTgfbr2fl/flSmad4fl/WT with NKp46-iCreTgfbr2fl/flSmad4fl/fl. Although the primary purpose is to establish Smad4's independence from TGF-β, it also allows for a comparison between Smad4fl/fl and Smad4fl/WT in the presence of Cre. In the critical phenotype we assessed, NKp46-iCreTgfbr2fl/flSmad4fl/fl (compared with NKp46-iCreTgfbr2fl/flSmad4fl/WT) exhibited the same phenotype as NKp46-iCreSmad4fl/fl (compared with NKp46WTSmad4fl/fl). This suggests that Cre's influence on NK cells may be within a reasonable and controllable range. Furthermore, in contrast to the decrease in Ncr1 expression caused by Cre, the reduction in the expression levels of genes targeted by Loxp knockout, such as Prdm1 in this study (Figure 1 E), is more significant. Therefore, with the current techniques and research methods, we believe that the data provided in this study can support the role of Prdm1 in

NK cells.

Comment 2: Several of the phenotypic findings on NK cells have been described before by Kallies et al. in 2011 (Ref 29), although using a different genetic Prdm1-ablation model (Prdm1-GFP/GFP knockin/knockout model). This study reported impaired NK cell maturation, reduced Gzmb expression, impaired in vivo cytotoxicity against subcutaneous RMA-S cells, impaired in vitro proliferation, comparable in vitro killing, increase in BM NK cell numbers. The authors should discuss/mention this more prominently in their manuscript, and highlight where they confirm or refine these previous findings, and where they actually provide new information.

Response 2: We appreciate your valuable suggestions. The article you referred to, published in Blood, is indeed an excellent work. While we had cited this article, our discussion regarding its specific content was limited. Based on your advice, we have made revisions and included the following content in our discussion section (page 24; line 489-493):

“In a study involving systemic knockout combined with competitive transplantation, it was found that Prdm1 promotes NK cell maturation and the expression of Gzmb. On the contrary, the same study also found that NK cells with Prdm1 deficiency exhibit heightened proliferation, increased survival, enhanced migratory abilities towards tumors, and greater cytotoxicity against subcutaneously implanted RMAS tumors (31).”.

Comment 3: What is the reason to refer to the enriched cluster in Blimp1-deficient NK cells as "Junbhi"? There is no follow-up for a function of Junb, and there are many other genes upregulated in these cells. Most critically, these cells seem to represent exactly the c-Kithi cells that Kallies et al. already showed and discussed in their paper. The authors should stain for Kit, and also refer to this. Also, MacKay et al. performed Blimp1-Chip-Seq (in T cells), maybe it would be interesting to check to which of the identified DEGs Blimp1 can bind.

Response 3: We appreciate the suggestion from the reviewer. We think a gene that supports the development of lymphocytes doesn't necessarily positively regulate their function. For example, JunB is essential for T cell development but can also induce T cell exhaustion (Lynn et al., Nature. 2019). Therefore, while Prdm1 has been shown to promote NK cell development, it cannot be assumed that it always positively regulates NK cell function, especially for anti-cancer immune surveillance. In this respect, we try to find a driving-factor of the impaired anti-tumor ability of Prdm1_Δ_Ncr1 NK cells. Although there are many other genes upregulated in this cluster (e.g. Kit), JunB attracts more our interest of its potential for regulating NK cells functions in cancer, whereas c-Kit is more likely a marker of NK cells maturation, which has been well-demonstrated by Kallies et al. and other studies. Our previous studies also showed that the expression of c-kit was decreased in mature NK cells, compared immature NK cells (Wang et al., J Clin Invest, 2018). 

The lack of following experiments of Junb is because we cannot find valuable surface markers to investigate the follow-up function of _Junb_hi cNK cluster. If we use intracellular markers, it is more likely an analysis of gene expression pattern, which has been well-described in our RNA-seq data. As we describe above, our study did not aim to further investigate the role of prdm1 in NK cells maturation, as the c-Kit expression was upregulated in Prdm1-kncok NK cells and correlated with NK cell maturation, which has been validated by Kallies et al.. 

We also have discussed the potential DEGs that could be bound and regulated by Prdm1 in our revised manuscript (page 27-28; line 561-571):

“Prdm1 and Hobit directly bound and repressed Tcf7 (18), which encoded TCF-1, a TF binding and limiting the activity of Gzmb regulatory element (69). Gzmb has been demonstrated directly bound and activated by Junb in NK cells, which suggested Gzmb expression regulated by multiple Prdm1/Hobit downstream signals (26). In human T cells, binding motif of JUNB was enriched in the binding sites of PRDM1 (70), indicating the essential role of PRDM1-JUNB axis during NK cell and T cell development. In NK cells deficient in Prdm1 expression, we noted a decrease in Gzmb levels alongside with an elevation in Junb expression. This indicates that Prdm1 not only facilitates the expression of Gzmb in NK cells but also suppresses Junb expression. Given that Junb is recognized as a positive regulator of Gzmb (71), this presents a complex interplay that seems contradictory. Therefore, it is imperative to develop a theoretical framework to comprehensively understand and interpret this paradoxical relationship.”.

Comment 4: cNK cells are considered circulating cells, that transiently pass through the liver.

Previous studies have suggested almost identical gene expression patterns in hepatic and splenic NK cells. In functional tests, they often "perform" identically. I am therefore a bit surprised that the authors find a differential dependency of Blimp1 for the IFNg production of splenic (no role of Blimp1) versus hepatic (Blimp1 regulating IFNg production) NK cells (Fig S3). Do the authors have any suggestions on that? The analyses are performed by 12+4h stimulations with IL12/18, which could involve the effects of altered bystander cells (as suggested by Figure 6). Therefore, these analyses should be provided upon standard 4h stimulations with IL12/18 and also with PMA/I under BFA. Note: liver and splenic cNK cells look quite different in the chosen histograms in Figures 7 A, B, C, yet there is massive variability in these analyses - is there any systematic/technical problem?

Response 4: We appreciate the valuable suggestion from the reviewer. Studies have suggested that, at the gene expression or transcriptomic level, liver NK cells exhibit more similarity to splenic NK cells while displaying greater divergence from liver ILC1s. However, we do not think that splenic NK cells or peripheral blood NK cells (which are more abundant in circulation) are entirely indistinguishable from liver NK cells. Notably, there are substantial differences in their maturity levels, with liver NK cells being more mature. Since we are examining the protein levels, a 4-hour stimulation period may not fully capture these distinctions. Even when considering the potential impact of bystander cells, the experimental design specifically targets Prdm1 knockout within NK cells, ensuring that the study accurately elucidates the role of Prdm1 in NK cells. For each experiment, we have implemented control measures, and any variances observed in the figures may be attributed to individual variations among the animals. It is also possible that the MFI values measured by flow cytometry exhibit larger variations than a percentage.

Comment 5: Figure 4 H/I - In contrast to NK cells in Fig 4E, F, the KO and WT ILC1s seem to co-cluster largely. Authors should validate differentially expressed genes. How strong is the effect of Blimp1 in ILC1s? Or is Blimp1 a critical TF driving effector differentiation in NK cells, while it has only subtle effects in ILC1 (these may be regulated by Hobit?)? This seems an interesting finding that should at least be discussed. For these types of small differences in ILC1, FACS confirmation analyses should be performed and findings be reevaluated using Cre-expressing controls (see above).

Response 5: We appreciate the suggestion from the reviewer. As request, we analyze the DEGs in liver cNK cells and ILC1s from our scRNA-seq data (revised Supplemental Figure 8, A and B). There only a few valuable DEGs in ILC1s compared to cNK cells. It’s likely that Prdm1 have more essential effect of cNK cells transcriptional program, while it plays more important role in keep the homeostasis of ILC1s population. We have discussed these points to better inform the readers. (page 27; line 554-561): 

“Previous studies have identified Hobit and Prdm1 as central regulators instructing tissue-dependent programs and retention of diverse tissue-resident lymphocytes (18, 51, 53). Liver ILC1s required Hobit, but not necessary for cNK cells (6). Expression of Prdm1 was remarkably higher in cNK cells versus ILC1s (18). While in our study, cNK cells and liver ILC1s reduced simultaneously in Prdm1ΔNcr1 mice, and even more significant in ILC1s. This indicates that while Prdm1 is expressed at lower levels in ILC1s, its role in preserving the quantity of ILC1s may be more crucial. Thus, Prdm1 and Hobit may have parallel program in instructing ILC1s functional development and maturation.”. 

We cannot find valuable surface marker to evaluate the change in ILC1s, as most of changes are intracellular markers.

Comment 6: The authors describe and discuss some of Figure 1 and 2 data as if Blimp1 would be involved in alternative NK versus ILC1 fates, but there is no evidence for this.

Response 6: There is no evidence that Prdm1 could alter the fate decision of the progenitor towards liver cNK or ILC1s. Although some studies reported the conversion between cNK cells and ILC1s in special contexts, it was widely accepted that liver cNK cells and ILC1s originated from different progenitors. While we observed changes in the proportions of liver cNK cells and ILC1 in Prdm1 KO mice, we still lack sufficient evidence to support the relative independence of NK and ILC1 development, as well as evidence to indicate that Prdm1 is exclusively responsible for NK and ILC1.

Regarding the changes in NK and ILC1 proportions after Prdm1 KO, we believe that both NK and ILC1 cells require Prdm1 to maintain their populations, with ILC1 possibly requiring it to a greater extent. This is the reason for the altered balance between NK and ILC1 cells following Prdm1 KO. We wish to clarify this point to prevent any misconceptions among readers. To address this, we have added the following content to the discussion section (page 25; line 509-516):

“Furthermore, although both liver NK cells and liver ILC1s require Prdm1 to maintain their quantity, liver ILC1s demonstrate a more pronounced dependency on Prdm1. However, it is currently widely believed that liver NK cells and liver ILC1s originate from different progenitors. It is worth noting that while we observed changes in the NK and ILC1 proportions after Prdm1 knockout, our data does not support the hypothesis that Prdm1 affects progenitor differentiation decisions, thereby influencing the fate selection of NK and ILC1. Further research may be needed to elucidate how Prdm1 regulates the balance between NK cells and ILC1s.”.

Comment 7: There are several recent studies suggesting a role for Hobit, homologue of Blimp1, in NK cells and in ILC1, and in the control of liver metastases. The authors should discuss similar and unique functions of Hobit and Blimp1, also in the regulation of gene expression patterns, and should refer to these studies.

Response 7: We would like to express our gratitude to the reviewer for your insightful comments, which bring forth a critical perspective. In accordance with the reviewer's suggestion, we have updated our discussion to include the diverse functions guided by Hobit and Prdm1 in regulating the development and function of cNK cells and ILC1s (page 27; line 554-561):

“Previous studies have identified Hobit and Prdm1 as central regulators instructing tissue-dependent programs and retention of diverse tissue-resident lymphocytes (18, 51, 53). Liver ILC1s required Hobit, but not necessary for cNK cells (6). Expression of Prdm1 was remarkably higher in cNK cells versus ILC1s (18). While in our study, cNK cells and liver ILC1s reduced simultaneously in Prdm1ΔNcr1 mice, and even more significant in ILC1s. This indicates that while Prdm1 is expressed at lower levels in ILC1s, its role in preserving the quantity of ILC1s may be more crucial. Thus, Prdm1 and Hobit may have parallel program in instructing ILC1s functional development and maturation.”.

As shown in Supplemental Figure 8, we analyzed two published scRNA-seq data performed with Hobit_KO mice and integrated DEGs in cNK cells and ILC1s with our data. We observed overlaps of DEGs in _Prdm1_Δ_Ncr1 and Hobit_KO between cNK cells and ILC1s, such as _Junb, Tcf7, Gzmb, and Prf1 (Supplemental Figure 8), indicating the similar regulatory network of Prdm1 and Hobit. These data are now described on page 19; lines 386-395:   

“We also compared the gene expression patterns between Prdm1 and Hobit (homologue of Blimp1) with two published scRNA-seq data (51, 53). Following the knockout of Hobit, the DEGs were primarily identified within ILC1s. Conversely, after the knockout of Prdm1, a greater number of DEGs were observed in cNK cells. This indicates that Prdm1 likely possesses a broader range of target genes within cNK cells, whereas Hobit appears to have a more pronounced impact on gene expression within ILC1s (Supplemental Figure 8, C-F). There are some overlaps between the downstream transcriptional profile of Prdm1 and Hobit in liver cNK cells and ILC1s (Supplemental Figure 8, G and H), such as Junb, Fosb, Tcf7, Kit, Gzmb, Prf1, and Cxcr6 was simultaneously upregulated or downregulated in both Prdm1ΔNcr1 and _Hobit_KO liver cNK cells or ILC1s, indicating the similar regulatory networks of Prdm1 and Hobit.”.

Comment 8: Figure 4: The authors should discuss (and cross-validate) their liver gene expression analyses in the context of published datasets of NK and ILC1, such as the ones by Lopez et al, Friedrich et al, Ducimetiere et al and Yomogida et al.

Response 8: We thank the reviewer for raising this important point. To address this question, we have now analyzed the gene expression of liver cNK cells and ILC1 in two published data mentioned above, also in the context of Hobit-knock. We compared gene expression of different clusters and described in our revised manuscript (page 19; lines 386-395). 

“We also compared the gene expression patterns between Prdm1 and Hobit (homologue of Blimp1) with two published scRNA-seq data (51, 53). Following the knockout of Hobit, the DEGs were primarily identified within ILC1s. Conversely, after the knockout of Prdm1, a greater number of DEGs were observed in cNK cells. This indicates that Prdm1 likely possesses a broader range of target genes within cNK cells, whereas Hobit appears to have a more pronounced impact on gene expression within ILC1s (Supplemental Figure 8, C-F). There are some overlaps between the downstream transcriptional profile of Prdm1 and Hobit in liver cNK cells and ILC1s (Supplemental Figure 8, G and H), such as Junb, Fosb, Tcf7, Kit, Gzmb, Prf1, and Cxcr6 was simultaneously upregulated or downregulated in both Prdm1ΔNcr1 and _Hobit_KO liver cNK cells or ILC1s, indicating the similar regulatory networks of Prdm1 and Hobit.”.

Recommendations For The Authors:

Comment 9: The use of a paired t-test analysis when comparing cells/groups from different mice is not correct. Instead, the authors should consider using e.g. an unpaired t-test and re-test the indicated significance (e.g. Figure 1F, Figure 2H).

Response 9: We thank the reviewer’s comments. As we used littermates for the experiments and they are compared side by side, so the paired t-test analysis is acceptable. We reanalysis the significance in the results of Figure 1F, and Figure 2H using unpaired t-test. The statistics significance of Figure 1F using unpaired t-test was same as using t-test. However, in Figure 2H, the reduced IFN-γ production not reach statistics significance when used un-paired t-test (Supplemental Figure 12B). It may attribute to the variation between different littermates, but the trend is still under the scope of our conclusion. We believe that employing a paired t-test between littermates could be also meaningful. As such, we kept both statistical methodologies to ensure a thorough evaluation.

Comment 10: In several instances, it is unclear whether data are pooled or representative (and if so, of how many analyses). This information needs to be provided for all analyses. 

Response 10: We apologize for the lack of details and have now provided the sufficient information in our figure legends. 

For example, we delete the number in original histogram to avoid the misunderstanding of the unclear whether data are pooled or representative (e.g. original Figure7 A-C; revised Figure7 A-C). Furthermore, we added the “representative” in figure legends of all flow cytometric plots to better inform readers (e.g. original Figure2, D and F; revised Figure2, B and D).

Comment 11: In the title and abstract authors use "type 1 ILCs" for both NK cells and ILC1, and it is difficult to understand which phenotypes correspond to cNK cells versus ILC1. Most of the analyses clearly separate these two different cell types. I would appreciate a lot being more accurate in the abstract, and describing cNK and ILC1 phenotypes in a clear way.

Response 11: We are really sorry for our inaccurate descriptions. According to Spits et al., (Spits et al., Nature Reviews Immunology, 2013) and other related studies, we have now adopted a more appropriate nomenclature as “Conventional NK cells” correspond to “cNK cells”, “Type 1 innate lymphoid cells” to “ILC1s”, and “Group 1 ILC” as the collective name of cNK and ILC1s. 

The definition of these cells was described in the introduction (page 4, line 52-53; line58-62): 

“Group 1 ILCs consist of cNK cells and ILC1s (1, 2), with distinct developmental trajectories and effect molecules (3).”, “In a state of homeostasis, liver group 1 ILCs (CD45+CD3-NK1.1+NKp46+) can be discriminated into cNK cells and ILC1s by the differential expression of CD49a and CD49b (2): cNK cells are marked by the expression of CD49b, while liver ILC1s exhibit a distinctive positivity for CD49a. Tumor Necrosis Factor Related Apoptosis Inducing Ligand (TRAIL) is also expressed on liver ILC1s, but not on cNK cells (10, 11).”. 

We also describe cNK and ILC1 phenotypes in our scRNA-seq data, as shown in page 13; line 259-261: 

“cNK cells expressed high levels of Itga2 (CD49b) and Eomes, while ILC1s had high levels expression of Itga1 (CD49a) and Tnfsf10 (Supplemental Figure 5, F and G).”.

Comment 12: In the abstract authors state "The present study unveiled a novel regulatory mechanism of Prdm1 in liver Type 1 ILCs, showing promising potential for developing innovative immune therapy strategies against liver cancer." - maybe authors should discuss how their findings could be used for therapeutic approaches?

Response 12: We appreciate comments from the reviewer. As there hasn't been a clear consensus on the role of Prdm1 in NK cells prior to this, some studies have suggested that Prdm1 can inhibit cytokine secretion by NK cells. Particularly, Kallies et al. in their 2011 article in Blood found that Prdm1 might suppress NK cell anti-tumor activity. Hence, there hasn't been any immunotherapy targeting Prdm1 in NK cells for cancer treatment. Our research demonstrates the enhancing role of Prdm1 in NK cell anti-tumor activity, providing theoretical support for NK cell therapy targeting Prdm1. 

We added the following content to the discussion section (page 29; line 605-609): 

“Further research may provide deeper insight into the role of PRDM1 in the anti-tumor function of human NK cells, enabling a more direct investigation of its application in cancer therapies. Given its important role in preserving liver cNK cells and ILC1s functional heterogeneity, enhancing Prdm1 function in human NK cells could potentially be a strategy to promote NK cell-based immunotherapy for cancer.”.

Comment 13: The authors should explain or interpret their data a bit more (e.g. what is the consequence of GSEA enriched in negative regulation of Il6 production? (Fig. 3D)  do NK cells produce Il6 (Figure 3)? What's the impact of Il17 signaling in NK/ILC1 (Figure 5). Do the authors suggest JunB-driven metabolic reprogramming (Suppl. Fig 6D-F?).

Response 13: We appreciate comments from the reviewer. The question of IL-6 production in NK cell also raised by another reviewer. We have checked the GSEA results, and found no valuable genes in IL-6 production in NK cells. According to the suggestions of another reviewer (Response to Reviewer 2 Comment, Comment 14), it may be prudent to omit this figure.

IL-17 signaling indicated the plasticity of ILC1s, that may be originated from the differentiation of ILC3, we added more discussion of this part (page 17; line 341-344). 

“Several ILC3 signature genes, such as Rora, Tmem176a, and Tmem176b (45), highly expressed in this cluster (Supplemental Figure 7D). Considering the close relationship between IL-17 mediated immunity response and ILC3 (1, 46), it is plausible that _Il7r_hi ILC1 cluster may be attributed, at least in part, to potential plasticity between ILC1 and ILC3 subsets.”.

The decreased mitochondrial function may have more relevance to NK cell exhaustion in tumors. Our data suggest that the elevated expression of JunB in NK cells may predispose them to exhaustion. Currently, our hypothesis regarding the promotion of NK cell exhaustion by high JunB expression is based on the observed correlation between JunB expression levels and exhaustion phenotypes (at the gene expression and IFN-γ secretion levels) and the findings in reference 67 (Lynn et al., Nature, 2019), where JunB was found to promote T cell exhaustion. However, we have not demonstrated causation between high JunB expression and exhaustion in NK cells. We propose that in NK cells, especially mature NK cells, excessive JunB expression may make them more sensitive to exhaustion inducers. Nevertheless, further research is needed to confirm this. To clarify this, we added the following content in the discussion section (page 26; line 537-543): 

“While our current data is not sufficient to definitively classify these cells as exhausted NK cells, it supports that a subpopulation, referred to Junbhi cluster, demonstrates an exhaustion-like phenotype.

The significant increase in this cell population following Prdm1 knockout in NK cells may potentially be one of the reasons why Prdm1ΔNcr1 mice lose their tumor-killing capacity. Whether the excessive expression of JunB in NK cells is also a contributing factor to their exhaustion, similar to T cells(65), requires further investigation.”.

Comment 14: Ref 25 and Ref 57 are the same publication?

Response 14: We are really sorry for our careless mistakes. We have checked all the reference and corrected the wrong format.

Comment 15: Figure 1, E - The method description of RT-PCR is missing. I apologize if I have overlooked this information.

Response 15: We have now added the description of RT-PCR in our revised method section (page 31; line 638-644):

“RNA was extracted from FACS-sorted NK cells or splenocytes using RNASimple Total RNA Kit (TIANGEN Biotech, 4992858) and subsequently reverse transcribed to cDNA with SuperScript VILO Master Mix (Thermo Fisher Scientific, 11755050) according to manufacturer’s instructions. qPCR was performed with SYBR Green Mix (Thermo Fisher Scientific, A25742) and CFX Opus 96 Real-Time PCR System (Bio-Rad). The relative mRNA expression level was calculated using 2-ddCt method. Primer sequences:           Prdm1: 5’-CAGAAACACTACTTGGTACA-3’; 5’-GATTGCTTGTGCTGCTAA-3’.”

Comment 16: Figure 1, F - The NKp46+CD3- gate for the liver seems to cut the population, not all cells are included.

Response 16: We appreciate the review’s comment and apologize for our carelessness. We expend our data with more samples and reanalyzed them with a more convincing gating strategy. We now update our figures (revised Figure 1G; revised Supplemental Figure 2A). Several changes have occurred in the data and conclusions, and we have accordingly revised these contents in our manuscript.

The original text is:

“Proportion and absolute number of cNK cells in blood, bone marrow, lung, liver, spleen, and lymph nodes were analyzed by flow cytometry. Compared with Prdm1+/+ mice, the percentage of cNK cells (CD3-NK1.1+NKp46+) among lymphocytes was decreased in all of these tissues except bone marrow and lymph nodes (Figure 1F; Supplemental Figure 2A). However, no significant difference was observed in the percentage of cNK cells among bone marrow-derived lymphocytes between Prdm1ΔNcr1 and Prdm1+/+ mice. The absolute number of cNK cells in blood, lung, liver, and spleen also decreased in Prdm1ΔNcr1 mice (Figure 1F; Supplemental Figure 2A). Only a slight decrease in the number of cNK cells was observed in the lymph nodes of Prdm1ΔNcr1 mice, which did not reach statistical significance either (Supplemental Figure 2A). In contrast, the absolute number of cNK cells in Prdm1fl/fl mice bone marrow is moderately higher than Prdm1ΔNcr1 mice (Figure 1F).”

The revised text is (page 8; line 142-146):

“Proportion and absolute number of cNK cells in blood, bone marrow, lung, liver, spleen, and lymph nodes were analyzed by flow cytometry. Compared with Prdm1+/+ mice, the percentage and absolute number of NK cells (CD45+CD3-NK1.1+NKp46+) among lymphocytes was decreased in all of these tissues, whereas increased number of NK cells were observed in bone marrow (Figure 1G; Supplemental Figure 2A).”

Comment 17: Figure 1, The y-axis labeling of lung CD3-NKp46+ cells (x10^3) is not correct.

Response 17: We are really sorry for our carelessness. We now check the labels and make sure they are correct.

Comment 18: Figure 1, The statistical significance of absolute numbers of NKp46+ cells in the bone marrow should be reviewed.

Response 18: We expend our data with more samples and reanalyzed them with a more convincing gating strategy. We observed significant increase of bone marrow NK cells quantity in our updated data. These changes are now described in our revised manuscript.

The original text is: 

“However, no significant difference was observed in the percentage of cNK cells among bone marrow-derived lymphocytes between Prdm1ΔNcr1 and Prdm1+/+ mice”, “In contrast, the absolute number of cNK cells in Prdm1fl/fl mice bone marrow is moderately higher than Prdm1ΔNcr1 mice (Figure 1F).”

The revised text is (page 8; line 142-146):

“Proportion and absolute number of cNK cells in blood, bone marrow, lung, liver, spleen, and lymph nodes were analyzed by flow cytometry. Compared with Prdm1+/+ mice, the percentage and absolute number of NK cells (CD45+CD3-NK1.1+NKp46+) among lymphocytes was decreased in all of these tissues, whereas increased number of NK cells were observed in bone marrow (Figure 1G; Supplemental Figure 2A).”

Comment 19: Figure 1, G - CD27 and CD11b are used to define maturation stages within NK cells. Here the authors are analyzing group 1 ILC instead (containing both NK cells and ILC1, especially in the liver). It would be better to pre-gate on Eomes+ or CD49b+ NK cells for this analysis.

Response 19: We apologize for the lack of details in this analysis. We have pre-gate CD49b+ NK cells for the maturation stages analysis. We have now added this statement in our revised manuscript and figure legend (page 8; line 149-151)

“The maturation of cNK cells (gated by CD45+CD3-NK1.1+NKp46+CD49b+) from blood, bone marrow, lung, liver, spleen, and lymph nodes were assessed, based on the expression of CD11b and CD27.”.

Comment 20: Supplementary Figure 1, A - The NKp46+CD3- gate seems to cut the population, not all cells are included. y-axis labeling of spleen CD3-NKp46+ cells (%) is not correct.

Response 20: Thanks, we have corrected these errors and shown in our revised supplementary Figure 2A.

Comment 21: Figure 2, D-G - Did the authors analyse the ILC1/NK compartment of the tumor? What is the abundance and phenotype of these cells dependent on Prdm1 expression? Proper Crecontrols should be used (see above).

Response 21: We appreciate the suggestions from the reviewer. As request, we have now added the analysis of cNK/ILC1s population in the context of tumor. The proportion changes of cNK cells and ILC1s in Prdm1_Δ_Ncr1 mice was similar with the no tumor-burden condition, while the number of both cNK cells and ILC1s decreased in tumor bearing liver (revised Figure 7D). These contents have been updated in our revised manuscript (page 23; line 479-481):

“The proportion changes of cNK cells and ILC1s in Prdm1ΔNcr1 mice was similar with the no tumorburden condition, while the number of both cNK cells and ILC1s have significant decreased in tumor-bearing liver (Figure 7D).”.

The reason why we did not use Cre-controls was described in comment 1.

Comment 22: Figure 2, H - Prdm1-deficient NK and ILC1 produce less Ifng in response to in vitro stimulations with Il-12 and /or Il-18, and bulk Seq analysis (Fig 3F) shows reduced Il12rb2 expression. Does the expression of cytokine receptors correlate with the maturation of NK cells? This could be analyzed from the single-cell RNA-seq dataset. The statistical significance of %Ifng after Il12/Il18 stimulation should be revisited (see above).

Response 22: We thank the reviewer for the suggestions. To address this question, we explored the expression of IL-12 and IL-18 receptors in cNK and ILC1 clusters. Within cNK clusters, Il12rb2, Il18r1 and Il18rap was highly expressed in Prf1hi and Cxcr3hi cNK clusters (revised Supplemental Figure 6H), indicating the IL-18 receptor expression correlated with the NK cell maturation. While in ILC1, these receptors mostly expressed on Il7r_hi and _Gzmb_hi ILC1 clusters (revised Supplemental Figure 7C). Significant decreased of _Il18r1 expression in Prdm1_Δ_Ncr1 cNK cells and ILC1s may associated with the impaired ability to produce IFN-γ. We now added this analysis (page 18; line 364-368):

“Within cNK cells, Il12rb2, Il18r1 and Il18rap was highly expressed in Prf1hi and Cxcr3hi cNK clusters (Supplemental Figure 6I), indicating the IL-18 receptor expression correlated with the NK cell maturation. While in ILC1, these receptors mostly expressed on Il7r_hi and _Gzmb_hi ILC1 clusters (Supplemental Figure 7D). Significant decreased of _Il18r1 expression in Prdm1ΔNcr1 cNK cells and ILC1s may associated with the impaired ability to produce IFN-γ.”.

The un-paired t test of IFN-γ production was displayed in revised supplemental Figure 12 B. Difference in IFN-γ production was found to be not significant when analyzed using an unpaired ttest in original Figure 2 H. However, significance was observed in tumor-bearing liver cNK cells and ILC1s, specifically under the context of IL-12/IL-18 stimulation, as depicted in the original Figure 7E using an unpaired t-test. These variations may be attributed to differences among different littermates. Despite these variations, the trend remains consistent with our overall conclusions. We believe that employing a paired t-test between littermates could be also meaningful. As such, we kept both statistical methodologies to ensure a thorough evaluation.

Comment 23: Figure 3, A-E - For bulk sequencing analysis, splenic CD3-NK1.1+NKp46+ were isolated. This population also contains ILC1 in the spleen (e.g. Flommersfeld et al.), although much less abundant compared to NK cells, and compared to the liver compartment. However, have the authors tested the abundance of splenic ILC1 in Prdm1-deficient mice, which may impact the gene expression data? In line with this the detection of altered Cxcr6 expression in Figure F, which is usually expressed by ILC1 rather than NK cells, may indicate an alteration in ILC1 numbers. The authors should validate the altered expression of CXCR6, Itga1, and Cx3cr1 on NK cells by flow cytometry.

Response 23: We cited the work of Flommersfeld et al. into our manuscript and have expanded our Results section to include the following information (page 19; line 377-385):

“Previous research found that spleen NK cells could be divided into three distinct groups based on their expression levels of CD27, CD62L, CD49a, and CD49b (52). CD27+CD62L- NK cells have remarkable high expression of Batf3, while it was only barely expressed in CD27+CD62L+ and CD27-CD62L+ NK cells (52). Based the sequencing data published by Flommersfeld et al., (GSE180978), a notable negative correlation was observed between the expression levels of Prdm1 and Batf3 (Supplemental Figure 8I). On top of that, our findings unveiled the negative regulatory influence of Prdm1 on Batf3 within both spleen and liver NK cells. This discovery highlights a potential upstream mechanism that may influence the hemostasis of the spleen NK cell subpopulations through Batf3.”.

We validated the expression of CD49a (Itga1) and CX3CR1 in liver cNK cells and ILC1s in our revised manuscript, which is described in our revised manuscript (page 9; line 170-174, page 14; line 231-233):

“Increased CD49a expression was also observed in Prdm1ΔNcr1 liver ILC1s, while it showed decreased expression in NKp46+ cells in the liver, bone marrow, and lymph nodes (Supplemental Figure 2, F and G).”, “The percentage of CX3CR1+ cNK cells was significantly decreased in multiple tissues of Prdm1_Δ_Ncr1 mice, while the proportion of CX3CR1+ ILC1 was increased in the liver (Figure 3F).”

Comment 24: Figure 3, F - Tnfsf26: which gene is this? is this a typo? Is a function of this gene in NK cells reported? Altered Batf3 expression suggests an impact on ILC1-like NK cells (Flommersfeld et al).

Response 24: We are very sorry for our mistakes. We have removed Tnfrsf26 from the heatmap.

Comment 25: Figure 3, G-J refer to Kallies data?! 

Response 25: Kallies‘s data has mentioned the reduced GzmB expression in Blimp1gfp/gfp mice. However, compared with Kallies’s study, we further analyzed the GzmB and Perforin expression in different mature stages of NK cells. Reduced GzmB expression not only due to the less mature phenotype in Prdm1-deficient NK cells, highlighting the role of Prdm1 in regulating NK cell function. So, we added these contents in the revised manuscript (page 12; line 233-242):

“Lower GZMB and PRF1 production was observed in Prdm1-deficient splenic cNK cells, liver cNK cells and ILC1s (Figure 3, H-K; Supplemental Figure 4, A-I). Notably, the proportion of GZMB+ and PRF1+ cNK cells was decreased among almost all of the maturation stages of cNK cells (Figure 3, J and K). The relative mean fluorescent intensities (MFIs) of GZMB and PRF1 consistently show a reduction across all developmental stages in PrdmΔNcr1 NK cells (Supplemental Figure 4, H and I). Yet, no statistical difference of PRF1 was found within the CD11b-CD27+ and CD11b+CD27+ subsets, likely due to the relatively lower perforin levels in these populations (Supplemental Figure 4I). These findings suggest that Prdm1 may directly influence cytotoxic molecule in NK cells, rather than impacting their anti-tumor abilities solely by affecting the maturation phenotype of Prdm1-deficient NK cells.”

In Discussion section (Kallies’s work is cited here in revised manuscript) (page 24; line 500-502):

“Our results not only confirmed a decrease in cytotoxic molecules in Prdm1-deficient NK cells (31) but also showed that the reduction in Gzmb and perforin is not solely attributable to the diminished maturation of these cells.”

Comment 26: Figure 3, G, I - How do the authors explain the high variability of GzmB and Prf1 in Prdm1+/+ cells? 2 samples have comparable values to Prdm1-deficient cells.

Response 26: This may be due to the inherent differences in MFI among different samples. In the revised version, we have added data on percentages, which exhibit much less variability (Figure 3, H and I). The MFIs of GZMB and PRF1 are moved to supplemental Figure 4 E and F.

Comment 27: Did the authors test the mice for potential germline recombination of the floxed allele, which has been suggested as a potential problem of Ncr1cre?

Response 27: We appreciate the insightful comments provided by the reviewer, and this is a really good question. In Prdm1fl/fl mice, germline recombination typically results in a systemic knockout of Prdm1, which can lead to embryonic lethality. Given that mice were successfully born in the current study, it is almost unlikely that germline recombination of Prdm1 occurred due to leaky expression of Cre.

To confirm this issue, we isolated splenocytes and assessed Prdm1 expression using qPCR. We observed no significant difference in Prdm1 expression between splenocytes from Prdm1+/+ and Prdm1ΔNcr1 mice (revised Figure 1F). This also indicated that germline recombination issues are unlikely to be present in the Prdm1ΔNcr1 mice.

Comment 28: Histograms do not show MFI

Response 28: We appreciate the comments provided by the reviewer. The MFI value was omitted.

Comment 29: Supplementary Figure 4, B - FACS plot labelling: Typo, Histograms do not show MFI.

Response 29: We sincerely thank the reviewer for careful reading. The typo in this figure was corrected. The MFI is omitted.

Comment 30: Figure 4, A - What are the cells in the red cluster in the middle of the UMAP, do they belong to B cells? Why do they cluster so separately? It is interesting, but also surprising that NK and ILC1 cluster map so far apart from each other (rather with CD8 or B cells? or NKT cells) - do the authors have any comments?

Response 30: We sincerely apologize for the mistakes in labeling a group of cells in our previous analysis. Upon a thorough re-evaluation, we have corrected the labels of several cell clusters that were previously misidentified. The revised heatmap (revised Supplemental Figure 5C) represents the marker genes for each cluster. Additionally, in our updated analysis (revised Figure 4A), we have included clusters for Epithelial cells, CD4+ T cells, NKT cells, and Kupffer cells. Please note, the red cluster identified in the center of the original heatmap corresponds to the CD4+ T cells.

We checked the markers of cNK cell and ILC1 clusters and confirmed they are labeled correctly, as Ncr1 and Klrb1c (NK1.1) was highly expressed in these clusters compared to others (revised Supplemental Figures 5E).

Comment 31: Does Junb expression correlate with the maturation stages of NK cells?

Response 31: Our previous research indicated that during the maturation process of NK cells, there was a decrease in the expression levels of Junb (negative correlation), whereas there was an increase in the expression levels of Prdm1 (Wang et al., J Clin Invest, 2018; Supplemental Figure 5c and Supplemental Figure 11).

Comment 32: The authors may consider validating their scRNA-seq data (e.g. by FACS analysis for highlighted markers, eg. cKit, Tcf7, Gzma, Cxcr3).

Response 32: We appreciate the suggestion from the reviewer. We validated several marker genes, including Gzmb, Prf1, and Cx3cr1 by FACS, as shown in the revised Figure 3 F-K. Currently, FACS cannot distinguish liver NK cells into as many distinct clusters as can be achieved through scRNAseq analysis. However, we expect that as technology progresses, we will be able to enhance our validation of the scRNA-seq data.

Comment 33: It is a bit unclear to me why authors refer to Cxcr3hi NK cells as tissue-resident. This is based on Cxcr3 and Ccr2 expression. To make this statement, a much more detailed analysis would be required. How are CD69, CD49a, or CXCR6 expression of these cells?

Response 34: We appreciate the suggestion from the reviewer. The primary reason for classifying this specific cluster of NK cells as tissue-resident is derived from the differential expression genes (DEGs) and Gene Ontology (GO) analysis, which demonstrate significant chemokine receptor activity within this cluster.

To make this statement more clearly, we check the expression of the above markers, but only Cd69 had expression in cNK clusters, which was highly expressed in _Junb_hi and _Cxcr3_hi cNK cells (revised Supplemental Figure 6D). We also used top30 DEGs in ILC1s versus cNK to calculate the module score in all cNK clusters, as _Cxcr3_hi cNK had highest score among these clusters (revised Supplemental Figure 6D). This part has been updated in our manuscript (page 15; line 298-308):

“Expression of tissue-resident markers Cd69 was also highly expressed in this clusters (Supplemental Figure 6D). The enrichment of chemokine receptors in the genes upregulated in the Cxcr3_hi cluster implying a greater likelihood of this cluster being tissue-resident compared with other cNK cell clusters (Figure 4H). To further confirmed tissue-resident properties of this clusters, we calculated the module score based on top30 DEGs in ILC1 versus cNK clusters, including _Cxcr6, Itga1, Cd160, Cd226, etc. _Cxcr3_hi cNK clusters have the highest score among all cNK clusters (Supplemental Figure 6H), indicating the similarity with liver ILC1s. In the tumor microenvironment, reports indicated that NK cells could transform into ILC1s (25). If this conversion of cNK cells into ILC1s also occurred under normal physiological conditions, then _Cxcr3_hi cNK cell cluster might be the most susceptible to such transformation.”

Comment 35: The authors suggest that Prdm1 regulates chemokine receptor expression. An alternative explanation could be that this is an indirect effect of altering the abundance of NK cell subsets.

Response 35: We are sorry for lacking the details in these figures. The input cell number of each genotype has now been added in following figure legends. 

Figure 4F, “Proportions of cNK cells among total cNK cells (left; 211 cells in Prdm1+/+, and 141 cells in Prdm1ΔNcr1) and within clusters (right).”; Figure 5C, “Proportions of ILC1s among total ILC1s in different genotypes (left; 114 cells in Prdm1+/+, and 63 cells in Prdm1ΔNcr1) and within each cluster (right).”; Figure 6C, “Proportions of MDMs and KCs among total macrophages in different genotypes (510 cells in Prdm1+/+, and 624 cells in Prdm1ΔNcr1).”

To minimize the effects of discrepancies in input numbers between samples with different genotypes, we represented the relative proportions of each cluster within its specific genotype (e.g. Supplemental Figure 6B; Supplemental Figure 7B; Supplemental Figure 9B).

Comment 36: Supplementary Figures 6 and 7, A - The formatting of gene annotations does not fit the heat maps (the gene names on the last rows are missing).

Response 36: We apologize for our careless mistakes. We have now addressed these mistakes.

Comment 37: Supplementary Figures 6 and 7, What is the consequence of compromised mitochondrial function? Increase apoptosis?

Response 37: In our experiments, we did not find that Prdm1 has an effect on the apoptosis of NK cells. Conversely, previous studies have found that Prdm1 might inhibit the proliferation of NK cells (C. Kucuk, et. al., PNAS, 2011). We acknowledge that there is ongoing debate regarding the precise definition of NK cell exhaustion. In our experiments, no changes were detected in the expression levels of surface markers (TIGIT) associated with exhaustion on NK cells following the knockout of Prdm1. However, we did note a significant reduction in the cytokine secretion capacity and tumor control efficacy of NK cells after Prdm1 knockout. We prefer to say that the consequence of compromised mitochondrial function might be increased exhaustion. As we mentioned in discussion part (line 482-483), mitochondrial fragmentation has been confirmed to be closely associated with NK cell exhaustion in tumor (Zheng et al. Nature immunology, 2019). Although the evidence to define the exhausted NK cells in Prdm1_Δ_Ncr1 was not sufficient, our data may support the compromised mitochondrial functions, at least in part, associated with the exhausted phenotype of Prdm1_Δ_Ncr1 NK cells in cancer. 

We have discussed these points in our revised manuscript (page 26; line 529-543): 

“Mitochondria are pivotal organelles crucial for cellular metabolism. Disruptions in mitochondrial function have been linked to T Cell exhaustion, attributed to glycolytic reprogramming (66). Similarly, mitochondrial fragmentation has been closely associated with NK cell exhaustion (67).

However, the concept of NK cell exhaustion isn't as firmly established as it is for T cells. Exhausted NK cells should primarily exhibit diminished functions. This is characterized by a diminished ability to destroy tumor cells, a reduced capability to activate other components of the immune system, and compromised proliferation and survival rates. Additionally, this reduced functionality is associated with a decline in the expression of molecules responsible for cytotoxic activity, lower production of IFN-γ, and metabolic disturbances that may arise from mitochondrial dysfunction. While our current data is not sufficient to definitively classify these cells as exhausted NK cells, it supports that a subpopulation, referred to Junb_hi cluster, demonstrates an exhaustion-like phenotype. The significant increase in this cell population following _Prdm1 knockout in NK cells may potentially be one of the reasons why Prdm1ΔNcr1 mice lose their tumor-killing capacity. Whether the excessive expression of JunB in NK cells is also a contributing factor to their exhaustion, similar to T cells(65), requires further investigation.”.

Comment 38: Figure 5, Describing the scRNA Seq data, the authors are switching a lot between Figure 4 and Figure 5. Maybe a reorganization of the Figures (Figure 4: NK cell; Figure 5: ILC1) could help.

Response 38: We appreciate the reviewer’s suggestion. We have now reorganized the Figure 4 and Figure 5.

Comment 39: Figure 5, We suggest naming one of the ILC1 clusters "Gzmbhi" to keep it consistent with the FACS data.

Response 39: We agree with this excellent suggestion and have now renaming the “Gzmahi” ILC1 cluster as “Gzmbhi” ILC1 cluster.

Comment 40: Figure 5, C - How was the JunB score derived (which genes were used)?

Response 40: The JunB score was calculated based on the expression of marker genes in _Junb_hi cNK clusters (DEGs in _Junb_hi cNK cluster compared to other clusters, as shown in revised Supplemental figure 6A). The score was calculated using “AddModuleScore” R package.

Comment 41: Figure 5, G, I - The authors highlight Il17 signaling pathway, what is the impact of Il17 on NK/ILC1? Did the authors check for ILC3 (Rorc expression) within the ILC1 cluster?

Response 41: The enrichment of IL-17 signaling pathway in Il7r_hi ILC1 indicated that this cluster encompass ILC1s originate from the conversion of Rorγt+ ILC3s. Although the Rorc expression was undetectable in all ILC1 clusters, we found several ILC3 marker genes highly expressed in this clusters (e.g. Rora, Tmem176a, Tmem176b) according to the ILC3 transcriptomes (Robinette et al., _Nature Immunology, 2015). 

We have added these contents in our revised manuscript (page 17; line 341-344): 

“Several ILC3 signature genes, such as Rora, Tmem176a, and Tmem176b (45), highly expressed in this cluster (Supplemental Figure 7D). Considering the close relationship between IL-17 mediated immunity response and ILC3 (1, 46), it is plausible that _Il7r_hi ILC1 cluster may be attributed, at least in part, to potential plasticity between ILC1 and ILC3 subsets.”.

Comment 42: Figure 5, The authors detect more Ly49E+ cytotoxic ILC1 in Prdm1fl Ncr1cre mice.

How does this observation fit to the reduced cytotoxicity of NK cells?

Response 42: The proportion of _Klra_hi ILC1 was increased, while the _Gzmb_hi ILC1 was decreased in _Prdm1_ΔNcr1 mice. Moreover, total number of three ILC1 cluster was reduced in _Prdm1_ΔNcr1 mice.

Comment 43: Line 350/351: Citation required.

Response 43: We added the respective reference. (reference 55 and 56).

Comment 44: Figure 6, The Cell-chat analysis provides interesting suggestions, but none are experimentally addressed. It is also difficult to evaluate these analyses: are any of the Mac subsets altered in frequency or phenotype in either genotype? This could be analyzed from the single-cell data in Fig 4. At the very least, flow cytometric validation of predicted shifts in the Mac compartment should be confirmed.

Response 44: We gratefully thanks for these valuable suggestions. As requested, we analyzed macrophages and validated some of the scRNA-seq data by flow cytometry. We have re-written this part with the analysis of altered proportion of two macrophage clusters (Kupffer cells and Monocyte-derived macrophages) (page 20-21; line 399-436):

“The scRNA sequencing analysis identified two well-established subpopulations of liver macrophages: the resident Kupffer Cells (KCs) and the Monocyte-Derived Macrophages (MDMs) (Figure 6, A-C; Supplemental Figure 9A). When comparing the total proportion of macrophages within the immune cell population of the liver between WT and Prdm1ΔNcr1 mice, there is an increase in Prdm1ΔNcr1 mice (Figure 6C). To confirm these findings, we utilized flow cytometry to define macrophages, including both KCs and MDMs, gating by CD45+Ly6G-F4/80+CD11b+ (Figure 6D).

Our analysis showed that, following the deletion of Prdm1 in Group 1 ILCs, there is a significant increase in both the proportion and number of macrophages in the liver (Figure 6D).

According to the transcriptional profile, liver macrophages further clustered and were labeled as “Ly6c2_hi”; “_Cxcl2_hi”; “_Ear2_hi” MDMs, and “_Mrc1_hi”; “_C1q_hi” KCs (Figure 6A, Supplemental Figure 9, A-E). Increased proportion of MDMs and KCs was observed in _Prdm1ΔNcr1 cells (Supplemental Figure 9B). Within MDMs clusters, Ly6c2_hi MDMs mainly compose of _Prdm1+/+ cells, while Prdm1ΔNcr1 cells concentrated in Cxcl2_hi cluster (Figure 6C). The scRNA-seq data reveal that following Prdm1 knockout in NKp46+ cells, there is a decrease in the proportion of KCs within the macrophage population, while the proportion of MDMs increases (Figure 6D). CX3CR1, a chemokine receptor, is extensively utilized to distinguish KCs and MDMs within macrophages. Cells expressing CX3CR1 are identified as MDMs, whereas those without CX3CR1 expression are categorized as KCs (56). Employing flow cytometry and leveraging CX3CR1 expression, we assessed the ratios of KCs and MDMs. However, diverging from the scRNA-seq findings, flow cytometry indicates that post-Prdm1 knockout in group 1 ILCs, there is a minor increase in the proportion of KCs within the total liver macrophages, and a decrease in the proportion of MDMs (Figure 6D; Supplemental Figure 9B). This discrepancy could stem from the different bases of classification: scRNA-seq defines KCs based on gene expression profiles, whereas flow cytometry differentiates between KCs and MDMs using the single surface marker, CX3CR1. Analysis of the macrophage subsets identified by scRNA-seq reveals that, while MDM clusters generally show high CX3CR1 expression, there exists a subset within MDMs, labeled _Mrc1hi, that also exhibits high levels of CX3CR1 (Supplemental Figure 9C). Consequently, if flow cytometry solely employs CX3CR1 for differentiating between KCs and MDMs, it could result in disparities when compared to scRNA-seq outcomes. Both KCs and MDMs has significantly increased in Prdm1ΔNcr1 mice, which was consist with the scRNA-seq data (Supplemental Figure 9, B and F). Despite the decrease in the proportion of Ly6c2hi MDMs in Prdm1ΔNcr1 mice, the expression levels of Ly6c2 exhibited minimal variation between WT and Prdm1ΔNcr1 mice (Supplemental Figure 9D). Intriguingly, within certain cellular subsets, notably the Ear2hi cluster, the Ly6c2 expression levels in KO mice were found to be higher than those in WT mice. Additionally, we employed flow cytometry to examine Ly6C expression within the macrophages. Similar with the scRNA-seq findings, there were no notable differences in Ly6C expression levels between WT and KO mice (Figure 6E; Supplemental Figure 9G).”.

The changes of the macrophage compartment indicated the potential influence of functional NK cells to macrophages. We have revised these parts in our results and discussion (line 590-601). However, to address more analysis on macrophage is worthy but would go beyond the scope of this manuscript, which will be a direction of our further work.

Comment 45: Figure 6, C1qhi Mac only are few cells/events, and interactions (or cells?) seem to be gone in the Prdm1-floxed mice. Is that true? Does it make sense to perform cell-chat analysis on so few cells?

Response 45: We have now added KCs to the cell-chat analysis, and this cluster was belonged to C1qhi KCs. We have revised the analysis of corresponding parts in our manuscript (page 20-21; line 408-428):

“According to the transcriptional profile, liver macrophages further clustered and were labeled as “Ly6c2_hi”; “_Cxcl2_hi”; “_Ear2_hi” MDMs, and “_Mrc1_hi”; “_C1q_hi” KCs (Figure 6A, Supplemental Figure 9, A-E). Increased proportion of MDMs and KCs was observed in _Prdm1ΔNcr1 cells (Supplemental Figure 9B). Within MDMs clusters, Ly6c2_hi MDMs mainly compose of _Prdm1+/+ cells, while Prdm1ΔNcr1 cells concentrated in Cxcl2_hi cluster (Figure 6C). The scRNA-seq data reveal that following Prdm1 knockout in NKp46+ cells, there is a decrease in the proportion of KCs within the macrophage population, while the proportion of MDMs increases (Figure 6D). CX3CR1, a chemokine receptor, is extensively utilized to distinguish KCs and MDMs within macrophages. Cells expressing CX3CR1 are identified as MDMs, whereas those without CX3CR1 expression are categorized as KCs (56). Employing flow cytometry and leveraging CX3CR1 expression, we assessed the ratios of KCs and MDMs. However, diverging from the scRNA-seq findings, flow cytometry indicates that post-Prdm1 knockout in group 1 ILCs, there is a minor increase in the proportion of KCs within the total liver macrophages, and a decrease in the proportion of MDMs (Figure 6D; Supplemental Figure 9B). This discrepancy could stem from the different bases of classification: scRNA-seq defines KCs based on gene expression profiles, whereas flow cytometry differentiates between KCs and MDMs using the single surface marker, CX3CR1. Analysis of the macrophage subsets identified by scRNA-seq reveals that, while MDM clusters generally show high CX3CR1 expression, there exists a subset within MDMs, labeled _Mrc1hi, that also exhibits high levels of CX3CR1 (Supplemental Figure 9C). Consequently, if flow cytometry solely employs CX3CR1 for differentiating between KCs and MDMs, it could result in disparities when compared to scRNA-seq outcomes.”.

Comment 46: Figure 6, C - Here the interactions of both Mac+ILC1 and Mac+NK are shown together. It would be interesting to separate this analysis (also Suppl. Fig 9A-B) into comparisons of Mac+ILC1 vs Mac1+NK from WT or Prdm1fl Ncr1 mice.

Response 46: As request, we re-analyzed this part in each genotype, which was showed in the Supplemental Figure 10. These data have now been described in (page 22; line 445-447).

“The reduction of interaction mostly occurred in the cross-talk of ILC1-MDM and ILC1-KC, whereas no difference was observed in cNK-MDM and cNK-KC interaction (Supplemental Figure 10, A-H)”

Comment 47: Supplementary Figure 9, A, B - Is this analysis using WT and Prdm1fl Ncr1cre dataset together? 

Response 47: Yes, we used WT and Prdm1_Δ_Ncr1 data together. As the request above, we separate this analysis from WT or Prdm1_Δ_Ncr1 Ncr1 mice. These data have now been described in (page 22; line 445-460):

“The reduction of interaction mostly occurred in the cross-talk of ILC1-MDM and ILC1-KC, whereas no difference was observed in cNK-MDM and cNK-KC interaction (Supplemental Figure 10, A-H). A reduction in the interaction of ligand-receptor, such as Mif-CD74, Cxcl16-Cxcr6, and Cxcl10-Cxcr3 was observed in Prdm1ΔNcr1 mice compared to Prdm1+/+ mice (Supplemental Figure 11). Compared to Prdm1+/+ mice, the information flow of CXCL and MIF pathways significantly decreased in Prdm1ΔNcr1 mice (Figure 6, H and I; Supplemental Figure 10, B, D, F, and H). These pathways play a crucial role in facilitating macrophage migration. The CXCL signaling was sent from Ly6c2_hi _Cxcl2_hi MDMs and _C1q_hi KC, targeting all ILC1 clusters and _Cxcr3_hi cNK cell clusters (Figure 6J). Of note, although the population of _Cxcl2_hi macrophage primarily comprised cells from _Prdm1ΔNcr1 mice, the interaction within the CXCL pathway between macrophages and group 1 ILCs was obviously less than Prdm1+/+ sample (Figure 6J). These changes could be linked to a decreased population of ILC1s and Cxcr3_hi cNK cell cluster in _Prdm1ΔNcr1 mice, implying that the homeostasis of _Cxcl2_hi macrophages required sufficient signals from cNK cells and ILC1s. The impaired CXCLCXCR interactions might subsequently lead to reduced recruitment and activation of group 1 ILCs and macrophages within the tumor microenvironment.”.

Comment 48: Figure 7, A-C -What is the consequence/interpretation of reduced Mitotracker staining? Any metabolic assays performed? The definition of NK cell "exhaustion" is unclear, is reduced IFNg enough for that? Is the concept of NK cell exhaustion clearly established? Only shortly touched upon in the discussion, the rationale for suggesting an exhausted phenotype, should be explained.

Response 48: MitoTracker was used to assess the mitochondrial mass. The reduced staining indicated compromised mitochondria function, which associated with mitochondrial fragmentation.

We believe that the exhaustion of NK cells is not as well-established a concept as it is for T cells. The purpose of detecting mitochondria in this study is to provide evidence for the relationship between Prdm1 and the exhaustion of NK cells. In the discussion section, we have added the following content (page 26; line 529-543):

“Mitochondria are pivotal organelles crucial for cellular metabolism. Disruptions in mitochondrial function have been linked to T Cell exhaustion, attributed to glycolytic reprogramming (66). Similarly, mitochondrial fragmentation has been closely associated with NK cell exhaustion (67).

However, the concept of NK cell exhaustion isn't as firmly established as it is for T cells. Exhausted NK cells should primarily exhibit diminished functions. This is characterized by a diminished ability to destroy tumor cells, a reduced capability to activate other components of the immune system, and compromised proliferation and survival rates. Additionally, this reduced functionality is associated with a decline in the expression of molecules responsible for cytotoxic activity, lower production of IFN-γ, and metabolic disturbances that may arise from mitochondrial dysfunction. While our current data is not sufficient to definitively classify these cells as exhausted NK cells, it supports that a subpopulation, referred to Junb_hi cluster, demonstrates an exhaustion-like phenotype. The significant increase in this cell population following _Prdm1 knockout in NK cells may potentially be one of the reasons why Prdm1ΔNcr1 mice lose their tumor-killing capacity. Whether the excessive expression of JunB in NK cells is also a contributing factor to their exhaustion, similar to T cells(65), requires further investigation.”.

Comment 49: Figure 7, x-axis labelling (MFI) of histograms is not correct. Do bar graphs and FACS plots show the same data? Does the number in the FACS plots indicate the MFI? If so, the FACS plots do not show representative samples?

Response 48: We appreciate the valuable comments provided by the reviewer. In the revised Figure 7, the MFI values have been removed. Bar graphs now display summary data from FACS histograms.

A representative sample close to the group's mean value was chosen for display in the histograms.

Comment 50: Figure 7, D - How are these data different from Figure 2H? Why is it now called "exhaustion", but not in 2H? Is the detected IFNg only driven by ex vivo stimulation with Il12/Il18? As above, a "standard" 4h assay should also be provided to allow better interpretation of potential differences. In the introduction, the authors cite the Ducimetiere study (Ref 5) highlighting "the primary function of ILC1 in suppressing the seeding of metastatic tumor cells in liver tissue". Thus, it would be interesting to test Ifng production by liver ILC1 and NK cells ex vivo at early time points of tumor inoculation.

Response 50: Tumors grow and proliferate within tissues, constituting one of the major causes of lymphocyte exhaustion. This part of the current study aims to investigate whether Prdm1 aids NK cells or ILC1 in resisting the exhaustion induced by malignant tumors. Specifically, we seek to ascertain whether the absence of Prdm1 renders NK cells or ILC1 more susceptible to exhaustion within the tumor microenvironment. Therefore, we will consider the capacity to secrete IFN-γ upon IL-12/IL-18 stimulation as one indicative aspect of exhaustion. It's crucial to emphasize that this assessment serves as only one piece of evidence, not the sole determinant. Overnight stimulation is a conventional method for studying NK cells and has been widely used across different laboratories, including our lab (e.g. Bream et al., Blood, 2003; Yu et al., Immunity, 2006; Wang et al., J Clin Invest, 2018). It's essential to clarify that our approach does not involve stimulating with tumor cells to evaluate the secretion capacity of IFN-γ by NK cells or ILC1.

Reviewer 2 (Public Review):

Summary:

This study offers a significant advancement in understanding liver innate lymphoid cell (ILC) biology by elucidating the role of the transcription factor Prdm1. It shows that Prdm1 is crucial in maintaining the balance between conventional natural killer (cNK) cells and ILC1s in the liver, with knockout models revealing a vital role in cancer defense mechanisms. Despite not affecting direct cytotoxicity, Prdm1 deficiency leads to increased cancer metastasis and reduced secretion of key molecules like IFN-γ, pointing to its importance in immune regulation. The use of single-cell RNA sequencing further underscores Prdm1's role in cellular communication within the liver's immune milieu. This study is a robust contribution to the field, providing insights that could inform new immunotherapy approaches for liver cancer.

Strengths:

The study's strength lies in its comprehensive approach, combining the specificity of Prdm1 conditional deletion in Ncr1-cre mice with integrative omics analyses and cutting-edge cytometry to delineate Prdm1's role in liver Type 1 ILC biology and its functional implications in tumor immunity. This multifaceted strategy not only clarifies Prdm1's influence on ILC composition and maturation but also conveys potential therapeutic insights for liver cancer immunotherapy.

We sincerely appreciate your interest and critical assessment of our manuscript. We have carefully read your comments and suggestions, and I am truly grateful for your expert guidance. We have worked on addressing each of your concerns and comments, and below we provide a point-to-point response. Please find the detailed responses below:

Weakness

Comment 1: A notable weakness of the study is the limited scope of in vivo disease models, primarily relying on the B16F10 melanoma model, which may not fully capture the complex behavior of Type 1 ILCs across diverse cancer types. Furthermore, the absence of direct human data, such as the effects of PRDM1 deletion in human NK cells or stem cells during their differentiation into NK and ILC1, leaves a gap in translating these findings to clinical settings.

Response 1: We appreciate the reviewer for raising these important points, which we see as a unique opportunity for future work to transform our understanding of Prdm1 and its targets as opposed to a weakness of the present study. 

In our revised manuscript, we have discussed these limitations of our study (page 29; line 602-609):

“While our findings underscore the importance of Prdm1 in liver cNK cells and ILC1s tumor immune surveillance, it does not be validated in human NK cells, whereas previous studies have found that PRDM1 might inhibit the proliferation and function of human NK cells (33, 73). Furthermore, we not provided an in-depth evaluation in multiple tumor models. Further research may provide deeper insight into the role of PRDM1 in the anti-tumor function of human NK cells, enabling a more direct investigation of its application in cancer therapies. Given its important role in preserving liver cNK cells and ILC1s functional heterogeneity, enhancing Prdm1 function in human NK cells could potentially be a strategy to promote NK cell-based immunotherapy for cancer.”.

Recommendations For The Authors:

(Introduction) 

Comment 2: Reference 1 appears slightly misplaced. You might find the nomenclature discussion in Spits et al., Nature Reviews Immunology, 2013, more appropriate.

Response 2: We are really sorry for our inaccurate descriptions. According to Spits et al., (Spits et al., Nature Reviews Immunology, 2013) and other related studies, we have now adopted a more appropriate nomenclature as “Conventional NK cells” correspond to “cNK cells”, “Type 1 innate lymphoid cells” to “ILC1s”, and “Group 1 ILC” as the collective name of cNK and ILC1s. 

The definition of these cells was described in the introduction (page 4, line 52-53; line58-62): 

“Group 1 ILCs consist of cNK cells and ILC1s (1, 2), with distinct developmental trajectories and effect molecules (3).”, “In a state of homeostasis, liver group 1 ILCs (CD45+CD3-NK1.1+NKp46+) can be discriminated into cNK cells and ILC1s by the differential expression of CD49a and CD49b (2): cNK cells are marked by the expression of CD49b, while liver ILC1s exhibit a distinctive positivity for CD49a. Tumor Necrosis Factor Related Apoptosis Inducing Ligand (TRAIL) is also expressed on liver ILC1s, but not on cNK cells (10, 11).”. 

We also describe cNK and ILC1 phenotypes in our scRNA-seq data, as shown in page 13; line 259-261: 

“cNK cells expressed high levels of Itga2 (CD49b) and Eomes, while ILC1s had high levels expression of Itga1 (CD49a) and Tnfsf10 (Supplemental Figure 5, F and G).”.

Comment 3: It has come to my attention that Reference 9 has been retracted. I recommend removing this citation to maintain the integrity of your references (https://doi.org/10.1182/blood.2023022801).

Response 3: We thank the reviewer’s comment and we now have removed this citation.

Comment 4: For a more comprehensive context around reference 15, consider citing Thierry Walzer's work ([https://rupress.org/jem/article/211/3/563/41636/T-bet-and-Eomes-instruct-thedevelopment-of-two)]) which aligns closely with your discussion.

Response 4: We agree with the reviewer’s suggestion and have added this citation in our introduction (page 4; line 64-66):

“Liver environment facilitated T-bet expression in the early stage of NK cells development, which results in Eomes repression. The repression of T-bet is required for Eomes+ NK cells (17).”.

(Results) 

Comment 5: The NK cell signature referenced in 32 has been questioned for its reliability as discussed by Cursons et al., CRI 2019 (https://pubmed.ncbi.nlm.nih.gov/31088844/). Reanalysis of data in Figure 1 B/C and Supplementary Figure 1 with the refined NK cell signature from Curson's work would be advantageous.

Response 5: We thank the reviewer’s comment. As requested, we reanalyzed our data using the refined NK cell signature from Cursons et al. (revised Figure 1 A-C; revised Supplemental Figure 1). Of note, the overall survival of liver cancer (LIHC) patients only reached statistics significance when compared high and low expression of refined PRDM1-NK signature with a median cutoff (Figure 1, A-C). The overall survival performed with quartile high and low expression of refined PRDM1-NK signature was moved to supplemental figure 1, G-I. 

The original text is: “Examination of 363 liver hepatocellular carcinoma (LIHC) patient samples from The Cancer Genome Atlas (TCGA) revealed a positive correlation between the expression of NK cell-associated genes (NCR1, NCR3, KLRB1, CD160, and PRF1) (32) and PRDM1 expression (Figure 1A). Patients with top and bottom quartiles of NK-PRDM1 signature expression were chosen for survival analysis (Figure 1B). Notably, patients with the NK-PRDM1_hi signature had better overall survival compared to the these with NK-_PRDM1_lo signature (Figure 1C). Similar results were also found in skin cutaneous melanoma (SKCM, n=454) and lung adenocarcinoma (LUAD, n=497) patients (Supplemental Figure 1, A-F). These data suggested that _PRDM1 in NK cells might be essential for immune surveillance in some solid tumors, including liver cancer. These findings prompted us to investigate the impact and mechanism of PRDM1 in NK cells and ILC1 within the context of liver cancer.”

We have rewritten this part in our revised manuscript (page 7; line 119-132): 

“Examination of 363 liver hepatocellular carcinoma (LIHC) patient samples from The Cancer Genome Atlas (TCGA) revealed a positive correlation between the expression of NK cell-associated genes (34) (NCR1, KLRB1, CD160, PRF1, etc.) and PRDM1 expression (Figure 1A). The patients are ordered from highest to lowest based on the expression of NK-Prdm1 for survival analysis (Figure 1B). Notably, patients exhibiting higher levels of NK-PRDM1 expression (above the median) experienced better survival outcomes compared to those with lower levels of NK-PRDM1 expression (below the median) (Figure 1C). Similar results were also found in skin cutaneous melanoma (SKCM, n=454) and lung adenocarcinoma (LUAD, n=497) patients (Supplemental Figure 1, A-F). Patients within the highest quartile of NK-PRDM1 signature expression demonstrated enhanced overall survival, a result that achieved statistical significance in LUAD and SKCM patients (Supplemental Figure 1, G-I). These data suggested that PRDM1 in NK cells might be essential for immune surveillance in solid tumors, including liver cancer, and prompted us to investigate the function and mechanism of PRDM1 in NK cells and ILC1 within the context of liver cancer.”.

Comment 6: The origin of the Ncr1-cre mice utilised should be clarified; is this the line developed by Eric Vivier? (https://www.pnas.org/doi/10.1073/pnas.1112064108).

Response 6: We did not use the line developed by Eric Vivier, our Ncr1-cre mice was purchase from Shanghai Model Organism Center, Inc.. We described this in our method parts (page 29-30; line 612-614): 

“Prdm1fl/fl mice were purchased from The Jackson Laboratory. Ncr1-iCre and B2m-/- mice were purchased from Shanghai Model Organisms Center, Inc.. Six- to twelve-week-old littermates were used for the experiment.”

Comment 7: Considering the known reduction of Ncr1 expression in Ncr1-cre mice and its implications, it is recommended to repeat the B16F10 experiments with the correct control, Ncr1cre/+ Prdm1+/+.

Response 7: This is an excellent question, and it has been raised by another reviewer and comprehensively answered (Reviewer 1, Comment 1). The answer is below: 

The expression of Cre and the insertion of loxP sequences both have the potential to influence gene expression. This is because the region where loxP is inserted may contain regulatory sequences for the gene of interest. Ncr1-Cre is a frequently used transgenic mouse model in our laboratory. In our prior research, we also had concerns about the possible impact of Cre on NKp46 expression, which could lead to a decline in NK cell function. Therefore, in our previous studies focused on Smad4 expression in NK cells, we conducted similar experiments. In Figure 6 of our published paper in the Journal of Clinical Investigation (Wang et al., J Clin Invest, 2018), we compared NKp46iCreTgfbr2fl/flSmad4fl/WT with NKp46-iCreTgfbr2fl/flSmad4fl/fl. Although the primary purpose is to establish Smad4's independence from TGF-β, it also allows for a comparison between Smad4fl/fl and Smad4fl/WT in the presence of Cre. In the critical phenotype we assessed, NKp46iCreTgfbr2fl/flSmad4fl/fl (compared with NKp46-iCreTgfbr2fl/flSmad4fl/WT) exhibited the same phenotype as NKp46-iCreSmad4fl/fl (compared with NKp46WTSmad4fl/fl). This suggests that Cre's influence on NK cells may be within a reasonable and controllable range. Furthermore, in contrast to the decrease in Ncr1 expression caused by Cre, the reduction in the expression levels of genes targeted by Loxp knockout, such as Prdm1 in this study (Figure 1 E), is more significant. Therefore, with the current techniques and research methods, we believe that the data provided in this study can support the role of Prdm1 in NK cells.

Comment 8: The proportion of ILC1 in wild-type mouse livers is notably higher than standard references. Could you confirm whether liver perfusion was performed before analysis? This procedure was not clearly detailed in the methods section.

Response 8: We apologize that we did not provide enough detail regarding this point in our original method. We had performed the liver perfusion before analysis. This has now been clarified in the method section of the revised text (page 30-31; line 630-636): 

“Mice were perfused with 1◊ PBS by portal vein puncture before harvesting tissues. Liver and lung was digested with 0.05% collagenase II for 30 minutes and filtered through 70 µm cell strainers, and mononuclear cells were isolated after subjected to density gradient using 30% and 70% percoll. Spleen were also removed and pressed through 70 µm filterers to obtain splenocytes. Peripheral blood mononuclear cells were obtained from peripheral blood after lysis of red blood cells (Biolegend, 420301). Flushing femurs and mechanical disruption of inguinal lymph nodes were performed to obtain cells from bone marrow and lymph nodes.”.

The lymphocyte proportions in mice from different laboratories may exhibit slight variations, possibly due to genetic background disparities. To minimize the influence of genetic backgrounds, paired littermates were used in the current study, wherein one is Prdm1 WT and the other has the Prdm1 gene knocked out in NK cells.

Comment 9: There appears to be inconsistency in reference formatting; for instance, Ref 39 does not match the formatting of other references. A thorough review of your citation format is suggested.

Response 9: We apologize for the inadvertent errors and we reviewed the citation format.

Comment 10: The information in Figures 2B and C may be better suited to the supplementary section as it does not significantly contribute to the main text.

Response 10: We agree with the reviewer’s suggestion and these are now moved to supplementary figures (Supplemental Figure 2).

Comment 11: The citation of reference 40 could be strengthened by including Sathe et al., 2014, which directly pertains to your findings (https://www.nature.com/articles/ncomms5539).

Response 11: We added the suggested reference.

Comment 12: Can the findings presented in Figure 2D/F be replicated using alternative models?

This would substantiate the versatility of your results.

Response 12: The current predominant in vivo tumor model for NK cells is primarily based on the use of B16F10 melanoma cells. These melanoma cells, with their low expression of MHC-I molecules, evade T cell-mediated immune surveillance, rendering them ideal targets for NK cells. Typically, this experimental melanoma metastasis assay involves tail vein injection, followed by nodules' detection in the lungs. To align with our investigation of liver-resident cNK and ILC1, we've introduced splenic injection (via the portal vein) and evaluated melanoma metastasis in the liver to reflect the anti-tumor capabilities of liver group 1 ILCs. We also explored subcutaneous tumor models, but we believe they may not effectively support Prdm1's role in cNK cells, particularly liver-resident NK cells and ILC1. While we've experimented with models using mouse liver tumor cells like Hepa 1-6, we found them less stable than B16F10 and less conducive to quantification. Should more suitable models or cells line emerge, we remain open to exploring them in future research.

Comment 13: The absence of in vitro killing assessments against B16F10 and YAC-1 leaves a gap in the NK cell characterisation which would be valuable to address.

Response 13: Isolating NK cells for ex vivo cytotoxicity assays typically requires stimulation with high concentrations of IL-2. Under such high IL-2 stimulation, many intracellular differences that contribute to difference in cytotoxicity, such as changes in transcription factors, are often masked. Another issue is that current ex vivo NK cell cytotoxicity assays often only isolate NK cells from the spleen. Liver-resident NK cells, on the other hand, are often limited in quantity and isolation methods, making it challenging to conduct ex vivo cytotoxicity assays effectively. If more sensitive detection methods become available, we will also incorporate ex vivo data into our future research endeavors.

Comment 14: The suggestion that NK cells produce IL-6 is indeed a bold one, and without additional validation through intracellular cytokine detection or ELISA, it may be prudent to omit these claims.

Response 14: We have checked the GSEA results, and found no valuable genes in IL-6 production.

Therefore, we have removed this figure.

Comment 15: The lack of fluorescence minus one (FMO) controls in Figure 3 and Supplementary

Figure 4 is noted; including these would enhance the validity of your gating strategies.

Response 15: As requested, we add the FMO controls in aforementioned figures.

Comment 16: There seems to be a minor mix-up in referring to Figure 4A in the scRNAseq results section, perhaps it was intended to refer to Figure 3A?

Response 16: We have corrected this part (line 247). We also double checked corrected the inaccuracies in the references to the figures. we apologize for the inadvertent errors.

Comment 17: The rich datasets generated from bulk and scRNAseq are commendable. However, I urge you to make these datasets publicly accessible with a GEO accession number.

Response 17: We appreciate the suggestion from the reviewer. We plan to upload our datasets when in the last version of our manuscript, which is also the request of the eLife policy.

Comment 18: Figure 4K is insightful, yet a similar analysis of the ILC1 cluster could provide a more rounded understanding.

Response 18: We thank the reviewer for the comments. We provide the similar analysis of ILC1s, as showing in revised Figure 5H. 

Comment 19: The metabolic RNA signatures featured in Supplementary Figure 6 are intriguing and warrant further validation, perhaps through Seahorse analysis. Such validation could merit their inclusion in the main figures.

Response 19: This is a very good suggestion. Currently, our data offer only limited indications in this context. We have chosen to validate some aspects of Prmd1's influence on cytotoxicity molecules. As for Prdm1's impact on other aspects of NK cells, such as metabolic functions, we may explore further in future research. Additionally, we hope that by publishing our research findings, laboratories worldwide can draw insights for their own studies and conduct relevant research based on this data.

Comment 20: It is difficult to discern whether the cells depicted in Figure 7D are truly tumorinfiltrating ILC1 or NK cells that have adopted ILC1-like characteristics. Intravenous injection of CD45-PE could clarify this distinction, and if they are the latter, it may be more appropriate to refer to them as ILC1-like cells.

Response 20: We completely agree with the reviewer's suggestion that "tumor-infiltrating lymphocytes" may not be accurate for the current experiment. Therefore, in the revised manuscript, we have changed it to "liver cNK or ILC1 from tumor-bearing livers.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

In this study, Drougard et al. examined the consequences of an acute high fat diet (HFD) on microglia in mice. 3-day HFD influenced the regulation of systemic glucose homeostasis in a microglia-dependent and independent manner, as determined using microglial depletion with PLX5622. 3-day HFD increased microglial membrane potential and the levels of palmitate and stearate in cerebrospinal fluid in vivo. Using confocal imaging, respirometry and stable isotope-assisted tracing in primary microglial cultures, the authors suggest an increase in mitochondrial fission and metabolic remodeling occurs when exposed to palmitate, which increases the release of glutamate, succinate and itaconate that may alter neuronal metabolism. This acute microglial metabolic response following acute HFD is subsequently linked to improved higher cognitive function (learning and memory) in a microglia and DRP1-dependent manner.

Strengths:

Overall, this study is interesting and novel in linking acute high fat diet to changes in microglia and improved learning and memory in mice. The role for microglia and DRP1 in regulating glucose homeostasis and memory in vivo appears to be supported by the data.

Weaknesses:

The authors suggest that utilization of palmitate by microglia following HFD is the driver of the acute metabolic changes and that the release of microglial-derived lactate, succinate, glutamate and itaconate are causally linked to improvements in learning and memory. A major weakness is that the authors provide no mechanistic link between beta-oxidation of palmitate (or other fatty acids) in microglia and the observed systemic metabolic and memory phenotypes in vivo. Pharmacological inhibition of CPT1a could be considered or CPT1a-deficient microglia.

We thank Reviewer #1 for their time, effort and the critique. Indeed, we suggest that palmitate drives the aMMR response and associated improvements in learning and memory. In response to acute HFD we observe 1) increased in palmitate in CSF; 2) impaired mitochondrial ETC activity in primary microglia (within 12 hours of HFD); and 3) improved learning and memory. The greatest barrier to proving how acute palmitate uptake in microglia improves learning and memory in vivo is the protracted methodology required for microglial isolation and purification. The timeframes and relatively harsh digestion protocols required are currently incompatible with metabolomic tracing and well beyond those required for most cell-types used for metabolomic investigation.  We have tested and failed to obtain reproducible data across numerous in vivo protocols and finally settled on in vitro 13C palmitate treated neonatal microglia as the best current option. Primary neonatal microglia are accepted as one of the current best culture models by the microglial community (Valdercaos cell report 2014, Kim Cell Metab 2019). Using neonatal microglia we demonstrate that 13Cpalmitate label is processed to palmitoylcarnitine (Fig 4C) and acetylcarnitine (Fig 4D) indicating that microglial fatty acid metabolism acts via the canonical CPT1/CPT2 pathway. These experiments highlight that microglia process palmitate via beta oxidation generating acetyl coA and engaging the TCA cycle (Fig 4G-I).

We now acknowledge these technical limitations more clearly and highlight their impact on any conclusions regarding adult microglia in vivo:

Results “Microglia take up and metabolize free fatty acids”; 

“Due in part to the long isolation times required to generate pure primary adult microglia, metabolite tracing experiments on primary adult microglia are not currently feasible. We therefore chose primary murine neonatal microglia as our model of choice for more mechanistic experiments (Valdercaos, Cell Report 2014)”

And,

Discussion:

“We propose that aMMR could result from direct uptake, processing, and release of fatty acid derived carbons, and demonstrate that microglia are capable of metabolizing fatty acids towards diverse intracellular and extracellular pools.”

While acute ICV injection a CPT1a blocker would be of potential interest, the caveats associated with CPT1a inhibition in other cell-types (neurons, astrocytes, etc) and with targeting the appropriate brain region (currently unknown) currently preclude the effective use of this approach for to generate clear additional mechanistic insights. Similarly, given the time and resources required to generate, validate, optimize and experiment on a clean model of in vivo adult microglia-specific CPT1a knockout, this approach was deemed beyond the scope of this study. That said, the critique is important, and it should comprise a follow-up project.

Comment: Another major weakness is that the authors also suggest that 3-day HFD microglial response (increase membrane potential) is likely driven by palmitate-induced increases in itaconate feedforward inhibition of complex II/SDH. Whilst this is an interesting hypothesis, the in vitro metabolic characterization is not entirely convincing.

The reviewer is correct, we suggest that our data is consistent with a model where a palmitate-induced increase in itaconate inhibits complex II/SDH. While our findings do not comprise mechanistic proof, the hypothesis is supported by our Seahorse studies (Fig 2E) highlighting that a combined Palmitate + Succinate stimulation does not increase OCR beyond that of Palmitate alone; by primary microglial cell experiments highlighting that 3d-HFD treated adult primary microglia are refractory to succinate-induced mitochondrial membrane depolarization (Fig 2F); and by the identification of increased palmitate induced itaconate production/release in cultured primary neonatal microglia (Fig 4H). The data are consistent with an inhibition of complex II/ SDH and with increased itaconate secretion. They are also consistent with literature on more easily accessible myeloid lineages (Lampropoulou V, Cell Metab 2016).  

Comment: The authors suggest that acute palmitate appears to rapidly compromise or saturate complex II activity. Succinate is a membrane impermeable dicarboxylate. It can enter cells via MCT transporters at acidic pH. It is not clear that I) Succinate is taken up into microglia, II) If the succinate used was pH neutral sodium succinate or succinic acid, and III) If the observed changes are due to succinate oxidation, changes in pH or activation of the succinate receptor SUCNR1 on microglia. In the absence of these succinate treatments, there are no alterations in mitochondrial respiration or membrane potential following palmitate treatment, which does not support this hypothesis.

We thank Reviewer #1 for highlighting a lack of information in the material and methods. We have updated them accordingly as follows:

“For the electron transport chain experiments (ETC), the experiment was based on the Salabei et al. The cell suspension was incubated with the mitochondrial probe Tetramethylrhodamine TMRM (10mM; Abcam, Cat# ab228569) and fluorescent glucose analog 2-NBDG (Abcam, Cat# 235976) for 30min at 37degrees before FACS acquisition. For challenging the ETC, the cell pellet was resuspended in 500ul of warm MAS buffer solution + 1nM Plasma Membrane Permeabilizer (Agilent Seahorse XF PMP) in order to permeabilize the cells. Microglial cells were gated from CD45low-CD11b+ cells followed by singlet after forward and side scatter pattern. They were incubated each 90 seconds by the following drugs: 0,5ul of 100uM Rotenone (Sigma), 2ul of 2.5M Succinate adjusted to ph 7.4 with NaOH (succinic acid, Sigma) and 0.5ul of 1mM Antimycin (Sigma). Cytometry was performed on Fortessa (BD Bioscience) and analyzed with FlowJo v10 (Treestar).”

Following the updated protocol, we hope we highlighted that the succinate (solution of succinic acid ph 7.4) is reaching directly the ETC since the microglial cells have been permeabilized by the Plasma Membrane Permeabilizer (Agilent Seahorse XF PMP).

Comment: Intracellular itaconate measurements and quantification are lacking and IRG1 expression is not assessed. There also appears to be more labelled itaconate in neuronal cultures from control (BSA) microglia conditioned media, which is not discussed. What is the total level of itaconate in neurons from these conditioned media experiments? No evidence is provided that the in vivo response is dependent on IRG1, the mitochondrial enzyme responsible for itaconate synthesis, or itaconate. To causally link IRG1/itaconate, IRG1-deficient mice could be used in future work. 

We appreciate the interest, the exciting question, and the suggested future experiment. Indeed, our results suggest a difference in metabolite release between the BSA treated-microglia and palmitate treated-microglia and their impact on neurons comprises a prime question for future work. We have highlighted this in the discussion as well as adding a comment regarding relative levels of labelled itaconate as follows:

Results; Acute HFD induces widespread MMR and rapid modulation (…) memory  

“As a control for the direct uptake of 13C-glucose, we treated parallel neuronal cultures with the same fresh 13C-glucose tracing media originally added to the microglia. Intriguingly, and consistent with literature documenting poor direct glucose utilization by neurons [29], we found substantial m+3 lactate (as well as other metabolites) in neurons treated with microglial conditioned media, and at levels that far exceeded labelling triggered by glucose tracer alone (Fig 5A, middle column vs left column)(Suppl Fig S5B). The data indicate higher uptake of citrate and itaconate from the control microglia-conditioned media, further supporting the hypothesis that neuronal metabolism is reproducibly impacted by palmitate-triggered changes in microglial products. These data demonstrate that palmitate metabolism by microglia modulates neuronal carbon substrate use in vitro, and, they highlight the relative importance of this process compared to uptake of pure glucose. The data identify a candidate mechanism by which aMMR may alter neuronal function in vivo.”

Comment: While microglial DRP1 is causally implicated the role of palmitate is not convincing. Mitochondrial morphology changes are subtle including TOMM20 and DRP1 staining and co-localization - additional supporting data should be provided. Electron microscopy of mitochondrial structure would provide more detailed insight to morphology changes. Western blot of fission-associated proteins Drp1, phospho-Drp1 (S616), MFF and MiD49/51. Higher magnification and quality confocal imaging of DRP1/TOMM20. Drp1 recruitment to mitochondrial membranes can be assessed using subcellular fractionation.

We appreciate the reviewer’s comment. Previous work by others, already cited elsewhere in our manuscript

(PMCID: PMC7251564), has clearly demonstrated increased mitochondrial fragmentation and

phosphorylated DRP1 in 3d HFD animals. This very specific result can therefore be considered confirmatory / validating of existing literature, and important for inclusion of DRP1 in our overall model. We have made sure to better highlight this important literature accordingly:

Results; A rapid Microglial Mitochondria response to high fat diet

“Consistent with the in vivo observations above, in vitro palmitate exposure decreased microglial mitochondrial length within 24 hours, indicating that fatty acid exposure itself is sufficient to trigger mitochondrial fission in a cell autonomous manner (Fig 2G upper panels). This result also confirms observations by Kim et al. who observed mitochondrial fission and DRP1 phosphorylation upon 3d-HFD treated mice [Kim JD et al, Microglial UCP2 mediates Inflammation and Obesity induced by High Fat feeding, Cell Metab 2019].”

Comment: No characterization of primary microglia from DRP1-knockout mice is performed with palmitate treatment. Authors demonstrate an increase in both stearate and palmitate in CSF following 3day HFD. Only palmitate was tested in the regulation of microglial responses, but it may be more informative to test stearate and palmitate combined.

Testing stearate and palmitate combined is an interesting experiment for mimicking the global effect of HFD which is highly enriched with these two satured fatty acids, and then, more informative. In vitro stimulation of microglia model cells has been previously published by Valdearcos and al. (Cell Reports 2014) who studied the effect of a mix of stearate and palmitate on the Mediobasal Hypothalamus inflammation. Here, we build on their important findings by demonstrating that these 2 compounds are actually found in the CSF of 3d-HFD mice. Studies from other labs have also shown the presence of stearate and palmitate in the CSF of chronically obese and diabetic patients which highlights the importance of these findings (Melo HM et al. cell report 2020). While a systematic dissection of the roles of HFD-regulated CSF metabolites (including direct (diet containing) and indirect (secondary) is beyond the scope of this study, this point is important, not least because it highlights less well-studied metabolites and the potential of possible combinatorial interactions. We have highlighted this idea in the results as follows:

Results; A rapid Microglial Mitochondria response to high fat diet

“To test whether these observed fatty acid changes in the CSF might directly trigger aMMR, we switched to an in vitro primary neonatal microglia model and examined the effects of the more abundant of these, palmitate (Fig S2A-B).”

and, in the discussion as follows:

“Studies have identified stearate and palmitate in the CSF of patients with chronic obesity and with diabetes, reports that highlight the importance of these findings (Melo HM et al. cell report 2020). While a systematic dissection of the roles of HFD-regulated CSF metabolites (including direct (diet containing) and indirect (secondary)) is beyond the scope of this study, they represent priority areas for future investigation, particularly given the wide-range of fatty-acid specific biological effects in the literature, and the potential for combinatorial interactions.” 

Reviewer #1 (Recommendations For The Authors):

Congratulations on this interesting and novel work. Please see public review for details on potential experiments. While I would not expect all the experiments to be performed for this current study, it’s important to not overstate what the data is showing. For example, there is no causal link between palmitate oxidation in microglia or released metabolites (itaconate etc) from microglia in the effect on systemic glucose metabolism or memory. To make such claims more supporting data would be required.

We thank Reviewer #1 for their highly constructive critique_._

Reviewer #2 (Public Review):

The study "A rapid microglial metabolic response controls metabolism and improves memory" by Drougard et al. provides evidence that short-term HFD has a beneficial effect on spatial and learning memory through microglial metabolic reprogramming. The manuscript is well-written and the statistics were properly performed with all the data. However, there are concerns regarding the interpretation of the data, particularly the gap between the in vivo observations and the in vitro mechanistic studies.

In the PLX-5622 microglial depletion study, it is unclear what happened to the body weight, food intake, and day-night behavior of these mice compared to the vehicle control mice. It is important to address the innate immunity-dependent physiology affected by a long period of microglial depletion in the brain (also macrophages in the periphery). Furthermore, it would be beneficial to validate the images presented in Fig.1F by providing iba1 staining in chow diet-fed mice with or without PLX-5622 for 7-10 days. Additionally, high-quality images, with equal DAPI staining and comparable anatomical level, should be provided in both chow diet-fed mice and HFD-fed mice with or without PLX-5622 in the same region of hypothalamus or hippocampus. These are critical evidences for this project, and it is suggested that the authors provide more data on the general physiology of these mice, at least regarding body weight and food intake.

We are grateful to Reviewer #2 for their constructive comments and for their time and effort; and for highlighting the lack of experimental details regarding the PLX-5622 microglial depletion study. We followed the protocol established in Feng et al JCI 2017. No adverse effects on body weight, food intake and day-night behavior have been described in this study as well as in other studies for longer treatment (Sonia George et al Molecular Neurodegeneration 2019). We didn’t observe any differences in body weight and the food intake within or between groups, upon PLX administration. These data have been included as new Supplementary Fig 6 A-B.

The material and method was updated as follows:

“Animals were administered PLX5622-containing diet for 7-9 days without observable impact on the body weight or food intake (Fig S6A-B), using protocols adopted from [Feng et al JCi 2017, Sonia George et al Molecular Neurodegeneration 2019].”

Comment: It is also unclear whether the microglia shown in Fig.3A were isolated from mice 4 weeks after Tamoxifen injection. It is suggested that the authors provide more evidence, such as additional images or primary microglia culture, to demonstrate that the mitochondria had more fusion upon drp1 KO. It is recommended to use mito-tracker green/red to stain live microglia and provide good resolution images.

We thank Reviewer #2 for pointing out the lack of detailed information about Fig 3A. Microglial cells were indeed isolated from mice after the tamoxifen injection for highlighting the deletion. We updated the Material and methods with the text below;

“For the colocalization experiment, microglia were isolated from 10 to 12-week old drp1ko mice and their littermate controls, immediately fixed in PFA and stained with DRP1 (diluted 1:50 Cell signaling; Cat#8570) and tomm20 antibodies (diluted 1:1000, SantaCruz; Cat#sc177615).”

This experiment was performed as an additional control of the drp1 deletion from our knockout-mice. For this experiment we used Tomm20 since the microglia cells weren’t live after the addition of PFA. 

Comment: Regarding the data presented in Fig.5A, it is suggested that the authors profile the metabolomics of the microglial conditioned media (and provide the methods on how this conditioned media was collected) to determine whether there was already abundant lactate in the media. Any glucose-derived metabolites, e.g. lactate, are probably more preferred by neurons as energy substrates than glucose, especially in embryonic neurons (which are ready to use lactate in newborn brain).

With regards to Fig 5A, metabolomics of microglia conditioned media are provided as Fig 5A, Supp Figure 5Band we provided a supplementary table 2.

We thank Reviewer #2 for noting the lapse of technical detail. We updated the Material and methods with the following:

“For conditioned media experiments, microglial cells were incubated with DMEM (Gibco) without lactate completed with BSA-conjugated palmitate or Control BSA. Conditioned media was collected after the incubation, centrifuged 15min at 300g (4oC) and the supernatant transferred and frozen in a fresh tube avoiding the cells and debris pellet. Sample were immediately snap frozen or use for the neurons incubation.”

Any glucose-derived metabolites, e.g. lactate, are more preferred by neurons as energy substrates than glucose as described first in the literature by Prof. Pellerin and Prof. Magistretti via the astrocyte-neuron cooperation (PNAS 1994). Since their discovery, lactate has been explored and is well known as a key signaling molecule (Magistretti PJ Nat Rev Neurosciences 2018). We explored the role of lactate released from the microglia, and we demonstrated that it is taken up by neurons independently of any microglial pretreatment. This experiment highlights microglia as another lactate provider for the neurons (Fig 4N and Fig 5A). 

Comment: Finally, it is important to address whether PLX-5622 affects learning and spatial memory in chow diet-fed animals. Following the findings shown in Fig 5J and 5K, the authors should confirm these by any morphological studies on synapse, e.g. by synaptophysin staining or ultrastructure EM study in the area shown in Fig 5I.

We appreciate the comment and question. We performed the controls and included them now as Fig 5J and Fig S5 E-F-G. We do not observe any adverse effects of PLX5622 on learning and spatial memory in normal chow-fed animals. 

While we were unable to study the synapses as requested, it is important to note that no changes are expected given publications from other labs using the same protocol (Feng x JCI 2017 ,Spangenberg E Nat Com 2019), or longer PLX5622 treatment (Niiyama T eNeuro 2023, Witcher KG J neurosciences 2021), all four of which did not find morphological differences at synapses. 

Reviewer #2 (Recommendations For The Authors):

The authors should provide more evidence that palmitate is derived from HFD to prove that it mediates the HFD effects on the microglial mitochondria response. This could be done by adding 13C-palmitate into the HFD and performing metabolomics in isolated microglia from control mice (and Drp1-MG-KO mice, if possible).

We thank the Reviewer #2 for the enthusiastic revision. Unfortunately, we were unable to attempt this final suggested experiment. We have adjusted our wording accordingly and appreciate the reviewer’s understanding.

Reviewer #3 (Public Review):

Drougard et al. explore microglial detection of a switch to high-fat diet and a subsequent metabolic response that benefits memory. The findings are both surprising and novel in the context of acute highfat intake, with convincing evidence of increased CSF palmitate after 3 days of HFD. While the authors demonstrate compelling signs of microglial activation in multiple brain regions and unique metabolite release in tracing studies, they should address the following areas prior to acceptance of this manuscript.

Major Points:

(1) It appears that the authors perform key metabolic assays in vitro/ex vivo using primary microglia from either neonatal or adult mice, which should be more clearly delineated especially for the 13C-palmitate tracing. In the case of experiments using primary microglia derived from mixed glial cultures stimulated with M-CSF, this system relies on neonatal mice. This is understandable given the greater potential yield from neonatal mice, but the metabolic state and energetic demands of neonatal and adult microglia differ as their functional roles change across the lifespan. The authors should either show that the metabolic pathways they implicate in neonatal microglia are also representative of adult microglia or perform additional experiments using microglia pooled from adult mice, especially because they link metabolites derived from neonatal microglia (presumably not under the effects of acute HFD) to improved performance in behavioral assays that utilize adult mice.

We thank Reviewer #3 for their constructive critique and encouraging words. As indicated, the 13C-palmitate experiments were performed with primary microglia derived from mixed glial cultures stimulated with M-CSF and we demonstrated our primary cultures were almost pure by the supplementary experiments (supp Fig2A and B). Additional minor details in these contexts have been added to the Material and Methods.

The experiments focusing on the mitochondrial ETC were performed on sorted microglia from adult mice and parallels demonstrated with the neonatal cultures (the primary model for metabolic tracing). Compromised complex II activity under conditions of acute HFD/palmitate stimulation for instance were shown in both systems. Unfortunately, despite best-efforts, attempts to run 13C-palmitate tracing experiments on primary adult microglia failed, attributable in large part to the long (~4 hour) and harsh microglial extraction and sorting process. These experiments will require substantial follow-up efforts including the establishment and validation ideally of an adult microglia-neuron co-culture model that faithfully recapitulates most aspects of in vivo metabolic cross-talk. This noble aim is beyond the scope of this study. We have made sure to temper the  conclusions made in the manuscript and to not overstate the impact and interpretation of the in vitro work including updating the following sentences.

Results “Microglia take up and metabolize free fatty acids”; 

“Due in part to the long isolation times required to generate pure primary adult microglia, metabolite tracing experiments on primary adult microglia are not currently feasible. We therefore chose primary murine neonatal microglia as our model of choice for more mechanistic experiments (Valdercaos cell Report 2014)”

and Discussion:

“We propose that aMMR could result from direct uptake, processing, and release of fatty acid derived carbons, and demonstrate that microglia are capable of metabolizing fatty acids towards diverse intracellular and extracellular pools.”

Comment: The authors demonstrate that 3 days of HFD increases circulating palmitate by CSF metabolomics and that microglia can readily metabolize palmitate, but the causal link between palmitate metabolism specifically by microglia and improved performance in behavioral paradigms remains unclear. A previous body of research, alluded to by the authors, suggests that astrocyte shuttling of lactate to neurons improves long-term and spatial memory. The authors should account for palmitate that also could be derived from astrocyte secretion into CSF, and the relative contribution compared to microglia-derived palmitate. Specifically, although microglia can metabolize the palmitate in circulation, there is no direct evidence that the palmitate from the HFD is directly shuttled to microglia and not, for example, to astrocytes (which also express CX3CR1). 

We appreciate the comment. Indeed, this issue highlights one of the greatest challenges for efforts aimed at tracing (beyond doubt) that a single minor cell population contributes towards metabolic cross-talk in vivo. Our experiments show: increased CSF palmitate levels within one feeding cycle of HFD; rapidly induced microglial metabolic activation (characterized by increased mitochondrial membrane potential and impaired complex II activity); and that microglia mount a comparable mitochondrial activation profile in vitro when exposed to palmitate. They show in vitro using neonatal microglia that microglia take up and metabolize palmitate; that they release metabolites with neuro-modulatory potential; that neurons take these metabolites up and modulate their function differentially when exposed to control vs palmitate-treated microglia-conditioned media (in the absence of astrocytes). The experiments show through acute PLX-induced elimination of microglia, however crude, that this compartment impacts the acute HFD response, and using conditional deletion, that full DRP1 expression is required CX3CR1-CreERT2 targeted cells (primarily microglia deleting; Zhao et al 2019).  While these experiments cannot rule out a contribution of astrocytes to the observations in vivo, comparable experiments rarely can and we cannot rationalize why microglia should not have equal access to CSF palmitate for uptake or to neurons for substrate provisioning. We now better highlight this important issue, and temper our conclusions accordingly:

“Tanycytes and astrocytes have both been documented to release select metabolites into the extracellular environment [33, 34]. While suggestive, the experiments highlighted here do not rule out a contribution of these or cell types in coupling acute HFD intake to memory and learning.”

Comment: Thus, the Barnes Maze results could be attributed to multiple cell types. Furthermore, the evidence provided in Figure 5J is insufficient to claim a microglia-dependent mechanism without showing data from mice on HFD with and without microglia depletion (analogous to the third and fourth bars in panel K).

Agreed. We appreciate the comment. We have now added the requested HFD condition to Figure 5J. The data support our previous interpretation of the data. 

Comment: Given the emphasis on improved cognitive function, there is minimal discussion of the actual behavioral outcomes in both the results and discussion sections. The data that HFD-treated animals outperform controls should be presented in more detail both in the figure and in the text. For example, data from all days/trials of the Barnes Maze should be shown, including the day(s) HFD mice outperform controls. Furthermore, the authors should either cite additional literature or provide experimental evidence supporting the notion that microglia release of TCA-associated substrates into the extracellular milieu after HFD specifically benefits neuronal function cellularly or regionally in the brain, which could translate to improved performance in classical behavioral paradigms. The single reference included is a bit obscure, given the study found that increased lactate enhances fear memory which is a neural circuit not studied in the current manuscript. Are there no additional studies on more relevant metabolites (e.g., itaconate, succinate)?

We agree. We have now re-plotted the behavioral test to better highlight that the HFD-treated animals outperform controls, as requested (Fig S7 and S8). We also added the requested literature. While we cannot be sure our search captured all relevant studies, we find a relative paucity of studies that characterize CSF metabolite changes in the context of acute high fat feeding or that demonstrate the ability of CSF substrates to convincingly improve memory and learning in vivo at physiological levels. Indeed, while simple, we feel the findings are of substantial novelty and highlight an area for significant future research. We have tempered our conclusions throughout and added to the discussion as follows:

“Such substrate release could mediate the learning and memory effects that accompany aMMR; they are consistent with the data of other studies that have examined metabolite associations with learning and memory (itaconate [Morgunov IG, microorganisms 2020; Xiong J, Neuromolecular med 2023], succinate [Serra FT neurosciences letter 2022; Cline BH, BMC neurosciences 2012].”

Minor Points:

(1) In Figure 5J the latency to find the hole was noticeably higher (mean around 150s) than the latency in panel K (mean around 100s for controls, and 60s for Drp1MGWT on HFD). This suggests high variability between experiments using this modified version of the Barnes Maze, despite the authors assertion that a standard Barnes Maze was employed and the results were reproducible at multiple institutions. Why do Drp1MGWT mice on control diet find the escape hole significantly faster than WT mice on control diet in panel J? Given the emphasis on cognitive improvement following acute HFD as a novel finding, the authors should explain this discrepancy.

We appreciate this question and comment. Indeed, as the reviewer knows, behavioral tests including the Barnes test show variation with genetic background, and with environment and context (eg. age, caging density, litter size, behavioral state and more (Inglis A, Physiol Behavior 2019; Loos M Mamm Genome 2015; and unpublished observations). We do not know the exact origin of the difference mentioned above but our best guess would be that it stems from either environmental differences  that are ever present in vivaria (seasonal, mouse house room, cage-changing cycles, etc) and/or, differences between the background genetics (eg. presence of Cre transgene and linked genome, genetic drift) or precise experimental differences between the cohorts (eg. repeated tamoxifen-injection paradigm for the deletion group). All of our experiments were performed in parallel, with all relevant animal groups equally represented in every run, and,and used age- and sex-matched individuals from congenic strains. Wherever possible, controls and test animals were littermates to minimize within strain variance attributable to litter effects (litter size, maternal and paternal effects). Given our lab’s interest and focus on the mechanistic and developmental origins of variance heterogeneity, these differences are of high interest for future study. 

Comment: The authors highlight in the graphical abstract and again in Figure 4A the formation of lipid droplets following palmitate exposure as evidence of that microglia can process fatty acids. They later suggest that a lack of substantial induction of lipid droplet accumulation suggests that microglia are metabolically wired to release carbon substrates to neighboring cells. Clarification as to the role of lipid droplet formation/accumulation in explaining the results would eliminate any possible confusion.

We modified the wording in the manuscript accordingly:

Results “Microglia take up and metabolize free fatty acids”;

“Based on BODIPY fluorescence, we found that primary microglia increase lipid droplet numbers within 24h of in vitro exposure to palmitate (200uM; Fig 4A), demonstrating a capacity to take up fatty acids.”

Comment: In many bar graphs showing relatively modest effects, it would be helpful to use symbols to also show the distribution of sample and animal replicates (especially behavioral paradigms).

Agreed. Indeed, the results are both modest and impressive given the nature of the intervention (simple change in dietary macronutrient composition). We have now re-plotted the results from the behavioral experiments, accordingly (Fig S7 and Fig S8).

Reviewer #3 (Recommendations For The Authors):

This is a good manuscript deserving of publication assuming some of the concerns posed above are addressed.

We thank Reviewer #3 again for their time, effort, and dedication, and for their objective review of the manuscript.

Author response:

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

All of the reviewers indicate that their major concerns have been adequately addressed, but they each have a few comments that the authors should consider before submitting a final version (without further review) for publication. For example, a statement about the sex of the mice used in the studies and whether any differences were noted if both sexes were used. The idea that the loss of glutamate transport might affect NA loading into vesicles is also worth considering. Finally, the authors might want to mention that the role of neuropeptide release from NA neurons needs further examination. 

As noted in the prior submitted revision, all experiments contained both males and females and this was addressed in our re-submission. In our analysis of breathing and metabolism, sex was included in the analysis and no significant phenotypic difference was observed (The statement of no sex difference is in line 451-456). For the fate map and in situ experiments, although the group size is small, we did not see obvious differences in the expression patterns in the three glutamate transporters between females and males (line 347-350). All the anatomical and phenotypic data in this manuscript are presented as combined graphs (figure 1, figure 1 supplement 1, figure 2, figure 2 supplement 2, figure 4,5,6,7) and we had differentially labeled our data points by sex (female data is pink and male data is blue).

The possibility that loss of Vglut2 might affect NA release has been added in the discussion (line 485-491) of the current revision. Dopamine Beta Hydroxylase (DBH) converts dopamine to noradrenaline in the vesicles, thus, glutamate may not directly affect noradrenaline loading into vesicles. However, since loss of Vglut2 reduced dopamine release in subsets of dopaminergic neurons, it remains possible that glutamate affects dopamine loading in NA neurons and in turn perturbs DA to NA conversion in the vesicle by DBH and subsequent noradrenaline release. Future work could examine this hypothesis using fast-scan cyclic voltammetry (FSCV) or microdialysis.

The further examination of the role of neuropeptide release from NA neurons is mentioned in the discussion (line 491-494 and line 497-499 of the pre).

eLife assessment

Chang et al. provide glutamate co-expression profiles in the central noradrenergic system and test the requirement of Vglut2-based glutamatergic release in respiratory and metabolic activity under physiologically relevant gas challenges. Their experiments provide compelling evidence that conditional deletion of vesicular glutamate transporters from noradrenergic neurons does not impact steady-state breathing or metabolic activity in room air, hypercapnia, or hypoxia. This study provides an important contribution to our understanding of how noradrenergic neurons regulate respiratory homeostasis in conscious adult mice. 

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Chang et al. provide glutamate co-expression profiles in the central noradrenergic system and test the requirement of Vglut2-based glutamatergic release in respiratory and metabolic activity under physiologically relevant gas challenges. Their experiments show that conditional deletion of Vglut2 in NA neurons does not impact steady-state breathing or metabolic activity in room air, hypercapnia, or hypoxia. Their observations challenge the importance of glutamatergic signaling from Vglut2 expressing NA neurons in normal respiratory homeostasis in conscious adult mice. 

Strengths:

The comprehensive Vglut1, Vglut2, and Vglut3 co-expression profiles in the central noradrenergic system and the combined measurements of breathing and oxygen consumption are two major strengths of this study. Observations from these experiments provide previously undescribed insights into (1) expression patterns for subtypes of the vesicular glutamate transporter protein in the noradrenergic system and (2) the dispensable nature of Vglut2dependent glutamate signaling from noradrenergic neurons to breathing responses to physiologically relevant gas challenges in adult conscious mice. 

Weaknesses:

Although the cellular expression profiles for the vesicular glutamate transporters are provided, the study does not document that glutamatergic-based signaling originating from noradrenergic neurons is evident at the cellular level under normal, hypoxic, and/or hypercapnic conditions. The authors effectively recognize this issue and appropriately discuss their findings in this context. 

We thank the reviewer for the positive evaluation of our work.

Reviewer #2 (Public Review):

The authors characterized the recombinase-based cumulative fate maps for vesicular glutamate transporters (Vglut1, Vglut2 and Vglut3) expression and compared those maps to their realtime expression profiles in central NA neurons by RNA in situ hybridization in adult mice. Authors have revealed a new and intriguing expression pattern for Vglut2, along with an entirely uncharted co-expression domain for Vglut3 within central noradrenergic neurons. Interestingly, and in contrast to previous studies, the authors demonstrated that glutamatergic signaling in central noradrenergic neurons does not exert any influence on breathing and metabolic control either under normoxic/normocapnic conditions or after chemoreflex stimulation. Also, they showed for the first-time the Vglut3-expressing NA population in C2/A2 nuclei. In addition, they were also able to demonstrate Vglut2 expression in anterior NA populations, such as LC neurons, by using more refined techniques, unlike previous studies. 

A major strength of the study is the use of a set of techniques to investigate the participation of NA-based glutamatergic signaling in breathing and metabolic control. The authors provided a full characterization of the recombinase-based cumulative fate maps for Vglut transporters. They performed real-time mRNA expression of Vglut transporters in central NA neurons of adult mice. Further, they evaluated the effect of knocking down Vglut2 expression in NA neurons using a DBH-Cre; Vglut2cKO mice on breathing and control in unanesthetized mice. Finally, they injected the AAV virus containing Cre-dependent Td tomato into LC of v-Glut2 Cre mice to verify the VGlut2 expression in LC-NA neurons. A very positive aspect of the article is that the authors combined ventilation with metabolic measurements. This integration holds

particular significance, especially when delving into the exploration of respiratory chemosensitivity. Furthermore, the sample size of the experiments is excellent.  Despite the clear strengths of the paper, some weaknesses exist. It is not clear in the manuscript if the experiments were performed in males and females and if the data were combined. I believe that the study would have benefited from a more comprehensive analysis exploring the sex specific differences. The reason I think this is particularly relevant is the developmental disorders mentioned by the authors, such as SIDS and Rett syndrome, which could potentially arise from disruptions in central noradrenergic (NA) function, exhibit varying degrees of sex predominance. Moreover, some of the noradrenergic cell groups are sexually dimorphic. For instance, female Wistar rats exhibit a larger LC size and more LC-NA neurons than male subjects (Pinos et al., 2001; Garcia-Falgueras et al., 2005). More recently, a detailed transcriptional profiling investigation has unveiled the identities of over 3,000 genes in the LC. This revelation has highlighted significant sexual dimorphisms, with more than 100 genes exhibiting differential expression within LC-NA neurons at the transcript level. Furthermore, this investigation has convincingly showcased that these distinct gene expression patterns have the capacity to elicit disparate behavioral responses between sexes (Mulvey et al., 2018).

Therefore, the authors should compare the fate maps, Vglut transporters in males and females, at least considering LC-NA neurons. Even in the absence of identified sex differences, this information retains significant importance. 

An important point well raised by the authors is that although suggestive, these experiments do not definitively rule out that NA-Vglut2 based glutamatergic signaling has a role in breathing control. Subsequent experiments will be necessary to validate this hypothesis. 

An improvement could be made in terms of measuring body temperature. Opting for implanted sensors over rectal probes would circumvent the need to open the chamber, thereby preventing alterations in gas composition during respiratory measurements. Further, what happens to body temperature phenotype in these animals under different gas exposures? These data should be included in the Tables. 

Is it plausible that another neurotransmitter within NA neurons might be released in higher amounts in DBH-Cre; Vglut2 cKO mice to compensate for the deficiency in glutamate and prevent changes in ventilation? 

Continuing along the same line of inquiry is there a possibility that Vglut2 cKO from NA neurons not only eliminates glutamate release but also reduces NA release? A similar mechanism was previously found in VGLUT2 cKO from DA neurons in previous studies (Alsio et al., 2011; Fortin et al., 2012; Hnasko et al., 2010). Additionally, does glutamate play a role in the vesicular loading of NA? Therefore, could the lack of effect on breathing be explained by the lack of noradrenaline and not glutamate? 

We thank the reviewer for the positive evaluation and further suggestions. Please see our response in “Author Response” to the previous version of Reviewer #2 (Public review).

Reviewer #4 (Public Review): 

Summary:

Although previous research suggested that noradrenergic glutamatergic signaling could influence respiratory control, the work performed by Chang and colleagues reveals that excitatory (specifically Vglut2) neurons is dynamically and widely expressed throughout the central noradrenergic system, but it is not significantly crucial to change baseline breathing as well the hypercapnia and hypoxia ventilatory responses. The central point that will make a significant change in the field is how NA-glutamate transmission may influence breathing control and the dysfunction of NA neurons in respiratory disorders. 

Strengths:

There are several strengths such as the comprehensive analysis of Vglut1, Vglut2, and Vglut3 expression in the central noradrenergic system and the combined measurements of breathing parameters in conscious unrestrained mice. 

Other considerations :

These results strongly suggest that glutamate may not be necessary for modulating breathing under normal conditions or even when faced with high levels of carbon dioxide (hypercapnia) or low oxygen levels (hypoxia). This finding is unexpected, considering many studies have underscored glutamate's vital role in respiratory regulation, more so than catecholamines. This leads us to question the significance of catecholamines in controlling respiration. Moreover, if glutamate is not essential for this function, we need to explore its role in other physiological processes such as sympathetic nerve activity (SNA), thermoregulation, and sensory physiology. 

We thank the reviewer for the positive evaluation and further suggestions. The potential role of noradrenergic-derived glutamate in other processes, which is beyond the scope of this study, should be addressed in the future.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

All of my concerns were effectively resolved, leading me to accept the paper. However, I suggest that the authors consider investing in a more reliable system for measuring body temperature, as accurate measurements of this parameter are crucial for whole body plethysmography. 

Thank you for the suggestion. The real-time measurement of body temperature is a goal in future studies.

Reviewer #4 (Recommendations For The Authors):

Because I am revising a revised version, I believe the authors have addressed most, if not all, the concerns raised by already 3 reviewers. In my understanding the authors achieved their aims and the results are totally supported by the conclusions. The impact of this work on the respiratory field is significant and is likely to advance the field. The methods and data utilized, which combine standard techniques with genetic tools, will be highly beneficial to the research community. 

In my understanding I still have one concern that if glutamate is not critical, then what is? Could we potentially disable the noradrenergic (NA) system while preserving glutamate functionality to determine if the NA system is indeed crucial for respiratory physiology? This approach might provide clearer insights into the mechanisms underlying respiratory control. 

We agree that there remain several exciting questions about the respective roles of noradrenaline, glutamate, and other neuropeptides such as Neuropeptide Y (NPY) and galanin. We are currently devising strategies to address the respective and combinatorial roles for all these candidates in breathing control. Most simply, we can conditionally, mutagenized each of them in the central noradrenergic system in an acute manner using DBH-CreER mice to determine if any of them are critical to respiratory control with the advantage of minimizing developmental compensatory events.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, the authors evaluated a novel eIF2B activator, DNL343, in two mouse models representing different forms of the integrated stress response (ISR). They first assessed the pharmacokinetics of DNL343, demonstrating its ability to cross the blood-brain barrier and exhibit good bioavailability. In an acute ISR model induced by optic nerve crush (ONC) injury, DNL343 treatment reduced ISR-induced transcriptional changes and neuronal loss, demonstrating neuroprotective effects. Next, the authors generated an eIF2B loss-of-function mice model by knocking in disease-causing Eif2b5 variants. The model presents a chronic ISR and mimics vanishing white matter disease (VWMD). DNL343 treatment from the pre-symptomatic stage improved body weight and motor functions corrected transcriptional changes, and reversed proteomic and metabolomic alterations in the brain and cerebrospinal fluid. DNL343 treatment initiated at an advanced disease stage also showed positive effects, restoring body weight gain, suppressing ISR, reducing neurodegeneration biomarkers, and extending lifespan. These findings highlight DNL343 as an effective ISR inhibitor with potential applications in treating VWMD and other neurodegenerative disorders involving ISR.

Strengths:

The study's findings regarding the novel compound DNL343 offer significant promise in addressing VWMD, a condition currently lacking disease-modifying treatment. DNL343 directly targets eIF2B, the disease-causing complex in VWMD, and demonstrates notable efficacy in reversing the integrated stress response (ISR) and mitigating neurodegeneration in a VWMD mouse model. These results raise hope for the potential application of DNL343 in VWMD treatment, a development eagerly anticipated by patients and the VWMD research community. Moreover, the study hints at the broader potential of DNL343 in treating other ISR-related neurodegenerative disorders, such as amyotrophic lateral sclerosis, a prospect that holds broader interest. Additionally, the study's identification of potential biomarkers for VWMD represents a notable strength, potentially leading to improved disease progression assessment pending further confirmation in future research.

Weaknesses:

There are a couple of notable concerns in this study. Firstly, while the in vivo evidence strongly supports the efficacy of DNL343 in mitigating ISR and neurodegeneration, there is a lack of direct biochemical evidence to confirm its activity in eIF2B activation. Secondly, the potential for cardiovascular toxicity, which has been reported for a related eIF2B activator in a canine model (as mentioned in the manuscript), has not been evaluated for DNL343 in this study. This data gap regarding toxicity could be crucial for informing the future development of DNL343 for potential human use. Further investigation into these areas would be valuable for a comprehensive understanding of the compound's mechanisms and safety profile.

We thank the reviewer for the thoughtful feedback and an opportunity to provide further clarification. To address the first question regarding biochemical evidence of the mechanism of action of DNL343, we agree that additional data is helpful to interpreting the results presented in this manuscript. We now include a citation to Craig et al (Craig, R.A., 2nd, J. De Vicente, A.A. Estrada, J.A. Feng, K.W. Lexa, M.J. Canet, W.E. Dowdle, R.I. Erickson, B.N. Flores, P.C.G. Haddick, L.A. Kane, J.W. Lewcock, N.J. Moerke, S.B. Poda, Z. Sweeney, R.H. Takahashi, V. Tong, J. Wang, E. Yulyaningsih, H. Solanoy, K. Scearce-Levie, P.E. Sanchez, L. Tang, M. Xu, R. Zhang and M. Osipov (2024). "Discovery of DNL343: A Potent, Selective, and Brain-Penetrant eIF2B Activator Designed for the Treatment of Neurodegenerative Diseases." J Med Chem.) which includes the full details on the discovery and characterization of DNL343.

On the question of cardiovascular toxicity observed with previous eIF2B activating compounds, Craig et al also provides evidence in a non-human primate (cynomolgus monkey) model that DNL343 dosing did not result in QT prolongation or any functional cardiac changes. We have also completed a Phase 1 (NCT04268784) and Phase 1B double-blind (NCT05006352) trials in healthy and ALS participants, respectively and these trials are referenced on page 4, lines 102-103. The safety profile observed in these clinical studies supported further development of DNL343 for ALS in the Healey Platform trial (NCT04297683, Regimen G).

Reviewer #2 (Public Review):

Summary:

The authors developed DNL343, a CNS-penetrant small molecule integrated stress response (ISR) inhibitor, to treat neurodegenerative diseases caused by ISR.

Strengths:

DNL343 is an investigational CNS-penetrant small molecule integrated stress response (ISR) inhibitor designed to activate the eukaryotic initiation factor 2B (eIF2B) and suppress aberrant ISR activation. The therapeutic efficacy of DNL343 has been extensively characterized in two animal models. Importantly, plasma biomarkers of neuroinflammation and neurodegeneration can be reversed with DNL343 treatment. Remarkably, several of these biomarkers show differential levels in CSF and plasma from patients with vanishing white matter disease (VWMD) upon DNL343 treatment. Overall, this is a very exciting study to target ISR for therapeutic interventions.

Weaknesses:

My main questions center around the characterization of DNL343.

(1) Is there any biochemical evidence showing DNL343 activates eIF2B, such as binding assays or in vitro biochemical activity assays? A conference presentation was cited - "Osipov, M. (2022). Discovery of DNL343: a Potent Selective and Brain-penetrant eIF2B Activator Designed for the Treatment of Neurodegenerative Diseases. Medicinal Chemistry Gordon Research Conference. New London, NH." However, there needs to be public information about this presentation.

Information from this presentation and more details on the discovery and characterization of DNL343 can be found in Craig et al J Med Chem (2024) and this citation has been replaced.

(2) How was the selectivity of DNL343 demonstrated? What are the off-targets of DNL343, in particular when DNL343 is administered at a high dose? Thermal-proteasome profiling or photoaffinity labeling experiments could be considered.

Please see Craig et al J Med Chem (2024) for full details. In brief, there were no significant off target effects observed for DNL343 in a Cerep panel.

(3) What are the total drug concentrations in the brain and plasma? What are the unbound ratios?

Following a single oral dose of DNL343 in mice, unbound brain-to-unbound plasma exposures ratios (Kp,uu) of 0.8 to 1.1 were observed, indicating high CNS penetrance. This was further supported by CSF-to-unbound plasma exposures ratios at 0.9 in the same mouse study. The CNS penetrance was also confirmed in rats and NHP by CSF-to-unbound plasma ratios near unity as reported in Craig et al J Med Chem (2024).

(4) If DNL343 is given intravenously, what are the concentrations in the brain and plasma after 5 minutes and 1 hour or longer time points? In other words, does DNL343 cross BBB through passive diffusion or an active process?

Unbound brain-to-unbound plasma exposure ratios following a single oral dose in the mouse were 0.8 to 1.1 and showed no time dependence. These measurements were made prior to, near, and following plasma tmax of DNL343, indicating unbound DNL343 crosses the BBB through passive diffusion and rapidly reached equilibrium between the brain and systemic circulation. Details can be found in Craig et al J Med Chem (2024).

(5) What is the complete PK profile of DNL343 for intravenous and oral dosing?

DNL343 administered orally to mice as a suspension formulation showed plasma PK consistent with prolonged absorption with tmax ranging from 3 to 4 h, and a terminal elimination half-life (t1/2) of ~10 h. Details can be found in Craig et al J Med Chem (2024).

(6) Are there any major drug metabolites that could be of concern?

DNL343 metabolism is through Phase 1 biotransformation pathways. None of the in vivo circulating metabolites show potency towards eIF2B activation. Given that none of these metabolites are of concern, we believe this information is beyond the scope of the current manuscript.

Reviewer #3 (Public Review):

Summary:

ISR contributes to the pathogenesis of multiple neurodegenerative diseases, such as ALS, FTD, VWMD, etc. Targeting ISR is a promising avenue for potential therapeutics. However, previously identified ways to target ISR present some challenges. PERK inhibitors suppress ISR by inhibiting eIF2alpha phosphorylation and cause pancreatic toxicity in mice. In order to bypass eIF2alpha, previous studies have identified ISR suppressors that target eIF2B, such as ISRIB and 2BAct. These molecules suppress neurodegeneration but do not cause detrimental effects in mouse models. However, ISRIB is water-insoluble, and 2BAct causes cardiovascular complications in dogs, preventing their use in clinics. Here, the authors showed that DNL343, a new ISR inhibitor targeting eIF2B, suppresses neurodegeneration in mouse models. Combined with their previous results of a clinical phase I trial showing the safety of DNL343, these findings suggest the promise of DNL343 as a potential drug for neurodegenerative diseases in which ISR contributes to pathogenesis.

Strengths:

The finding is important and has disease implications, and the conclusion is not surprising.

Weaknesses:

The experimental design and data are hard to comprehend for an audience with a basic research background. This reviewer suggests that the authors use the same way that previous studies on ISRIB and 2BAct (e.g., Wong et al; eLife, 2019) designed experiments and interpret data.

We thank this reviewer for their feedback and recognition that DNL343 has a promising potential as treatment for neurodegenerative diseases. While our studies share some similarities to Wong et al., eLife (2019) and Abbink et al., ACTN (2019), our study design is intentionally distinct (e.g. inclusion of both prevention and treatment dosing paradigms, determining dose-response impact of drug treatment across biomarkers) which necessitates tailored data visualization to effectively communicate our findings. However, we understand the importance of clarity for a broader audience and to this end, we have made a number of changes to the data figures, in particular data from omics experiments in Figures 3 and 5. We also provided additional supplemental tables to aid data interpretation. This would hopefully cater to both audiences familiar with previous work and those with a less specialized background.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Demyelination is a significant pathological feature in the VWMD mouse model. The authors should clarify whether they observed similar demyelination in their study and if DNL343 had any impact on reversing this demyelination. These findings are crucial for assessing the compound's effectiveness in mitigating neurodegeneration.

Demyelination is indeed an important feature in the eIF2B LOF (VWMD) mouse model. Given that this phenotype and the ability to rescue the histological phenotype with this MOA (Wong et al; eLife, 2019, cited in introduction) is very well characterized, along with our limitation from the size and number of mouse tissues, we prioritized non-histological targeted and unbiased analyses that were aimed at identifying translatable biomarkers. Nonetheless, the totality of our data, in different mouse models and cell types, strongly supports DNL343 as a potent ISR inhibitor that is effective in attenuating neurodegeneration:

· In the optic nerve crush model, DNL343 dose-dependently reduced retinal cell degeneration

· In the VWMD mouse model, DNL343 attenuated the increase in a plasma biomarker of neurodegeneration, neurofilament-light, which corresponded to normalization in motor function.

· Metabolomic and lipidomic analyses in the VWMD mouse model brain showed increases in oxysterols, such as 7-ketocholesterol, and cholesterol esters and these lipids are associated with demyelination (Nugent et al, 2020). DNL343 treatment attenuated the levels of these oxysterols, indicating decreased demyelination.

· When initiated at an advance disease stage, reversal of plasma biomarkers of neurodegeneration (Nf-L) and neuroinflammation (GFAP) by DNL343 in this model was accompanied by extension in the lifespan that is otherwise shortened as the mutant animals succumb to disease.

These data highlight the potential therapeutic benefits of DNL343 in the broader context of ISR-mediated neurodegeneration which can include but may not be limited to VWMD.

(2) Figure 6 presents several biomarkers with significantly increased levels in VWMD mice and patient biofluids. However, these biomarkers are not reflected in the brain proteomics data presented in Figure 3. The discrepancy between these findings should be addressed and discussed in the manuscript to provide a more comprehensive understanding.

Proteins detected in Figure 6 were not detected by TMT proteomics in the CSF. In the brain, only GFAP was detected and the overall abundance in tissue were similar in both genetic groups. Cytokines such as TIMP1, MCP1 are usually present in low abundances and therefore are challenging to detect in broad discovery proteomics method applied in this study. Antibody-based immunoassays are better suited to specifically measure low abundant proteins than mass-spectrometry-based proteomics, while mass-spectrometry based methods offer wider dynamic range to detect more highly abundant proteins. Differences in detection sensitivity between immunoassay vs mass spectrometry assays has been previously noted (Petrera et al, J Proteome Res, 2021). We have added new text to address this point in the revised manuscript (page 7, line 274-277).

(3) Figure 7 discusses the effects of DNL343 treatment initiated at an advanced disease stage. Since the 4-week treatment did not rescue performance in the balance beam test (as shown in Figure 6A), it is important to clarify if a 20-week treatment had any impact on this parameter.

This reviewer raised an important question that we were unfortunately unable test. When the balance beam training was administered after 8 (out of 20) weeks of dosing, most animals of both wildtype and mutant genotypes struggled to remain on or maintain balance on the beam and were unable to progress traversing the beam, making the assay unsuccessful in this cohort. This impairment appeared to be driven by distinct factors in the two genotypes: age-associated obesity in wild-type animals and severe motor impairment in the eIF2B HOM mice, irrespective of treatment. While it is possible that other less demanding and more sensitive assays could reveal more nuanced differences, this, and our earlier data (Figure 4G-I), suggest that DNL343 could prevent but not reverse functional deterioration. This is in line with our understanding of DNL343 mechanism of action that does not include neuronal regeneration, a therapeutic effect that is likely required for functional recuperation. We have added this point to the manuscript (page 8, line 319-326).

Additionally, considering the significant increase in Gdf15 levels in the disease model, it would be valuable to know if DNL343 treatment affected Gdf15 levels. If these assays were conducted, reporting the data would greatly assist in evaluating the compound's efficacy when administered at an advanced disease stage.

We were not able to measure GDF15 levels in the 20-week study due to limitation in the in-life collected plasma samples which was dedicated to assessing biomarkers of neurodegeneration (Figure 7E-F). However, data from our 4-week treatment study, which was initiated at a similar age range to the 20-week treatment study (19-26 and 24-33 weeks of age, respectively), showed that DNL343 was able to reduce GDF15 levels in the brain (mRNA and protein) and CSF (protein) (Supplemental Figure 5A-C), suggesting that DNL343 reduces ISR activation at an advanced disease stage in the model. We expect that this reduction observed at 4 weeks of treatment would persist for the duration of the extended treatment in the 20-week cohort.

(4) A minor point. In Figures 5A, 5C, and 5E, it appears that the red-colored group should likely be labeled as "HOM 0 mg/kg" instead of "HOM 3 mg/kg".

This has been amended, thank you.

Reviewer #3 (Recommendations For The Authors):

Major concerns:

(1) The cellular function of DNL343 needs to be clarified. The authors claim that it activates eIF2B, but no cellular or molecular evidence is provided. Does it bind to eIF2B? Does it not affect eIF2alpha phosphorylation? Does it restore translation upon stress that causes eIF2alpha phosphorylation? Does it suppress stress granule assembly? The authors cited Sun, Tsai et al. 2023 and Osipov et al., 2022. However, these citations are conference abstracts with no published figures available for review.

We agree that additional data outlining the biochemical evidence of the mechanism of action of DNL343 was needed. We now include a citation to Craig et al J Med Chem (2024) that includes the full details on the discovery and molecular characterization of DNL343.

(2) It needs to be clarified how the authors selected the ISR marker genes. ISR genes are more than those selected. How about others? How did the authors measure the mRNA levels, bulk RNA-seq or RT-PCR? If the former, have the authors verified their results using RT-PCR? Have the authors measured the protein levels for nerve crush experiments (by both proteomic and individual protein analyses)? Also, no statistical analyses were found for the heat maps.

The ISR marker genes were selected by a combination of experimental and literature data. Transcriptomics analysis of the eIF2B HOM brains was conducted using untargeted RNAseq (Supplemental Figure 1B). Here, we found an enrichment of transcripts previously reported to be ISR dependent, namely Atf4, Chac1, Ddit3, Eif4ebp1, Ppp1r15a (Larhammar et al., 2017), Atf3, Asns, Mthfd2, Psat1, Sesn2, Slc1a5, Slc7a5, Slc7a11, Trib3 (Wong et al., 2019, Abbink et al., 2019).  These transcripts were assayed using targeted qPCR in the eIF2B HOM brains, spleen and PBMC (Supplemental Figure 1A, C, D) and in the retinas from the ONC experiments (Figure 2C). We have further clarified the analysis method for the gene expression data in the figure legends.

We did not interrogate the proteome of the retina in the ONC model. Our study in this model was intended as a proof-of-concept evaluation of DNL343 effects in this acute ISR-dependent model of neurodegeneration. To this end, we performed gene expression (Figure 2C) and immunofluorescence analyses (Figure 2D-F). Each of these analyses were conducted using dedicated whole retinas; conducting additional protein analyses would necessitate a separate cohort of animals.

We believe that heatmaps provide the best visualization of the data, particularly the dose dependent effects of DNL343 on multiple genes, but we understand the value for also providing statistical analyses. To address this, we provide additional Supplemental tables to show the outcome of statistical analyses undertaken. Statistical data relating to Figure 2C can be found on new Supplemental Tables 1 & 2; those relating to Supplemental Figures 1A, C, and D on new Supplemental Tables 3, 5, 6, respectively; that from Figure 4D on new Supplemental Table 8, and that from Figure 7D on new Supplemental Table 11.

(3) Both the authors and Wong et al. (eLife, 2019) performed transcriptomic analyses on HOM mice. How do the authors compare the two data sets? Are they the same?

In this work, transcriptomic approach was applied to confirm induction of ISR response in our in vivo model. While data are not identical, all of the top annotated genes shown in supplementary figure 1B were also deemed to be significant by Wong and coworkers (Bayes factor > 10). More importantly, as explained in our responses to question #2 from reviewer 3,  ISR genes highlighted in supplementary Figure 1B were also confirmed in two other studies (Larhammar et al., 2017, Abbink et al., 2019). These data support our interpretation that eIF2B HOM have elevated ISR relative to WT mice. We have added new text to line 164 on page 5 to clarify this point.

(4) Can the authors interpret their omic data using volcano plots for HOM rescue experiments, as Wong et al. did in eLife 2019? Heat maps with statistical analyses are more straightforward to comprehend. Can the authors verify some of these data using RT-PCR, Western blot, etc.?

We added additional pathway interpretation in our Figure 3 and 5 to highlight key biological processes altered in the brain and cellular compartment origin of CSF proteins changed in eIF2B HOM at baseline and following treatment with DNL343. Our treatment designed employed multiple dosing levels and as such, summarization by volcano plot would have resulted in creation of many figures that can be more easily captured by a single heat map plot. However, to provide additional quantitative information, we now added supplementary tables showing full statistical analysis for all heat maps for added clarity and transparency.

We demonstrated 100% correlation between the select genes we examined by qPCR in supplemental Figure 1A and those identified from brain by RNA-seq. In addition, question of reliability of RNA-seq data has been previously been examined in great detail (Everaet et al, Sci Rep 2017) and found ~85% concordance between RNA-seq and qPCR data and those that were discordant tended to have < 2 log2FC and were present in low abundance. Given that top core ISR genes identified in our study have >2 log2FC and have been verified by other independent labs (Larhammar et al., 2017, Abbink et al., 2019, Wong et al., 2019). Based on these, we do not think that there is a rationale need for technical confirmation of RNAseq data.

Risks for mis-annotation of proteins in TMT data were further mitigated by removing protein with coverage < 20% and having less than 8 unique peptides detected and setting protein annotation FDR to <1%.

Additionally, TMT-labelling based proteomics offers wider dynamic range and sensitivity than western blotting. Validation of TMT logFC data with western blot technique, which is less quantitative and has lower dynamic ranges of detection may not be very informative. Furthermore, similar trends of changes in key ISR genes and proteins shown in figures 4D and 5A (e.g PSAT, SLC7A11, SLC7A5) provides additional support for the authenticity of proteins identified in this work.

Also, for Figures 4E and F, it is assumed that each line represents an individual animal, but why their body weight gains are so different for the wild type? Can the authors plot the mean and s.e.m.? Also, there are no data about neurodegeneration. The authors need to show microscopy images, count the numbers, and assess the morphology of nerve cells.

The large data spread in the body weight gain in our wild-type mice reflect the normal variability of this endpoint which can be influenced by sex and age. Indeed, both factors are present in our cohorts as animals of both sexes were included and there was a 7-week age-range (10-17 weeks of age at dosing start). Each line in Figures 4E-F indeed represents data sampled from individual animal over time. We chose to represent the data this way for transparency and have provided additional visualization (new Supplemental Figure 3) showing both body weight gain and plasma Nf-L levels as mean ± SEM as requested by this reviewer.

In this study we chose to use a clinically-relevant biomarker of neurodegeneration, plasma neurofilament light chain (NfL) (Figure 4F). This allowed us to prioritize the tissue samples from these studies to execute comprehensive unbiased analyses for more complete characterization of the phenotype of these eIF2B LoF mice. NfL is a biomarker that has been recognized as a sensitive measurement of neuronal/axonal damage regardless of cause (Gaetani et al., 2018, Khalil et al., 2018). Elevated levels of plasma (and CSF) NfL levels has been demonstrated across neurodegenerative conditions such as Alzheimer’s disease (Giacomucci et al., 2022), multiple sclerosis (Ferreira-Atuesta et al., 2021), and in ALS (Huang et al., 2018).

(5) How ISR is connected to metabolomic changes? Can the authors explain it?

ISR caused significant increases in amino acid transporter and serine/glycine/1-carbon metabolism enzymes transcript and protein abundances that were highlighted in Figure 3A and C and lines 237-255 in the main text. Similar patterns were also observed in prior published studies (Larhammar et al., 2017, Abbink et al., 2019, Wong et al., 2019). Consistent with these changes we observed increased levels of Alanine (transported by SLC3A2, SLC7A11, SLC7A3) and decreased cystathionine levels (associated with increased expression of CTH).  ATF4 is one of the main orchestrator of ISR response to stress (e.g., amino acid deprivation) and it is required for expression of amino acid transporters and enzymes required for synthesis non-essential amino acids (PMID: 28494858). ATF4 increases cellular amino acid uptake and deliver AA needed for synthesis of proteins and glutathione needed for survival.

We also observed prominent changes in CE in eIF2B HOM and its normalization with DNL343 treatment shown in Figure 5C. We checked for changes in expression levels of CEL, CES1, LCAT, LIPA, SOAT1, and NCEH1 proteins involved in CE metabolism and failed to detect any changes in protein or RNA abundances.  This  suggests that a rapid demyelination is a more likely trigger for CE accumulation as reported in FTD-GRN (Marian OC et al., 2023 acta neuropathol commun 11, 52), and in experimental demyelination models (Nugent AA et al., 2020 Neuron). We have added new text to the discussion section of the manuscript page 9, lines 408-411 to discuss how these results relate to each other.

(6) It is hard to understand the biomarker part. The authors said "potential translational biomarkers are elevated..." Do the authors mean they are elevated so they can be potential biomarkers? If their levels are unchanged (e.g., TIMP-1), how can they be biomarkers? Also, this part needs a conclusion/summary. Also, what does "reversed biomarkers..." mean?

We have modified the text to clarify and included a concluding sentence for this section of the results (page 7, lines 297-299). In assessing whether a given protein could be a potential translational biomarker for human disease we evaluated if the following two conditions were met: (1) Increased or decreased gene expression or protein levels of the biomarker in the brain or biofluids (CSF or plasma) of Eif2b5 R191H homozygote mice relative to wild-type controls that is modulated or normalized by administration of DNL343 and (2) protein levels in biofluids from VWMD patients that show differential levels than healthy controls in the same directionality as what is seen in the mouse model. GDF-15, GFAP, and NfL meet these criteria, but TIMP-1 and MCP-1 do not.

Minor concerns:

(1) Please explain which multiple comparison tests the authors used.

This information has been further clarified in the figure legends.

(2) Administrating the drug at an advanced stage led to a trend of NfL reduction but did not rescue function. Can the authors discuss what this means?

Further elaboration and discussion about this finding have been added to the results section on page 8, line 319-325.

(3) For statistical analyses on the bar graphs, it would be better if the authors labeled the comparison pairs on the graphs.

We agree that labelling comparisons in bar graphs could aid the readership and have added this modification. Additionally, comparisons are indicated in the figure legend.

(4) The authors need to state clearly that 2BAct's cardiovascular toxicity was observed in dogs, not mice. The current study does not exclude similar DNL343 toxicity. However, previous clinical trials suggest that DNL343 may be safe for humans.

The suggestion to specify cardiovascular toxicity in dogs has been added (page 3, line 101), thank you. We now include a citation to Craig et al J Med Chem (2024) that provides evidence in a non-human primate (cynomolgus monkey) model that DNL343 dosing did not result in QT prolongation or any functional cardiac changes. We have also completed a Phase 1 (NCT04268784) and Phase 1B double-blind (NCT05006352) trials in healthy and ALS participants, respectively and now include reference to these trials on page 4, lines 102-104. The safety profile observed in these clinical studies supported further development of DNL343 for ALS in the Healey Platform trial (NCT04297683, Regimen G).

Author response:

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

Reviewer #1:

We thank Reviewer #1 for the assessment of our study.

Reviewer #2:

The authors should use DF/F to quantify over time the calcium response in photoreceptors. Furthermore, they should show that there is no concern of motion artifact when the pressure changes - as it could be a concern”.

We used the ΔR/R measure (as defined in Böhm et al. 2016) to correct for motion artifacts due to the larvae moving out of the focal plane at the onset of pressure stimulation. This measure calculates the ratio of the GCaMP signal and a reference fluorescent signal (tdTomato in our case). This ratiometric quantification can better correct for changes in fluorescence that are not related to changes in calcium concentration than the ΔF/F metric, which does not use an independent reference channel.

The authors have not shown

(1) how the off response to decrease of pressure is mediated

(2) which receptor/channel mediates in photoreceptors the response to increased pressure,

(3) nor how the integration of light and pressure information is integrated by photoreceptors in order to guide the behavior of the larvae.

These points are beyond the scope of the study. However, if possible within a short time frame, it would be really interesting to find out whether conflicting stimuli or converging stimuli (light & pressure) can cancel each other out or synergize. In particular since the authors cite unpublished results in the discussion: "Our unpublished results indeed suggest that green light determines the direction of swimming and can override upward swimming induced by pressure, which only influences the speed of swimming (LABC and GJ, unpublished)." Showing in one panel this very cool phenomenon would be exciting & open tons of questions for the field.”

We agree that investigating the interaction of light and pressure is a very exciting direction. However, doing it properly with the rigour we characterised pressure sensation here (across stages, pressure levels and genotypes) and phototaxis and UV avoidance in previous work (across stages, wavelengths, genotypes and stimulus direction; see Randel et al. 2014, Gühmann et al. 2015, Verasztó et al. 2018, Jokura et al. 2023) would require a separate in-depth study.

We agree with points 1-3 regarding the limitations and mentioned these in the discussion.

(1) Although we carried out pressure-release experiments to characterise in more detail the response to pressure OFF, our setup did not allow us to control pressure release as accurately as we could for pressure increase. Therefore, we decided not to address this aspect of the response in more detail in this study.

“Upon a decrease in pressure, three-day-old (but not two-day-old) larvae also show an off-response characterised by downward swimming. We have not analysed in detail the neuronal mechanisms of this response but it may depend on an inverted activation of the cPRC circuit, as happens during UV avoidance (Jokura et al., 2023)”

(2) We decided not to explore this important question in this study, due to the significant effort it would take to test the expression and function of potential candidate channels in pressure transduction mechanism. “The cellular and molecular mechanisms by which cPRCs sense and transduce changes in hydrostatic pressure deserve further enquiry. “ and “The molecular mechanisms of pressure detection remain unclear. Components of the phototransduction cascade may be involved in pressure sensation. Our results indicate that the ciliary opsin required for detecting UV light is not essential for pressure sensation.“ We hypothesise in the discussion that TRP channels may play a role in pressure transduction, due to their diversity, multiple modalities and participation in phototransduction cascades.

(3) We considered that the complexity of this question merits a separate study, where both cues can be accurately titrated and temporally combined to dissect the mechanisms of sensory integration. We have therefore removed the sentence referring to the interaction of phototaxis and the pressure response from the discussion.

“How UV and pressure signals are integrated by the cPRC and how other light responses such as phototaxis interact with pressure responses remain exciting avenues for future research.”

Author response:

We thank the reviewers for their positive evaluation and constructive comments.  In our revision, we will aim to improve the analysis of our existing data and perform new experiments to address questions raised by the reviewers. 

Reviewer 1 found it interesting that Kdm6b-deletion in hippocampal dentate gyrus (DG) neural stem cells causes precocious neuronal differentiation, whereas in contrast, Kdm6b is required for the maturation of neural progenitors in the ventricular-subventricular zone (V-SVZ). In the submitted manuscript, we did not provide much insight into the differences in Kdm6b function in these two neural stem cell populations. We plan on performing new experiments and expanding on our prior V-SVZ studies in a way that allows a direct comparison to the analyses of the DG. We hope that the addition of this data will shed light on why Kdm6b-deletion produces such different phenotypes in postnatal neural stem cells of the mouse brain. 

Reviewer 2 noted that our submitted manuscript lacked insight into how KDM6B regulates gene expression. In particular, this reviewer asked whether the function of KDM6B is mediated by its enzymatic activity. The CUT&RUN experiment in our manuscript revealed an increase in H3K27me3 levels at select neural maintenance genes in the DG of Kdm6b-deleted mice. However, we agree that this data is insufficient to assess the significance of KDM6B-mediated H3K27me3 demethylation in regulating the NSC transcriptome. To address this point, we are performing experiments that can directly test this mechanistic model of KDM6B function and answer the question of whether the H3K27me3 demethylase activity of KDM6B is required for its ability to activate transcription.  Reviewer 2 also had a specific question about the cell types observed in the developing hippocampus after Kdm6b-deletion, and we believe that additional analyses will provide clarity to the overall phenotype.  More generally, we will aim to improve data quality and visualization. 

Reviewer 3 raised the concern that because Kdm6b is not exclusively expressed in neural stem cells, the phenotype of precocious neuronal differentiation in mice with Kdm6b-deletion driven by the hGFAP-Cre transgene may be indirect, such as through changes in mature glial populations.  We will study the mature glia, as suggested by the reviewer.  We will also more thoroughly describe how our experiments targeting Kdm6b-deletion to adult neural stem cells with the tamoxifen-inducible Nestin-CreER provide evidence for the precocious neuronal differentiation phenotype being cell autonomous, at least in adult mice.  Reviewer 3 also had helpful suggestions for analyzing our single-cell RNA-seq data and behavioral studies, and we will address these comments in the revision. 

Again, we thank the editors and reviewers for their time and consideration.  We believe that our manuscript will be greatly improved through this review process and hope to construct a stronger understanding of the role of KDM6B in DG neurogenesis.

Author response:

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

In the revised manuscript we have included an additional study that significantly contributes to the conclusions and models of the original version. Briefly, Figure 3 now describes our characterization of the diaphragm and laryngeal muscle activities (electromyography, EMG) during endogenous vocalizations. These EMGs also serve as representations of the brainstem breathing central pattern generator (CPG) inspiratory and post-inspiratory generating neurons, respectively. In our original submission, we found that many of the vocalizations had changes in pitch that mirrored the change in expiratory airflow (we termed positive intonation), and we proposed that the coordination of breathing muscles (like the inspiratory muscles) and larynx patterned this. This mechanism is akin to our findings for how neonatal cries are rhythmically timed and produced (Wei et al. 2022). The newly presented EMG data re-inforces this idea. We found that for vocalizations with positive intonation, the inspiratory diaphragm muscle has an ectopic burst(s) of activity during the expiration phase which corresponds to a decrease in airflow and pitch, and this is followed by laryngeal muscle activity and increased pitch. This can be cycled throughout the expiration to produce complex vocalizations with oscillations in pitch. A basal breath is hardwired for the laryngeal muscle activity to follow the diaphragm, so the re-cycling of this pattern nested within an expiration (a ‘mini-breath’ in a ‘breath’) demonstrates that the vocalization patterning system engages the entire breathing CPG. This contrasts with the canonical model that activity of the laryngeal premotor neurons control all aspects of producing / patterning vocalizations. Furthermore, this mechanism is exactly how the iRO produces and patterns neonatal vocalizations (Wei et al. 2022) and motivates the likely use of the iRO in adult vocalizations.

Response to recommendations for the authors:

Reviewer #1:

(1) The authors should note in the Discussion that the cellular and circuit mechanisms by which the vocalization pattern generator integrates with the respiratory pattern generator to control expiratory airflow have not been fully worked out, requiring future studies.

This was noted in the discussion section “The iRO likely patterns intonation for endogenous phonation”.

(2) Please change the labeling of the last supplemental figure to Figure Supplemental 5.

Thank you for identifying this.

Reviewer #2:

Major concerns

(1) While it is true that modulation of activity in RAm modulates the laryngeal opening, this statement is an incomplete summary of prior work. Previous studies (Hartmann et al., 2020; Zhang et al., 1992, 1995) found that activation of RAm elicits not just laryngeal adduction but also the production of vocal sounds, albeit vocal sounds that were spectrally dissimilar from speciestypical vocalizations. Moreover, a recent study/preprint that used an activity-dependent labeling approach in mice to optogenetically activate RAm neurons that were active during USV production found that re-activation of these neurons elicits USVs that are acoustically similar to natural USVs (Park et al., 2023). While the authors might not be required to cite that recent preprint (as it is not yet peer-reviewed), the fact that activation of RAm elicits vocal sounds is clear evidence that its effects go beyond modulating the size of the laryngeal opening, as this alone would not result in sound production (i.e., RAm activation must also recruit expiratory airflow). The authors should include these relevant studies in their Introduction. Moreover, the rationale for the model proposed by the authors (that RAm controls laryngeal opening whereas iRO controls expiratory airflow) is unclear with regard to these prior studies. The authors should include a discussion of how these prior findings are consistent with their model (as presented in the Introduction, as well as in Figure 4 and relevant Discussion) that RAm modulates the size of laryngeal opening but not expiratory airflow.

An introduction and discussion of the Veerakumar et. al. 2023 and Park et. al. 2024 manuscripts describing RAm in mice has now been included.

The iRO serves to coordinate the breath airflow and laryngeal adduction to produce sound and the intonation within it that mirrors the breath airflow. This occurs because the iRO can control the breathing CPG (synaptic input to the preBötC inspiratory pacemaker) and is premotor to multiple laryngeal muscles (Wei et. al. 2022). The modulation of the expiratory airflow is by inducing momentary contraction of the diaphragm (via excitation of the preBötC) which opposes (a.k.a. slows) expiration. This change in flow results in a decrease in pitch (Fig. 3 in the revised manuscript, Wei et. al. 2022).

It is our understanding that the basic model for RAm evoked USVs is that RAm evokes laryngeal adduction (and presumed abdominal expiratory muscle activation) and this activity is momentarily stopped during the breath inspiration by inhibition from the preBötC (Park et. al. 2024). So, in this basic model, any change in pitch and expiratory airflow would be controlled by tuning RAm activity (i.e., extent of laryngeal adduction). In this case, the iRO induced inspiratory muscle activity should not occur during expiration, which is not so (Fig. 3). Note, the activity of abdominal expiratory muscles during endogenous and RAm evoked USVs has not been characterized, so the contribution of active expiration remains uncertain. This is an important next step.

We have now included a discussion of this topic which emphasizes that iRO and RAm likely have reciprocal interactions (supported by the evidence of this anatomical structure). These interactions would explain why excitation of either group can evoke USVs and, perhaps, the extent that either group contributes to a USV explains how the pitch / airflow changes. An important future experiment will be to determine the sufficiency of each site in the absence of the other.

(2) The authors provide evidence that the relationship between expiratory airflow and USV pitch is variable (sometimes positive, sometimes negative, and sometimes not related). While the representative spectrograms clearly show examples of all three relationship types, no statistical analyses are included to evaluate whether the relationship between expiratory airflow and USV pitch is different than what one would expect by chance. For example, if USV pitch were actually unrelated to expiratory airflow, one might nonetheless expect spurious periods of positive and negative relationships. The lack of statistical analyses to explicitly compare the observed data to a null model makes it difficult to fully evaluate to what extent the evidence provided by the authors supports their claims.

We have now included two null distributions and compared our observed correlation values to these. The two distributions were created by taking each USV / airflow pair and randomly shuffling either the normalized USV pitch values (pitch shuffled) or the normalized airflow values (airflow shuffled) to simulate the distribution of data should no relationship exist between the USV pitch and airflow.

(3) The relationship between expiratory airflow and USV pitch comes with two important caveats that should be described in the manuscript. First, even in USV types with an overall positive relationship between expiratory airflow and pitch contour, the relationship appears to be relative rather than absolute. For example, in Fig. 2E, both the second and third portions of the illustrated two-step USV have a positive relationship (pitch goes down as expiratory airflow goes down). Nonetheless, the absolute pitch of the third portion of that USV is higher than the second portion, and yet the absolute expiratory airflow is lower. The authors should include an analysis or description of whether the relationship between expiratory airflow and USV pitch is relative vs.

absolute during periods of 'positive intonation'.

The relationship between pitch and airflow is relative and this in now clarified in the text. To determine this, we visualized the relationship between the two variables by scatterplot for each of the USVs syllables and, as the reviewer notes, a given airflow cannot predict the resulting frequency and vice versa.

(4) A second important caveat of the relationship between expiratory airflow and USV pitch is  that changes in expiratory airflow do not appear to account for the pitch jumps that characterize mouse USVs (this lack of relationship also seems clear from the example shown in Fig. 2E). This caveat should also be stated explicitly.

The pitch jumps do not have a corresponding fluctuation in airflow, and this is now stated in the results and discussion.

(5) The authors report that the mode of relationship between expiratory airflow and USV pitch (positive intonation, negative intonation, or no relationship) can change within a single USV. Have the authors considered/analyzed whether the timing of such changes in the mode of relationship coincides with pitch jumps? Perhaps this isn’t the case, but consideration of the question would be a valuable addition to the manuscript.

We analyzed a subset of USVs with pitch jumps that were defined by a change >10 kHz, at least 5ms long, and had one or two jumps. The intonation relationships between the sub-syllables within a USV type were not stereotyped as evidenced by the same syllable being composed of combinations of both modes.

(6) The authors incorrectly state that PAG neurons important for USV production have been localized to the ventrolateral PAG. Tschida et al., 2019 report that PAG-USV neurons are located predominantly in the lateral PAG and to a lesser extent in the ventrolateral PAG (see Fig. 5A from that paper). The finding that iRO neurons receive input from VGlut2+ ventrolateral PAG neurons represents somewhat weak evidence that these neurons reside downstream of PAG-USV neurons. This claim would be strengthened by the inclusion of FOS staining (following USV production), to assess whether the Vglut+ ventrolateral PAG neurons that provide input to iRO are active in association with USV production.

This comment correctly critiques that our PAG à iRO tracing does not demonstrate that the labeled PAG neurons are sufficient nor necessary for vocalization. Directly demonstrating that activation and inhibition the PAG-iRO labeled neurons ectopically drives or prevents endogenous USVs is an important next step. While FOS implies this connectivity, it does not definitely establish it and so this experiment is impacted by some of the caveats of our tracing (e.g. PAG neurons that drive sniffing might be erroneously attributed to vocalization).

Our reading of the literature could not identify an exact anatomical location within the mouse PAG and this site appears to vary within a study and between independent studies (like within and between Tschida et. al. 2019 and Chen et. al. 2021). The labeling we observed aligns with some examples provided in these manuscripts and with the data reported for the retrograde tracing from RAm (Tschida et al 2019).

(7) In Figure S5A, the authors show that USVs are elicited by optogenetic activation of iRO neurons during periods of expiration. In that spectrogram, it also appears that vocalizations were elicited during inspiration. Are these the broadband vocalizations that the authors refer to in the Results? Regardless, if optogenetic activation of iRO neurons in some cases elicits vocalization both during inspiration and during expiration, this should be described and analyzed in the manuscript.

The sound observed on the spectrogram during inspiration is an artefact of laser evoked head movements that resulted in the fiber cable colliding with the plethysmography chamber. In fact, tapping an empty chamber yields the same broad band spectrogram signal. The evoked USV or harmonic band vocalization is distinct from this artefact and highlighted in pink.

(8) Related to the comment above, the authors mention briefly that iRO activation can elicit broadband vocalizations, but no details are provided. The authors should provide a more detailed account of this finding.

The broadband harmonic vocalizations we sometimes observe upon optogenetic stimulation of AAV-ChR2 expressing iRO neurons are akin to those previously described within the mouse vocal repertoire (see Grimsley et. al .2011). We have added this citation and mentioned this within the text. 

(9) The effects of iRO stimulation differ in a couple of interesting ways from the effects of PAGUSV activation. Optogenetic activation of PAG-USV neurons was not found to entrain respiration or to alter the ongoing respiratory rate and instead resulted in the elicitation of USVs at times when laser stimulation overlapped with expiration. In contrast, iRO stimulation increases and entrains respiratory rate, increases expiratory and inspiratory airflow, and elicits USV production (and also potentially vocalization during inspiration, as queried in the comment above). It would be informative for the authors to add some discussion/interpretation of these differences.

We have added a section of discussion to describe the how these different results may be explained by the iRO being a vocal pattern generator versus the PAG as a ‘gating’ signal to turn on the medullary vocalization patterning system (iRO and RAm). See discussion section ‘The iRO likely patterns intonation for endogenous phonation’.

(10) The analysis shown in Fig. 4D is not sufficient to support the author’s conclusion that all USV types elicited by iRO activation are biased to have more positive relationships between pitch and expiratory airflow. The increase in the relative abundance of down fm USVs in the opto condition could account for the average increase in positive relationship when this relationship is considered across all USV types in a pooled fashion. The authors should consider whether each USV type exhibits a positive bias. Although such a comparison is shown visually in Fig. 4G, no statistics are provided. All 7 USV types elicited by optogenetic activation of iRO should be considered collectively in this analysis (rather than only the 5 types currently plotted in Fig. 4G).

In the original submission the statistical analysis of r values between opto and endogenous conditions was included in the figure legend (‘panels E-G, two-way ANOVA with Sidak’s post-hoc test for two-way comparisons was used; all p-values > 0.05), and this has not changed in the revised manuscript. We have now provided the suggested comparison of opto vs endogenous USVs without down fm (Fig. 5D). This positive shift in r is statistically significant (…).

(11) The evidence that supports the author’s model that iRO preferentially regulates airflow and that RAm preferentially regulates laryngeal adduction is unclear. The current study finds that activation of iRO increases expiratory (and inspiratory) airflow and also elicits USVs, which means that iRO activation must also recruit laryngeal adduction to some extent. As the authors hypothesize, this could be achieved by recruitment of RAm through iRO’s axonal projections to that region.

Note, it is more likely that iRO is directly recruiting laryngeal adduction as they are premotor to multiple laryngeal muscles like the thyroarytenoid and cricothyroid (Wei et. al. 2022). The ‘Discussion’ now includes our ideas for how the iRO and RAm likely interact to produce vocalizations.

In the recent preprint from Fan Wang’s group (Park et al., 2023), those authors report that RAm is required for USV production in adults, and that activation of RAm elicits USVs that appear species-typical in their acoustic features and elicits laryngeal adduction (assessed directly via camera). Because RAm activation elicits USVs, though, it must by definition also recruits expiratory airflow. Can the authors add additional clarification of how the evidence at hand supports this distinction in function for iRO vs RAm?

See response to ‘Major Concern #1”.

Minor concerns 

(1) The authors might consider modifying the manuscript title. At present, it primarily reflects the experiments in Figure 2.

We have provided a title that we feel best reflects the major point of the manuscript. We hope that this simplicity enables it to be recognized by a broad audience of neuroscientists as well as specialists in vocalization and language.

(2) The statement in the abstract that "patterns of pitch are used to create distinct 'words' is somewhat unclear. Distinct words are by and large defined by combinations of distinct phonemes. Are the authors referring to the use of "tonemes" in tonal languages? If so, a bit more explanation could be added to clarify this idea. This minor concern includes both the Abstract, as well as the first paragraph of the Introduction.

We have clarified this line in the abstract to avoid the confusing comparison between mouse vocalizations and human speech. In the introduction we have expanded our explanation to clarify that variations in pitch are a component of spoken language that add additional meaning and depth to the underlying, phonemic structure. 

(3) Multiple terms are used throughout the manuscript to refer to expiratory airflow: breath shape (in the title), breath pattern, deviations in exhalation, power of exhalation, exhalation strength, etc. Some of these terms are vague in meaning, and a consolidation of the language would improve the readability of the abstract and introduction.

We have chosen a smaller selection of descriptive words to use when describing these breath features.

(4) Similarly, "exhalation" and "expiration" are both used, and a consistent use of one term would help readability.

See point 3.

(5) In a couple of places in the manuscript, the authors seem to state that RAm contains both laryngeal premotor neurons as well as laryngeal motor neurons. This is not correct to our knowledge., but if we are mistaken, we would ask that the authors add the relevant references that report this finding.

It is our understanding that the RAm is defined as the anatomical region consistent with the murine rostral and caudal ventral respiratory groups composed of multiple premotor neuron pools to inspiratory, expiratory, laryngeal, and other orofacial muscles. This is supported by neurons within RAm that reflect multiple phases of the inspiratory and expiratory cycle (Subramanian et. al. 2018) and excitation of sub-regions within RAm modulating multiple parts of the breathing control system (Subramanian et. al. 2018 and Subramanian 2009). Rabies tracing of the various premotor neurons which define the anatomical region of RAm in the mouse shows that they surround the motor neurons in the loose region of the nucleus ambiguus (the anatomical location of RAm) for multiple muscles of the upper airway system, such as the thyroarytenoid (Wu et. al. 2017, Dempsey et. al. 2021 and Wei et. al. 2022). Given that the name RAm reflects a broad anatomical location, we have used it to describe both the premotor and motor neurons embedded within it. We have now clarified this in the text.

(6) The statistical analysis applied in Figure 1C is somewhat confusing. The authors show two distributions that appear different but report a p-value of 0.98. Was the analysis performed on the mean value of the distributions for each animal, the median, etc.? If each animal has two values (one for USV+ breaths and one for USV- breaths), why not instead compare those with a paired t-test (or Wilcoxon rank sign)? Additional information is needed to understand how this analysis was performed.

The original manuscript version used a two-way anova to compare the normalized histogram of instantaneous frequency for breaths with (USV+) or without (USV-) for each animal (first factor: USV+/-, second factor: Frequency). The p-value for the first factor (USV) was 0.98 showing no statistically significant effect of USV on the distribution of the histogram.

For simplicity, we have instead performed the analysis as suggested and include a bar graph. This analysis shows that the instantaneous frequency of USV breaths is, in fact, statistically significantly lower than those without USVs. We have updated the figure legend and text to reflect this.

(7) The use of the word "syllable" to describe parts of a USV that are produced on a single breath may be confusing to some scientists working on rodent USVs. The term 'syllable' is typically used to describe the entirety of a USV, and the authors appear to use the term to describe parts of a USV that are separated by pitch jumps. The authors might consider calling these parts of USVs "sub-syllables".

We have clarified these descriptions throughout the text. We now refer to the categories as ‘syllable types’, define ‘syllables’ as ‘a continuous USV event’ with no more than 20ms of silence within and finally ‘sub-syllables’ to refer to components of the syllable separated by jumps in frequency (but not gaps in time).

(8) In Figure S3, final row, the authors show a USV produced on a single breath that contains two components separated by a silent period. This type of bi-syllabic USV may be rare in adults and is similar to what the authors showed in their previous work in pups (multiple USVs produced on a single expiration, separated by mini-inspirations). One might assume that the appearance of such USVs in pups and their later reduction in frequency represents a maturation of vocalrespiratory coordination. Nonetheless, the appearance of bi-syllabic USVs has not been reported in adult mice to our knowledge, and the authors might consider further highlighting this finding.

We were also struck by the similarity of these USVs to our study in neonates and such types of similarities sparked an interest in the role of the iRO in patterning adult USVs. We now include a description of the presence and abundance of bi- and tri-syllablic calls observed in our recordings to highlight this finding.

(9) Figure 4 is referenced at the end of the second Results section, but it would seem that the authors intended to reference Figure 2. 

For simplicity we included some of the referenced data within Fig. S5. We appreciate the recommendation.

(10) In the optogenetic stimulation experiments, the authors should clarify why bilateral stimulation was applied. Was unilateral stimulation ineffective or less effective? The rationale provided for the use of bilateral stimulation (to further localize neural activation) is unclear.

The iRO is bilateral and, we presume, functions similarly. So, we attempted to maximally stimulate the system. We have clarified this in the methods.

(11) Figure Supplemental '6' should be '5'.

Thanks!

(12) Last sentence of the Introduction: "Lasty" should be "lastly".

Thanks!

(13) There are two references for Hage et al., 2009. These should be distinguished as 2009a and 2009b for clarity.

Thanks!

Author response:

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

We thank the reviewers and editor for their careful review of our work. We believe the resulting manuscript is much stronger. We agree with the comments made by Reviewer #2 regarding additional histology and neuronal data analysis, which will be presented in subsequent work.

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

Reviewer 1 (Public Weaknesses):

It was not always clear what the lesion size was. This information is important for future applica- tions, for example, in the visual cortex, where neurons are organized in retinotopy patterns.

We thank the reviewer for this feedback. While there is some variation in lesion volume for a given parameter set, we have added more details of the volumes of lesions created in our testing (Fig. 4 and Fig. 5).

It would be helpful if the author could add some discussion about whether and how this method could be used in other types of array/multi-contact electrodes, such as passive neuropixels, S- probes, and so on. In addition, though an op-amp was used in the design, it would still be helpful if the author could provide a recommended range for the impedance of the electrodes.

We thank the reviewer for this suggestion. We have both added a demonstration of use in a differ- ent multielectrode probe type (with a U-probe) in Fig. 8, and we have added a discussion about which types of multielectrode probes would be suitable on Page 15, Line 420.

“We demonstrated that our electrolytic lesioning technique works with a linear multicontact probe by testing with a U-Probe in ex vivo rabbit cortex. There are no particular limitations that would prevent our specific electrolytic lesioning technique and device from working with any passive multielectrode probe. The main requirements for use are that the probe has two electrodes that can directly (via whatever necessary adapters) connect to the lesioning device, such that arbitrary current can be passed into them as the anode and cathode. This would limit use of probes, like Neuropixels, where the on-chip acquisition and digitization circuitry generally precludes direct connection to electrodes [1], [2]. The impedance of the multielectrode probe should not be an issue, due to the use of an op amp. We showed use  with a Utah array (20-800 kΩ) and a U-Probe (1-1.5 MΩ). The specific op amp used here has a voltage range of ± 450 V, which assuming a desired output of 150 µA of current would limit electrode impedance to 6 MΩ. Though a different op amp could easily be used to accommodate a higher electrode impedance, it is unlikely that this would be necessary, since most electrodes have impedances between 100 kΩ to 1 MΩ [3].”

Reviewer 2 (Public Weaknesses):

In many of the figures, it is not clear what is shown and the analysis techniques are not well described.

We thank the reviewer for this feedback. We hope that our edits to both the figures and the text have improved clarity for readers.

The flexibility of lesioning/termination location is limited to the implantation site of the multielec- trode array, and thus less flexible compared to some of the other termination methods outlined in Appendix 2.

We thank the reviewer for this point. You are right that the lesioning location is limited to the multielectrode array’s implantation site, while other methods in Appendix 2 do not require prox- imity of the lesion location and the electrophysiology recording site. However, we believe that the closeness of the lesioning location to the microelectrode array is a strength - guaranteeing record- ings from the perilesional area - even with the small negative of reduced flexibility. Multielectrode arrays can be implanted in many areas of cortex. If one wanted to study distal effects of a lesion, additional electrophysiology probes could be implanted to record from those areas. We have noted this on Page 3, Line 117.

“While the link between the lesion location and the multielectrode location technically con- strains the lesion to an area of cortex in which a multielectrode array could be implanted, we see the connection as a positive, because it ensures recording some neuroelectrophysiology from the perilesional area in which recovery is hypothesized to occur (see Appendix 1Data Availabilityappendix.41).”

Although the extent of the damage created through the Utah array will vary based on anatomical structures, it is unclear what is the range of lesion volumes that can be created with this method, given a parameter set. It was also mentioned that they performed a non-exhaustive parameter search for the applied current amplitude and duration (Table S1/S2) to generate the most suitable lesion size but did not present the resulting lesion sizes from these parameter sets listed. Moreover, there’s a lack of histological data suggesting that the lesion size is precise and repeatable given the same current duration/amplitude, at the same location.

We thank the reviewer for this thoughtful feedback. We have added figures (Figs. 4 and 5), where we show the relationship between estimated lesion volume and the current amplitude and duration parameters. These figures include more data from the tests in Supplementary File 1 and Supplementary File 2. While there is some variation in lesion volume for a given current amplitude and duration, there is still a clear relationship between the parameters and lesion volume.

It is unclear what type of behavioral deficits can result from an electrolytic lesion this size and type (∼3 mm in diameter) in rhesus macaques, as the extent of the neuronal loss within the damaged parenchyma can be different from past lesioning studies.

While we appreciate the reviewer’s interest in the behavioral deficits associated with our lesions in rhesus macaques, reporting these falls beyond the scope of this manuscript. Future work will explore the behavioral deficits associated with these lesions

The lesioning procedure was performed in Monkey F while sedated, but no data was presented for Monkey F in terms of lesioning parameters, lesion size, recorded electrophysiology, histological, or behavioral outcomes. It is also unclear if Monkey F was in a terminal study.

We apologize for not being more explicit about the parameters used for the lesion in Monkey F. We have added this in Results on Page 5, Line 209 and in Methods on Page 19, Line 586.

“After this validation and refinement, one proof-of-concept lesion (150 µA direct current passed through adjacent electrodes for 45 seconds) was performed in an in vivo sedated rhe- sus macaque (Monkey F) in order to validate the safety of the procedure.”

“This lesion was created by applying 150 µA of direct current to two adjacent electrodes in the microelectrode array for 45 seconds.”

We also clarified the parameters used for the other lesions in Monkeys H and U in Results on Page 7, Line 233 and in Methods on Page 19, Line 586.

“In all of the fourteen lesions across two awake-behaving rhesus macaques (150 µA direct current passed through adjacent electrodes for 30 or 45 seconds (30s for Monkey U and 45s for Monkey H, except lesion H200120 which was for 50 seconds)), the current source worked as expected, providing a constant current throughout the duration of the procedure.”

“In these lesions, 150 µA of direct current was applied to two adjacent electrodes in the mi- croelectrode array for 30 or 45 seconds (30s for Monkey U, 45s for Monkey H), except in lesion H200120 where current was applied for 50 seconds.”

Monkey F was euthanized shortly after the lesion, so we now mention this on Page 19, Line 583.

“Based on this, and a lack of physiological signs of pain from the anaesthetized pig studies, a lesion was performed on a sedated rhesus macaque who was subsequently euthanized due to unrelated health complications (Monkey F; 16 year-old adult, male rhesus macaque) in order to further verify safety before use in awake-behaving rhesus.”

Because Monkey F was sedated and then euthanized shortly after, there is no behavioral data. As the lesion in sedated Monkey F was used to validate the safety of the procedure, any further data and analysis fall beyond the scope of this manuscript.

As an inactivation method, the electrophysiology recording in Figure 5 only showed a change in pairwise comparisons of clustered action potential waveforms at each electrode (%match) but not a direct measure of neuronal pre and post-lesioning. More evidence is needed to suggest robust neuronal inactivation or termination in rhesus macaques after electrolytic lesioning. Some exam- ples of this can be showing the number of spike clusters identified each day, as well as analyzing local field potential and multi-unit activity.

The reviewer has pointed out some short comings of the original analysis, which we believe have since been addressed with the revised analysis. LFP and spiking activity are functional measures that are more ambiguous in terms of loss and are also the subject of another manuscript currently under revision.

The advantages over recently developed lesioning techniques are not clear and are not discussed.

We thank the reviewer for noting this. We have added a section, also responding to their later request for us to compare our work to Khateeb et al. 2022, by adding a section to the Discussion on Page 16, Line 434.

“Perhaps the most unique advantage of our technique in comparison with other existing inactivation methods lies in Design Consideration #1: stable electrophysiology pre- and post-inactivation (Appendix 1Data Availabilityappendix.41). While several methods exist that allow for localization and size control of the inactivation (Design Consideration #2) and cross compatibility across regions and species (Design Consideration #3), few have achieved compatibility with stable electrophysiology. For example, some studies record electrophysiology only after the creation of the lesion, preventing comparison with baseline neuronal activity [4]. One recent study, Khateeb, et al., 2022, developed an inactivation method that is effectively combined with stable electrophysiology by creating photothrombotic lesions through a chronic cranial window integrated with an electrocorticography (ECoG) array [5], which may be appropriate for applications where local field potential (LFP) recording is sufficient. This approach has trade-offs with regards to the three design considerations presented in Appendix 1Data Availabilityappendix.41.

While Khateeb, et al., present a toolbox with integrated, stable electrophysiology from an ECoG array pre- and post- inactivation (Design Consideration #1), it demonstrated recordings from an ECoG array with limited spatial resolution. While a higher density ECoG array that would provide higher spatial resolution could be used, increasing the density of opaque electrodes might occlude optical penetration and constrain photothrombotic lesions. Further, ECoG arrays are limited to recording LFP, not electrophysiology at single neuron resolution, potentially missing meaningful changes in the neuronal population activity after lesioning. Khateeb, et al., demonstrated localization and control the size of inactivation (Design Consideration #2). In this manuscript, we have shown that the amount and duration of direct current are significant determinants of lesion size and shape, while with photothrombotic lesions, light intensity and aperture diameter are the significantly relevant parameters. One potential advantage of photothrombotic approaches is the use of optical tools to monitor anatomical and physiological changes after lesioning through the cranial window, though the research utility of this monitoring remains to be demonstrated.

Although the method presented by Khateeb, et al., shows some cross-compatibility (Design Consideration #3), it has greater limitations in comparison with the method presented here. For example, while Khateeb, et al., notes that the approach could be adapted for use in smaller organisms, no modification is needed for use in other species with this work’s approach–so long as a multielectrode probe is implantable. In this manuscript we demon- strate electrolytic lesioning spanning two multielectrode probes across rabbits, pigs, sheep, and rhesus macaques, and our same device could be easily used with other smaller species, like rats, in which multielectrode probes have been successfully implanted [6]. Further, the approach in Khateeb, et al., is limited to superficial brain structures, due to the need for opti- cal accessibility. As noted, fiber optics could allow access to deeper structures, which would bring associated additional tissue damage, but deeper structure lesioning was not demon- strated. In contrast, the approach presented here can be used in any region of cortex in which a multielectrode probe can be implanted, which, depending on the probe used, does not limit it to surface structures. For example, we demonstrated use of our lesioning tech- nique with a linear U-probe (Fig. 8figure.caption.25), which could be used to reach deeper layers of cortex or specific deep cortical structures. In both techniques, the location of the lesion is tied to the location of the electrophysiology (for Khateeb et al., wherever the cra- nial window and ECoG array are; for this technique, wherever the multielectrode probe has been implanted), which ensures that the electrophysiology will include recordings from the perilesional area. Neither work addresses the potential of their technique to induce chronic post-lesion behavioral effects, which is a key goal for future work.”

There is a lack of quantitative histological analysis of the change in neuronal morphology and loss.

We appreciate the reviewer’s desire for a quantitative histological analysis, however this falls out- side of the scope of this manuscript. We are not attempting to make strong claims about the number of neurons lost through lesioning or thoroughly characterize morphological changes in the neurons. The histology is intended to show that lesioning did lead to a loss of neurons, but the precise num- ber of neurons lost is neither in scope nor is likely to be highly conserved across lesions.

There is a lack of histology data across animals and on the reliability of their lesioning techniques across animals and experiments.

We thank the reviewer for this point. As stated above, we have now added Fig. 4 and Fig. 5, which includes volume estimates based on the histology from more of our ex vivo and in vivo testing across animals.

There is a lack of data on changes in cortical layers and structures across the lesioning and non- lesioning electrodes.

We acknowledge that the histology does not have the level of detail that is expected from many modern studies. However, the goal here was dramatically different: we sought to calibrate a novel lesion device, ensure it’s safe use in large mammals (specifically, non-human primates) and pro- vide estimates of the lesion size to compare with the literature. The extent of histology that could be performed and the tools available to us prevent such an in depth analysis. We can say based on shank length of the Utah arrays used and known anatomy that we have affected layer 2/3 and maybe a bit of layer 4.

Reviewer 1 (Recommendations For The Authors):

Figure 5b. It would be helpful if the author could plot the delta match separately for the lesion elec- trodes, near neighbor electrodes, and far neighbors. This would help understand the lesion effect, specifically whether the effect is selective (e.g., more potent for the lesion and adjacent electrodes.)

The fact that neuron loss is not particularly selective can already be seen in the spike waveform plots, arranged spatially on the array. Plenty of clear change is observed far from the lesion elec- trodes (marked with black dots) as well as nearby. We have made mention of this localized non- specificity in the main text and have ensured to remphasize in the figure legened. While a nice suggestion, we currently don’t feel this result rises to the level of a figure given it is not highly specific spatially.

Reviewer 2 (Recommendations For The Authors):

Overall the quality of the paper, the figures and the analysis used could be significantly improved. There is a lack of scientific rigor in the presentation of figures and analysis techniques. It is not clear what the authors are trying to communicate through the figures and their choice of figures to show is confusing (see below).

We thank the reviewer for their pointed critiques and believe we have addressed their concerns with many changes to the text, a revamped waveforms analysis, and both the expansion and addition of results.

The neurophysiology data shown doesn’t suggest neuronal loss, it only shows change which needs strong control data to show it is due to a lesion.

As detailed below, we have presented a revised analysis that provides this control. While the reviewer is right to point out we can distinguish actual neuron loss from neuron silencing, we be- lieve the new analysis rigorously indicates new rates of sample turnover beyond those expected from healthy state.

The histology figure should be replaced with a high-quality representation without folds.

We understand the reviewer’s suggestion. While ideally we would have many histology slices from each lesion, due to cost, we were only able to collect one histology slice per lesion. The folds were introduced by the company that performed the H&E staining, and we unfortunately cannot remove the folds. Therefore, despite the folds, this is the best and only image from this lesion. We hope that the markings on the figure and the comment in the caption is sufficient to explain to readers that the folds are not a result of the lesion but instead a result of the histology process.

The authors suggest that this lesioning method will be compatible with any available multielec- trode probe theoretically. Since all testing was done with a Utah array, it will be helpful to add an explanation about potential constraints that will make a given array compatible with this method.

We thank the reviewer for this suggestion. As stated above, we have both added a demonstration of use in a different multielectrode probe type (with a U-probe) in Fig. 8, and we have added a discussion about which types of multielectrode probes would be suitable on Page 15, Line 420.

The authors should cite and discuss previous studies using electrolytic lesioning in awake-behaving animals to study the causal connection between the brain and behavior. (One example study: Morissette MC, Boye SM. Electrolytic lesions of the habenula attenuate brain stimulation reward. Behavioural brain research. 2008 Feb 11;187(1):17-26.)

We thank the reviewers for this suggestion. We have added a mention of existing electrolytic le- sioning studies on Page 2, Line 88.

“Prior termination studies mostly measure behavioral output, with no simultaneous measures of neuronal activity during the behavior, impairing their ability to provide insight into the causal connection between the brain and behavior [7]–[11], or with no baseline (i.e., pre- lesion) measures of neuronal activity [4].”

The authors should compare their technique with other recent lesioning studies in primates (e.g. Khateeb et al, 2022)

We again thank the reviewer for this point. Specifically not mentioning Khateeb et al. 2022 was a submission error on our part; we cited the paper in Appendix 2 in the version uploaded to the eLife submission portal, but we had uploaded the version prior to citing it to bioRxiv. We have combined addressing this with addressing a previous comment, as mentioned above, with a section in the Discussion on Page 16, Line 434.

In Appendix 2, the authors suggest that a major limitation of optogenetics and chemogenetic in- activation methods is the lack of rhesus-compatible constructs. However, several viral constructs have successful implementation in rhesus monkeys so far (e.g. Galvan A, Stauffer WR, Acker L, El-Shamayleh Y, Inoue KI, Ohayon S, Schmid MC. Nonhuman primate optogenetics: recent advances and future directions. Journal of Neuroscience. 2017 Nov 8;37(45):10894-903; Tremblay et al, Neuron 2020)

We thank the reviewer for pointing us to these papers. We have added a more thorough description of what we meant by lack of rhesus-compatible constructs in that Appendix.

“However, other challenges exist with using optogenetics as an inactivation method in nonhu- man primates, including difficulty reliably affecting behavior [12]. While several constructs for rhesus macaques have been developed [13], [14], reports of successfully inducing be- havioral effects have a small effect size and are less numerous than might be expected [12], and several null results have been published [15]–[17]. Other remaining challenges include the need to develop a head-mounted, battery powered light delivery system for multi-day delivery of light and difficulty integrating illumination with simultaneous chronic neuro- electrophysiology.”

For Figure 5b, only pairwise comparison results from monkey U (L11-14) are shown. It is unclear why such results from monkey H were shown in Figure 5a but not in 5b.

We thank the reviewer for pointing out this unconventional one monkey result. As described in the original submission, we previously omitted Monkey H from the analysis in Figure 5b (now Figure 7) since some of the lesions were closely spaced together, preventing well defined pre- and post- lesion rates of turnover. Never-the-less we have included Monkey H in all the revised analysis and believe even the less cleanly separated data shows useful indications of neuron loss or silencing evoked by the lesion.

Behavioral data (during a motor task) from the awake behaving monkeys (U and H) would greatly strengthen the claim that this lesioning method is capable of creating a behavioral effect and can be adopted to study the relationship between neural function and behavior outcomes.

While we are grateful for the reviewer’s interest in the application of our lesioning technique to studies involving behavior, a behavioral analysis of the effects of our electrolytic lesions falls be- yond the scope of this Tools and Resources manuscript. We would also like to point out that we do not claim that we have achieved a behavioral deficit in this manuscript.

Figure 2 would benefit from an illustration of the Utah array placement and the location of the sites used for lesioning. The authors can either overlay the illustrations on the current ex-vivo and histology images or create a separate schematic to demonstrate that for the readers. Also, Figure 2B needs to be replaced with one without the folds to avoid confusion for the readers.

We have added Figure 2 - figure supplement 1, which shows both the location within the Utah array of the two electrodes used to create the lesions as well as the relative size of the surface area of the lesion and the array. Unfortunately, as the lesion was created under the array, the exact location of the array relative to the lesion is unknown.

As mentioned above, Figure 2B is the only histological image from that lesion. We hope that the markings in the image as well as the caption sufficiently explain that the folds are unrelated to the lesion itself.

Figure 3, the conical region is not well delineated. Data across animals and lesion volume with respect to different parameters should be included.

We have included a supplemental figure, Figure 3 - figure supplement 1, where we have used a dashed white line to clearly indicate the area of damaged parenchyma, in case it was not clear in Figure 3a. We have also added volume estimates from lesions across animals and different param- eters. The ex vivo estimates are shown in Figure 4 and the in vivo estimates are shown in Figure 5.

Figure 4: it is not clear what is being communicated, and where the voltage traces are from.

We thank the reviewer for noting this confusion. We have added some lines in the text to explain what the voltage traces show, both in the caption to Fig. 6 and in the text on Page 7, Line 238.

“Traces only capture the values while the lesioning device was turned on (45 seconds for most lesions and 50 seconds for lesion H200120). A) Voltage traces. Discontinuity at the beginning of the traces indicates transient voltages that were too rapid to be captured by the voltmeter, lasting between 0.13 and 0.33 s. The fluctuating voltages, especially the rapid in- crease in voltage at the beginning of lesioning, emphasize the importance of using a current source to deliver consistent amounts of current into the brain.”

“The voltage across the microelectrode array fluctuated much more than the current did, em- phasizing that we made the correct choice in using a current source to ensure delivery of consistent amounts of current into the brain (Fig. 6figure.caption.19).”

Figure 5: why did the authors choose to use matching units as a measure of the lesion? It is surprising that there are still units on the location that the authors claim to be a lesion. To clarify that it would be helpful to show the location of the lesion in Figure 4a. Also, what can we conclude about the lesion induction when we see units on the lesion electrode? The change in unit match shows that there is a change in the network (although the authors need to show control for that so we know those changes don’t happen due to natural dynamics). It is not clear what is the time duration for pre-pre and post-post (i.e. minutes, seconds, hours). Do these comparisons come from the same time frame or are they coming from two fragments of time for both pre and post- conditions?

Aside from post-mortem histology and tissue assays, there is no good way to confirm neuron loss with chronically implanted electrode arrays in nonhuman primates. Waveforms were chosen as they are the one readily isolated physical measure of the system we are injuring. Although functional measures of activity could indicate neuron loss (topic of following papers), there are many conceivable changes in firing rate patterns that could manifest spuriously as loss, making the estimation of loss even more ambiguous and challenging this way.

We believe the new Figure 7 will make the procedure much more clear, while also providing the control requested by the reviewer, illustrating that new statistical categories of altered waveforms emerge during a lesion, beyond those associated with typical changes in waveform composition within multi-unit recordings seen during recording sample turnover fom healthy animals. We further note that by confining this analysis to four day spans at most, we have limited the impact of daily sample turnover described in the literature (Gallego, 2020).

The time duration for pre-session versus pre-session (pre-post and post-post), is some multiple of the approximate 24 hours between each daily recording session. Therefore, since restricting our- selves to four days separation, between 24 and 96 hours. Spikes are sampled from successful trial periods (so on the order of seconds, compiled into minutes across the whole recording session). Although already described in the main text, these points have been reemphasized in the figure legend.

CNO (line 931) needs to be explained.

We thank the reviewer for this point. We have defined CNO and its relevance in Appendix 2.

“Additionally, chronic inactivation over days may be logistically challenging, as the half life of clozapine N-oxide (CNO, a ligand used to activate DREADD receptors) is on the order of hours.”

Author response:

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

Reviewer #1 (Public Review):

Summary:

This study examines the spatial and temporal patterns of occurrence and the interspecific associations within a terrestrial mammalian community along human disturbance gradients. They conclude that human activity leads to a higher incidence of positive associations.

Strengths:

The theoretical framework of the study is brilliantly introduced. Solid data and sound methodology. This study is based on an extensive series of camera trap data. Good review of the literature on this topic.

Weaknesses:

The authors use the terms associations and interactions interchangeably.

This is not the case. In fact, we state specifically that "... interspecific associations should not be directly interpreted as a signal of biotic interactions between pairs of species…" However, co-occurrence can be an important predictor of likely interactions, such as competition and predation. We stand by our original text.

It is not clear what the authors mean by "associations". A brief clarification would be helpful.

Our specific definition of what is meant here by spatial association can be found in the Methods section. To clarify, the calculation of the index of associations is based on the covariance for the two species of the residuals (epsilon) after consideration of all species-specific response to known environmental covariates. These covariances are modelled to allow them to vary with the level of human disturbance, measured as human presence and human modification. After normalization, the final index of association is a correlation value that varies between -1 (complete disassociation) and +1 (complete positive association).

Also, the authors do not delve into the different types of association found in the study. A more ecological perspective explaining why certain species tend to exhibit negative associations and why others show the opposite pattern (and thus, can be used as indicator species) is missing.

Suggesting the ecological underpinnings of the associations observed here would mainly be speculation at this point, but the associations demonstrated in this analysis do suggest promising areas for the more detailed research suggested.

Also, the authors do not distinguish between significant (true) non-random associations and random associations. In my opinion, associations are those in which two species co-occur more or less than expected by chance. This is not well addressed in the present version of the manuscript.

Results were considered to be non-random if correlation coefficients (for spatial association) or overlap (for temporal association) fell outside of 95% Confidence Intervals. This is now stated clearly in the Methods section.  In Figure 3—figure supplement 1-3 and Figure 4—figure supplement 1-3, p<0.01 levels are also presented.

The obtained results support the conclusions of the study.

Anthropogenic pressures can shape species associations by increasing spatial and temporal co-occurrence, but above a certain threshold, the positive influence of human activity in terms of species associations could be reverted. This study can stimulate further work in this direction.

Reviewer #2 (Public Review):

Summary:

This study analyses camera trapping information on the occurrence of forest mammals along a gradient of human modification of the environment. The key hypotheses are that human disturbance squeezes wildlife into a smaller area or their activity into only part of the day, leading to increased co-occurrence under modification. The method used is joint species distribution modelling (JSDM).

Strengths:

The data source seems to be very nice, although since very little information is presented, this is hard to be sure of. Also, the JSDM approach is, in principle, a nice way of simultaneously analysing the data.

Weaknesses:

The manuscript suffers from a mismatch of hypotheses and methods at two different levels.

(1) At the lower level, we first need to understand what the individual species do and "like" (their environmental niche). That information is not presented, and the methods suggest that the representation of each species in the JSDM is likely to be extremely poor.

The response of each species to the environmental covariates provides a window into their environmental niche, encapsulated in the beta coefficients for each environmental covariate. This information is presented in Figure 2.

(2) The hypothesis clearly asks for an analysis of the statistical interaction between human disturbance and co-occurrence. Yet, the model is not set up this way, and the authors thus do a lot of indirect exploration, rather than direct hypothesis testing.

Our JSDM model is set up specifically to examine the effect of human disturbance on co-occurrence, after controlling for shared responses to environmental variables.  It directly tests the first hypothesis, since, if increase in indices of human disturbance had not tended to increase the measured spatial correlations between species as detected by the model, we would have rejected our stated hypothesis that human modification of habitats results in increased positive spatial associations between species.

Even when the focus is not the individual species, but rather their association, we need to formulate what the expectation is. The hypotheses point towards presenting the spatial and the temporal niche, and how it changes, species for species, under human disturbance. To this, one can then add the layer of interspecific associations.

Examining each species one by one and how each one responds to human disturbance would miss the effects of any meaningful interactions between species.  The analysis presented provides a means to highlight associations that would have been overlooked.  Future research could go on to analyze the strongest associations in the community and the strongest effects of human disturbance so as to uncover the underlying interactions that give rise to them and the mechanisms of human impact.  We believe that this will prove to be a much more productive approach than trying to tackle this problem species by species and pair by pair.

The change in activity and space use can be analysed much simpler, by looking at the activity times and spatial distribution directly. It remains unclear what the contribution of the JSDM is, unless it is able to represent this activity and spatial information, and put it in a testable interaction with human disturbance.

The topic is actually rather complicated. If biotic interactions change along the disturbance gradient, then observed data are already the outcome of such changed interactions. We thus cannot use the data to infer them! But we can show, for each species, that the habitat preferences change along the disturbance gradient - or not, as the case may be.

Then, in the next step, one would have to formulate specific hypotheses about which species are likely to change their associations more, and which less (based e.g. on predator-prey or competitive interactions). The data and analyses presented do not answer any of these issues.

We suggest that the so-called “simpler” approach described above is anything but simple, and this is precisely what the Joint Species Distribution Model improves upon.  As pointed out in the Introduction, simply examining spatial overlap is not enough to detect a signal of meaningful biotic interaction, since overlap could be the result of similar responses to environmental variables.  With the JSDM approach, this would not be considered a positive association and would then not imply the possible existence of meaningful interaction.

Another more substantial point is that, according to my understanding of the methods, the per-species models are very inappropriate: the predictors are only linear, and there are no statistical interactions (L374). There is no conceivable species in the world whose niche would be described by such an oversimplified model.

While interaction terms can be included in the JSDM, this would considerably increase the complexity of the models.  In previous work, we have found no strong evidence for the importance of interaction terms and they do not improve the performance of the models.

We have no idea of even the most basic characteristics of the per-species models: prevalences, coefficient estimates, D2 of the model, and analysis of the temporal and spatial autocorrelation of the residuals, although they form the basis for the association analysis!

The coefficient estimates for response to environmental variables used in the JSDM are provided in Figure 2 and Figure 2—source data 1.

Why are times of day and day of the year not included as predictors IN INTERACTION with niche predictors and human disturbance, since they represent the temporal dimension on which niches are hypothesised to change?

Also, all correlations among species should be shown for the raw data and for the model residuals: how much does that actually change and can thus be explained by the niche models?

The discussion has little to add to the results. The complexity of the challenge (understanding a community-level response after accounting for species-level responses) is not met, and instead substantial room is given to general statements of how important this line of research is. I failed to see any advance in ecological understanding at the community level.

We agree that the community-level response to human disturbance is a complex topic, and we believe it is also a very important one.  This research and its support of the spatial compression hypothesis, while not providing definitive answers to detailed mechanisms, opens up new lines of inquiry that makes it an important advance.  For example, the strong effects of human disturbance on certain associations that were detected here could now be examined with the kind of detailed species by species and pair by pair analysis that this reviewer appears to demand.

Reviewer #1 (Recommendations For The Authors):

L27 indicates instead of "idicates".

We thank the reviewer for catching that error.

L64 I would refer to potential interactions or just associations. It is always hard to provide evidence for the existence of true interactions.

We have revised to “potential interactions” to qualify this statement.

L69 Suggestion: distort instead of upset.

We thank the reviewer for catching that error.

L70-71 Here, authors use the term associations. Please, be consistent with the terminology throughout the manuscript.

We thank the reviewer for raising this important point.  The term “co-occurrence” appears to be used inconsistently in the literature, so we have tried to refer to it only when referencing the work of us. For us, co-occurrence means “spatial overlap” without qualification as to whether it is caused by interaction or simply by similar responses to environmental factors (see Blanchet et al. 2020, Argument 1). In our view, interactions refer to biotic effects like predation, competition, commensalism, etc., while associations are the statistical footprint of these processes.   In keeping with this understanding, in Line 73, we changed "association" to the stronger word "interaction," but in Line 76, we keep the words "spatiotemporal association", which is presumed to be the result of those interactions. In Line 91, we have changed “interactions” to “associations,” as we do not believe interactions were demonstrated in that study. 

L76 "Species associations are not necessarily fixed as positive or negative..." This sentence is misleading. I would say that species associations can vary across time and space, for instance along an environmental gradient.

We thank the reviewer for pointing out the potential for confusion.  In Line 79, we have changed as suggested.

L78 "Associations between free-ranging species are especially context-dependent" Loose sentence. Please, explain a bit further.

We have changed the sentence to be more specific; ”Interactions are known to be context-dependent; for example, gradients in stress are associated with variation in the outcomes of pairwise species interactions.”

L83-85 This would be a good place to introduce the 'stress gradient' hypothesis, which has also been applied to faunal communities in a few studies. According to this hypothesis, the incidence of positive associations should increase as environmental conditions harden.

In our review of the literature, we find that the stress gradient hypothesis is somewhat controversial and does not receive strong support in vertebrates.  We have added the phrase “…the controversial stress-gradient hypothesis predicts that positive associations should increase as environmental conditions become more severe…”

L86-88 Well, overall, the number of studies examining spatiotemporal associations in vertebrates is relatively small. That is, bird associations have not received much more attention than those of mammals. I find this introductory/appealing paragraph a bit rough. I think the authors can do better and find a better justification for their work.

We thank the reviewer for the comments.  We have rewritten the paragraph extensively to make it clearer and to provide a stronger justification for the study.

L106 "[...] resulting in increased positive spatial associations between species" I'd say that habitat shrinking would increase the level of species clustering or co-occurrence, but in my opinion, not necessarily the incidence of positive associations. It is not clear to me if the authors use positive associations as a term analogous to co-occurrence.

We thank the reviewer for raising this very important distinction.  Habitat shrinking would increase levels of species co-occurrence, but this is not particularly interested.  We wanted to test whether there were effects on species interactions, as revealed by associations.  We find that the terms association and co-occurrence are used somewhat loosely in the literature and so have made some new effort to clarify and systematize this in the manuscript.  For example, there appear to be a differences in the way “co-occurrence” is used in Boron 2023 and in Blanchet 2020. We do not use the term "positive spatial association" as analogous to "spatial co-occurrence.". Spatial co-occurrence, which for us has the meaning of spatial overlap, could simply be the result of similar reactions to environmental co-variates, not reflecting any biotic interaction. Joint Species Distribution Models enable the partitioning of spatial overlap and segregation into that which can be explained by responses to known environmental factors, and that which cannot be explained and thus might be the result of biotic interactions.  It is only the latter that we are calling spatial association, which can be positive or negative.   These associations may be the statistical footprint of biotic interactions.

Results:

Difference between random and non-random association patterns. It is not clear to me if the reported associations are significant or not. The authors only report the sign of the association (either positive or negative) but do not clarify if these associations indicate that two species coexist more or less than expected by chance. In my opinion, that is the difference between true ecological associations (e.g., via facilitation or competition effects) and random co-existence patterns. This is paramount and should be addressed in a new version of the manuscript.

This information is provided in Figure 3—figure supplement 1,2,3 and Figure 4—figure supplement 1,2,3.  This is referenced in the text as follows, “… correlation coefficients for 18 species pairs were positive and had a 95 % CI that did not overlap zero, and the number increased to 65 in moderate modifications but dropped to 29 at higher modifications" and so on. This criterion for significance (ie., greater than expected by chance) is now stated at the end of the Materials and methods.  In Figure 3—figure supplement 1,2,3 and Figure 4—figure supplement 1,2,3, those correlations that were significant at p<0.01 are also shown.

I am also missing a more ecological explanation for the observed findings. For instance, the top-ranked species in terms of negative associations is the red fox, whereas the muntjac seems to be the species whose presence can be used as an indicator for that of other species. What are the mechanisms underlying these patterns? Do red foxes compete for food with other species? Do the species that show positive associations (red goral, muntjac) have traits or a diet that are more different from those of other species? More discussion on these aspects (role of traits and the trophic niche) would be necessary to better understand the obtained results.

The purpose of this paper was to test the compression hypotheses, and we have tried to keep that as the focus.  However, the analysis does open up interesting lines of inquiry for future research to decipher the details of the interactions between species and the mechanisms by which human disturbance facilitates or disrupts these interactions. The reviewer raises some interesting possibilities, but at this point, any discussion along these lines would be largely speculation and could lengthen the paper without great benefit. 

Reviewer #2 (Recommendations For The Authors):

The manuscript should be accompanied by all data and code of analysis.

All data and RScripts have been made available in Science Data Bank: https://doi.org/10.57760/sciencedb.11804.

The sentence "not much is known" is weak: it suggests the authors did not bother to quantify what IS known, and simply waved any previous knowledge aside. Surely we have some ideas about who preys on whom, and which species have overlapping resource requirements (e.g., due to jaw width). For those, we would expect a particularly strong signal, if the association is indeed indicative of interactions.

We believe that the reviewer is referring to the statement in Line 90-92 about the lack of understanding of the resilience of terrestrial mammal associations to human disturbance.  We have added a reference to one very recent publication that addresses the issue (Boron et al., 2023), but otherwise we stand by our statement. We have, however, added a qualifier to make it clear that we did indeed look for previous knowledge; "However, a review of the literature indicates that ...."

Figures:

Fig. 1. This reviewer considers that this is too trivial and should be deleted.

This is a graphical statement of the hypotheses and may be helpful to some readers.

Fig. 2. Using points with error bars hides any potential information.

Done as suggested.

That only 4 predictors are presented is unacceptably oversimplified.

Only 4 predictors are included because, in previous work, we found that adding additional predictors or interactions did little to improve the model’s performance (Li et al. 2018, 2021 and 2022) and could lead to over-fitting.

Fig. 5. and 6. aggregate extremely strongly over species; it remains unclear which species contribute to the signal, and I guess most do not.

The number of detection events presented in Table 1 should help to clarify the relative contribution of each species to the data presented in Figures 5 and 6.

This reviewer considers that the introduction 'oversells' the paper.

L55: can you give any such "unique ecological information"

L60: Lyons et al. (Kathleen is the first name) has been challenged by Telford et al. (2016 Nature) as methodologically flawed.

The first name has been deleted.  The methodological flaw has to do with interpretation of the fossil record and choice of samples, not with the need to partition shared environmental preferences and interactions.

L61 contradicts line 64: Blanchet et al. (2022, specifying some arguments from Dormann et al. 2018 GEB) correctly point out that logically one cannot infer the existence or strength from co-occurrence data. It is thus wrong to then claim (citing Boron et al.) that such data "convey key information about interactions". The latter statement is incorrect. A tree and a beetle can have extremely high association and nothing to do with each other. Association does not mean anything in itself. When two species are spatially and temporally non-overlapping, they can exhibit perfect "anti-association", yet, by the authors' own definition, cannot interact.

We believe that the reviewer’s concerns arise from a misunderstanding of how we use the term association.  In our usage, an association is not the same as co-occurrence or overlap, which may simply be the result of shared responses to environmental variables.  The co-occurring tree and beetle would not be found to have any association in our analysis, only shared environmental sensitivities.  In contrast, associations can be the statistical footprint of interactions, and would be overlaid onto any overlap due to similar responses to the environment.  In the case of negative associations, such as might be the result of competitive exclusion or avoidance of predators, the two species would share environmental responses but show lower than expected spatial overlap.  Even though they might be only rarely found in the same vicinity, they would indeed be interacting when they were together.

Joint Species Distribution Models "allow the partitioning of the observed correlation into that which can be explained by species responses to environmental factors... and that which remains unexplained after controlling for environmental effects and which may reflect biotic interactions." (Garcia Navas et al. 2021). It is the latter that we are calling “associations.”

L63: Gilbert reference: Good to have a reference for this statement.

This point is important, but the reviewer’s comments below have made it clear that it is even more important to point out that strong interactions should be expected to lead to significant associations.  We have added a statement to clarify this.

L70-72: Incorrect, interactions play a role, not associations (which are merely statistical).

In this, we agree, and we have revised the statement to refer to interactions, not associations. In our view, an interaction is a biological phenomenon, while an association is the resulting statistical signal that we can detect.

L75: Associations tell us nothing, only interactions do. Since these can not be reliably inferred, this statement and this claim are wrong.

We thank the reviewer for raising this point, but we beg to disagree. Strong interactions should be expected to lead to significant associations that can be detected in the data. Associations, which can be measured reliably, are the evidence of potential interactions, and hence associations can tell us a great deal.  We have added a note to this effect after the Gilbert reference above to clarify this point.

However, we do accept that associations must be interpreted with caution. As Blanchet et al. 2020 explain, " …the co-occurrence signals (e.g. a significant positive or negative correlation value) estimated from these models could originate from any abiotic factors that impact species differently. Therefore, this correlation cannot be systematically interpreted as a signal of biotic interactions, as it could instead express potential non-measured environmental drivers (or combinations of them) that influence species distribution and co-distribution.”  Or alternatively an association could be the result of interaction with a 3rd species. 

L87: Regarding your claim, how would you know you DO understand? For that, you need to formulate an expectation before looking at the data and then show you cannot show what you actually measure. (Jaynes called this the "mind-projection fallacy".)

We are not sure if the reviewer is criticizing our paper or the entire field of community ecology.  Perhaps it is the statement that “….resilience of interspecific spatiotemporal associations of terrestrial mammals to human activity remains poorly understood….”  Since we are confident that the reviewer believes that mammals do interact, we guess that it is the term “association” that is questioned.  We have revised this to “…the impacts of human activity on interspecific interactions of terrestrial mammals remains poorly understood…” 

In this particular case, we did formulate an expectation before looking at the data, in the form of the two formal hypotheses that are clearly stated in the Introduction and illustrated in Figure 1. If the hypotheses had not been supported, then we would have accepted that we do not understand. But as the data are consistent with the hypotheses, we submit that we do understand a bit more now.

Author response:

We thank the reviewers for their critical appraisal of our manuscript. We will address the points of confusion and/or lack of clarity in a revised manuscript. We agree with reviewer 1 that applying the best practice pipeline(s) on new experimental data and comparing this approach with current practices would be a useful demonstration of how this alters the biological interpretation. This is something we are in the process of completing but believe this is best addressed in a separate manuscript where we can focus on the associated biological findings, allowing this manuscript to remain focused on the accurate quantification of tRNA-Seq data.

Author response:

We thank the reviewers for their positive evaluation and constructive feedback on our study.

We acknowledge the concern regarding the use of HEK293T cells. In the revised manuscript, we will provide a more detailed explanation of the role of the PKA pathway in the regulation of GSIS by PGE2. To validate this regulation through Kv2.2, we will overexpress the Kv2.2 mutant channel in beta cells and assess its impact. Additionally, we will verify the specificity of the antibodies for EP1-EP4 receptors by knockdown. To confirm the receptors involved in PGE2 function, we will use additional EP receptor blockers or perform receptor knockdown experiments.

We will clarify that the described signaling pathway operates under normal physiological conditions and differs from pathological changes.

We once again thank the reviewers for their positive evaluation and constructive suggestions.

Author response:

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

Reviewer #1

We modified the text regarding PRC1 according to the reviewer’s recommendation.

Reviewer #2

Following the reveiwer’s advise, we introduced the holdup assay, as well as the native holdup assay in more details.

This new part now also discusses the question of replicates in more details. We do not agree with the eLife assessment on this matter, but we think that this assessment was made because analyzing holdup data requires a different approach compared to more conventional interactomic approaches and these differences were not introduced in sufficient depth. We hope that the inclusion of more background reasoning, as well as by providing a more detailed comparison of the measured independent BIN1 interactomes, now included on Figure S4, will eliminate all confusion in the reader.

We thank the reviewer for guiding us to a previous work that was done on Grb2. Indeed, the finding of this earlier work aligns perfectly with our finding suggesting general similarities in SH3 domain mediated interactions.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Benner et al. identify OVO as a transcriptional factor instrumental in promoting the expression of hundreds of genes essential for female germline identity and early embryo development. Prior data had identified both ovo and otu as genes activated by OVO binding to the promoters. By combining ChIP-seq, RNA-seq, and analysis of prior datasets, the authors extend these data to hundreds of genes and therefore propose that OVO is a master transcriptional regulator of oocyte development. They further speculate that OVO may function to promote chromatin accessibility to facilitate germline gene expression. Overall, the data compellingly demonstrate a much broader role for OVO in the activation of genes in the female germline than previously recognized. By contrast, the relationship between OVO, chromatin accessibility, and the timing of gene expression is only correlative, and more work will be needed to determine the mechanisms by which OVO promotes transcription.

We fully agree with this summary.  

Strengths:

Here Benner et al. convincingly show that OVO is a transcriptional activator that promotes expression of hundreds of genes in the female germline. The ChIP-seq and RNA-seq data included in the manuscript are robust and the analysis is compelling.

Importantly, the set of genes identified is essential for maternal processes, including egg production and patterning of the early embryo. Together, these data identify OVO as a major transcriptional activator of the numerous genes expressed in the female germline, deposited into the oocyte and required for early gene expression. This is an important finding as this is an essential process for development and prior to this study, the major drivers of this gene expression program were unknown.

We are delighted that this aspect of the work came across clearly. Understanding the regulation of maternal effect genes has been something of a black-box, despite the importance of this class of genes in the history of developmental genetics. The repertoire of essential oogenesis/embryonic development genes that are bound by and respond to OVO are well characterized in the literature, but nothing is known about how they are transcriptionally regulated. We feel the manuscript will be of great interest to readers working on these genes.

Weaknesses:

The novelty of the manuscript is somewhat limited as the authors show that, like two prior, well-studied OVO target genes, OVO binds to promoters of germline genes and activates transcription. The fact that OVO performs this function more broadly is not particularly surprising.

Clearly, transcription factors regulate more than one or two genes. Never-the-less we were surprised at how many of the aspects of oogenesis per se and maternal effect genes were OVO targets. It was our hypothesis that OVO would have a transcriptional effect genome-wide, however, it was less clear whether OVO would always bind at the core promoter, as is with the case of ovo and otu. Our results strongly support the idea that core promoter proximal binding is essential for OVO function; a conclusion of work done decades ago, which has not been revisited using modern techniques. 

A major challenge to understanding the impact of this manuscript is the fact that the experimental system for the RNA-seq, the tagged constructs, and the expression analysis that provides the rationale for the proposed pioneering function of OVO are all included in a separate manuscript.

This is a case where we ended up with a very, very long manuscript which included a lot of revisiting of legacy data. It was a tough decision on how to break up all the work we had completed on ovo to date. In our opinion, it was too much to put everything into a single manuscript unless we wanted a manuscript length supplement (we were also worried that supplemental data is often overlooked and sometimes poorly reviewed). We therefore decided to split the work into a developmental localization/characterization paper and a functional genomics paper. As it stands both papers are long. Certainly, readers of this manuscript will benefit from reading our previous OVO paper, which we submitted before this one. The earlier manuscript is under revision at another journal and we hope that this improved manuscript will be published and accessible shortly.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Benner et al. interrogate the transcriptional regulator OVO to identify its targets in the Drosophila germline. The authors perform ChIP-seq in the adult ovary and identify established as well as novel OVO binding motifs in potential transcriptional targets of OVO. Through additional bioinformatic analysis of existing ATAC-seq, CAGE-seq, and histone methylation data, the authors confirm previous reports that OVO is enriched at transcription start sites and suggest that OVO does not act as part of the core RNA polymerase complex. Benner et al. then perform bulk RNA-seq in OVO mutant and "wildtype" (GAL4 mediated expression of OVO under the control of the ovo promoter in OVO mutants) ovaries to identify genes that are differentially expressed in the presence of OVO. This analysis supports previous reports that OVO likely acts at transcription start sites as a transcriptional activator. While the authors propose that OVO activates the expression of genes that are important for egg integrity, maturation, and for embryonic development (nanos, gcl, pgc, bicoid), this hypothesis is based on correlation and is not supported by in vivo analysis of the respective OVO binding sites in some of the key genes. A temporal resolution for OVO's role during germline development and egg chamber maturation in the ovary is also missing. Together, this manuscript contains relevant ChIP-seq and RNA-seq datasets of OVO targets in the Drosophila ovary alongside thorough bioinformatic analysis but lacks important in vivo experimental evidence that would validate the high-quality datasets.

We thank reviewer 2 for the appreciation of the genomics data and analysis. Some of the suggested in vivo experiments are clear next steps, which are well underway. These are beyond the scope of the current manuscript. 

Temporal analysis of ovo function in egg chamber development is not easy, as only the weakest ovo alleles have any egg chambers to examine. However, we will also point out the long-known phenotypes of some of those weak alleles in the text (e.g. ventralized chambers in ovoD3/+). We will need better tools for precise rescue/degradation during egg chamber maturation.     

Strengths:

The manuscript contains relevant ChIP-seq and RNA-seq datasets of OVO targets in the Drosophila ovary alongside thorough bioinformatic analysis

Thank you. We went to great lengths to do our highly replicated experiments in multiple ways (e.g. independent pull-down tags) and spent considerable time coming up with an optimized and robust informatic analysis.

Weaknesses:

(1) The authors propose that OVO acts as a positive regulator of essential germline genes, such as those necessary for egg integrity/maturation and embryonic/germline development. Much of this hypothesis is based on GO term analysis (and supported by the authors' ChIP-seq data). However accurate interpretation of GO term enrichment is highly dependent on using the correct background gene set. What control gene set did the authors use to perform GO term analysis (the information was not in the materials and methods)? If a background gene set was not previously specified, it is essential to perform the analysis with the appropriate background gene set. For this analysis, the total set of genes that were identified in the authors' RNA-seq of OVO-positive ovaries would be an ideal control gene set for which to perform GO term analysis. Alternatively, the total set of genes identified in previous scRNA-seq analysis of ovaries (see Rust et al., 2020, Slaidina et al., 2021 among others) would also be an appropriate control gene set for which to perform GO term analysis. If indeed GO term analysis of the genes bound by OVO compared to all genes expressed in the ovary still produces an enrichment of genes essential for embryonic development and egg integrity, then this hypothesis can be considered.

We feel that this work on OVO as a positive regulator of genes like bcd, osk, nos, png, gnu, plu, etc., is closer to a demonstration than a proposition. These are textbook examples of genes required for egg and early embryonic development. Hopefully, this is not lost on the readers by an over-reliance on GO term analysis, which is required but not always useful in genome-wide studies. 

We used GO term enrichment analysis as a tool to help focus the story on some major pathways that OVO is regulating. To the specific criticism of the reference gene-set, GO term enrichment analysis in this work is robust to gene background set. We will update the GO term enrichment analysis text to indicate this fact and add a table using expressed genes in our RNA-seq dataset to the manuscript and clarify gene set robustness in greater detail in the methods of the revision. We will also try to focus the reader’s attention on the actual target genes rather than the GO terms in the revised text.

We have updated the GO term analysis by including all the expressed genes in our RNA-seq datasets as a background control. Figure 6 has been updated to include the significant GO terms. We have outlined changes in the methods section below.

Lines 794-801:

“Gene ontology enrichment analysis was completed with g:Profiler’s g:GOSt software (Raudvere et al. 2019) on the set of genes overlapping OVO ChIP peaks over the TSS and significantly upregulated in the presence of ectopic OVO (525 genes in total). All genes that were considered to be expressed in our RNA-seq datasets were used as a background control (10,801 genes in total). Default parameters were used for the enrichment analysis except for ‘statistical domain scope’ was set to ‘custom’ (our control background genes were uploaded here), ‘significance threshold’ was set to ‘Bonferroni correction’, and only GO biological process terms were searched for enrichment with the gene list. The GO terms listed in Figure 6 represent the 24 smallest GO term sizes according to Table S5.”

(2) The authors provide important bioinformatic analysis of new and existing datasets that suggest OVO binds to specific motifs in the promoter regions of certain germline genes. While the bioinformatic analysis of these data is thorough and appropriate, the authors do not perform any in vivo validation of these datasets to support their hypotheses. The authors should choose a few important potential OVO targets based on their analysis, such as gcl, nanos, or bicoid (as these genes have well-studied phenotypes in embryogenesis), and perform functional analysis of the OVO binding site in their promoter regions. This may include creating CRISPR lines that do not contain the OVO binding site in the target gene promoter, or reporter lines with and without the OVO binding site, to test if OVO binding is essential for the transcription/function of the candidate genes.

Exploring mechanism using in vivo phenotypic assays is awesome, so this is a very good suggestion. But, it is not essential for this work -- as has been pointed out in the reviews, in vivo validation of OVO binding sites has been comprehensively done for two target genes, ovo and otu. The “rules” appear similar for both genes. That said, we are already following up specific OVO target genes and the detailed mechanism of OVO function at the core promoter. We removed some of our preliminary in vivo figures from the already long current manuscript. We continue to work on OVO and expect to include this type of analysis in a new manuscript.

(3) The authors perform de novo motif analysis to identify novel OVO binding motifs in their ChIP-seq dataset. Motif analysis can be significantly strengthened by comparing DNA sequences within peaks, to sequences that are just outside of peak regions, thereby generating motifs that are specific to peak regions compared to other regions of the promoter/genome. For example, taking the 200 nt sequence on either side of an OVO peak could be used as a negative control sequence set. What control sequence set did the authors use as for their de novo motif analysis? More detail on this is necessary in the materials and methods section. Re-analysis with an appropriate negative control sequence set is suggested if not previously performed.

We apologize for being unclear on negative sequence controls in the methods. We used shuffled OVO ChIP-seq peak sequences as the background for the de novo motif analysis, which we will better outline in the methods of the revision. This is a superior background set of sequences as it exactly balances GC content in the query and background sequences. We are not fond of the idea of using adjacent DNA that won’t be controlled for GC content and shadow motifs. Furthermore, the de novo OVO DNA binding motifs are clear, statistically significant variants of the characterized in vitro OVO DNA binding motifs previously identified (Lu et al., 1998; Lee and Garfinkel, 2000; Bielinska et al., 2005), which lends considerable confidence. We also show that the OVO ChIP-seq read density are highly enriched for all our identified motifs, as well as the in vitro motifs. We provide multiple lines of evidence, through multiple methods, that the core OVO DNA binding motif is 5’-TAACNGT-3’. We have high confidence in the motif data.

We have added the below text to the methods section for further clarity on motif analysis parameters.

Lines 808-812

“The default parameters were used for de novo motif enrichment analysis, including the use of shuffled input sequences as a control. After identifying ‘OVO Motif One’, OVO ChIP peaks that contained that sequence were removed and the resulting ChIP peaks were resubmitted for STREME analysis deriving derivative OVO DNA binding motifs like above.”

(4) The authors mention that OVO binding (based on their ChIP-seq data) is highly associated with increased gene expression (lines 433-434). How many of the 3,094 peaks (conservative OVO binding sites), and what percentage of those peaks, are associated with a significant increase in gene expression from the RNA-seq data? How many are associated with a decrease in gene expression? This information should be added to the results section.

Not including the numbers of the overlapping ChIP peaks and expression changes in the text was an oversight on our part. The numbers that relate to this (666 peaks overlapping genes that significantly increased in expression, significant enrichment according to Fishers exact test, 564 peaks overlapping genes that significantly decreased in expression, significant depletion according to Fishers exact test) are found in figure 4C and will be added to the text.

We have modified the results section to include the overlap between the RNA-seq and ChIP-seq data.

Lines 463-468

“We found that 2,298 genes that were expressed in our RNA-seq data overlapped an OVO ChIP peak. 666 genes significantly increased in expression and were bound by OVO, which is a significant enrichment according to a Fisher’s exact test (Figure 4C, cyan dots, p < 0.01, odds ratio = 2.21). While conversely, 564 genes decreased in expression and were bound by OVO, indicating a significant depletion according to a Fisher’s exact test (Figure 4C, blue dots, p < 0.01, odds ratio = 0.85).”

(5) The authors mention that a change in endogenous OVO expression cannot be determined from the RNA-seq data due to the expression of the OVO-B cDNA rescue construct. Can the authors see a change in endogenous OVO expression based on the presence/absence of OVO introns in their RNA-seq dataset? While intronic sequences are relatively rare in RNA-seq, even a 0.1% capture rate of intronic sequence is likely to be enough to determine the change in endogenous OVO expression in the rescue construct compared to the OVO null.

This is a good point. The GAL4 transcript is downstream of ovo expression in the hypomorphic ovoovo-GAL4 allele. We state in the text that there is a nonsignificant increase in GAL4 expression with ectopic rescue OVO, although the trend is positive. We calculated the RPKM of RNA-seq reads mapping to the intron spanning exon 3 and exon 4 in ovo-RA and found that there is also a nonsignificant increase in intronic RPKM with ectopic rescue OVO (we will add to the results in the revision). We would expect OVO to be autoregulatory and potentially increase the expression of GAL4 and/or intronic reads, but the ovoovoGAL4>UASp-OVOB is not directly autoregulatory like the endogenous locus. It is not clear to us how the intervening GAL4 activity would affect OVOB activity in the artificial circuit. Dampening? Feed-forward? Is there an effect on OVOA activity? Regardless, this result does not change our interpretation of the other OVO target genes.

We have added the analysis of intronic ovo RNA-seq to the results as outlined below.

Lines 512-520

“Transcriptionally, ovo RNA-seq reads are likely derived from the UASp-3xFHA-OVO-B cDNA rescue or are indistinguishable between the genomic locus and rescuing cDNA transgene. We found a nonsignificant increase in exon 3 to exon 4 intronic ovo reads with the expression of ectopic rescue OVO (log2 fold change = 0.76, p-adj = 0.26). These intronic reads would be derived from the endogenous ovo locus, but it is difficult to conclusively determine if the endogenous ovo locus would respond transcriptionally to ectopic OVO downstream of UASp (for example, the pathway for ovo is no longer autoregulatory in ovoovo-GAL4/ovoΔBP; UASp-3xFHA-OVO-B germ cells, there is an additional GAL4>UASp activation step). So, we could not confidently assess whether ovo responded transcriptionally to ectopic rescue OVO.”

(6) The authors conclude with a model of how OVO may participate in the activation of transcription in embryonic pole cells. However, the authors did not carry out any experiments with pole cells that would support/test such a model. It may be more useful to end with a model that describes OVO's role in oogenesis, which is the experimental focus of the manuscript.

We did not complete any experiments in embryonic pole cells in this manuscript and base our discussion on the potential dynamics of OVO transcriptional control and our previous work showing maternal and zygotic OVO protein localization in the developing embryonic germline. Obviously, we are highly interested in this question and continue to work on the role of maternal OVO. We agree that we are extended too far and will remove the embryonic germ cell model in the figure. We will instead focus on the possible mechanisms of OVO gene regulation in light of the evidence we have shown in the adult ovary, as suggested.

We have removed figure 7 and have re-written the last two paragraphs of the discussion as below.

Lines 645-663

“The requirement for OVO at the TSS of target genes has been well characterized at its own locus as well as its downstream target otu. Our OVO ChIP and expression data confirm findings from previous work that OVO is binding to these target promoters, and in the case of otu, strongly responds transcriptionally to the presence of OVO. Although we did not test the requirement for OVO DNA binding motifs at other OVO bound genes in this work, this has been extensively explored before, showing that removal of OVO

DNA binding sites overlapping the TSS results in a strong decrease in reporter expression (Lü et al. 1998; Bielinska et al. 2005; Lü and Oliver 2001). Removal of more distal upstream OVO DNA binding sites also reduces reporter expression to a lesser degree. However, for most cases tested, removal of OVO DNA binding sites while leaving the rest of the enhancer regions intact, never totally abolished reporter expression. These dynamics are highly similar to work that has been completed on the pioneer factor zelda (zld). Adding zld DNA binding motifs to a stochastically expressed transcriptional reporter increases the activity and response of the reporter (Dufourt et al. 2018). Distally located zld DNA binding motifs influenced reporter expression to a lesser degree than proximal sites. A single zld DNA binding site adjacent to the TSS produced the strongest reporter activity. Importantly, just like the activity of OVO transgenic reporters, there is not an absolute requirement for zld DNA binding to activate reporter expression, however, the addition of TSS adjacent zld DNA binding motifs does strongly influence reporter response. We know that zld achieves this reporter response through its pioneering activity (Xu et al. 2014; Harrison et al. 2011), whether OVO achieves this similar effect on gene expression through a shared mechanism, or in cooperation with other transcription factors needs to be further explored.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The Results section could be streamlined by limiting the discussion of analysis to only those details that are unusual or essential for understanding the science. For example, the fact that MACS3 was used to call peaks seems most suitable for the Methods section.

We have removed the below excerpts from the results section to streamline the text.

‘We compared immuno-purified OVO associated DNA with input DNA as a control, for a total of 12 ChIPseq libraries, which we sequenced using the Illumina system. After quality control and alignment to the Drosophila r6.46 genome (Gramates et al. 2022), we used MACS3 (Zhang et al. 2008)’

The Supplemental Tables are referred to out of order. Table S2 is referred to on line 143 while Table S1 is not referred to until the Methods section.

We have reorganized the order of the tables in the manuscript text.

In the analysis of CAGE-seq data, it is unclear whether there is anything distinctive about the ~2000 regions bound by OVO but that is not near TSS in the ovary dataset. Are these TSS that are not active in the ovary or are these non-promoter bound OVO sites? If they are TSS of genes not in the CAGE-seq data set, are these genes expressed in other tissues or just expressed at lower levels in the ovary?

This was a good point that prompted us to take a closer look at the characteristics of OVO binding and its relationships to promoters and other gene elements. 45% of OVO ChIP peaks overlapped the TSS while 55% were either non-overlapping downstream or upstream of the TSS. When plotting OVO ChIP read density, there was still a striking enrichment of OVO binding over the TSS, even though the ChIP peak was not overlapping the TSS (new figure 1K). This is possibly due to weaker direct OVO binding at the TSS that was not considered significant in the peak calling software or were indirect interactions of the distal OVO binding and the TSS. We outline this in the below text added to the results section on the OVO ChIP. To showcase these results, we have included a new panel in figure 1K. We removed the panel showing the enrichment over the cage-seq TSS, but this same data remains in the heatmap shown in figure 1L, so no information is lost. To directly answer the Cage-seq questions considering the OVO bound over the annotated TSS results, we found that 1,047 chip peaks overlapped CAGE-seq TSS, which is only 347 fewer than the annotated TSS overlap (1,394). Of the 1,394 genes that were bound by over the TSS, all of them were considered to be expressed in our RNA-seq dataset, indicating that these might just be more lowly expressed genes that for whatever reason were not considered to be enriched TSSs in the CAGE-seq data. This difference is likely not significant.

Lines 235-251

“Although OVO ChIP peaks overlapping genes showed a strong read density enrichment over the TSS, we found that only 45% (1,394/3,094) of OVO ChIP peaks directly overlapped a TSS. 43% (1,339/3,094) of OVO ChIP peaks were found to overlap the gene body downstream of the TSS (intronic and exonic sequences) and 12% (366/3,094) did not overlap any gene elements, indicating that they were intergenic.

We were interested in the differences between OVO binding directly over the TSS or at more distal upstream and downstream sites. We decided to plot the OVO ChIP read density of these different classes of OVO binding patterns and found that OVO bound over the TSS produced a sharp read density enrichment over the TSS which was consistent with what was found for all OVO bound genes (Figure 1K). OVO binding along the gene body surprisingly also showed a read density enrichment over the TSS, although the magnitude of read density enrichment was notably less than TSS OVO binding. Intergenic OVO binding also showed these same characteristics with a notable upstream read density enrichment possibly indicative of enhancer binding. This indicates that although the significantly called OVO ChIP peaks did not overlap the TSS, there was still a propensity for TSS sequences to be enriched with OVO ChIP over the input control. This could be due to weaker direct in vivo binding of OVO to these TSSs or indirect interactions between the upstream/downstream OVO bound sequences and the TSS, possibly through a looping enhancer-promoter interaction. However, regardless of the location of the OVO ChIP peak, OVO seemed to always be enriched at or in close proximity to TSSs.”

It would be helpful for the authors to provide a bit more detailed analysis of chromatin states of OVObound regions in GSC, 8c NC, and 32c NC (or some more clarity in the current analysis). Are the regions that are bound by OVO accessible in all these cell types or specifically enriched for accessibility in a subset? The authors state that OVO binding is correlated with open chromatin, but whether these are regions that are open in all cell types analyzed or a subset is not clear from the data presented. Promoters are often accessible regardless of cell type, so it is unclear what exactly is to be concluded from this association. Also, is the proximity to open chromatin features for OVO-bound promoters (as shown in Figure 2C) different than non-OVO-bound promoters (the two classes shown Figure 1L, for example)?

We utilized previously published datasets of staged germ cell chromatin status to look at the association of chromatin status and OVO binding. Unfortunately, not all the same germ cell stages were profiled for each chromatin mark from the datasets derived for these two papers. For example, only H3K4me3 data exists for GSCs, and only gsc and 8c data exists for H3K9me3, while the other chromatin marks had more profiles, even including later stages. We focused specifically on gsc and 32c (essentially stage 5 egg chambers) for the other chromatin marks since that is when the ovo hypomorphic egg chambers arrest. A nice control would have been chromatin states in somatic follicle cells of the ovary, since we know germ cell genes such as ovo and otu are not expressed and presumably the chromatin states in somatic cell types would be different than germ cells. However, chromatin states for somatic follicle cells were not published in these two papers and we are not aware of any other existing datasets to compare too. Essentially, we need to determine the changes in chromatin states with and without OVO, which we are currently working on. 

We did further analyze chromatin states and differential OVO binding in respect to gene elements, and found that OVO binding, regardless of the relationship to the gene element, is always open (gsc and 32c ATAC). OVO binding over the gene body shows the same enrichment for open chromatin and transcriptionally active histone marks. We compared the profiles of these chromatin marks and the promoters of OVO bound and not bound genes and consistent with the suggestion that promoters are generally open, we found that this was the case. However, there is an enrichment for open chromatin and transcriptionally active histone marks for OVO bound genes compared to non-OVO bound genes. This could be a consequence of OVO binding or indirect consequence of a downstream OVO target. Regardless, as has been suggested, future experiments directly measuring chromatin status and OVO needs to be performed. The below excerpts have been added to the text to supplement the comments provided above.

Lines 328-343

“The association of OVO binding with active histone marks and open chromatin was striking, but open chromatin is likely a general phenomenon of promoters (Haines and Eisen, 2008). Indeed, when measuring the read density for GSC and 32C ATAC-seq for OVO bound and OVO non-bound promoters, there is an enrichment for open chromatin at the TSS regardless of OVO binding. However, we did notice an increase in enrichment for OVO bound promoters compared to OVO non-bound promoters (Figure S1G), possibly suggesting that OVO bound promoters are more open or have an increase in accessibility when compared to non-OVO bound promoters. This same relationship held true for the transcriptionally active histone mark H3K27ac in GSCs (Figure S1H). Since only 45% of OVO ChIP peaks overlapped TSSs, we plotted the read density of the above chromatin marks over OVO ChIP peak maximums for OVO bound over the TSS, gene body, or intergenic regions (Figure S2A-D). We found that OVO bound regions that were not overlapping the TSS still showed the same propensity for enrichment of open chromatin and active histone marks. Intergenic regions were especially enriched for open chromatin measured through ATAC-seq. Altogether suggesting that OVO binding genome-wide is tightly associated with open chromatin regardless of germ cell stage, and active transcription in GSCs. In other words, chromatin state data suggests OVO is acting positively on its target genes and raises the possibility that OVO-binding and open chromatin are related.”

For clarity, it would help the reader if the authors mentioned the male-specific TATA-associated factors as a rationale for testing the role of OVO binding in core promoter function. This is currently mentioned in the Discussion on lines 575-577, but would help in understanding the motivation behind the detailed analysis of the promoter binding of OVO in the Results and make the negative result more clearly impactful.

We have introduced the male specific tata factors as suggested and have condensed the two intro paragraphs in this section into one, as shown below.

Lines 347-363

“Our data thus far clearly indicates that OVO binding occurs at or very near the core promoter, a region recognized by an enormous collection of factors that associate with RNA polymerase to initiate transcription (Aoyagi and Wassarman 2000; Vo Ngoc, Kassavetis, and Kadonaga 2019). The highly organized polymerase complex has sequence-specific DNA recognition sites with incredibly precise spacing between them, with an overall DNA footprint of a little less than 100bp (Rice, Chamberlin, and Kane 1993; FitzGerald et al. 2006; Ohler et al. 2002). There are upstream binding sites such as TATA, sites at transcription start, such as the initiator (INR), and downstream promoter elements (DPE) (Vo Ngoc, Kassavetis, and Kadonaga 2019). The combinations of these DNA motifs is not random in mammals and Drosophila (FitzGerald et al. 2006), and distinct combinations of different motifs at the TSS of genes expressed in Drosophila are conserved over tens of millions of years of evolution (Chen et al. 2014). The male germline expresses a number of TATA-associated factors that have been implicated in male-specific promoter usage for gene expression (M. Hiller et al. 2004; M. A. Hiller et al. 2001; Lu et al. 2020; V. C. Li et al. 2009). It is possible that OVO is a female germline specific TATA-associated factor, and if so, OVO binding sites at core promoters should share precise spacing with other core promoter elements, suggesting it is likely part of the complex. If not, then OVO is more likely to facilitate binding of the basal transcriptional machinery. Because of the extended footprint of engaged RNA polymerase, OVO and the basal machinery would not be likely to occupy the same region at the same time.”

The description of the system used for the RNA-seq would benefit from additional clarity. It is not clear as written why it is "Lucky" that there is an mRNA isoform with extended exon 2 required for egg chamber development beyond stage 5. How does this requirement compare to the global requirement for OVO, which seems to be required for germ cell development even before stage 5? Understanding this system is essential for interpreting the RNA-seq results. Indeed, the authors have a separate manuscript (currently on bioRxiv) that explains the details of this system. As such, the current description requires that the reader refer to this additional pre-print. Could the authors include a diagram to better illustrate this system? Furthermore, since this RNA-seq is being performed on tissue that includes nurse cells, follicle cells, and germ cells from multiple stages of development, it is important for the authors to clearly state in which cell types OVO is expressed and likely functional. (While this is well beyond this manuscript, this analysis is the type that might benefit from the use of single-cell sequencing as a means to deconvolute the phenotypic effects of OVO loss.)

We have rewritten the text to better describe the system for RNA-seq. We have also included a figure (Figure S1A) showing the alleles used that should help provide clarity for the readers. We agree that moving forward single cell experiments will be critical to have a better understanding of the transcriptional changes and chromatin dynamics with and without OVO. We have included the below changes to the text.

Lines 409-423

“Previous work from our lab has identified a transheterozygous ovo allelic combination (ovoovo-GAL4/ovoΔBP) that greatly reduces OVO activity resulting in sterility, however, female germ cells are able to survive up until at least stage 5 of oogenesis (Benner et al. 2023). ovoovo-GAL4 is a CRISPR/Cas9 derived T2A-GAL43xSTOP insertion upstream of the splice junction of exon 3 in the ovo-RA transcript (Figure S1A).

Importantly, this insertion in the extended exon 3 would disrupt roughly 90% of the ovo-B transcripts. However, since about 10% of ovo-B transcripts utilize an upstream splice junction in exon 3, these transcripts would not be disrupted with the T2A-GAL4-3xSTOP insertion and thus allow for enough OVO activity for germ cell survival (Benner et al. 2023). Since ovoovo-GAL4 expresses GAL4 in place of full length OVO due to the T2A sequences, we can drive expression of a rescuing OVO-B construct downstream of UASp to generate OVO+ female germ cells, which in fact does rescue the arrested germ cell phenotype of ovoovo-GAL4/ovoΔBP ovaries. Therefore, in order to determine genes that are transcriptionally responsive to OVO, we compared the gene expression profiles in sets of ovaries that had the ovo hypomorphic phenotype with a negative control rescue construct (ovoovo-GAL4/ovoΔBP; UASp-GFP)(Figure 4A) versus those that drive expression of the rescue construct expressing OVO-B (ovoovo-GAL4/ovoΔBP; UASp-3xFHAOVO-B)(Figure 4B).”

Lines 427-432

“The adult female ovary contains somatic cells, germline stem cells, and germline derived nurse cells that would be profiled in a bulk ovary tissue RNA-seq experiment. Although OVO is only required and expressed in germline derived cell types, we chose to dissect one day old post-eclosion ovoovoGAL4/ovoΔBP; UASp-3xFHA-OVO-B female ovaries to enrich for early stages of oogenesis and collected only ovarioles containing the germarium through previtellogenic egg chambers.”

On lines 526-532, it is unclear why the genes fs(1)N, fs(1)M3, and closca are particularly sensitive to the ovoD3 allele. What is this allele trans heterozygous with in the assay that allows development through egg laying? Why might these genes be unique in their sensitivity?

These genes are not particularly sensitive, the transheterozygous hypomorphic ovo ovaries are weak enough to reveal the role of OVO for these genes. We rewrote this paragraph to try and provide more clarity to the relationship between OVO+ binding at these vitelline membrane genes and the phenotype of OVOD3 expressing females.

Lines 562-577

“We also found that the genes fs(1)N, fs(1)M3, and closca, were all bound by OVO and responded transcriptionally to the presence of ectopic rescue OVO. These genes are significant because they constitute a set of genes that are expressed in the germline and the encoded proteins are eventually incorporated into the vitelline membrane providing the structural integrity and impermeability of the egg (Mineo, Furriols, and Casanova 2017; Ventura et al. 2010). Loss-of-function of these three genes results in flaccid eggs that are permeable to dye and fail to develop. The loss-of-function phenotype of fs(1)N, fs(1)M3, and closca closely resembles the dominant antimorph ovoD3 phenotype. The ovoD3 allele is the weakest of the original dominant-negative ovo alleles and produces defective eggs allowing us to explore the role of OVO in late stages (Busson et al. 1983; Komitopoulou et al. 1983). ovoD3/ovo+ transheterozygous females express a repressive form of OVO that results in dominant sterility, and importantly, these females lay flaccid eggs with compromised vitelline membranes that are permeable to the dye neutral red (Oliver, Pauli, and Mahowald 1990). Since OVO+ is bound at the TSS of fs(1)N, fs(1)M3, and closca, and these three genes respond transcriptionally to OVO+, then it is plausible that the repressive OVOD3 is negatively regulating these three genes that are required for vitelline membrane formation. This is evidence that OVO is not only involved in regulating the expression of numerous essential maternal pathways for embryonic development, but it is also essential for regulating genes that are required for egg integrity and maturation.”

The Discussion of OVO as a pioneer factor is highly speculative and based only on correlative data. In fact, the expression data in the embryonic germline is not included in this manuscript, but rather in a separate bioRxiv preprint. This makes it challenging to understand, why this is extensively discussed here. However, there are experiments that could begin to test this proposal. OVO could be expressed in an exogenous tissue and test whether it promotes accessibility. Also, mutations could be made (using gene editing) to identify previously known OVO binding sites in the otu and/or other promoters and these could be assayed for accessibility. By selecting promoters of genes that are not essential for germline development, the authors could directly test the role of OVO in promoting chromatin accessibility. Alternatively, are there reasons that the system used for RNA-seq couldn't be similarly used for ATACseq? It is imperfect but could provide insights into chromatin accessibility in the absence of OVO.

We have largely removed the speculation on pioneering activity, reference to embryonic germline OVO dynamics included in the previous work, and Figure 7. These are excellent suggestions for experiments and ones we are currently pursuing. Below is the modified discussion. 

Lines 645-663

“The requirement for OVO at the TSS of target genes has been well characterized at its own locus as well as its downstream target otu. Our OVO ChIP and expression data confirm findings from previous work that OVO is binding to these target promoters, and in the case of otu, strongly responds transcriptionally to the presence of OVO. Although we did not test the requirement for OVO DNA binding motifs at other OVO bound genes in this work, this has been extensively explored before, showing that removal of OVO

DNA binding sites overlapping the TSS results in a strong decrease in reporter expression (Lü et al. 1998; Bielinska et al. 2005; Lü and Oliver 2001). Removal of more distal upstream OVO DNA binding sites also reduces reporter expression to a lesser degree. However, for most cases tested, removal of OVO DNA binding sites while leaving the rest of the enhancer regions intact, never totally abolished reporter expression. These dynamics are highly similar to work that has been completed on the pioneer factor zelda (zld). Adding zld DNA binding motifs to a stochastically expressed transcriptional reporter increases the activity and response of the reporter (Dufourt et al. 2018). Distally located zld DNA binding motifs influenced reporter expression to a lesser degree than proximal sites. A single zld DNA binding site adjacent to the TSS produced the strongest reporter activity. Importantly, just like the activity of OVO transgenic reporters, there is not an absolute requirement for zld DNA binding to activate reporter expression, however, the addition of TSS adjacent zld DNA binding motifs does strongly influence reporter response. We know that zld achieves this reporter response through its pioneering activity (Xu et al. 2014; Harrison et al. 2011), whether OVO achieves this similar effect on gene expression through a shared mechanism, or in cooperation with other transcription factors needs to be further explored.”

The authors suggest that OVO binding is essential for transcriptional activation, but that this may be indirect and that expression of other transcription factors might be necessary for activating gene expression. Did the motif analysis of the OVO-bound regions suggest additional transcription factors that might provide this function?

We did find other motifs significantly enriched in OVO ChIP peaks. We performed XSTREME analysis on the same set of OVO ChIP peaks which allowed us to determine if any of these motifs were significant matches to DNA binding motifs of known transcription factors. Notably, the DNA binding motifs of GAF and CLAMP were enriched in OVO ChIP peaks. GAF is required in germline clones and the potentially for co-regulation of genes is possible. Other enriched motifs did not match any known binding motifs of other transcription factors but we reported some of the most significantly enriched motifs that were alongside of OVO in Figure S1C-F. The below text outlines changes made to the text incorporating these findings.

Lines 170-182

“Along with the OVO DNA binding motif, other motifs were also significantly enriched in OVO ChIP peaks. The motif 5’-GWGMGAGMGAGABRG-3’ (Figure S1C) was found in 18% of OVO ChIP peaks and is a significant match to the DNA binding motifs of the transcription factors GAF (Trl) (Omelina et al. 2011) and CLAMP (Soruco et al. 2013). Trl germline clones are not viable, indicating that GAF activity is required in the germline during oogenesis (Chen et al. 2009). The possibility that OVO binds with and regulates genes alongside of GAF given the enrichment of both transcription factors DNA binding motifs is intriguing. Other significantly enriched motifs 5’-ACACACACACACACA-3’ (29% of peaks, Figure S1D), 5’RCAACAACAACAACA-3’ (26% of peaks, Figure S1E), and 5’-GAAGAAGAAGAAGAR-3’ (17% of peaks,

Figure S1F) were present in OVO ChIP peaks, however, these motifs did not significantly match known

DNA binding motifs of other transcription factors. Determining the factors that bind to these sequences

will certainly help elucidate our understanding of transcriptional control with relationship to OVO in the female germline.”

The figures would benefit from a bit more detail in the legends (see comments below).

Minor comments:

In multiple places throughout the document, the citations are inadvertently italicized (see lines 57-59, 91, and 327 as examples.)

We have changed this in these locations and other instances in the text.

On line 76, when discussing OVO as a transcription factor this is referencing the protein and not the gene. Thus, should be written OVO and not ovo.

We have made the correction ovo to OVO.

On line 349, "core" promoters is likely what is meant rather than "care" promoters.

We have corrected ‘care’ to ‘core’ in the text.

On line 404, the authors state that they wanted to use a "less conservative log2 fold change" but it is not clear what they are comparing to. This is important to understand the motivation.

We are talking about the gene expression comparison between the ectopic ovo rescue and ovo hypomorphic ovaries. “less conservative” was an unfortunate phrasing. We have rewritten the text to state this directly to the reader.

Lines 435-444

“We then performed RNA-seq in quadruplicate and measured the changes in gene expression between ectopic rescue OVO and hypomorphic OVO ovaries. We used a significance level of p-adj < 0.05 and a log2 fold change cutoff of >|0.5| to call differential expression between these two sets of ovaries. We utilized these log2 fold change cutoffs for two reasons. Our control ovary genotype (ovoovo-GAL4/ovoΔBP; UASp-GFP) has hypomorphic OVO activity, hence germ cells can survive but are arrested. With the addition of ectopic rescue OVO in ovoovo-GAL4/ovoΔBP; UASp-3xFHA-OVO-B ovaries, we predicted that genes that were directly regulated by OVO would transcriptionally respond, however, we were unsure as to what degree the response would be in comparison to hypomorphic OVO. We reasoned that if the changes were not significant between genotypes, then minor changes in gene expression would not matter.”

On line 615, it is unclear what is meant by "showing expression with only 10s of bp of sequence in reporters."

This is in reference to some of the previously studied ovo reporter deletion lines, however, we have decided to remove the below text in the revised discussion.

“, despite being remarkably compact. The OVO-dependent ovo core promoter is very compact; showing expression with only 10s of bp of sequence in reporters.” 

It would be useful to cite and discuss Dufourt et al. Nature Communications 2018 (PMID30518940) regarding the role of Zelda in potentiating transcriptional activation when mentioned on line 624.

We have added this and the relationship to previous similar work on OVO in the discussion.

Lines 645-663

“The requirement for OVO at the TSS of target genes has been well characterized at its own locus as well as its downstream target otu. Our OVO ChIP and expression data confirm findings from previous work that OVO is binding to these target promoters, and in the case of otu, strongly responds transcriptionally to the presence of OVO. Although we did not test the requirement for OVO DNA binding motifs at other OVO bound genes in this work, this has been extensively explored before, showing that removal of OVO

DNA binding sites overlapping the TSS results in a strong decrease in reporter expression (Lü et al. 1998; Bielinska et al. 2005; Lü and Oliver 2001). Removal of more distal upstream OVO DNA binding sites also reduces reporter expression to a lesser degree. However, for most cases tested, removal of OVO DNA binding sites while leaving the rest of the enhancer regions intact, never totally abolished reporter expression. These dynamics are highly similar to work that has been completed on the pioneer factor zelda (zld). Adding zld DNA binding motifs to a stochastically expressed transcriptional reporter increases the activity and response of the reporter (Dufourt et al. 2018). Distally located zld DNA binding motifs influenced reporter expression to a lesser degree than proximal sites. A single zld DNA binding site adjacent to the TSS produced the strongest reporter activity. Importantly, just like the activity of OVO transgenic reporters, there is not an absolute requirement for zld DNA binding to activate reporter expression, however, the addition of TSS adjacent zld DNA binding motifs does strongly influence reporter response. We know that zld achieves this reporter response through its pioneering activity (Xu et al. 2014; Harrison et al. 2011), whether OVO achieves this similar effect on gene expression through a shared mechanism, or in cooperation with other transcription factors needs to be further explored.”

On line 1006 (Figure 1 legend), it is unclear what is meant by "The percentage of OVO ChIP peaks each motif was found". Is a word missing?

This was unclear, we have revised the sentence below.

Lines 1035-1036

“The percentage of OVO ChIP peaks containing each motif and their corresponding p-value are indicated to the right.”

In the Figure 1 legend, please include citations for the Garfinkel motif and Oliver motif.

Included, as below.

Lines 1036-1039

“H) OVO ChIP minus input control ChIP-seq read coverage density centered on the location of the four de novo OVO DNA binding motifs and previously defined in vitro OVO DNA binding motifs (Lü et al. 1998, Bielinska et al. 2005, Lee and Garfinkel 2000).”

In Figure 2 legend, it is unclear if B is all instances of a given motif or the DNA motifs that are bound by ChIP. Please clarify.

We meant only the OVO DNA binding motifs that were within significant OVO ChIP peaks. We have revised the legend below.

Lines 1049-1052

“A, B) OVO ChIP minus input control, GSC and 32c ATAC-seq, GSC H3K27ac, H3K4me3, H3K27me3, H3K9me3, 8c NC H3K9me3, 32c NC H3K27ac, and H3K27me3 ChIP-seq read coverage density centered on each OVO peak maximum or OVO DNA binding motif located within a significant OVO ChIP peak.”

The Figure legend for 2D could use more explanation. What do the lines and circles indicate?

These lines and circles indicate the amount of overlapping peaks measured between the two datasets with solid circles. We have included a better description of what these indicate in the figure legend.

Lines 1054-1058

“D) Total number of significant peaks (left) and the total number of overlapping peaks (top) between OVO

ChIP and GSC and 32c ATAC-seq, GSC H3K27ac, H3K4me3, H3K27me3, H3K9me3, 8c NC H3K9me3, 32c NC H3K27ac, and H3K27me3 ChIP-seq. Lines connecting solid dots indicates the amount of overlapping peaks between those two corresponding datasets.”

In Figure 4C, bring the 564 blue dots forward so they are not masked by the yellow dots.

We have brought the colored dots forward in both figure 4C and 4D.

In Figure 4E, what is the order of the heatmaps?

The order is genes with the highest to lowest OVO read density enrichment. We have included this in the figure 4 legend.

Lines 1086-1087

“The order of the heatmap is genes with the highest to lowest amount of OVO ChIP read density.”

In Figure 5, the order of the tracks is not immediately obvious. It appears to be those chromatin features most associated with OVO ChIP and those less correlated. Additional clarity could be provided by showing these tracks (and in Supplemental Figure S2) in different colors with a reference to the figure legend about what the colors might indicate.

We have changed the colors and order of the tracks to be more similar and consistent in both figures.

Lines 1090-1093

“ovo gene level read coverage tracks for OVO ChIP minus input (black), GSC and 32c ATAC-seq (light blue), GSC and 32C H3K27ac (green), H3K4me3 (dark blue), GSC and 32c H3K27me3 (orange), and GSC and 8c H3K9me3 (pink) ChIP-seq, and ovoΔBP/ovoovo-GAL4; UASp-3xFHA-OVO-B minus ovoΔBP/ovoovo-GAL4; UASp-GFP RNA-seq (red).”

In Figure S1 legend, what is the reference to the da-GAL4 X UAS transgene in the title?

This was an error on our part and we have removed it.

Reviewer #2 (Recommendations For The Authors):

Overall, the manuscript would benefit from revisions of the writing style. At times it is difficult to distinguish between hypothesis and results. The use of colloquial phrases/prose was distracting while reading, which the authors may consider revising. Some sentences were confusing or extraneous, and the authors may consider revising those. Occasionally sentences within the results sections seem more appropriate for the materials and methods.

(1) The manuscript is generally clear; however, it is at times difficult to distinguish between hypothesis and results. The use of colloquial phrases/prose was distracting while reading, which the authors may consider revising. Examples include:

a)  Lines 48-49 "While thematic elements of this complex orchestration have been well studied, coordinate regulation of the symphony has not."

We have edited this sentence below.

Lines 48-50

“While the complex interactions between maternally supplied mRNAs and proteins have been well studied, transcriptional regulation driving the expression of these pathways are less well understood.“

b)  Lines 232-233 "In other words, where exactly does transcription start at these genes."

We have removed this sentence.

c)  Line 385, the word "sham" could be changed to "negative control" or "GFP control"

We have rewritten this sentence below.

Lines 419-423

“Therefore, in order to determine genes that are transcriptionally responsive to OVO, we compared the gene expression profiles in sets of ovaries that had the ovo hypomorphic phenotype with a negative control rescue construct (ovoovo-GAL4/ovoΔBP; UASp-GFP)(Figure 4A) versus those that drive expression of the rescue construct expressing OVO-B (ovoovo-GAL4/ovoΔBP; UASp-3xFHA-OVO-B)(Figure 4B)”

d)  Line 490 "For the big picture"

We have removed this and revised with the below sentence.

Lines 530-531

“To do this, we performed Gene Ontology enrichment analysis with gProfiler software (Raudvere et al. 2019).”

(2) Some sentences were confusing or extraneous, and the authors may consider revising them. Examples include:

a)  Lines 195-196 "Therefore, we plotted the significant ChIP (minus input) read density peaks centered on the location of the motif itself."

We have removed the word ‘peaks’ and ‘itself’, as below.

Lines 200-201

“Therefore, we plotted the significant ChIP (minus input) read density centered on the location of the motif.”

b)  Lines 201-203 "... over the location of the motifs, strongly reinforces the idea that our dataset contains regions centered on sequence-specifically bound OVO transcription factor in the ovary."

We have edited this sentence to clarify below.

Lines 204-208

“While it is possible that OVO comes into contact with regions of DNA in three-dimensional nuclear space non-specifically, the presence of OVO motifs within a large percentage of significant ChIP peaks in vivo and enrichment of OVO ChIP read density at the location of the motifs, strongly reinforces the idea that our OVO ChIP dataset contains regions centered on sequences specifically bound by OVO in the ovary.”

c)  Lines 326-328 "The combinations of these elements...tens of millions of years of evolution."

We have revised this sentence below.

Lines 354-357

“The combinations of these DNA motifs is not random in mammals and Drosophila (FitzGerald et al. 2006), and distinct combinations of different motifs at the TSS of genes expressed in Drosophila are conserved over tens of millions of years of evolution (Chen et al. 2014).”

d)  Lines 444-446 "To address this directly, we tested the idea that genes with... and thus downstream of OVO."

We have removed this sentence in its entirety.

e)  Line 579-580 "Where OVO binding in close proximity, in any ...activates transcription"

We have removed this sentence in its entirety.

(3)    Occasionally sentences within the results sections seem more appropriate for the materials and methods. For example, lines 213-218.

(4)    At the end of line 375, do the authors mean "only" instead of "also"?

We have modified this sentence below.

Lines 411-414

“ovoovo-GAL4 is a CRISPR/Cas9 derived T2A-GAL4-3xSTOP insertion upstream of the splice junction of exon 3 in the ovo-RA transcript (Figure S1A). Importantly, this insertion in the extended exon 3 would disrupt roughly 90% of the ovo-B transcripts. However, since about 10% of ovo-B transcripts utilize an upstream splice junction in exon 3, these transcripts would not be disrupted with the T2A-GAL4-3xSTOP insertion and thus allow for enough OVO activity for germ cell survival (Benner et al. 2023).”

(5)    In line 392 the authors say that they dissected ovaries "one day post-eclosion" but the methods section says that ovaries were 3-5 days old. Please clarify.

We meant one day old for the RNAseq experiments. We have changed this in the text.

Lines 679-681

“Twenty, one day old post-eclosion ovoΔBP/ovoovo-GAL4; UASp-GFP and ovoΔBP/ovoovo-GAL4; UASp-3xFHAOVO-B ovaries were dissected and germariums through previtellogenic egg chambers were removed with microdissection scissors and placed in ice cold PBS making up one biological replicate.”

(6)    In line 668 the authors mention CRISPR/Cas9 in the methods, but no such experiment was described.

We have removed this from the Methods header.

Author response:

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

Reviewer #1:

We thank the reviewer for their careful reading of our manuscript and have taken all of their grammatical corrections into account.

Reviewer #2 (Public Review):

Weaknesses: 

The paper contains multiple instances of non-scientific language, as indicated below. It would also benefit from additional details on the cryo-EM structure determination in the Methods and inclusion of commonly accepted requirements for cryo-EM structures, like examples of 2D class averages, raw micrographs, and FSC curves (between half-maps as well as between rigid-body fitted (or refined) atomic models of the different polymorphs and their corresponding maps). In addition, cryo-EM maps for the control experiments F1 and F2 should be presented in Figure 9.

We tried to correct the non-scientific language and have included the suggested data on the Cryo-EM analyses including new Figures 11-17.  We did not collect data on the sample used for the seeds in the cross seeding experiments because we had already confirmed in multiple datasets that the conditions in F1 and F2 reproducibly produce fibrils of Type 1 and Type 3, respectively. We have now analyzed cryo-EM data for 6 more samples at pH 7.0 and found that several kinds of polymorphs (Types 1A, 1M, 2A, 2B and 5) are accessible at this pH, however the Type 3 polymorphs are not formed at pH 7.0 under the conditions that we used for aggregation.

Reviewer #2 (Recommendations For The Authors):

Remove unscientific language: "it seems that there are about as many unique atomicresolution structures of these aggregates as there are publications describing them"   

We have rephrased this sentence.

For same reason, remove "Obviously, " 

Done

What does this mean? “polymorph-unspecific” 

Rephrased as non-polymorph-specific

What does this mean? "shallow amyloid energy hypersurface"  

By “shallow hypersurface” we mean that the minimum of the multi-dimensional function that describes the energy of the amyloid is not so deep that subtle changes to the environment will not favor another fold/energy minimum. We have left the sentence because while it may not be perfect, it is concise and seems to get the point across.

"The results also confirm the possibility of producing disease-relevant structure in vitro." -> This is incorrect as no disease-relevant structure was replicated in this work. Use another word like “suggest”.

We have changed to “suggest” as suggested.

Remove "historically" 

Done

Rephrase “It has long been understood that all amyloids contain a common structural scaffold” 

Changed to “It has long been established that all amyloids contain a common structural scaffold..” 

"Amyloid polymorphs whose differences lie in both their tertiary structure (the arrangement of the beta-strands) and the quaternary structure (protofilamentprotofilament assembly) have been found to display distinct biological activities [8]" -> I don't think this is true, different biological activities of amyloids have never been linked to their distinct structures.  

We have added 5 new references (8-12) to support this sentence.

Reference 10 is a comment on reference 9; it should be removed. Instead, as for alphasynuclein, all papers describing the tau structures should be included.  

We have removed the reference, but feel that the addition of all Tau structure references is not merited in this manuscript since we are not comparing them.

Rephrase: "is not always 100% faithful"

Removed “100%”

What is pseudo-C2 symmetry? Do the authors mean pseudo 2_1 symmetry (ie a 2-start helical symmetry)?

Thank for pointing this out.  We did indeed mean pseudo 21 helical symmetry.  

Re-phrase: "alpha-Syn's chameleon-like behavior" 

We have removed this phrase.

"In the case of alpha-Syn, the secondary nucleation mechanism is based on the interaction of the positively charged N-terminal region of monomeric alpha-Syn and the disordered, negatively charged C-terminal region of the alpha-Syn amyloid fibrils [54]" -> I would say the mechanisms of secondary nucleation are not that well understood yet, so one may want to tune this down a bit. 

We have changed this to “mechanism has been proposed to be”

The paragraphs describing experiments by others are better suited for a Discussion rather than a Results section. Perhaps re-organize this part? 

We have left the text intact as we are using a Results and Discussion format.

A lot of information about Image processing seems to be missing: what steps were performed after initial model generation? 

We have added more details in the methods section on the EM data processing and model analysis.

Figure 1: Where is Type 4 on the pH scale?

We have adjusted the Fig 1 legend to clarify that pH scale is only applicable to the structures presented in this manuscript. 

Figure 2: This might be better incorporated as a subpanel of Figure 1.

We agree that this figure is somewhat of a loner on its own and we only added it in order to avoid confusion with the somewhat inconsistent naming scheme used for the Type 1B structure. However, we prefer to leave it as a separate figure so that it does not get dilute the impact of figure 1.

Figure 3: What is the extra density at the bottom of Type 3B from pH 5.8 samples 1 and 2. pH 5.8 + 50mM NaCl (but not pH 5.8 + 100 mM NaCl)? Could this be an indication of a local minimum and the pH 5.8 + 100 mM NaCl structure is correct? Or is this a real difference between 0/50mM NaCl and 100 mM NaCl? 

We did not see the extra density to which the reviewer is referring, however the images used in this panel are the based on the output of 3D-classification which is more likely to produce more artifacts than a 3D refinement. With this in mind, we did not see any significant differences in the refined structures and therefore only deposited the better quality map and model for each of the polymorph types.

Figure 3: To what extent is Type 3B of pH 6.5 still a mixture of different types? The density looks poor. In general, in the absence of more details about the cryo-EM maps, it is hard to assess the quality of the structures presented.

In order to improve the quality of the images in this panel, a more complete separation of the particles from each polymorph was achieved via the filament subset selection tool in RELION 5. In each case, an unbiased could be created from the 2D classes via the relion_helix_inimodel2D program, further supporting the coexistence of 4 polymorphs in the pH 6.5 sample. The particles were individually refined to produce the respective maps that are now used in this figure.

Many references are incorrect, containing "Preprint at (20xx)" statements.  

This has been corrected.

Reviewer #3 (Public Review):

Weaknesses: 

(1) The authors reveal that both Type 1 monofilament fibril polymorph (reminiscent of JOSlike polymorph) and Type 5 polymorph (akin to tissue-amplified-like polymorph) can both form under the same condition. Additionally, this condition also fosters the formation of flat ribbon-like fibril across different batches. Notably, at pH 5.8, variations in experimental groups yield disparate abundance ratios between polymorph 3B and 3C, indicating a degree of instability in fibrillar formation. The variability would potentially pose challenges for replicability in subsequent research. In light of these situations, I propose the following recommendations: 

(a) An explicit elucidation of the factors contributing to these divergent outcomes under similar experimental conditions is warranted. This should include an exploration of whether variations in purified protein batches are contributing factors to the observed heterogeneity.

We are in complete agreement that understanding the factors that lead to polymorph variability is of utmost importance (and was the impetus for the manuscript itself). However the number of variables to explore is overwhelming and we will continue to investigate this in our future research. Regarding the variability between batches of purified protein, we also think that this could be a factor in the polymorph variability observed for otherwise “identical” aggregation conditions, particularly at pH 7 where the largest variety of polymorphs have been observed. However, even variation between identical replicates (samples created from the same protein solution and simply aggregated simultaneously in separate tubes) can lead to different outcomes (see datasets 15 and 16 in the revised Table 1) suggesting that there are stochastic processes that can determine the outcome of an individual aggregation experiment. While our data still indicates that Type 1,2 and 3 polymorphs are strongly selected by pH, the selection between interface variants 3B vs. 3C and 2A vs. 2B might also be affected by protein purity. Our standard purification protocol produces a single band by coomassie-stained SDS-PAGE however minor truncations and other impurities below a few percent would go undetected and, given the proposed roles of the N and C-termini in secondary nucleation, could have a large effect on polymorph selection and seeding. In line with the reviewer’s comments we now include a batch number for each EM dataset. While no new conclusions can be drawn from the inclusion of this additional data, we feel that it is important to acknowledge the possible role of batch to batch variability. 

(b) To enhance the robustness of the conclusions, additional replicates of the experiments under the same condition should be conducted, ideally a minimum of three times.  

The pH 5.8 conditions that yield Type 3 fibrils has already been repeated several times in the original manuscript. Since the pH 7.4 conditions produce the most common a-Syn polymorph (Type 1A) and were produced twice in this manuscript (once as an unseeded and once as a cross-seeded fibrilization) we decided to focus on the intermediate condition where the most variability had been seen (pH 7.0). The revised table 1 now has 6 new datasets (11-16) representing 6 independent aggregations at pH 7.0 starting from two different protein purification batches. The results is that we now produce the type 2A/B polymorphs in three samples and in two of these samples we once again observed the type 1M polymorph.  The other samples produced Type 1A or non-twisted fibrils.

(c) Further investigation into whether different polymorphs formed under the same buffer condition could lead to distinct toxicological and pathology effects would be a valuable addition to the study.  

The correlation of toxicity with structure would in principle be interesting. However the Type 1 and Type 3 polymorphs formed at pH 5.8 and 7.4 are not likely to be biologically relevant. The pH 7 polymorphs (Type 5 and 1M) would be more interesting because they form under the same conditions and might be related to some disease relevant structures. Still, it is rare that a single polymorph appears at 7.0 (the Type 5 represented only 10-20% of the fibrils in the sample and the Type 1M also had unidentified double-filament fibrils in the sample). We plan to pursue this line of research and hope to include it in a future publication.

(2) The cross-seeding study presented in the manuscript demonstrates the pivotal role of pH conditions in dictating conformation. However, an intriguing aspect that emerges is the potential role of seed concentration in determining the resultant product structure. This raises a critical question: at what specific seed concentration does the determining factor for polymorph selection shift from pH condition to seed concentration? A methodological robust approach to address this should be conducted through a series of experiments across a range of seed concentrations. Such an approach could delineate a clear boundary at which seed concentration begins to predominantly dictate the conformation, as opposed to pH conditions. Incorporating this aspect into the study would not only clarify the interplay between seed concentration and pH conditions, but also add a fascinating dimension to the understanding of polymorph selection mechanisms.

A more complete analysis of the mechanisms of aggregation, including the effect of seed concentration and the resulting polymorph specificity of the process, are all very important for our understanding of the aggregation pathways of alphasynuclein and are currently the topic of ongoing investigations in our lab.

Furthermore, the study prompts additional queries regarding the behavior of cross-seeding production under the same pH conditions when employing seeds of distinct conformation. Evidence from various studies, such as those involving E46K and G51D cross-seeding, suggests that seed structure plays a crucial role in dictating polymorph selection. A key question is whether these products consistently mirror the structure of their respective seeds. 

We thank the reviewer for reminding us to cite these studies as a clear example of polymorph selection by cross-seeding. Unfortunately, it is not 100% clear from the G51D cross seeding manuscript (https://doi.org/10.1038/s41467-021-26433-2) what conditions were used in the cross-seeding since different conditions were used for the seedless wild-type and mutant aggregations… however it appears that the wildtype without seeds was Tris pH 7.5 (although at 37C the pH could have dropped to 7ish) and the cross-seeded wild-type was in Phosphate buffer at pH 7.0. In the E46K cross-seeding manuscript, it appears that pH 7.5 Tris was used for all fibrilizations (https://doi.org/10.1073/pnas.2012435118).  In any event, both results point to the fact that at pH 7.0-7.5 under low-seed conditions (0.5%) the Type 4 polymorph can propagate in a seed specific manner.

(3) In the Results section of "The buffer environment can dictate polymorph during seeded nucleation", the authors reference previous cell biological and biochemical assays to support the polymorph-specific seeding of MSA and PD patients under the same buffer conditions. This discussion is juxtaposed with recent research that compares the in vivo biological activities of hPFF, ampLB as well as LB, particularly in terms of seeding activity and pathology. Notably, this research suggests that ampLB, rather than hPFF, can accurately model the key aspects of Lewy Body Diseases (LBD) (refer to: https://doi.org/10.1038/s41467-023-42705-5). The critical issue here is the need to reconcile the phenomena observed in vitro with those in in-vivo or in-cell models. Given the low seed concentration reported in these studies, it is imperative for the authors to provide a more detailed explanation as to why the possible similar conformation could lead to divergent pathologies, including differences in cell-type preference and seeding capability.  

We thank the reviewer for bring this recent report to our attention. The findings that ampLB and hPFF have different PK digestion patterns and that only the former is able to model key aspects of Lewy Body disease are in support of the seed-specific nature of some types of alpha-synuclein aggregation.  We have added this to the discussion regarding the significant role that seed type and seed conditions likely play in polymorph selection.

(4) In the Method section of "Image processing", the authors describe the helical reconstruction procedure, without mentioning much detail about the 3D reconstruction and refinement process. For the benefit of reproducibility and to facilitate a deeper understanding among readers, the authors should enrich this part to include more comprehensive information, akin to the level of detail found in similar studies (refer to:

https://doi.org/10.1038/nature23002).

As also suggested by reviewer #2, we have now added more comprehensive information on the 3D reconstruction and refinement process.

(5) The abbreviation of amino acids should be unified. In the Results section "On the structural heterogeneity of Type 1 polymorphs", the amino acids are denoted using three-letter abbreviation. Conversely, in the same section under "On the structural heterogeneity of Type 2 and 3 structures", amino acids are abbreviated using the one-letter format. For clarity and consistency, it is essential that a standardized format for amino acid abbreviations be adopted throughout the manuscript.

That makes perfect sense and had been corrected.

Reviewing Editor:

After discussion among the reviewers, it was decided that point 2 in Reviewer #3's Public Review (about the experiments with different concentrations of seeds) would probably lie outside the scope of a reasonable revision for this work. 

We agree as stated above and will continue to work on this important point.

Author response:

Reviewer #1 (Public Review):

Summary and Strengths:

The ability of Wolbachia to be transmitted horizontally during parasitoid wasp infections is supported by phylogenetic data here and elsewhere. Experimental analyses have shown evidence of wasp-to-wasp transmission during coinfection (eg Huigins et al), host to wasp transmission (eg Heath et al), and mechanical ('dirty needle') transmission from host to host (Ahmed et al). To my knowledge this manuscript provides the first experimental evidence of wasp to host transmission. Given the strong phylogenetic pattern of host-parasitoid Wolbachia sharing, this may be of general importance in explaining the distribution of Wolbachia across arthropods. This is of interest as Wolbachia is extremely common in the natural world and influences many aspects of host biology.

Weaknesses:

The first observation of the manuscript is that the Wolbachia strains in hosts are more closely related to those in their parasitoids. This has been reported on multiple occasions before, dating back to the late 1990s. The introduction cites five such papers (the observation is made in other studies too that could be cited) but then dismisses them by stating "However, without quantitative tests, this observation could simply reflect a bias in research focus." As these studies include carefully collected datasets that were analysed appropriately, I felt this claim of novelty was rather strong. It is unclear why downloading every sequence in GenBank avoids any perceived biases, when presumably the authors are reanalysing the data in these papers.

Thank you for bringing this to our attention, and we will make the necessary amendments in our revised manuscript.

I do not doubt the observation that host-parasitoid pairs tend to share related Wolbachia, as it is corroborated by other studies, the effect size is large, and the case study of whitefly is clearcut. It is also novel to do this analysis on such a large dataset. However, the statistical analysis used is incorrect as the observations are pseudo-replicated due to phylogenetic non-independence. When analysing comparative data like this it is essential to correct for the confounding effects of related species tending to be similar due to common ancestry. In this case, it is well-known that this is an issue as it is a repeated observation that related hosts are infected by related Wolbachia. However, the authors treat every pairwise combination of species (nearly a million pairs) as an independent observation. Addressing this issue is made more complex because there are both the host and symbiont trees to consider. The additional analysis in lines 123-124 (including shuffling species pairs) does not explicitly address this issue.

We concur with your observation regarding the non-independence of the data due to phylogenetic relationships. While common phylogenetic correction methods are indeed not directly applicable to wsp distances between species pairs, we are investigating the potential of phylogenetic mixed models to address this issue. We hope to include a revised analysis using this approach in our revised manuscript.

The sharing of Wolbachia between whitefly and their parasitoids is very striking, although this has been reported before (eg the authors recently published a paper entitled "Diversity and Phylogenetic Analyses Reveal Horizontal Transmission of Endosymbionts Between Whiteflies and Their Parasitoids"). In Lines 154-164 it is suggested that from the tree the direction of transfer between host and parasitoid can be inferred from the data. This is not obvious to me given the poor resolution of the tree due to low sequence divergence. There are established statistical approaches to test the direction of trait changes on a tree that could have been used (a common approach is to use the software BEAST).

Thank you for your insightful comments regarding the transfer direction of Wolbachia between whiteflies and their parasitoids. We acknowledge the concern about the resolution of the phylogenetic tree and the inference of the direction of Wolbachia transmission based on the available data. We considered the high infection frequency and obligate nature of Wolbachia in En. formosa, which exhibits a 100% infection rate, as a strong indicator that recent transmission of Wolbachia in this clade likely occurred from En. formosa to B. tabaci. We appreciate your recommendation and will ensure that our conclusions are supported by a more statistically sound approach. As you suggested, we will employ the software BEAST to rigorously test the direction of transmission, and we will revise our statements accordingly.

Reviewer #2 (Public Review):

The paper by Yan et al. aims to provide evidence for horizontal transmission of the intracellular bacterial symbiont Wolbachia from parasitoid wasps to their whitefly hosts. In my opinion, the paper in its current form consists of major flaws.

Weaknesses:

The dogma in the field is that although horizontal transmission events of Wolbachia occur, in most systems they are so rare that the chances of observing them in the lab are very slim.

For the idea of bacteria moving from a parasitoid to its host, the authors have rightfully cited the paper by Hughes, et al. (2001), which presents the main arguments against the possibility of documenting such transmissions. Thus, if the authors want to provide data that contradict the large volume of evidence showing the opposite, they should present a very strong case.

In my opinion, the paper fails to provide such concrete evidence. Moreover, it seems the work presented does not meet the basic scientific standards.

We are grateful for your critical perspective on our work. Nonetheless, we are confident in the credibility of our findings regarding the horizontal transmission of Wolbachia from En. formosa to B. tabaci. Our study has documented this phenomenon through phylogenetic tree analyses, and we have further substantiated our observations with rigorous experiments in both cages and petri dishes. The horizontal transfer of Wolbachia was confirmed via PCR, with the wsp sequences in B. tabaci showing complete concordance with those in En. formosa. Additionally, we utilized FISH, vertical transmission experiments, and phenotypic assays to demonstrate that the transferred Wolbachia could be vertically transmitted and induce significant fitness cost in B. tabaci. All experiments were conducted with strict negative controls and a sufficient number of replicates to ensure reliability, thereby meeting basic scientific standards. The collective evidence we present points to a definitive case of Wolbachia transmission from the parasitoid En. formosa to the whitefly B. tabaci.

My main reservations are:

• I think the distribution pattern of bacteria stained by the probes in the FISH pictures presented in Figure 4 looks very much like Portiera, the primary symbiont found in the bacterium of all whitefly species. In order to make a strong case, the authors need to include Portiera probes along with the Wolbachia ones.

We are very grateful for your critical evaluation regarding the specificity of FISH in our study. We assure the reliability of our FISH results based on several reasons.

1) We implemented rigorous negative controls which exhibited no detectable signal, thereby affirming the specificity of our hybridization. 2) The central region of the whitefly nymphs is a typical oviposition site for En. formosa. Post-parasitism, we observed FISH signals around the introduced parasitoid eggs, distinct from bacteriocyte cells which are rich in endosymbionts including Portiera (FIG 3e-f). This observation supports the high specificity of our FISH method. 3) In the G3 whiteflies, we detected the presence of Wolbachia in bacteriocytes in nymphs and at the posterior end of eggs in adult females (FIG 4). This distribution pattern aligns with previously reported localizations of Wolbachia in B. tabaci (Shi et al., 2016; Skaljac et al., 2013). Furthermore, the distribution of Wolbachia in the whiteflies does indeed exhibit some overlap with that of Portiera (Skaljac et al., 2013; Bing et al., 2014). 4) The primers used in our FISH assays have been widely cited (Heddi et al., 1999) and validated in studies on B. tabaci and other systems (Guo et al., 2018; Hegde et al., 2024; Krafsur et al., 2020; Rasgon et al., 2006; Uribe-Alvarez et al., 2019; Zhao et al., 2013). Taking all these points into consideration, we stand by the reliability of our FISH results.

References:

Bing XL, Xia WQ, Gui JD, Yan GH, Wang XW, Liu SS. 2014. Diversity and evolution of the Wolbachia endosymbionts of Bemisia (Hemiptera: Aleyrodidae) whiteflies. Ecol Evol, 4(13): 2714-37.

Guo, Y, Hoffmann, AA, Xu, XQ, Zhang X, Huang HJ, Ju JF, Gong JT, Hong XY. 2018. Wolbachia-induced apoptosis associated with increased fecundity in Laodelphax striatellus (Hemiptera: Delphacidae). Insect Mol Biol, 27: 796-807.

Heddi A, Grenier AM, Khatchadourian C, Charles H, Nardon P. 1999. Four intracellular genomes direct weevil biology: Nuclear, mitochondrial, principal endosymbiont, and Wolbachia. Proc Natl Acad Sci USA, 96: 6814-6819.

Hegde S, Marriott AE, Pionnier N, Steven A, Bulman C, Gunderson E, et al. 2024. Combinations of the azaquinazoline anti-Wolbachia agent, AWZ1066S, with benzimidazole anthelmintics synergise to mediate sub-seven-day sterilising and curative efficacies in experimental models of filariasis. Front Microbiol, 15: 1346068.

Krafsur AM, Ghosh A, Brelsfoard CL. 2020. Phenotypic response of Wolbachia pipientis in a cell-free medium. Microorganisms, 8: 1060.

Rasgon JL, Gamston, CE, Ren X. 2006. Survival of Wolbachia pipientis in cell-free medium. Appl Environ Microbiol, 72: 6934-6937.

Shi P, He Z, Li S, An X, Lv N, Ghanim M, Cuthbertson AGS, Ren SX, Qiu BL. 2016. Wolbachia has two different localization patterns in whitefly Bemisia tabaci AsiaII7 species. PLoS One, 11: e0162558.

Skaljac M, Zanić K, Hrnčić S, Radonjić S, Perović T, Ghanim M. 2013. Diversity and localization of bacterial symbionts in three whitefly species (Hemiptera: Aleyrodidae) from the east coast of the Adriatic Sea. Bull Entomol Res, 103(1): 48-59.

Uribe-Alvarez C, Chiquete-Félix N, Morales-García L, Bohórquez-Hernández A, Delgado-Buenrostro N L, Vaca L, et al. 2019. Wolbachia pipientis grows in Saccharomyces cerevisiae evoking early death of the host and deregulation of mitochondrial metabolism. MicrobiologyOpen, 8: e00675.

Zhao DX, Zhang XF, Chen DS, Zhang YK, Hong XY, 2013. Wolbachia-host interactions: Host mating patterns affect Wolbachia density dynamics. PLoS One, 8: e66373.

• If I understand the methods correctly, the phylogeny presented in Figure 2a is supposed to be based on a wide search for Wolbachia wsp gene done on the NCBI dataset (p. 348). However, when I checked the origin of some of the sequences used in the tree to show the similarity of Wolbachia between Bemisia tabaci and its parasitoids, I found that most of them were deposited by the authors themselves in the course of the current study (I could not find this mentioned in the text), or originated in a couple of papers that in my opinion should not have been published to begin with.

We appreciate your meticulous examination of the sources for our sequence data. All the sequences included in our phylogenetic analysis were indeed downloaded from the NCBI database as of July 2023. The sequences used to illustrate the similarity of Wolbachia between B. tabaci and its parasitoids include those from our previously published study (Qi et al., 2019), which were sequenced from field samples. Additionally, some sequences were also obtained from other laboratories (Ahmed et al., 2009; Baldo et al., 2006; Van Meer et al., 1999). We acknowledge that in our prior research (Qi et al., 2019), the sequences were directly submitted to NCBI and, regrettably, we did not update the corresponding publication information after the article were published. It is not uncommon for sequences on NCBI, with some never being followed by a published paper (e.g., FJ710487- FJ710511 and JF426137-JF426149), or not having their associated publication details updated post-publication (for instance, sequences MH918776-MH918794 from Qi et al., 2019, and KF017873-KF017878 from Fattah-Hosseini et al., 2018). We recognize that this practice can lead to confusion and apologize for the oversight in our work.

References:

Ahmed MZ, Shatters RG, Ren, SX, Jin GH, Mandour NS, Qiu BL. 2009. Genetic distinctions among the Mediterranean and Chinese populations of Bemisia tabaci Q biotype and their endosymbiont Wolbachia populations. J Appl Entomol, 133: 733-741.

Baldo L, Hotopp JCD, Jolley KA, Bordenstein SR, Biber SA, Choudhury RR, et al. 2006. Multilocus sequence typing system for the endosymbiont Wolbachia pipientis. Appl Environ Microbiol, 72: 7098-110.

Fattah-Hosseini S, Karimi J, Allahyari H. 2014. Molecular characterization of Iranian Encarsia formosa Gahan populations with natural incidence of Wolbachia infection. J Entomol Res Soc, 20: 85–100.

Qi LD, Sun JT, Hong XY, Li YX. 2019. Diversity and phylogenetic analyses reveal horizontal transmission of endosymbionts between whiteflies and their parasitoids. J Econ Entomol, 112(2): 894-905.

Van Meer MM, Witteveldt J, Stouthamer R. 1999. Phylogeny of the arthropod endosymbiont Wolbachia based on the wsp gene. Insect Mol Biol, 8: 399-408.

• The authors fail to discuss or even acknowledge a number of published studies that specifically show no horizontal transmission, such as the one claimed to be detected in the study presented.

Thank you for bringing this to our attention. We will address and discuss the published studies that report no evidence of horizontal transmission, as you've highlighted, in the revised version of our manuscript.

Reviewer #3 (Public Review):

This is a very ordinary research paper. The horizontal of endosymbionts, including Wolbachia, Rickettsia etc. has been reported in detail in the last 10 years, and parasitoid vectored as well as plant vectored horizontal transmission is the mainstream of research. For example, Ahmed et al. 2013 PLoS One, 2015 PLoS Pathogens, Chiel et al. 2014 Enviromental Entomology, Ahmed et al. 2016 BMC Evolution Biology, Qi et al. 2019 JEE, Liu et al. 2023 Frontiers in Cellular and Infection Microbiology, all of these reported the parasitoid vectored horizontal transmission of endosymbiont. While Caspi-Fluger et al. 2012 Proc Roy Soc B, Chrostek et al. 2017 Frontiers in Microbiology, Li et al. 2017 ISME Journal, Li et al. 2017 FEMS, Shi et al. 2024 mBio, all of these reported the plant vectored horizontal transmission of endosymbiont. For the effects of endosymbiont on the biology of the host, Ahmed et al. 2015 PLoS Pathogens explained the effects in detail.

Thank you very much for your insightful comments and for highlighting the relevant literature in the field of horizontal transmission of endosymbionts, including Wolbachia and Rickettsia. After careful consideration of the studies you have mentioned, we believe that our work presents significant novel contributions to the field. 1) Regarding the parasitoid-mediated horizontal transmission of Wolbachia, most of the cited articles, such as Ahmed et al. 2013 in PLoS One and Ahmed et al. 2016 in BMC Evolutionary Biology, propose hypotheses but do not provide definitive evidence. The transmission of Wolbachia within the whitefly cryptic species complex (Ahmed et al. 2013) or between moths and butterflies (Ahmed et al. 2016) could be mediated by parasitoids, plants, or other unknown pathways. 2) Chiel et al. (2014 in Environmental Entomology reported “no evidence for horizontal transmission of Wolbachia between and within trophic levels” in their study system. 3) The literature you mentioned about Rickettsia, rather than Wolbachia, indirectly reflects the relative scarcity of evidence for Wolbachia horizontal transmission. For example, the evidence for plant-mediated transmission of Wolbachia remains isolated, with Li et al. 2017 in The ISME Journal being one of the few reports supporting this mode of transmission. 4) While the effects of endosymbionts on their hosts are not the central focus of our study, the effects of transgenerational Wolbachia on whiteflies are primarily demonstrated to confirm the infection of Wolbachia into whiteflies. Furthermore, the effects we report of Wolbachia on whiteflies are notably different from those reported by Ahmed et al. 2015 in PLoS Pathogens, likely due to different whitefly species and Wolbachia strains. 6) More importantly, our study reveals a mechanism of parasitoid-mediated horizontal transmission of Wolbachia that is distinct from the mechanical transmission suggested by Ahmed et al. 2015 in PLoS Pathogens. Their study implies transmission primarily through host-feeding contamination, without the need for Wolbachia to infect the parasitoid, suggesting host-to-host transmission at the same trophic level. In contrast, our findings demonstrate transmission from parasitoids to hosts through unsuccessful parasitism, which represents cross-trophic level transmission. To our knowledge, this is the first experimental evidence that Wolbachia can be transmitted from parasitoids to hosts. We believe these clarifications and the novel insights provided by our research contribute valuable knowledge to the field.

References:

Ahmed MZ, De Barro PJ, Ren SX, Greeff JM, Qiu BL. 2013. Evidence for horizontal transmission of secondary endosymbionts in the Bemisia tabaci cryptic species complex. PLoS One, 8: e53084.

Ahmed MZ, Li SJ, Xue X, Yin XJ, Ren SX, Jiggins FM, Greeff JM, Qiu BL. 2015. The intracellular bacterium Wolbachia uses parasitoid wasps as phoretic vectors for efficient horizontal transmission. PLoS Pathog, 10: e1004672.

Ahmed MZ, Breinholt JW, Kawahara AY. 2016. Evidence for common horizontal transmission of Wolbachia among butterflies and moths. BMC Evol Biol, 16: 118. doi.org/10.1186/s12862-016-0660-x.

Caspi-Fluger A, Inbar M, Mozes-Daube N, Katzir N, Portnoy V, Belausov E, Hunter MS, Zchori-Fein E. 2012. Horizontal transmission of the insect symbiont Rickettsia is plant-mediated. Proc Biol Sci, 279(1734): 1791-6.

Chiel E, Kelly SE, Harris AM, Gebiola M, Li X, Zchori-Fein E, Hunter MS. 2014. Characteristics, phenotype, and transmission of Wolbachia in the sweet potato whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae), and its parasitoid Eretmocerus sp. nr. emiratus (Hymenoptera: Aphelinidae). Environ Entomol, 43(2): 353-62.

Chrostek E, Pelz-Stelinski K, Hurst GDD, Hughes GL. 2017. Horizontal transmission of intracellular insect symbionts via plants. Front Microbiol, 8: 2237.

Li SJ, Ahmed MZ, Lv N, Shi PQ, Wang XM, Huang JL, Qiu BL. 2017. Plantmediated horizontal transmission of Wolbachia between whiteflies. ISME J, 11: 1019-1028.

Li YH, Ahmed MZ, Li SJ, Lv N, Shi PQ, Chen XS, Qiu BL. 2017. Plant-mediated horizontal transmission of Rickettsia endosymbiont between different whitefly species. FEMS Microbiol Ecol, 93(12). doi: 10.1093/femsec/fix138.

Liu Y, He ZQ, Wen Q, Peng J, Zhou YT, Mandour N, McKenzie CL, Ahmed MZ, Qiu BL. 2023. Parasitoid-mediated horizontal transmission of Rickettsia between whiteflies. Front Cell Infect Microbiol, 12: 1077494. DOI: 10.3389/fcimb.2022.1077494

Qi LD, Sun JT, Hong XY, Li YX. 2019. Diversity and phylogenetic analyses reveal horizontal transmission of endosymbionts between whiteflies and their parasitoids. J Econ Entomol, 112: 894-905.

Shi PQ, Wang L, Chen XY, Wang K, Wu QJ, Turlings TCJ, Zhang PJ, Qiu BL. 2024. Rickettsia transmission from whitefly to plants benefits herbivore insects but is detrimental to fungal and viral pathogens. mBio, 15(3): e0244823.

Weaknesses:

In the current study, the authors downloaded the MLST or wsp genes from a public database and analyzed the data using other methods, and I think the authors may not be familiar with the research progress in the field of insect symbiont transmission, and the current stage of this manuscript lacking sufficient novelty.

We appreciate your critical perspective on our study. However, we respectfully disagree with the viewpoint that our manuscript lacks sufficient novelty.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

(1) Original blots in Figures 2E and 2H should be shown as well as the quantification of miR-182-5p overexpression in HepG2 cells. miR-182-5p expression in T2D patients was 2.3-fold higher than ND patients. The lack of insights into the degree of miR-182-5p overexpression precluded proper interpretation of the data presented.

Thank you very much for these comments. We now include the original uncut blots and relevant bands (new supplementary figure 3A) as well as the quantification of miR-182-5p expression in mimic-treated HepG2 cells in the supplement (new supplementary figure 2).

(2) What are the upstream transcriptional regulators of miR-182-5p?

To the best of our knowledge the upstream transcriptional regulators of miR-182-5p are currently unknown.

(3) What's the purpose of the weight cycling cohort? Figure 3A only showed that miR-182-5p expression was highly correlated to body weight, but the cohort can not explain why the human cohort has different miR-182-5p expression. GTT and ITT data are lacking for this cohort and thus cannot demonstrate a causal link between insulin sensitivity and miR-182-5p. The lack of histological evidence cannot show the relationship between NAFLD and miR-182-5p.

The purpose of the weight cycling cohort was to demonstrate that miR-182-5p is dynamically altered and that it can be reversed to almost control levels by weight loss. Thereby we validate in mice that obesity is associated with miR-182-5p upregulation (HFD group without intervention) and we propose that the adverse effects of increased miR-182-5p in obesity might be reversible by weight loss.  We did not perform ITTs and GTTs in this weigh cycling cohort because the HFD-model in C57BL/6 mice is well established and it can be assumed that glucose- and insulin-tolerance deteriorated during HFD feeding (doi.org/10.1038/oby.2007.608; doi:10.1007/978-1-61779-430-8_27 and improved after weight loss (doi:10.1038/s41598-023-40514-w). To corroborate this assumption, we provide plasma insulin along with as other important metabolic marker of the weight cycling model in supplemental figure 5A.

(4) Loss-of-function of miR-182-5p and/or gain-of-function of Lrp6 in vivo or in vitro would clarify the importance of the miR-182-5p-Lrp6 axis and provide more direct evidence for its potential as a therapeutic target.

We absolutely agree with the reviewer that loss of miR-182 and gain of LRP6 function experiments are missing. However, we provide miR-182 gain of function experiments that impressively show increased liver triglycerides after only seven days of miR-182 overexpression. Because these in vivo data are only short-term, we stated our conclusions carefully and point out that we do not have evidence for a direct involvement of miR-182-5p in insulin signaling. We are now planning follow-up studies in which miR-182-5p will be overexpressed and also antagonized for a longer time. However, for the timeframe of this revision process these extensive studies are not feasible and we ask the reviewer for his/her understanding.

(5) The schematic summary is too complex and includes too many assumptions to faithfully represent the data shown in this study.

We agree, the schematic summary is very complex. Therefore we simplified the upper part (new figure 5) and only focused on the clearly regulated genes and main pathways.

Reviewer #2 (Recommendations For The Authors):

(1) Although lots of microarray analyses were performed in this study, the authors didn't systemically investigate the function of miR-182 in T2DM or NAFLD. The current data provided in this manuscript may only support that miR-182 is involved in the homeostasis of glucose or insulin.

We thank the reviewer for this comment and agree that the nature of or data is mostly correlative. We tried to overcome this by performing mechanistic in vitro data. Because overexpression of miR-182-5p decreases inulin signaling in vitro and induces hyperinsulinemia in vivo we still strongly believe that miR-182-5p is highly relevant for the homeostasis of glucose and insulin.

(2) The authors used miRNA mimics to overexpress miR-182 in mice. How to emphasize the target specificity in the liver? Normally, adeno-associated virus 8 (AAV8) is used to specifically target the liver.

Tail vein injections as used in our experimental set-up are known to deliver compounds directly to the liver via the portal vein. For modulation of microRNAs in the liver it is an established technique to deliver mimics (or inhibitors) via the tail vein (doi:10.1007/978-1-62703-435-7_18; doi: 10.1089/10430349950017734). To account for off-target effects we quantified miR-182-5p and target gene expression in spleen and heart. Although miR-182-5p concentrations in mimic treated mice were strongly increased in these tissues, expression in the liver was still highest (new supplementary figure 6A).

(3) The HE and Oil red staining of the mouse liver should be shown in miR-182-5p overexpressing mice compared with the control mice, which could provide a more intuitive view of the fat content in the mouse liver.

Unfortunately the livers were flash frozen and not optimally prepared for later histological analyses. Nevertheless, we performed H&E stainings in all livers and provide representative HE stainings of two control and two miR-182-mimic treated mice (new supplementary figure 5D). The increase hepatic lipid content is clearly visible in the H&E staining of miR-182-mimic treated mice and supports our previous findings of increased hepatic triglycerides (Figure 4H). Due to the freezing process, livers were damaged and Oil red staining was impossible.

(4) After overexpression of miR-182-5p in mice, the serum insulin levels were increased. Does miR-182-5p affect insulin resistance in mice? The insulin tolerance test (ITT) experiment needs to be performed.

We thank the reviewer for this comment. Indeed, the performance of an ITT would have clarified the effects of miR-182 on insulin tolerance best. Because we did not see differences in the GTT after treating mice acutely with the miR-182 mimic we decided to not perform the ITT in this short-term. The increased fasting serum levels after miR-182-5p mimic treatment (Fig. 4G) suggest that rather insulin sensitivity than insulin secretion is disturbed by miR-182-5p. We are aware, that in future experiments mice should be treated for a longer period with miR-182-5p mimics and that an ITT should be performed in these more chronic studies.

(5) In Figure 2H, the author measured the level of p-Akt/Akt to indicate the effect of miR-182-5p on insulin resistance in HepG2 cells. It is best to provide the western blotting results of p-AKT and t-AKT after HepG2 cells are treated with or without insulin.

We now provide the full blots for all western blotting experiments as new supplemental figure 3B. The HepG2 cells were stimulated with 20 nM insulin 10 min before harvest as described in 2.11 and consequently Akt and p-Akt were quantified. We did not analyze Akt and p-Akt without stimulation because Akt is rarely phosphorylated in the basal non-insulin stimulated state.

(6) This study suggests that miR-182-5p may promote insulin resistance and hyperinsulinemia by downregulating LRP6. Nevertheless, to confirm this conclusion, we suggest you transfect miR-182-5p after downregulating the level of LRP6 with its siRNA for further validation.

Because miR-182-5p targets LRP6 as we have validated by luciferase-assays, LRP6 levels are already low after miR-182-5p overexpression. Thus, the additional downregulation of LRP6 by other means (such as siRNAs) does not make sense in our opinion.

(7) The author described that serum miR-182-5p was neither altered in T2D nor correlated with hepatic miR-182-5p expression, so is it suitable as the biomarker of T2D?

Yes, as the reviewer stated correctly, serum concentrations of miR-182-5p were not related to its liver concentrations or the type 2 diabetic state. We therefore suggest that circulating miR-182-5p levels are not a suitable biomarker for T2D. We clarified this in the discussion.

(8) What are the changes in fasting blood glucose levels in HFD, HC, and YoYo mouse models? Is there a correlation between miR-182-5p level and fasting blood glucose level in T2D patients and mouse models?

Unfortunately, we did not measure the fasting blood glucose levels in this mouse model and therefore cannot answer this question. However, we provide the fasting insulin levels of our mouse models and their positive correlations with miR-182-5p (Fig. 3D and Suppl.Fig. 5D). In T2D humans, hepatic miR-182-5p correlates positively with fasting glucose (Fig. 2B).

(9) The capitalization of the letters in "STrengthening the Reporting of OBservational studies in Epidemiology" should be checked. What does the "Among these is miRNAs miR-182-5p" mean? Please clarify it.

The “STrengthening the Reporting of OBservational studies in Epidemiology “ report form is abbreviated as “STROBE” list. We this capitalized the letters that are used to build the abbreviation.

“Among these is miRNAs miR-182-5p” is a typo for which we apologize. It should mean “Among these conserved miRNAs is miR-182-5p.” We corrected this error.

Reviewer #3 (Recommendations For The Authors):

(1) The functional importance of miR-182 on gene expression is not rigorously tested.

(A) Many of the target genes in Fig. 1C and Fig. 3 are controlled by multiple factors that are known to be increased with obesity (e.g., lipogenic genes are increased by hyperinsulinemia), making it likely that their association with miR-182 is correlative rather than a consequence of miR-182 increases.

We thank the reviewer for this comment and agree that miR-182 is not the only factor regulating the here investigated genes. We rather propose, that miR-182 could be an additional upstream regulator that holds the potential to modify entire pathways of insulin signaling and lipogenesis. However, miR-182 should be not viewed as an on/off-switch as it likely plays a modulating role. Although, our in vivo data stemming from humans and mice are correlative we believe that the in vitro data derived in HepG2 cells clearly show a causal role for miR-182-5ß in decreasing LRP6 and insulin signaling, indicated by lower AKT phosphorylation after miR-182-5p overexpression.

(B) 500-fold overexpression of miR-182 does not significantly change gene expression. The authors need to knockdown miR-182 in mice and then feed them a chow versus high-fat diet. If miR-182 is a significant regulator of these genes, the effects of the diet will be blunted.

We thank the reviewer for the constructive criticism and agree that an optimal experiment would be to antagonize miR-182-5p in mice to rescue glucose and lipid metabolism. There here presented in vivo upregulation of miR-182-5p was a proof-of-concept study to confirm our hypothesis in a reasonable timeframe. We are aware, that follow-up studies are needed, and we are now planning studies in which miR-182-5p will be overexpressed and also antagonized for a longer time. However, for the timeframe of this revision process these extensive studies are not feasible and we ask the reviewer for his/her understanding. 

(2) It has previously been shown that miR-182 is in a polycistrionic microRNA locus that is activated directly by SREBP-2. Is this also true in humans? If so, this would indicate that miR-182 is a marker of SREBP activity. How does the nuclear active form of SREBP1 and SREBP2 change in the human livers and HFD-fed mice?

We thank the reviewer for this very interesting question. Suitable experiments to investigate if miR-182-5p is activated by SREBF would be EMSAs or ChIPs. Unfortunately we have only frozen protein lysate of the human livers left in which such experiments cannot be performed. We agree that this should be prioritizes in the future.

(3) Similarly, to test the role of LRP6 in mediating the effects of miR-182, the authors should compare the effects of miR-182 overexpression in the presence and absence of LRP6.

Because miR-182-5p targets LRP6 as we have validated by luciferase-assays, LRP6 levels are already low after miR-182-5p overexpression. Thus, the additional downregulation of LRP6 by other means (such as siRNAs) does not make sense in our opinion.

(4) The methods are a bit confusing. The authors state that "we applied a logistic regression analysis for the 594 mature miRNAs using the NAFLD activity score (NAS) as a cofactor to exclude any bias by hepatic fat content, lobular inflammation, and fibrosis." However, they later showed that miR-182 levels are correlated with NAS. Please clarify.

We excluded NAFLD explicitly as driving factor for the association to T2D by including a surrogate (the NAFLD activity score) as cofactor. It is well known that NAFLD and T2D are indeed likely associated to each other. Since not all our included individuals with T2D have NAFLD and vice versa, a second correlation with NAS revealed also that a high NAS is associated with higher expression of miR-182.

(5) Does two-fold overexpression of miR-182 (which mimics the effects of HFD) have any effect on chow-fed mice?

This is a very interesting question that we unfortunately cannot answer right now. We are planning further mouse studies in which we will include a chow-fed mice as controls.

Author response:

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

Public reviews:

Reviewer 1:

Weaknesses:

While I generally agree with the author's interpretations, the idea of Saccorhytida as a divergent, simplified off-shot is slightly contradictory with a probably non-vermiform ecdysozoan ancestor. The author's analyses do not discard the possibility of a vermiform ecdysozoan ancestor (importantly, Supplementary Table 4 does not reconstruct that character),

Saccorhytids are only known from the early Cambrian and their unique morphology has no equivalent among any extinct or extant ecdysozoan groups. This prompted us to consider them as a possible dead-end evolutionary off-shot. The nature of the last common ancestor of ecdysozoan (i.e. an elongated worm-like or non-vermiform animal with capacities to renew its cuticle by molting) remains hypothetical. At present, palaeontological data do not allow us to resolve this question. The animal in Fig. 4b at the base of the tree is supposed to represent an ancestral soft-bodied form with no cuticle from which ecdysozoan evolved via major innovations (cuticular secretion and ecdysis). Its shape is hypothetical as indicated by a question mark. Our evolutionary model is clearly intended to be tested by further studies and hopefully new fossil discoveries.

…and outgroup comparison with Spiralia (and even Deuterostomia for Protostomia as a whole) indicates that a more or less anteroposteriorly elongated (i.e., vermiform) body is likely common and ancestral to all major bilaterian groups, including Ecdysozoa. Indeed, Figure 4b depicts the potential ancestor as a "worm". The authors argue that the simplification of Saccorhytida from a vermiform ancestor is unlikely "because it would involve considerable anatomical transformations such as the loss of vermiform organization, introvert, and pharynx in addition to that of the digestive system". However, their data support the introvert as a specialisation of Scalidophora (Figure 4a and Supplementary Table 4), and a pharyngeal structure cannot be ruled out in Saccorhytida. Likewise, loss of an anus is not uncommon in Bilateria. Moreover, this can easily become a semantics discussion (to what extent can an animal be defined as "vermiform"? Where is the limit?).

We agree that “worm” and “vermiform” are ill-defined terms. They are widely used in various palaeontological and biological papers to describe elongated tubular animals such as edydsozoans and annelids (see Giribet and Edgecombe 2017; popular textbook written by Nielsen 2012; Schmit-Rhaesa 2013; Brusca et al. 2023; Giribet and Edgecombe 2020). Very few other animals are termed “worms”. Changes have been made in the text to solve this semantic problem, for example in the abstract where we added (i.e elongated and tubular) to better define what we mean by “vermiform”.

Priapulid worms or annelids are examples of extremely elongated, tubular animals. In saccorhytids, the antero-posterior elongation is present (as it is in the vast majority of bilaterians) but extremely reduced, Saccorhytus and Beretella having a sac-like or beret-shape, respectively. That such forms may have derived from elongated, tubular ancestors (e.g. comparable with present-day priapulid worms) would require major anatomical transformations that have no equivalent among modern animals. We agree that further speculation about the nature of these transformations is unnecessary and should be deleted simply because the nature of these ancestors is purely hypothetical. We also agree that the loss of anus and the extreme simplification of the digestive system is common among extant bilaterians. In Figure 4b, the hypothetical pre-ecdysozoan animal is slightly elongated (along its antero-posterior axis) but in no way comparable with a very elongated and cylindrical ecdysozoan worm (e.g. extant or extinct priapulid).

Therefore, I suggest to leave the evolutionary scenario more open. Supporting Saccorhytida as a true group at the early steps of Ecdysozoa evolution is important and demonstrates that animal body plans are more plastic than previously appreciated. However, with the current data, it is unlikely that Saccorhytida represents the ancestral state for Ecdysozoa (as the authors admit), and a vermiform nature is not ruled out (and even likely) in this animal group. Suggesting that the ancestral Ecdysozoan might have been small and meiobenthic is perhaps more interesting and supported by the current data (phylogeny and outgroup comparison with Spiralia).

We agree to leave the evolutionary scenario more open, especially the evolutionary process that gave rise to Saccorhytida. Again, we know nothing about the morphology of the ancestral ecdysozoan (typically the degree of body elongation, whether it had a differentiated introvert or not, whether it had a through gut or not). In Fig.4, the ancestral ecdysozoan is supposed to have evolved from a soft-bodied epibenthic animal through key innovations such as the secretion of a cuticle and ecdysis. It is a hypothesis that needs to be tested by further studies and fossil discoveries. Speculations concerning the process through which saccorhytids may have arisen have been deleted.

Reviewer 2:

Weaknesses:

The preservations of the specimens, in particular on the putative ventral side, are not good, and the interpretation of the anatomical features needs to be tested with additional specimens in the future. The monophyly of Cycloneuralia (Nematoida + Scalidophora) was not necessarily well-supported by cladistic analyses, and the evolutionary scenario (Figure 4) also needs to be tested in future works.

Yes, we agree that the animal described in our manuscrip remains enigmatic (e.g. the natures of its internal organs, its lifestyle, etc..). Whereas the dorsal side of the animal is well documented (consistent pattern of pointed sclerites), uncertainties remain concerning its ventral anatomy (typically the mouth location and shape). Additional better-preserved specimens will hopefully provide the missing information. Concerning Cycloneuralia, their monophyly is generally better supported by analyses based on morphological characters than in molecular phylogenies.

Reviewer 3:

Weaknesses:

I, as a paleontology non-expert, experienced several difficulties in reading the manuscript. This should be taken into consideration when assuming a wide range of readers including non-experts.

We have ensured that the text is comprehensible to biologists. The main results are summarized in relatively simple diagrams (e.g. Fig. 4) that can be understood by non-specialized readers. We are aware that technical descriptive terms may appear obscure to non-specialists. We can hardly avoid them in the descriptive parts. However, our figures (e.g. SEM images and 3D-reconstruction) are clear enough to give the reader a clear idea of the morphology of Beretella.

Recommendations for the authors:

All three reviewers appreciate the discovery and found the merit of publishing this manuscript. They also raised some concerns about the data presentation. The authors are requested to perform no additional analysis but to go through all the reviewer comments and rebut or intake them in revising the manuscript.

Reviewer 1:

- Line 41: comma after "ecdysozans".

OK, done.

- Formatting style: add a space before references.

OK, done.

- Line 169: B. spinosa in italics

OK, done.

- Line 157: could the "relatively large opening" in the flattened ventral side of a mouth (even when altered by the fossilisation process)?

Most bilaterians have a mouth. There is no opening on the relatively well-preserved dorsal side of Beretella, that could be interpreted as a mouth. In contrast the flattened ventral side often show a depressed area that could potentially bear a mouth. This ventral area is often pushed in and poorly preserved. The cuticle of this ventral side might have been relatively thinner, perhaps more flexible than that of the dorsal one (with strong sclerites). These differences might explain why the possible oral area is poorly preserved.

- Line 178: "position of the mouth"

OK, done.

- Line 219: "These sclerites, unknown..."

OK, done.

- Line 282: update reference formatting

OK, done.

- Line 298: remove reference to Supplementary Table 4, as it does not refer to the possible vermiform nature of the last common ecdysozoan ancestor?

OK, done.

- Figure 4a: change "paired legs" for "paired appendages"?

OK, done.

- Supplementary Table 4: For TGE and Introvert, the state 0 (absent) should be in bold and underlined (as it is the most likely state).

OK, done.

Reviewer 2:

Line 25: "from the early Cambrian" should be changed into "from the lower Cambrian"

OK, done.

Line 126: The range of maximum length should be reported in µm (rather than mm) just like those of maximum width and height.

OK, done.

Lines 191-192: Please recheck the figure panels of Saccorhytus (Supplementary Figure 4c) and scalidophoran worm (Supplementary Figure 4d). Perhaps, the former should refer to Figure 4d, and the latter to Figure 4c?

OK, done.

Lines 239 and 241: "1" and "2" appear to stand for citations (the other journal style), but I am not certain what they are.

To avoid confusing, we replace ‘1’ and ‘2’ by ‘i’ and ‘ii’.

Figures 3d and 4a: "Cycloneuralia" should be included in the phylogenetic trees.

OK, done.

Figure 3: The caption for the panel d is redundant. It should be changed into, for example, "Phylogenetic tree obtained from cladistic analyses using maximum likelihood (IQTREE)."

OK, done.

Supplementary Figures 6-9: In the captions, more detailed explanations of the results (for example, "50% majority rule consensus of XXX trees" and "strict consensus of all 4 most-parsimonious trees") should be provided.

OK, done.

Supplementary Figures 8 and 9: The caption explains that Cycloneuralia is resolved as a paraphyletic group, but it is not certain because Nematoida, Scalidophora, and Panarthropoda are resolved in a polytomy.

We changed the sentence into:

“Note that Cycloneuralia does not appear as a monophyletic clade”

Reviewer 3:

Line 25 'tiny' - I suggest giving an absolute measure of the size.

We add ‘maximal length 3 mm’.

Line 29 'both forms' - This is hard to follow by a non-expert. Can this be replaced with 'fossil species'?

OK, done.

Line 32 'dead-end' - Is this word necessary? I suggest to skip this word, as it is obvious that this lineage is extinct.

OK, done.

Lines 80, 94, and 172 'Remarks' - I, as a palaeontology non-expert, cannot get this manuscript structure with a repetition of this same section title.

Our systematic descriptions follow the standard rules in palaeontology.

Line 119 - I could not get what this 'Member 5' that was not introduced earlier means.

In Stratigraphy, ‘member’ is a lithostratigraphic subdivision (a Formation is usually subdivided into several Members).

Lines 104, 105, 417, ... - The name of the organization or database hosting these IDs (CUB.... and ELIXX....) should also be supplied.

OK, done.

Lines 341 and 361 - These two Figures (Figures 1 and 2) have the same caption (with an addition to the one for Figure 1). There should be a distinction based on what is presented in each figure.

We corrected the caption of Figure 2 and wrote the following: ‘Beretella spinosa gen. et sp. nov.’.

Line 362-367 - There is no guide about what the individual figure panels (e.g., Figure 2g, 2h, and 2i) show in detail. This guide should be supplied. This also applies to Figure 3a-c - are they anterolateral (a), dorsal (b), and posterolateral (c) views? It is better to write clearly in this way.

OK, done.

Figure 3d - The color contrast is not sufficient, and this figure does not look reader-friendly. Plus, the division into Cycloneuralia and Panarthropoda is indicated above the tree, but it is not clear what range of lineages these clades include. For example, is Pliciloricidae included in Cycloneuralia? Also, is Collinsium included in Panarthropoda? This figure looks quite unreliable, and it should be easy to fix.

OK, done.

Line 277 legend of Figure 3 - Including the parenthesis only with the program name (IQTREE) is not useful at all. Isn't it enough to describe it in Methods?

OK, done. We remove (IQTREE).

Line 380 legend of Figure 3 - I could not get where 'thicker bars' are.

Known fossil record indicated by thicker vertical bars. We added “vertical”.

Line 453 - Give full names of the methods, maximum parsimony, and maximum-likelihood.

OK, done.

Line 489 - State clearly what 'the recent paper' means.

Replace ‘recent’ by ‘present’.

Author Response:

We thank the reviewers for careful reading, acknowledging the strength of our manuscript, and pointing out its weakness, which we will address in the revised version as described below.

(1) We will supplement our analysis with finer statistical testing and analysis, such as cross-validation and a more detailed analysis of the relation between the inferred model and the intrinsic timescales of the system. For the effect of the drug TIMP-1 on the animal, we will first explore the possibility of assessing the results using a multifactor ANOVA test, with the caveat that the distribution of interactions is not Gaussian. We will further test the effect of different group size on the significance of our results by considering subgroups of animals in the drug group, and compare the statistics between the (subsampled) drug group and the controlled group.

(2) Our manuscript is similar with that of Shemesh et al. in that we both analyze socially interacting mice by constructing maximum entropy models (MEM) of the co-localization patterns of mice. The difference is in the setup and the number of mice (4 mice in Shemesh et al, 10-15 in our work), as we outlined in the manuscript. To further supplement our current argument of the difference of our results in the Discussion section, we will learn a MEM model up to triplet interactions for our Eco-HAB mice data, and compare to our current MEM model up to pairwise interactions using test-set validation or the Bayesian information criterion (BIC).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

(1) The manuscript by Lu et al aims to study the effects of tubulin post-translational modification in C. elegans touch receptor neurons. Authors use gene editing to engineer various predicted PTM mutations in a-tubulin MEC-12 and b-tubulin MEC-7. Authors generate and analyze an impressive battery of mutants in predicted phosphorylation site and acetylation site of b-tubulin MEC-7, K40 acetylation site in a-tubulin MEC-12, enzymatic site of the a-tubulin acetyltransferase MEC-17, and PTM sites in the MEC-12 and MEC-7 C-tails (glutamylation, detyrosination, delta-tubulin). This represents a lot of work, and will appeal to a readership interested in C. elegans touch receptor neurons. The major concern/criticism of this manuscript is whether the introduced mutation(s) directly affects a specific PTM or whether the mutation affects gene expression, protein expression/stability/localization, etc. As such, this work does convincingly demonstrate, as stated in the title, that "Editing of endogenous tubulins reveals varying effects of tubulin posttranslational modifications on axonal growth and regeneration." 

We thank the reviewer for the constructive comments. With regards to the major concern or criticism, we like to point out that we have previously characterized ~100 missense mutations in mec-7 and mec-12 (Zheng et al., 2017, PMID: 28835377; Lee et al., 2021, PMID: 33378215). So, we are familiar with the phenotypes associated with mutations that affect gene expression or protein stability, which mostly result in a null phenotype. When analyzing the PTM site mutants, we compared their phenotypes with the previously categorized phenotypes of null alleles, neomorphic mutations that increase microtubule stability, and antimorphic mutations that prevent polymerization or disrupt microtubule stability. For example, in the case of mec-7 S172 mutations, we found that S172P mutants had the same phenotype as the mec-7 knockout (mild neurite growth defects), suggesting that S172P likely affects protein folding or stability, resulting in the loss of MEC-7. In contrast, S172A and S172E mutations showed phenotypes similar to neomorphic alleles (the emergence of ectopic ALM posterior neurite) and antimorphic alleles (the severe shortening of all neurites in the TRNs), respectively. These phenotypic differences suggested to us that the effects of S172A and S172E mutations cannot be simply attributed to the loss of protein expression and stability. Similar logic was applied to the studies of other PTM-inactivating or -mimicking mutations.

(2) For example, the authors manipulate the C-terminal tail of MEC-12 and MEC-7, to test the idea that polyglutamylation may be an important PTM. These mutants displayed subtle phenotypes. The authors show that branch point GT335 and polyglutamyation polyE recognizing antibodies stain cultured embryonic touch receptor neurons (TRNs), but did not examine staining in C. elegans TRNs in situ. To my knowledge, these antibodies have not been shown to stain the TRNs in any published papers, raising the question of how these "glutamylation" mutations are affecting mec-12 and -7. The rationale for using cultured embryonic TRNs and the relevance of the data and its interpretation are not clear. 

The GT335 and polyE antibodies were used by previous studies (O’Hagan et al., 2011, PMID: 21982591; and O’Hagan et al., 2017, PMID: 29129530) to detect the polyglutamylation signals in the sensory cilia of C. elegans. We initially tried to stain the whole animals using these antibodies but could not get clear and distinct signals in the TRNs. We reason that the tubulin polyglutamylation signals in the TRNs may be weak, and the in situ staining method which requires the antibodies to penetrate multiple layers of tissues (e.g., cuticles and epidermis) to reach the TRN axons may be not sensitive enough to detect the signal. In fact, the TRN axons are located deeper in the worm body compared to the sensory cilia that are mostly exposed to the environment. Another reason could be that the tissues (mostly epidermis) surrounding the TRN axons also have polyglutamylation staining, which makes it difficult to recognize TRN axons. This is a situation different from the anti-K40 acetylation staining, which only occurs in the TRNs because MEC-12 is the only a-tubulin isotype that carries K40. Due to these technical difficulties, we decided to use the in vitro cultured TRNs for the staining experiment, which allows both easy access of the antibodies (thus higher sensitivity) and the dissociation of the TRNs from other tissues. The fact that we were able to observe reduced staining in the ttll mutants and the tubulin mutants that lost the glutamate residues suggest that these antibodies indeed detected glutamylation signals in the cells.

(3) The final paragraph of the discussion is factually incorrect. The C. elegans homologs of the CCP carboxypeptidases are called CCPP-1 and CCPP-6. There are several publications on their functions in C. elegans.

We thank the reviewer for pointing out the mistake in the text. We intended to say that “there is no C. elegans homolog of the known tubulin carboxypeptidases that catalyze detyrosination”, which is true given that the detyrosinase vasohibins (VASH1/VASH2) homologs cannot be found in C. elegans. We are aware of the publications on CCPP-1 and CCPP-6; CCPP-1 is known to regulate tubulin deglutamylation in the cilia of C. elegans (O’Hagan et al., 2011 and 2017), while CCPP-6 may function in the PLM to regulate axonal regeneration (Ghosh-Roy et al., 2012). In the revised manuscript, we have corrected the error.

Reviewer #2 (Public Review):

Summary:

The tubulin subunits that make up microtubules can be posttranslationally modified and these PTMs are proposed to regulate microtubule dynamics and the proteins that can interact with microtubules in many contexts. However, most studies investigating the roles of tubulin PTMs have been conducted in vitro either with purified components or in cultured cells. Lu et al. use CRISPR/Cas9 genome editing to mutate tubulin genes in C. elegans, testing the role of specific tubulin residues on neuronal development. This study is a real tour de force, tackling multiple proposed tubulin modifications and following the resulting phenotypes with respect to neurite outgrowth in vivo. There is a ton of data that experts in the field will likely reference for years to come as this is one of the most comprehensive in vivo analyses of tubulin PTMs in vivo.

This paper will be very important to the field, however would be strengthened if: 1) the authors demonstrated that the mutations they introduced had the intended consequences on microtubule PTMs, 2) the authors explored how the various tubulin mutations directly affect microtubules, and 3) the findings are made generally more accessible to non C. elegans neurobiologists.

(1) The authors introduce several mutations to perturb tubulin PTMs, However, it is unclear to what extent the engineered mutations affect tubulin in the intended way i.e. are the authors sure that the PTMs they want to perturb are actually present in C. elegans. Many of the antibodies used did not appear to be specific and antibody staining was not always impacted in the mutant cases as expected. For example, is there any evidence that S172 is phosphorylated in C. elegans, e.g. from available phosphor-proteomic data? Given the significant amount of staining left in the S172A mutant, the antibody seems non-specific in this context and therefore not a reliable readout of whether MTs are actually phosphorylated at this residue. As another example, there is no evidence presented that K252 is acetylated in C. elegans. At the very least, the authors should consider demonstrating the conservation of these residues and the surrounding residues with other organisms where studies have demonstrated PTMs exist. 

We thank the reviewer for the comments. To our knowledge, there are very few phosphor-proteome data available for C. elegans. We searched a previously published dataset (Zielinska et al., 2009; PMID: 19530675) and did not find the S172 phosphorylation signal in MEC-7. This is not surprising, given that only six touch receptor neurons expressed MEC-7 and the abundance of MEC-7 in the whole animal lysate may be below the detection limit. However, this phosphorylation site S172 is highly conserved across species and tubulin isotypes (Figure 1-figure supplement 1 in the revised manuscript), suggesting that this site is likely phosphorylated in MEC-7.

In the case of K252, the potential acetylation site and the flanking sequences are extremely conserved across species and isotypes. In fact, the 20 amino acids from 241-260 a.a. are identical among the tubulin genes of C. elegans, fruit flies, Xenopus, and humans (Figure 4-figure supplement 1B). Thus, although K252 acetylation was found in the HeLa cells, this site can possibly be acetylated. 

In the case of K40, we observed sequence divergence at the PTM site and adjacent sequences among the tubulin isotypes in C. elegans. MEC-12 is the only C. elegans a-tubulin isotype that has the K40 residue, and the 40-50 a.a. region of MEC-12 appears to be more conserved than other isotypes when compared to Drosophila, frog, and human a-tubulins (Figure 4-figure supplement 1A).

(2) Given that the authors have the mutants in hand, it would be incredibly valuable to assess the impact of these mutations on microtubules directly in all cases. MT phenotypes are inferred from neurite outgrowth phenotypes in several cases, the authors should look directly at microtubules and/or microtubule dynamics via EBP-2 when possible OR show evidence that the only way to derive the neurite phenotypes shown is through the inferred microtubule phenotypes. For example, the effect of the acetylation or detyrosination mutants on MTs was not assessed. 

We thank the reviewer for the suggestions. In this study, we created >20 tubulin mutants. Due to limited time and resources, we were not able to examine microtubule dynamics in every mutant strain using EBP-2 kymographs. We assessed the effects of the tubulin mutations mostly based on the changes on neurite growth pattern. From our previous experience of analyzing ~100 mec-7 and mec-12 missense mutations (Zheng et al., 2017, MBoC; Lee et al., 2021, MBoC), we found that the changes in microtubule dynamics are correlated with the changes in neuronal morphologies. For example, the growth of ectopic ALM-PN is correlated with fewer EBP-2 comets and potentially reduced microtubule dynamics; this correlation holds true for several mec-7 neomorphic missense alleles we examined before (Lee et al., 2021, MBoC) and the PTM site mutants [e.g., mec-7(S172A) and mec-12(4Es-A)] analyzed in this study. Similarly, the shortening of TRN neurites is correlated with more EBP-2 comets and increased microtubule dynamics. For the mutants that don’t show neurite growth defects, our previous experience is that they are not likely to show altered microtubule dynamics in EBP-2 tracking experiments. So, we did not analyze the acetylation mutants (which had no defects in neurite growth) and the detyrosination mutants (which had weak ALM-PN phenotype). Nevertheless, we agree with the reviewer that we could not rule out the possibility that there may be some slight changes to microtubule dynamics in these mutants.

Using tannic acid staining and electron microscopy (EM), we previously examined the microtubule structure in several tubulin missense mutants (Zheng et al., 2017, MBoC) and found that the loss-of-function and antimorphic mutations significantly reduced the number of microtubules and altered microtubule organizations by reducing protofilament numbers. These structural changes are consistent with highly unstable microtubules and defects in neurite growth. On the other hand, neomorphic mutants had only slight decrease in microtubule abundance, maintained the 15-protofilament structure, and had a more tightly packed microtubule bundles that filled up most of the space in the TRN neurite (Zheng et al., 2017, MBoC). These structural features are consistent with increased microtubule stability and ectopic neurite growth. Although we did not directly examine the microtubule abundance and structure using EM in this study, we would expect similar changes that are correlated with the neurite growth phenotypes in the PTM mutants. We agree with the reviewer, it will be informative to conduct more comprehensive analysis on these mutants using EM and other structural biology methods.

(3) There is a ton of data here that will be important for experts working in this field to dig into, however, for the more general cell biologist, some of the data are quite inaccessible. More cartoons and better labeling will be helpful as will consistent comparisons to control worms in each experiment.

Response: We thank the reviewer for the comment. In the revised manuscript, we added some cartoons to Figure 2G to show the location of the synaptic vesicles. The neurite growth phenotype should be quite straightforward. Nevertheless, we added one more Figure (Figure 8) to summarize all the results in the study with cartoons that depicted the changes to neuronal morphologies.

(4) In addition, I am left unconvinced of the negative data demonstrating that MBK does not phosphorylate tubulin. First, the data described in lines 207-211 does not appear to be presented anywhere. Second, RNAi is notoriously finicky in neurons, thus necessitating tissue-specific degradation using either the ZF/ZIF-1 or AID/TIR1 systems which both work extremely well in C. elegans. Third, there appears to be increasing S172 phosphorylation in Figure 3 Supplement 2 with added MBK-2, but there is no anti-tubulin blot to show equal loading, so this experiment is hard to interpret.

We added the results of mbk-1, mbk-2, and hpk-1 mutants and cell-specific knockdown of MBK-2 into Figure 3-figure supplement 1D. Considering the reviewer’s suggestion, we attempted to use a ZIF-1 system to remove the MBK-2 proteins specifically in the TRNs using a previously published method (PMID: 28619826). We fused endogenous MBK-2 with GFP by gene editing and then expressed an anti-GFP nanobodies fused with ZIF-1 in the TRNs to induce the degradation of MBK-2::GFP. To our surprise, unlike the mbk-2p::GFP transcriptional reporter, the MBK-2::GFP did not show detectable expression in the TRNs, although expression can be seen in early embryos, which is consistent with the “embryonic lethal” phenotype of the mbk-2(-) mutants (Figure 3-figure supplement 2A-B in the revised manuscript). We reason that either endogenous MBK-2 is not expressed in the TRNs or is expressed at a very low level. We then crossed mbk-2::GFP with ItSi953 [mec-18p::vhhGFP4::Zif-1] to trigger the degradation of any potential MBK-2 proteins and did not observe the ectopic growth of ALM-PN (Figure 3- figure supplement 2C). These results suggest that MBK-2 is not likely to regulate tubulin phosphorylation in the TRNs, which is consistent with the results of other genetic mutants and the RNAi experiments.

For Figure 3 Supplement 2 (Figure 3-figuer supplement 3 in revised manuscript), because we added the same amount of purified MEC-12/MEC-7 to all reactions and had established equal loading in Figure 3E, we did not do the anti-tubulin staining in this experiment. Since higher concentration (1742 nM) of MBK-2 did not produce stronger signal than the condition with 1268 nM, we don’t think the 1268 nM band represents true phosphorylation. Moreover, the signal is not significantly stronger than the control without MBK-2 and is much lower than the signal generated by CDK1 in Figure 3E. Based on these results, we concluded that MBK-2 is not likely to phosphorylate MEC-7.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

General:

A summary table would help the reader digest the vast amount of phenotypic data.

Cartoons to help a non-C. elegans reader understand the figures. 

We added Figure 8 to summarize and illustrate the effects of the various mutants analyzed in this study.

Specific:

The authors engineered mutations into the predicted phosphorylation site of b-tubulin mec-7. These CRISPR-alleles mutations phenocopied previously identified loss-of-function, gain-of-function, and neomorphic mec-7 alleles identified in genetic screens by the Chalfie lab. Next, the authors sought to identify the responsible kinase, taking a candidate gene approach. The most likely family - minibrain - had no effect when knocked down/out. The authors showed that cdk-1 mutants displayed ectopic ALM-PN outgrowth. Whether cdk-1 specifically acts in the TRNs was not demonstrated, calling into question whether CDK-1 phosphorylates S172 in vivo. In their introduction (lines 45-59), the authors built a case for engineering PTM mutations directly into tubulins, because the PTM enzymes may have multiple substrates. This logic applies to the cdk-1 experiment and its interpretation. 

The reviewer is right. Since CDK1 and minibrain kinase are the only known kinases that catalyze S172 phosphorylation, our results suggest that CDK-1 is more likely to catalyze S172 phosphorylation in the TRNs compared to MBK-1/2. Genetic studies found that cdk-1(-); mec-7(S172A) double mutants did not show stronger phenotype than the two single mutants, suggesting that they function in the same pathway. Nevertheless, we could not rule out the possibility that other kinases may also control S172 phosphorylation, and the effect of CDK-1 is indirect. We mentioned this possibility in the revised manuscript.

For a-tubulin MEC-12, acetyl-mimicking K40Q and unmodifiable K40R mutants failed to stain with the anti-acetyl-a-tubulin (K40) antibody and displayed subtle TRN phenotypes. The enzymatically dead MEC-17 had phenotypes similar to those described by Topalidou (2012), confirming the Chalfie lab finding that MEC-17 has functions in addition and independent of its acetyltransferase activity. The authors moved onto a predicted acetylation site in MEC-7 and observed TRN developmental defects, and acknowledged that this may be due to tubulin instability and not a PTM. This is a concern for all mutants, as there is no way to measure whether the protein is expressed, stable, or localized properly. 

We acknowledge that this is a caveat of mutational studies. An amino acid substitution at the PTM site may have multiple effects, including the change of the PTM state and potential alteration of protein conformation. Without direct evidence for enzymatic modification of the PTM site in the neurons, we could not rule out the possibility the phenotype we observed is not related to PTM and instead is the result of abnormal protein conformation and function caused by the mutation.

Nevertheless, as stated in our above response to the first point in the public review, we can phenotypically differentiate loss-of-function and gain-of-function mutants. If the mutation reduces expression or general protein stability, it is more likely to cause a loss-of-function phenotype. For most PTM site mutants, this is not the case. We observed mostly gain-of-function phenotype, suggesting that the missense mutations did not simply inactivate the tubulin protein and instead affected the functional properties of the protein.

From here, the authors manipulate the C-terminal tail of MEC-12 and MEC-7, testing the idea that polyglutamylation may be an important PTM. These mutants displayed subtle phenotypes. The authors show that branch point GT335 and polyglutamyation polyE recognizing antibodies stain cultured embryonic TRNs, but did not examine staining in TRNs. To my knowledge, these antibodies have not been shown to stain the TRNs in any published papers (see next point). The rationale for using cultured embryonic TRNs is not clear. 

See our response to the second point in the public review.

Lines 548-553 There are several publications on CCPP-1 and CCPP-6 functions in TRNs and ciliated sensory neurons. See

PMID: 20519502

PMID: 21982591

PMID: 21943602

PMID: 23000142

PMID: 29129530

PMID: 33064774

PMID: 36285326

PMID: 37287505 

We thank the reviewer for pointing out these references, some of which were cited in the revised manuscript. We made a mistake in the Discussion by saying that there are no C. elegans homologs of tubulin carboxypeptidases while we intended to state that there is no homolog of tubulin detyrosinase in C. elegans. We are aware of the studies of CCPP-1 and CCPP-6 and have corrected the mistake in revised manuscript (also see our response to the third point in the public review).

Reviewer #2 (Recommendations For The Authors):

Figures: 

As stated in the public review, more cartoons and better labeling will be helpful as will consistent comparisons to control worms in each experiment. A good example of this issue is demonstrated in Figure 2 and Figure 4: 

(1) Figure 2: Please label images with what is being probed in each panel. 

We added labels to the panels.

(2) Figure 2G is very hard to interpret - cartoon diagramming what is being observed would be helpful. 

We added cartoons to help illustrate the images.

(3) Line 182-185: is this referring to your data or to Wu et al? It is not clear in this paragraph when the authors are describing published work versus their own data presented here. 

It is from our data. We have made it clear in the revised manuscript.

(4) Figure 2 - 2K is not well described. What experiment is being done here? What is dlk-1 and why did you look at this mutant? 

Figure 2K showed that both wild-type animals and S172A mutants could reconnect the severed axons after laser axotomy. Previous studies have found that dlk-1(-) mutants were not able to regenerate axons due to altered microtubule dynamics (PMID: 19737525; PMID: 23000142). We used dlk-1(-) mutants as a negative control, because DLK-1 promotes microtubule growth following axotomy, and the DLK-1 pathway is essential for regeneration (PMID: 23000142). We want to highlight the phenotypic difference between dlk-1(-) mutants and the S172E mutants. Although both mutants showed similar regrowth length, dlk-1(-) mutants showed unbranched regrowth probably due to the lack of microtubule polymerization, whereas the S172E mutants showed a mesh-like regrowth pattern likely due to highly dynamic and unstable microtubules. We explained the different phenotypes in the revised manuscript.

(5) Figure 4C: this phenotype is hard to interpret. Where is the wt control? Where is the quantification? 

In the Figure legend, we have referred the readers to Figure 1G for the wild-type image. Quantification is provided in the text (~20% of the animals showed the branching defects).

(6) There are no WT comparison images in Figure 4I, making the quantification difficult to interpret 

In the Figure legend, we have referred the readers to Figure 1A for the wild-type control. Moreover, we included a new Figure 8 to summarize the phenotypes of all mutants.

Experimental:

(1) Is it clear that only MEC-7/MEC-12 are the only a- and b-tubulin present in the TRNs? The presence of other tubulins not mutated would complicate the interpretation of the results. 

According to the mRNA levels, the expression of MEC-7 and MEC-12 are >100 fold higher than other tubulin isotypes. For example, single-cell transcriptomic data (Taylor et al., 2021) showed that mec-7 mRNA is at 135,940 TPM in ALM neurons, whereas two other tubulin isotypes, tbb-1 and tbb-2, have expression value of 54 and 554 TPM, respectively in the ALM. So, even if there are some other tubulin isotypes, their abundance is much lower than mec-7 and mec-12 and are not likely to interfere with the effects of the mec-7 and mec-12 mutants.

(2) The in vitro kinase assays should be quantified. 

We have added the quantification.

(3) The idea that Cdk1 phosphorylates tubulin in interphase is surprising and I am left wondering how the authors propose that Cdk1 is activated in interphase. Is cyclin B (or another cyclin) present in interphase in this cell type? Expression but not activation of Cdk1 is not discussed. 

CDK1 can work with cyclin A and cyclin B. C. elegans has one cyclin A gene (cya-1) and four cyclin B genes (cyb-1, cyb-2.1, cyb-2.2, and cyb-3). According to single-cell transcriptomic data of L4 animals, cya-1 and cyb-1 showed weak expression in many postmitotic neurons (including the ALM neurons), while cyb-2.1, cyb-2.2, and cyb-3 had no expression in neurons. So, it is possible that cya-1/cyclin A and cyb-1/cyclin B has low level of expression in the TRNs. A previous study also found the expression of cell cycle regulators (including cyclins) in postmitotic neurons in mouse brain (Akagawa et al., 2021; PMID: 34746147).

(4) What is the significance of neurite swelling and looping in Figure 4H? The underlying cause of this phenotype is not described. 

The neurite swelling and looping phenotype of mec-17(-) mutants were described by Topalidou et al., (2012; PMID: 22658602) and were caused by the bending of the microtubules. It appears that the loss of the a-tubulin acetyltransferase altered the organization of microtubules in the TRNs. These defects were partially rescued by the enzymatically dead MEC-17, suggesting that MEC-17 may play a non-enzymatic (and likely structural) role in regulating microtubule organization. We added more explanation in the revised manuscript.

(5) It is quite surprising that polyglutamylation is not affected in the quintuple ttll mutant. Since the authors made the sextuple ttll mutant, could they demonstrate whether polyglutamylation is further reduced in this mutant via GT335 staining? 

We did not make the comparison of the quintuple and sextuple ttll mutants because they were crossed with TRN markers with different colors for technical reasons. The quintuple mutants CGZ1475 carried uIs115 [mec-17p::TagRFP] IV, whereas the sextuple mutants CGZ1474 carried zdIs5 [mec-4p::GFP] I. As a result, we need to use different secondary antibodies for the antibody staining, which makes the results not compatible.

Polyglutmaylation signal in the cell body was strongly affected by the ttll mutations. In fact, in the ttll-4(-); ttl-5(-); ttll-12(-) triple mutants, the signal is significantly reduced in the cell body of the TRNs, as well as the cell body of other cells. What’s surprising is that the signal in the axons persisted in the ttll triple and quintuple mutants. As the reviewers suggested, we also stained the sextuple mutants and found similar pattern as the triple and quintuple mutants (new Figure 6-figure supplement 1C in the revised manuscript), although the results are not quantitatively comparable due to the use of secondary antibodies with different fluorophores.

Writing:

(1) The beginning of the results section is quite jarring. The information in lines 96-104 should be in the Introduction. 

Due to the nature of this paper, each section deals with a particular PTM. We think it is helpful to discuss some background information before describing our results on each PTM rather than giving all in the introduction. Nevertheless, we modified the beginning of the results to make it more coherent and more connected with the preceding paragraphs.

(2) Line 122-126: conclusions are not supported by the data: it is suggested from previous experiments, but authors do not look at MTs directly. 

We have rephrased the statement to acknowledge that we made such conclusion based on phenotypic similarity with mutants we previously examined.

(3) I am confused by the usage of both mec-12(4EtoA) and mec-12(4Es-A). Are these the same mutations? If so, there needs to be consistency. If not, each case needs to be defined. 

They are the same. We have corrected the mistake and are now using mec-12(4Es-A) to refer to the mutants.

Line 105: phosphor --> phospho 

Line 187: were --> was 

Line 298: is --> are

The above typos are corrected.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

I still find it really impressive that the Purkinje cell stimulation so closely mimics the pathogenic phenotypes - in my opinion, the strongest part of the paper. I would like just a little clarification on some of my previous questions.

Major points:

(1) Can the authors clarify where the new units came from? Are these units that were recorded before the initial submission and excluded, but are now included? If so, why were they excluded before? Or are these units that were recorded since the original submission?

The number of units increased in Figure 1 for three reasons: 1) We have now plotted the classifier results in Figure 1 instead of the validation results, which have been moved to Figure 1 Supplement 3. 2) In response to reviewer comments, we no longer include units that had >60 s of recording in both our model creation and validation. We had previously used 30 s for creating the model and a different 30 s for validating the model, if an additional 30 s were available. 3) We changed our model creation and validation strategy based on previous reviewer comments. The new units in Figures 2-4 were taken from our pool of previously collected but unanalyzed data (we collect neural data on a rolling basis and thus these data were not initially available). We were fortunate to have these data to analyze in order to address the concerns about the number of cells included in the manuscript. The number of units increased in Figure 5 because new units were recorded in response to reviewer comments.

(2) Why did some of the neuron counts go down? For example, in Pdx1Cre;Vglut2fl/fl mice, the fraction of units with the control signature went from 11/21 to 7/23. Is this because the classifier changed between the original submission and the revision?

Yes, the proportion of cells matching each classification changed due to the different parameters and thresholds used in the updated classifier model.

Minor points:

In the Discussion: "We find some overlap and shared spike features between the different disease phenotypes and show that healthy cerebellar neurons can adapt multiple disease-associated spike train signatures." I think "adapt" should be "adopt"

In the Discussion: "compare" is misspelled as "compared"

Thank you for bringing these typos to our attention. We will upload a new version of the text with the typos corrected.

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

We would like to thank the Reviewers for providing excellent and constructive suggestions that have enabled us to strengthen our overall presentation of our data. We have addressed each of the comments by altering the text, providing additional data, and revising the figures, as requested.

Below are our explanations for how we have altered the manuscript in this revised version.

Recommendations for the authors:

I think you will have seen from the comments that there was great enthusiasm for the importance of this study. There were also shared concerns about how the classifier may be inadequate in its current format, as well as specific suggestions to consider to improve. I hope that you will consider a revision to really amplify the impact of the importance of this study.

Reviewer #1 (Recommendations For The Authors):

Distinct motor phenotypes are reflected in different neuronal firing patterns at different loci in motor circuits. However, it is difficult to determine if these altered firing patterns: 1) reflect the underlying neuropathology or phenotype, 2) whether these changes are intrinsic to the local cell population or caused by larger network changes, and 3) whether abnormal firing patterns cause or reflect abnormal movement patterns. This manuscript attempts to address these questions by recording neural firing patterns in deep cerebellar nucleus neurons in several models of cerebellar dysfunction with distinct phenotypes. They develop a classifier based on parameters of single unit spike trains that seems to do an inconsistent job of predicting phenotype (though it does fairly well for tremor). The major limitation of the recording/classifier experiments is the low number of single units recorded in each model, greatly limiting statistical power. However, the authors go on to show that specific patterns of Purkinje cell stimulation cause consistent changes in interposed nucleus activity that map remarkably well onto behavioral phenotypes. Overall, I did not find the recording/classifier results to be very convincing, while the stimulation results strongly indicate that interposed nucleus firing patterns are sufficient to drive distinct behavioral phenotypes.

We thank the reviewer for their comments. We describe below how we have addressed the major concerns.

Major concerns:

(1) I don't think it's legitimate to use two 30-second samples from the same recording to train and validate the classifier. I would expect recordings from the same mouse, let alone the same unit, to be highly correlated with each other and therefore overestimate the accuracy of the classifier. How many of the recordings in the training and validation sets were the same unit recorded at two different times?

We previously published a paper wherein we measured the correlation (or variability) between units recorded from the same mouse versus units recorded from different mice (see: Van der Heijden et al., 2022 – iScience, PMID: 36388953). In this paper we did not find that nuclei neuron recordings from the same mouse were more correlated or similar to each other than recordings from different mice. 

Upon this reviewer comment, however, we did observe strong correlations between the two 30-second samples from the same recording units. We therefore decided to no longer validate our classifier based on a training and validation sets that had overlapping units. Instead, we generated 12 training sets and 12 non-overlapping validation sets based on our entire database. We then trained 12 classifier models and ranked these based on their classification ability on the validation sets (Figure 1 – supplemental Figure 3). We found that the top two performing classifier models were the same, and used this model for the remainder of the paper. 

(2) The n's are not convincing for the spike signature analyses in different phenotypic models. For example, the claim is that Pdx1Cre;Vglut2fl/fl mice have more "control" neurons than ouabain infusion mice (more severe phenotype). However, the numbers are 11/21 and 7/20, respectively. The next claim is that 9/21 dystonic neurons are less than 11/20 dystonic neurons. A z-test for proportions gives a p-value of 0.26 for the first comparison and a pvalue of 0.44 for the second. I do not think any conclusions can be drawn based on these data.

We included more cells in our analyses and found that the z-test for n the proportion of cells with the “control” and “dystonia” signature is indeed statistically significant. 

(3) Since the spiking pattern does not appear to predict an ataxic phenotype and the n's are too small to draw a conclusion for the dystonic mice, I think the title is very misleading - it does not appear to be true that "Neural spiking patterns predict behavioral phenotypes...", at least in these models.

We have changed the title to: “Cerebellar nuclei cells produce distinct pathogenic spike signatures in mouse models of ataxia, dystonia, and tremor.” We feel that this new title captures the idea that we find differences between spike signatures associated with ataxia, dystonia, and tremor and that these signatures induce pathological movements.

(4) I don't think it can be concluded from the optogenetic experiments that the spike train signatures do not depend on "developmental changes, ...the effect of transgene expression, ... or drug effects outside the cerebellum." The optogenetic experiments demonstrate that modulating Purkinje cell activity is sufficient to cause changes in DCN firing patterns and phenotypes (i.e., proof-of-principle). However, they do not prove that this is why DCN firing is abnormal in each model individually.

Thank you for highlighting this section of the text. We agree that the optogenetic experiments cannot explain why the DCN is firing abnormally in each model. We have edited this section of the text to prevent this conclusion from being drawn by the readers.

Minor points:

(1) It would be nice to see neural recordings in the interposed nucleus during Purkinje terminal stimulation to verify that the firing patterns observed during direct Purkinje neuron illumination are reproduced with terminal activation. This should be the case, but I'm not 100% certain it is.

We have edited the text to clarify that representative traces and analysis of interposed nucleus neurons in response to Purkinje terminal stimulation are the data in Figure 5.

(2) How does the classifier validation (Fig. 1E) compare to chance? If I understand correctly, 24/30 neurons recorded in control mice are predicted to have come from control mice (for example). This seems fairly high, but it is hard to know how impressive this is. One approach would be to repeat the analysis many (1000s) of times with each recording randomly assigned to one of the four groups and see what the distribution of "correct" predictions is for each category, which can be compared against the actual outcome.

We have now also included the proportion of spike signatures in the entire population of neurons and show that the spike signatures are enriched in each of the four groups (control, ataxia, dystonia, tremor) relative to the presence of these signatures in the population (Figure 1E). 

(3) I don't think this is absolutely necessary, but do the authors have ideas about how their identified firing patterns might lead to each of these phenotypes? Are there testable hypotheses for how different phenotypes caused by their stimulation paradigms arise at a network level?

We have added some ideas about how these spike signatures might lead to their associated phenotypes to the discussion.

Reviewer #2 (Recommendations For The Authors):

(1) As mentioned earlier, my main concern pertains to the overall architecture and training of the classifier. Based on my reading of the methods and the documentation for the classifier model, I believe that the classifier boundaries may be biased by the unequal distribution of neurons across cerebellar disease groups (e.g., n=29 neurons in control versus n=19 in ataxics). As the classifier is trained to minimize the classification error across the entire sample, the actual thresholds on the parameters of interest may be influenced by the overrepresentation of neurons from control mice. To address this issue, one possible solution would be to reweight each group so that the overall weight across classes is equal. However, I suggest a better strategy might be to revise the classifier architecture altogether (as detailed below).

We have retrained the classifier model based on equal numbers of ataxic, dystonic, and tremor cells (n=20) but we intentionally included more control cells (n=25). We included more control cells because we assume this is the baseline status for all cerebellar neurons and wanted to avoid assigning disease signatures to healthy neurons too easily. 

(2) As the authors make abundantly clear, one mouse model of disease could potentially exhibit multiple phenotypes (e.g., a mouse with both ataxia and tremor). To address this complexity, it might be more valuable to predict the probability of a certain CN recording producing specific behavioral phenotypes. In this revised approach, the output of the classifier wouldn't be a single classification (e.g., "this is an ataxic mouse") but rather the probability of a certain neural recording corresponding to ataxia-like symptoms (e.g., "the classifier suggests that this mouse has a 76% likelihood of exhibiting ataxic symptoms given this CN recording"). This modification wouldn't require additional data collection, and the exemplar disease models could still be used to train such a revised network/classifier, with each mouse model corresponding to 0% probability of observing all other behavioral phenotypes except for the specific output corresponding to the disease state (e.g., L7CreVgat-fl/fl would be 0% for all categories except ataxia, which would be trained to produce a score of 100%). This approach could enhance the validation results across other mouse models by allowing flexibility in a particular spike train parameter to produce a diverse set of phenotypes.

This is a great comment. Unfortunately, our current dataset is constrained to fully address this comment for the following reasons:

- We have a limited number of neurons on which we can train our classifier neurons. Further dividing up the groups of neurons or complicating the model limited the power of our analyses and resulted in overfitting of the model on too few neurons.

- The recording durations (30 seconds) used to train our model are likely too short to find multiple disease signatures within a single recording. We feel that the complex phenotypes are likely resulting from cells within one mouse exhibiting a mix of disease signatures (as in the Car8wdl/wdl mice).

We think this question would be great for a follow-up study that uses a large number of recordings from single mice to fully predict the mouse phenotype based on the population spike signatures. 

To limit confusion about our classifier model, we have also altered the language of our manuscript and refer to the cells exhibiting a spike signature instead of predicting a phenotype. 

However, the paper falls short in terms of the classifier model itself. The current implementation of this classifier appears to be rather weak. For instance, the crossvalidated performance on the same disease line mouse model for tremor is only 56%. While I understand that the classifier aims to simplify a high-dimensional dataset into a more manageable decision tree, its rather poor performance undermines the authors' main objectives. In a similar vein, although focusing on three primary features of spiking statistics identified by the decision tree model (CV, CV2, and median ISI) is useful for understanding the primary differences between the firing statistics of different mouse models, it results in an overly simplistic view of this complex data. The classifier and its reliance on the reduced feature set are the weakest points of the paper and could benefit from further analysis and a different classification architecture. Nevertheless, it is commendable that the authors have collected high-quality data to validate their classifier. Particularly impressive is their inclusion of data from multiple mouse models of ataxia, dystonia, and tremor, enabling a true test of the classifier's generalizability.

We intentionally simplified our parameter space from a high-dimensional dataset into a more manageable decision tree. We did this for the following reasons:

- The parameters, even though all measuring different features, are highly correlated (see Figure 1 – supplemental Figure 2). Further, we were training our dataset on a limited number of recordings. We found that including all parameters (for example using a linear model) caused overfitting of the data and poor model performance.

- Describing the spike signatures using a lower number of parameters allowed us to design optogenetic parameters that would mimic this parameter space. This would be infinitely more complex with a bigger parameter space. 

We agree with the reviewer that inclusion of multiple mouse models in addition to the optogenetics experiments provide the classifier’s generalizability. 

Minor Comments:

(1) The blown-up CN voltage traces in Figures 5C and Supplementary Figure 2B appear more like bar plots than voltage traces on my machine.

Thank you for bringing this to our attention. We have improved the rendering of the traces.

(2) The logic in lines 224-228 is somewhat confusing. The spike train signatures are undoubtedly affected by all the factors mentioned by the authors. What, I believe, the authors intend to convey is that because changes in CN firing rates can be driven by multiple factors, it is the CN firing properties themselves that likely drive disease-specific phenotypes.

We agree that our discussion of the CN firing needs clarification. We have made the appropriate edits in the text.

Reviewer #3 (Recommendations For The Authors):

It's quite astounding that this can be done from single spike trains from what are almost certainly mixed populations of neurons. Could you add something to the discussion about this? Some questions that could be addressed would be would multiple simultaneous recordings additionally help classify these diseases, or would non-simultaneous recordings from the same animal be useful? Also more discussion about which cells you are likely recording from would be useful.

Thank you for this suggestion. We have added discussion about multiple recordings, simultaneous vs non-simultaneous recordings, and our thoughts on the cell population recorded in this work.

Data in figure 2 is difficult to understand - it appears that the majority of dysregulated cells in 2 ataxic models are classified as dystonia cells, not ataxic cells. This appears surprising as it seems to be at odds with earlier data from Fig 1. In my opinion, it is not discussed adequately in the Results or Discussion section.

We have added further discussion of the ataxia models represented in Figures 1 and 2.

Minor comment:

The colours of the subdivisions of the bars in 2C and 3C, and the rest of the paper appear to be related to the groups in the middle (under "predicted"), but the colours are much paler in the figure than in the legend, although the colours in the bars and the legends match in the first figure (1E). Does this signify something?

These figures were remade with the same colors across the board.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The study by Prieto et al. faces the increasingly serious problem of bacterial resistance to antimicrobial agents. This work has an important element of novelty proposing a new approach to control antibiotic resistance spread by plasmids. Instead of targeting the resistance determinant, plasmid-borne proteins are used as antigens to be bound by specific nanobodies (Nbs). Once bound plasmid transfer was inhibited and Salmonella infection blocked. This in-depth study is quite detailed and complex, with many experiments (9 figures with multiple panels), rigorously carried out. Results fully support the authors' conclusions. Specifically, the authors investigated the role of two large molecular weight proteins (RSP and RSP2) encoded by the IncHI1 derivative-plasmid R27 of Salmonella. These proteins have bacterial Ig-like (Big) domains and are expressed on the cell surface, creating the opportunity for them to serve as immunostimulatory antigens. Using a mouse infection model, the authors showed that RSP proteins can properly function as antigens, in Salmonella strains harboring the IncHI1 plasmid. The authors clearly showed increased levels of specific IgG and IgA antibodies against these RSP proteins proteins in different tissues of immunized animals. In addition, non-immunized mice exhibited Salmonella colonization in the spleen and much more severe disease than immunized ones. 

However, the strength of this work is the selection and production of nanobodies (Nbs) that specifically interact with the extracellular domain of RSP proteins. The procedure to obtain Nbs is lengthy and complicated and includes the immunization of dromedaries with purified RPS and the construction of a VHH (H-chain antibody variable region) library in E. coli. As RSP is expressed on the surface of E. coli, specific Nbs were able to agglutinate Salmonella strains harboring the p27 plasmid encoding the RSP proteins. 

The authors demonstrated that Nbs-RSP reduced the conjugation frequency of p27 thus limiting the diffusion of the amp resistance harbored by the plasmid. This represents an innovative and promising strategy to fight antibiotic resistance, as it is not blocked by the mechanism that determines, in the specific case, the amp resistance of p27 but it targets an antigen associated with HincHI- derivative plasmids. Thus, RPS vaccination could be effective not only against Salmonella but also against other enteric bacteria. A possible criticism could be that Nbs against RSP proteins reduce the severity of the disease but do not completely prevent the infection by Salmonella.

It is true that vaccina2on of mice with purified RSP protein did not provide complete protec2on against infec2on with a Salmonella strain harboring an IncHI plasmid. As this finding is based on an animal model, further inves2ga2on is required to evaluate its clinical efficacy. In any case, even par2al protec2on provided by nanobodies or by a vaccine could poten2ally improve survival rates among cri2cally ill pa2ents infected with a pathogenic bacterium harboring an IncHI plasmid. An addi2onal beneficial aspect of our approach is that it will reduce dissemina2on of IncHI plasmids among pathogenic bacteria, which would reduce the presence of an2bio2c resistance plasmids in the environment and in the bacteria infec2ng pa2ents. 

Reviewer #2 (Public Review):

Summary:

This manuscript aims to tackle the antimicrobial resistance through the development of vaccines. Specifically, the authors test the potential of the RSP protein as a vaccine candidate. The RSP protein contains bacterial Ig-like domains that are typically carried in IncHl1 plasmids like R27. The extracellular location of the RSP protein and its role in the conjugation process makes it a good candidate for a vaccine. The authors then use Salmonella carrying an IncHl plasmid to test the efficacy of the RSP protein as a vaccine antigen in providing protection against infection of antibioticresistant bacteria carrying the IncHl plasmid. The authors found no differences in total IgG or IgA levels, nor in pro-inflammatory cytokines between immunized and non-immunized mice. They however found differences in specific IgG and IgA, attenuated disease symptoms, and restricted systemic infection.

The manuscript also evaluates the potential use of nanobodies specifically targeting the RSP protein by expressing it in E. coli and evaluating their interference in the conjugation of IncHl plasmids. The authors found that E. coli strains expressing RSPspecific nanobodies bind to Salmonella cells carrying the R27 plasmid thereby reducing the conjugation efficacy of Salmonella. 

Strengths:

The main strength of this manuscript is that it targets the mechanism of transmission of resistance genes carried by any bacterial species, thus making it broad.

The experimental setup is sound and with proper replication.

Weaknesses:

The two main experiments, evaluating the potential of the RSP protein and the effects of nanobodies on conjugation, seem as parts of two different and unrelated strategies.

In preparing our manuscript, we were aware that we included two different strategies to combat an2microbial resistance. However, we deemed it valuable to include both in the paper. The development of new vaccines and the inhibi2on of the transfer of an2bio2c resistance determinants are currently considered relevant approaches to combat an2microbial resistance. Our inten2on in the ar2cle is to integrate these two strategies. 

The survival rates shown in Figure 1A and Figure 3A for Salmonella pHCM1 and non-immunized mice challenged with Salmonella, respectively, are substantially different. In the same figures, the challenge of immunized mice and Salmonella pHCM1 and mice challenged with Salmonella pHCM1 with and without ampicillin are virtually the same. While this is not the only measure of the effect of immunization, the inconsistencies in the resulting survival curves should be addressed by the authors more thoroughly as they can confound the effects found in other parameters, including total and specific IgG and IgA, and pro-inflammatory cytokines.

Overall the results are inconsistent and provide only partial evidence of the effectiveness of the RSP protein as a vaccine target.

To address the concerns regarding the disparities in survival rates depicted in Figures 1A and 3A, it is important to refer to several factors that contribute to these variations. Firstly, it should be noted that the data depicted in these figures stem from distinct experimental sets conducted at different times employing different batches of mice. Despite the use of the same strain and supplier, individual animals and their batches can exhibit variability in susceptibility to infection due to inherent biological differences.

Unlike in vitro cell culture experiments, which can achieve high replicability due to the homogeneity of cell lines, in vivo animal studies often exhibit greater variability. This variability is influenced not only by genetic variations within animal populations, even if originating from the same supplier, but also by environmental factors within the animal facility. These factors include temperature variations, the concentration y of non-pathogenic microorganisms in the facility, which can modify the immune responses, or the density of animals in the environment, consequently affecting human traffic and generating potential disturbances. 

When designing experiments with animals, it is desirable for the results to be consistent across different animal batches. If one bacterial strain exhibits higher mortality rates than another across multiple experimental series, this pattern should be reproducible despite the inherent variability in in vivo studies. It is more important to demonstrate consistency in trends than to focus on absolute figures when validating experimental results. 

It is also important to clarify that when we refer to survival rates, it doesn’ t necessarily mean that the animals were found deceased. The animal procedures were approved by the Ethics Committee of Animal Experimentation of the Universitat de Barcelona, which include an animal monitoring protocol. Our protocol requires close daily monitoring of several health and behavioral parameters, each evaluated according to specific criteria. When an animal reaches a predetermined score threshold indicating severe distress or suffering, euthanasia is administered to alleviate further suffering. At this point, biological samples are collected for subsequent analysis.

The conjugative experiments use very long conjugation times, making it harder to assess if the resulting transconjugants are the direct result of conjugation or just the growth of transconjugants obtained at earlier points in time. While this could be assessed from the obtained results, it is not a direct or precise measure.

In the conjuga2on experiments we u2lized a reduced number of donor cells expressing the RSP protein and of recipient cells, as well as long conjuga2on 2mes, to reflect more accurately a situa2on that may occur naturally in the environment. Short conjuga2on 2mes are efficient in controlled laboratory condi2ons using high densi2es of donor and recipient cells, but these condi2ons are not commonly found in the environment. For the interference of the conjuga2ve transfer of the IncHI plasmid we used an E. coli strain displaying the nanobody binding RSP to simulate a process that could be also scaled-up in a natural environment (i.e., a probio2c strain in a livestock farm) and that could be cost effec2ve. See discussion sec2on, lanes 326-328.   

While the potential outcomes of these experiments could be applied to any bacterial species carrying this type of plasmids, it is unclear why the authors use Salmonella strains to evaluate it. The introduction does a great job of explaining the importance of these plasmids but falls short in introducing their relevance in Salmonella.

The prevalence of IncHI plasmids in Salmonella was indicated in the introduc2on sec2on, lanes 65-67. Nevertheless, we understand the reviewer’s cri2cisms and have modified both these sentences in the introduc2on sec2on and also added comments in the results sec2on (lanes 118-128).

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I understand working with mice can be challenging in terms of repeating experiments to further support the study's claims. For this reason, I think the authors need to discuss more thoroughly the following things:

Can the authors comment on why the presence of Ampicillin leads to a lower upregulation of proinflammatory cytokines in the spleen despite harboring resistance against ampicillin?

At the intestinal level, physiological inflammatory responses play a crucial role in enabling the host to identify foreign and commensal bacterial antigens and initiate a highly regulated and "controlled" immune response (Fiocchi, 2008. Inflamm Bowel Dis. 2008, 14 Suppl 2:S77-8). The administration of antibiotics such as ampicillin, reduces the load of intestinal resident microbiota, thereby lowering the extent of intestinal immune activation. This decline in immune activation extends to systemic levels, potentially accounting for the reduced expression of proinflammatory cytokines observed in the spleen.

There are inconsistent results in the survival rates in Figures 1A and 3A, please discuss how this could alter the observed differences in total and specific IgG and IgA, and pro-inflammatory cytokines.

To address the reviewer concerns regarding the discrepancies in survival rates shown in Figures 1A and 3A, and how these differences might influence the observed variations in total and specific IgG and IgA, as well as pro-inflammatory cytokines, it is important to clarify the terminology used in our study. In our context, "survival" does not solely refer to mortality per se, but encompasses the endpoints defined by our animal welfare protocols, which are rigorously supervised by the Animal Experimentation Ethics Committee of the University of Barcelona. Our protocol mandates close daily monitoring of several health and behavioral parameters, each scored according to specific criteria. When an animal reaches a predefined score threshold indicating severe distress or suffering, euthanasia is conducted to prevent further distress, at which point we collect biological samples for analysis.

In contrast to in vitro cell culture experiments, which often achieve high replicability thanks to the homogeneity of cell lines, in vivo animal studies frequently display greater variability. This variability stems not only from genetic differences within animal populations, even if originating from the same supplier, but also from environmental factors within the animal facility. These factors encompass variations in temperature, the presence of non-pathogenic microorganisms in the facility (capable of altering immune responses) and the density of animals, which can impact human traffic and potentially lead to disturbances. 

The experiments depicted in Figs. 1A and 3A were separated in time, and hence may be influenced by environmental factors within the animal facility. Nevertheless, in the comparative analysis performed between immunized and non-immunized animals, experiments were performed simultaneously and hence under similar environmental conditions in the animal facility. For several parameters (i.e., immunoglobulins and proinflammatory cytokines) statistically significant differences were observed. 

Regarding the conjugation assays, it is not entirely clear to me why the conjugation times are so long. It would be beneficial to have more data about the conjugation efficacy between the donor and recipient without any E. coli expressing the nanobodies at different time intervals. This would help to differentiate between transconjugants and transconjugants obtained from early conjugation events.

This comment is par2ally answered in a previous response, regarding the numbers of donor and recipient cells and dura2on of conjuga2on.  We note here that in fig. 9, the requested experiment with donor and recipient cells without E. coli interferent cells is already present, corresponding to the label “none”. To avoid confusion, we have modified the legend in fig. 9.

Author response:

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

Public Reviews:

Reviewer #2 (Public Review):

Although the study by Xiaolin Yu et al is largely limited to in vitro data, the results of this study convincingly improve our current understanding of leukocyte migration.

(1) The conclusions of the paper are mostly supported by the data and in the revised manuscript clarification is provided concerning the exact CCL5 forms (without or with a fluorescent label or His-tag) and amounts/concentrations that were used in the individual experiments. This is important since it is known that modification of CCL5 at the N-terminus affects the interactions of CCL5 with the GPCRs CCR1, CCR3 and CCR5 and random labeling using monosuccinimidyl esters (as done by the authors with Cy-3) is targeting lysines. The revised manuscript more clearly indicates for each individual experiment which form is used. However, a discussion on the potential effects of the modifications on CCL5 in the results and discussion sections is still missing.

Many thanks for the reviewer's suggestion. We fully agree it is important to clarify the potential issue of Cy3 labeling, and believe it is more suitable in the Materials and Methods section (line 312-314).

(2) In general, authors used high concentrations of CCL5 in their experiments. In their reply to the comments they indicate that at lower CCL5 concentrations no LLPS is detected. This is important information since it may indicate the need for chemokine oligomerization for LLPS. This info should be added to the manuscript and comparison with for instance the obligate monomer CCL7 and another chemokine such as CXCL4 that easily forms oligomers may clarify whether LLPS is controlled by oligomerization.

We are pleased by the help of the reviewers and accordingly inserted a brief discussion as suggested (line 240-246).

(3) Statistical analyses have been improved in the revised manuscript.

Thanks to the reviewer for his/her comment.

Author response:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors intended to prove that gut GLP-1 expression and secretion can be regulated by Piezo1, and hence by mechanistic/stretching regulation. For this purpose, they have assessed Piezo1 expression in STC-1 cell line (a mouse GLP-1 producing cell line) and mouse gut, showing the correlation between Piezo1 level and Gcg levels (Figure S1). They then aimed to generate gut L cell-specific Piezo1 KO mice, and claimed the mice show impaired glucose tolerance and GLP-1 production, which can be mitigated by Ex-4 treatment (Figures 1-2). Pharmacological agents (Yoda1 and GsMTx4) and mechanic activation (intestinal bead implantation) were then utilized to prove the existence of ileal Piezo1-regulated GLP-1 synthesis (Figure 3). This was followed by testing such mechanism in a limited amount of primary L cells and mainly in the STC-1 cell line (Figures 4-7).

While the novelty of the study is somehow appreciable, the bio-medical significance is not well demonstrated in the manuscript. The authors stated (in lines between lines 78-83) a number of potential side effects of GLP-1 analogs, how can the mechanistic study of GLP-1 production on its own be essential for the development of new drug targets for the treatment of diabetes. Furthermore, the study does not provide a clear mechanistic insight on how the claimed CaMKKbeta/CaMKIV-mTORC1 signaling pathway upregulated both GLP-1 production and secretion. This reviewer also has concerns about the experimental design and data presented in the current manuscript, including the issue of how proglucagon expression can be assessed by Western blotting.

Strengths:

The novelty of the concept.

Weaknesses:

Experimental design and key experiment information.

Current GLP-1-based therapies for diabetes use GLP-1 agonists/analogs. Although generally safe, there are some side effect or risks of GLP-1 agonists/analogs. We agree to the reviewer that a mechanistic study on the regulation of GLP-1 production will not directly lead to development of new drug targets for the treatment of diabetes. However, understanding the mechanism of GLP-1 production may shed light onto alternative treatment strategies for diabetes that targeting the production of GLP-1. In our previous studies, we have elucidated the role of mTOR/S6K pathway in regulating GLP-1 production in L cells. Using STC-1 cell line and different mouse models, including Neurog3-Tsc1−/− mice, rapamycin or L-lucine treatment to stimulate mTOR activity, we have demonstrated that mTOR stimulates proglucagon gene expression and thus GLP-1 production (Diabetologia 2015;58(8):1887-97； Mol Cell Endocrinol. 2015 Nov 15:416:9-18.). Based on our previous studies, we found that Piezo1 regulated mTOR/S6K pathway and thus proglucagon expression and GLP-1 production through Ca2+/CaMKKbeta/CaMKIV in our present study. Although we could not exclude involvement of other signaling pathways downstream of Piezo1 in regulating the cleavage of proglucagon, granule maturation and the final release of GLP-1, our present study provided evidence to support the involvement of the Ca2+/CaMKKbeta/CaMKIV/mTOR pathway in mediating the role Piezo1 in proglucagon expression and GLP-1 production. The reviewer also expressed concerns on the use of western blot to detect proglucagon expression. In fact, western blot is often used in detection of proglucagon. Here are some examples from other researchers: Diabetes. 2013 Mar;62(3):789-800. Gastroenterology. 2011 May;140(5):1564-74. 2004 Jul 23;279(30):31068-75. The proglucagon antibody we used in our study was purchased from abcam (Cat#ab23468), which can detect proglucagon of 21 kDa.

Reviewer #2 (Public Review):

Summary:

The study by Huang and colleagues focuses on GLP-1 producing entero-endocrine (EEC) L-cells and their regulation of GLP-1 production by a mechano-gated ion channel Piezo1. The study describes Piezo1 expression by L-cells and uses an exciting intersectional mouse model (villin to target epithelium and Gcg to target GLP-1-producing cells and others like glucagon-producing pancreatic endocrine cells), which allows L-cell specific Piezo1 knockout. Using this model, they find an impairment of glucose tolerance, increased body weight, reduced GLP-1 content, and changes to the CaMKKbeta-CaMKIV-mTORC1 signaling pathway using a normal diet and then high-fat diet. Piezo1 chemical agonist and intestinal bead implantation reversed these changes and improved the disrupted phenotype. Using primary sorted L-cells and cell model STC-1, they found that stretch and Piezo1 activation increased GLP-1 and altered the molecular changes described above.

Strengths:

This is an interesting study testing a novel hypothesis that may have important mechanistic and translational implications. The authors generated an important intersectional genetics mouse model that allowed them to target Piezo1 L-cells specifically, and the surprising result of impaired metabolism is intriguing.

Weaknesses:

However, there are several critical limitations that require resolution before making the conclusions that the authors make.

(1) A potential explanation for the data, and one that is consistent with existing literature [see for example, PMC5334365, PMC4593481], is that epithelial Piezo1, which is broadly expressed by the GI epithelium, impacts epithelial cell density and survival, and as such, if Piezo1 is involved in L-cell physiology, it may be through regulation of cell density. Thus, it is critical to determine L-cell densities and epithelial integrity in controls and Piezo1 knockouts systematically across the length of the gut, since the authors do not make it clear which gut region contributes to the phenotype they see. Current immunohistochemistry data are not convincing.

We appreciate the reviewer’s comment. We agree that Piezo1 may affect L-cell density and epithelial integrity. We will do quantification of L-cell density and test the epithelial integrity by examining the expression of tight junction proteins (ZO-1 and Occludin) and determine the transepithelial resistance in different regions of the gut

(2) Calcium signaling in L-cells is implicated in their typical role of being gut chemo-sensors, and Piezo1 is a calcium channel, so it is not clear whether any calcium-related signaling mechanism would phenocopy these results.

We will examine whether other calcium-related signaling mechanism also contribute the phenotype seen in the IntL-Piezo1-/- mice.

(3) Intestinal bead implantation, while intriguing, does not have clear mechanisms - and is likely to provide a point of intestinal obstruction and dysmotility.

To ascertain if intestinal bead implantation led to intestinal obstruction and dysmotility, we conducted a bowel transit time test. The results revealed no difference in bowel transit time between the sham-operated mice and those implanted with beads.

(4) Previous studies, some that are very important, but not cited, contradict the presented results (e.g., epithelial Piezo1 role in insulin secretion) and require reconciliation.

Overall, this study makes an interesting observation but the data are not currently strong enough to support the conclusions.

We will cite more previous studies on GLP-1 production and discuss the discrepancy between our study and others’ studies. The lack of changes in blood glucose seen in Villin-Piezo1-/- mice reported by Sugisawa et. al. is not surprising (Cell. 2020 Aug 6;182(3):609-624.e21.). Actually, in another recent study from our group, we found similar results when the Villin-Piezo1-/- mice Piezo1fl/fl control mice were fed with normal chow diet. Since Villin-1 is expressed in all the epithelial cells of the gut, including enterocytes and various types of endocrine cells, the effect of L-cell Piezo1 loss may be masked by other cell types under normal condition. However, impair glucose tolerance was seen in Villin-Piezo1-/- mice compared to the Piezo1fl/fl control mice after high fat diet for 8 weeks. We further found that Piezo1 in enterocytes exerted a negative effect on the glucose and lipid absorption. Loss of Piezo1 in enterocytes led to over-absorption of nutrients under high-fat diet （Tian Tao, Qing Shu, Yawen Zhao, Wenying Guo, Jinting Wang, Yuhao Shi, Shiqi Jia, Hening Zhai, Hui Chen, Cunchuan Wang*, Geyang Xu*, Mechanical regulation of lipid and sugar absorption by Piezo1 in enterocytes, Acta Pharmaceutica Sinica B , Accepted, 2024，https://doi.org/10.1016/j.apsb.2024.04.016）.

Author response:

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

Your editorial guidance, reviews, and suggestions have led us to make substantial changes to our manuscript. While we detail point-by-point responses in typical fashion below, I wanted to outline, at a high level, what we’ve done.

(1) Methods. Your suggestions led us to rethink our presentation of our methods, which are now described more cohesively in a new methods section in the main text.

(2) Model Validation & Robustness. Reviewers suggested various validations and checks to ensure that our findings were not, for instance, the consequence of a particular choice of parameter. These can be found in the supplementary materials.

(3) Data Cleaning & Inclusion/Exclusion. Finally, based on feedback, our new methods section fully describes the process by which we cleaned our original data, and on what grounds we included/excluded individual faculty records from analysis.

eLife assessment

Efforts to increase the representation of women in academia have focussed on efforts to recruit more women and to reduce the attrition of women. This study - which is based on analyses of data on more than 250,000 tenured and tenure-track faculty from the period 2011-2020, and the predictions of counterfactual models - shows that hiring more women has a bigger impact than reducing attrition. The study is an important contribution to work on gender representation in academia, and while the evidence in support of the findings is solid, the description of the methods used is in need of improvement.

Reviewer #1 (Public Review):

Summary and strengths

This is an interesting paper that concludes that hiring more women will do more to improve the gender balance of (US) academia than improving the attrition rates of women (which are usually higher than men's). Other groups have reported similar findings but this study uses a larger than usual dataset that spans many fields and institutions, so it is a good contribution to the field.

We thank the reviewer for their positive assessment of the contributions of our work.

Weaknesses

The paper uses a mixture of mathematical models (basically Leslie matrices, though that term isn't mentioned here) parameterised using statistical models fitted to data. However, the description of the methods needs to be improved significantly. The author should consider citing Matrix Population Models by Caswell (Second Edition; 2006; OUP) as a general introduction to these methods, and consider citing some or all of the following as examples of similar studies performed with these models:

Shaw and Stanton. 2012. Proc Roy Soc B 279:3736-3741

Brower and James. 2020. PLOS One 15:e0226392

James and Brower. 2022. Royal Society Open Science 9:220785 Lawrence and Chen. 2015.

[http://128.97.186.17/index.php/pwp/article/view/PWP-CCPR-2015-008]

Danell and Hjerm. 2013. Scientometrics 94:999-1006

We have expanded the description of methods in a new methods section of the paper which we hope will address the reviewer’s concerns.

We agree that our model of faculty hiring and attrition resembles Leslie matrices. In results section B, we now mention Leslie matrices and cite Matrix Population Models by Caswell, noting a few key differences between Leslie matrices and the model of hiring and attrition presented in this work. Most notably, in the hiring and attrition model presented, the number of new hires is not based on per-capita fertility constants. Instead, population sizes are predetermined fixed values for each year, precluding exponential population growth or decay towards 0 that is commonly observed in the asymptotic behavior of linear Leslie Matrix models.

We have additionally revised the main text to cite the listed examples of similar studies (we had already cited James and Brower, 2022). We thank the reviewer for bringing these relevant works to our attention.

The analysis also runs the risk of conflating the fraction of women in a field with gender diversity! In female-dominated fields (e.g. Nursing, Education) increasing the proportion of women in the field will lead to reduced gender diversity. This does not seem to be accounted for in the analysis. It would also be helpful to state the number of men and women in each of the 111 fields in the study.

We have carefully examined the manuscript and revised the text to correctly differentiate between gender diversity and women’s representation.

We have additionally added a table to the supplemental materials (Tab. S3) that reports the estimated number of men and women in each of the 111 fields.

Reviewer #2 (Public Review):

Summary:

This important study by LaBerge and co-authors seeks to understand the causal drivers of faculty gender demographics by quantifying the relative importance of faculty hiring and attrition across fields. They leverage historical data to describe past trends and develop models that project future scenarios that test the efficacy of targeted interventions. Overall, I found this study to be a compelling and important analysis of gendered hiring and attrition in US institutions, and one that has wide-reaching policy implications for the academy. The authors have also suggested a number of fruitful future avenues for research that will allow for additional clarity in understanding the gendered, racial, and socioeconomic disparities present in US hiring and attrition, and potential strategies for mitigating or eliminating these disparities.

We thank the reviewer for their positive assessment of the contributions of our work.

Strengths:

In this study, LaBerge et al use data from over 268,000 tenured and tenure-track faculty from over 100 fields at more than 12,000 PhD-granting institutions in the US. The period they examine covers 2011-2020. Their analysis provides a large-scale overview of demographics across fields, a unique strength that allows the authors to find statistically significant effects for gendered attrition and hiring across broad areas (STEM, non-STEM, and topical domains).

LaBerge et al. find gendered disparities in attrition-using both empirical data and their counterfactual model-that account for the loss of 1378 women faculty across all fields between 2011 and 2020. It is true that "this number is both a small portion of academia... and a staggering number of individual careers," as ." - as this loss of women faculty is comparable to losing more than 70 entire departments. I appreciate the authors' discussion about these losses-they note that each of these is likely unnecessary, as women often report feeling that they were pushed out of academic jobs.

LaBerge et al. also find-by developing a number of model scenarios testing the impacts of hiring, attrition, or both-that hiring has a greater impact on women's representation in the majority of academic fields in spite of higher attrition rates for women faculty relative to men at every career stage. Unlike many other studies of historical trends in gender diversity, which have often been limited to institution-specific analyses, they provide an analysis that spans over 100 fields and includes nearly all US PhD-granting institutions. They are able to project the impacts of strategies focusing on hiring or retention using models that project the impact of altering attrition risk or hiring success for women. With this approach, they show that even relatively modest annual changes in hiring accumulate over time to help improve the diversity of a given field. They also demonstrate that, across the model scenarios they employ, changes to hiring drive the largest improvement in the long-term gender diversity of a field.

Future work will hopefully - as the authors point out - include intersectional analyses to determine whether a disproportionate share of lost gender diversity is due to the loss of women of color from the professoriate. I appreciate the author's discussion of the racial demographics of women in the professoriate, and their note that "the majority of women faculty in the US are white" and thus that the patterns observed in this study are predominately driven by this demographic. I also highly appreciate their final note that "equal representation is not equivalent to equal or fair treatment," and that diversifying hiring without mitigating the underlying cause of inequity will continue to contribute to higher losses of women faculty.

Weaknesses

First, and perhaps most importantly, it would be beneficial to include a distinct methods section. While the authors have woven the methods into the results section, I found that I needed to dig to find the answers to my questions about methods. I would also have appreciated additional information within the main text on the source of the data, specifics about its collection, inclusion and exclusion criteria for the present study, and other information on how the final dataset was produced. This - and additional information as the authors and editor see fit - would be helpful to readers hoping to understand some of the nuance behind the collection, curation, and analysis of this important dataset.

We have expanded upon the description of methods in a new methods section of the paper.

We have also added a detailed description of the data cleaning steps taken to produce the dataset used in these analyses, including the inclusion/exclusion criteria applied. This detailed description is at the beginning of the methods section. This addition has substantially enhanced the transparency of our data cleaning methods, so we thank the reviewer for this suggestion.

I would also encourage the authors to include a note about binary gender classifications in the discussion section. In particular, I encourage them to include an explicit acknowledgement that the trends assessed in the present study are focused solely on two binary genders - and do not include an analysis of nonbinary, genderqueer, or other "third gender" individuals. While this is likely because of the limitations of the dataset utilized, the focus of this study on binary genders means that it does not reflect the true diversity of gender identities represented within the professoriate.

In a similar vein, additional context on how gender was assigned on the basis of names should be added to the methods section.

We use a free, open-source, and open-data python package called nomquamgender (Van Buskirk et al, 2023) to estimate the strengths of (culturally constructed) name-gender associations. For sufficiently strong associations with a binary gender, we apply those labels to the names in our data. We have updated the main text to make this approach more apparent.

We have also added language to the main text which explicitly acknowledges that our approach only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

I do think that some care might be warranted regarding the statement that "eliminating gendered attrition leads to only modest changes in field-level diversity" (Page 6). while I do not think that this is untrue, I do think that the model scenarios where hiring is "radical" and attrition is unchanged from present (equal representation of women and men among hires (ER) + observed attrition (OA)) shows that a sole focus on hiring dampens the gains that can otherwise be addressed via even modest interventions (see, e.g., gender-neutral attrition (GNA) + increasing representation of women among hires (IR)). I am curious as to why the authors did not include an additional scenario where hiring rates are equal and attrition is equalized (i.e., GNA + ER). The importance of including this additional model is highlighted in the discussion, where, on Page 7, the authors write: "In our forecasting analysis, we find that eliminating the gendered attrition gap, in isolation, would not substantially increase representation of women faculty in academia. Rather, progress towards gender parity depends far more heavily on increasing women's representation among new faculty hires, with the greatest change occurring if hiring is close to gender parity." I believe that this statement would be greatly strengthened if the authors can also include a comparison to a scenario where both hiring and attrition are addressed with "radical" interventions.

Our rationale for omitting the GNA + ER scenario in the presented analysis is that we can reason about the outcomes of this scenario without the need for computation; if a field has equal inputs of women and men faculty (on average) and equal retention rates between women and men (on average), then, no matter the field’s initial age and gender distribution of faculty, the expected value for the percentage of women faculty after all of the prior faculty have retired (which may take 40+ years) is exactly 50%. We have updated the main text to discuss this point.

Reviewer #3 (Public Review):

This manuscript investigates the roles of faculty hiring and attrition in influencing gender representation in US academia. It uses a comprehensive dataset covering tenured and tenure-track faculty across various fields from 2011 to 2020. The study employs a counterfactual model to assess the impact of hypothetical gender-neutral attrition and projects future gender representation under different policy scenarios. The analysis reveals that hiring has a more significant impact on women's representation than attrition in most fields and highlights the need for sustained changes in hiring practices to achieve gender parity.

Strengths:

Overall, the manuscript offers significant contributions to understanding gender diversity in academia through its rigorous data analysis and innovative methodology.

The methodology is robust, employing extensive data covering a wide range of academic fields and institutions.

Weaknesses:

The primary weakness of the study lies in its focus on US academia, which may limit the generalizability of its findings to other cultural and academic contexts.

We agree that the U.S. focus of this study limits the generalizability of our findings. The findings that we present in this work will only generalize to other populations–whether it be to an alternate industry, e.g., tech workers, or to faculty in different countries–to the extent that these other populations share similar hiring patterns, retention patterns, and current demographic representation. We have added a discussion of this limitation to the manuscript.

Additionally, the counterfactual model's reliance on specific assumptions about gender-neutral attrition could affect the accuracy of its projections.

Our projection analysis is intended to illustrate the potential gender representation outcomes of several possible counterfactual scenarios, with each projection being conditioned on transparent and simple assumptions. In this way, the projection analysis is not intended to predict or forecast the future.

To resolve this point for our readers, we now introduce our projections in the context of the related terms of prediction and forecast, noting that they have distinct meanings as terms of art: On one hand, prediction and forecasting involve anticipating a specific outcome based on available information and analysis, and typically rely on patterns, trends, or historical data to make educated guesses about what will happen. Projections are based on assumptions and are often presented in a panel of possible future scenarios. While predictions and forecasts aim for precision, projections (which we make in our analysis) are more generalized and may involve a range of potential outcomes.

Additionally, the study assumes that whoever disappeared from the dataset is attrition in academia. While in reality, those attritions could be researchers who moved to another country or another institution that is not included in the AARC (Academic Analytics Research Centre) dataset.

In our revision, we have elevated this important point, and clarified it in the context of the various ways in which we count hires and attritions. We now explicitly state that “We define faculty hiring and faculty attrition to include all cases in which faculty join or leave a field or domain within our dataset.” Then, we enumerate the number of situations that could be counted as hires and attritions, including the reviewer’s example of faculty who move to another country.

Reviewer #1 (Recommendations For The Authors):

Section B: The authors use an age structured Leslie matrix model (see Caswell for a good reference to these) to test the effect of making the attrition rates or hiring rates equal for men and women. My main concern here is the fitting techniques for the parameters. These are described (a little too!) briefly in section S1B. Some specific questions that are left hanging include:

A 5th order polynomial is an interesting choice. Some statistical evidence as to why it was the best fit would be useful. What other candidate models were compared? What was the "best fit" judgement made with: AIC, r^2? What are the estimates for how good this fit is? How many data points were fitted to? Was it the best fit choice for all of the 111 fields for men and women?

We use a logistic regression model for each field to infer faculty attrition probabilities across career ages and time, and we include the career age predictor up to its fifth power to capture the career-age correlations observed in Spoon et. al., Science Advances, 2023. For ease of reference, we reproduce the attrition risk curves in Fig S4.

We note that faculty attrition rates start low and then reach a peak around 5-7 years after earning PhD, and then decline until around 15-20 years post-PhD, after which, attrition rates increase as faculty approach retirement.

This function shape starts low and ends high, and includes at least one local minimum, which indicates that career age should be odd-ordered in the model and at least order-3, but only including career age up to its 3rd order term tended to miss some of the overserved career-age/attrition correlations. We evaluated the fit using 5-fold cross validation with a Brier score loss metric, and among options of polynomials of degree 1, 3, 5, or 7, we found that 5th order performed well overall on average over all fields (even if it was not the best for every field), without overfitting in fields with fewer data. Example fits, reminiscent of the figure from Spoon et al, are now provided in Figs S4 and S5.

While the model fit with fifth order terms may not be the best fit for all 111 fields (e.g., 7th order fits better in some cases), we wanted to avoid field-specific curves that might be overfitted to the field-specific data, especially due to low sample size (and thus larger fluctuations) on the high career age side of the function. Our main text and supplement now includes justifications for our choice to include career age up to its fifth order terms.

You used the 5th order logistic regression (bottom of page 11) to model attrition at different ages. The data in [24] shows that attrition increases sharply, then drops then increases again with career age. A fifth order polynomial on its own could plausibly do this but I associate logistic regression models like this as being monotonically increasing (or decreasing!), again more details as to how this worked would be useful.

Our first submission did not explain this point well, but we hope that Supplementary Figures S4 and S5 provide clarity. In short, we agree of course that typical logistic regression assumes a linear relationship between the predictor variables and the log odds of the outcome variable. This means that the relationship between the predictor variables and the probability of the outcome variable follows a sigmoidal (S-shaped) curve. However, the relationship between the predictor variables and the outcome variable may not be linear.

To capture more complex relationships, like the increasing, decreasing and then increasing attrition rates as a function of career age, higher-order terms can be added to the logistic regression model. These higher-order terms allow the model to capture nonlinear relationships between the predictor variables and the outcome variable — namely the non-monotonic relationship between rates of attrition and career age — while staying within a logistic regression framework.

"The career age of new hires follows the average career age distribution of hires" did you use the empirical distribution here or did you fit a standard statistical distribution e.g. Gamma?

We used the empirical distribution. This information has been added to the updated methods section in the main text.

How did you account for institution (presumably available)? Your own work has shown that institution types plays a role which could be contributing to these results.

See below.

What other confounding variables could be at play here, what is available as part of the data and what happens if you do/don't account for them?

A number of variables included in our data have been shown to correlate with faculty attrition, including PhD prestige, current institution prestige, PhD country, and whether or not an individual is a “self-hire,” i.e., trained and hired at the same institution (Wapman et. al., Nature, 2022). Additional factors that faculty self-report as reasons for leaving academia include issues of work-life balance, workplace climate, and professional reasons, and in some cases to varying degrees between men and women faculty (Spoon et. al., Sci. Adv., 2023).

Our counterfactual analysis aims to address a specific question: how would women’s representation among faculty be different today if men and women were subjected to the same attrition patterns over the past decade? To answer this question, it is important to account for faculty career age, which we accept as a variable that will always correlate strongly with faculty attrition rates, as long as the tenure filter remains in place and faculty continue to naturally progress towards retirement age. On the other hand, it is less clear why PhD country, self-hire status, or any of the other mentioned variables should necessarily correlate with attrition rates and with gendered differences in attrition rates more specifically. While some or all of these variables may underlie the causal roots of gendered attrition rates, our analysis does not seek to answer causal questions about why faculty leave their jobs (e.g., by testing the impact of accounting for these variables in simulations per the reviewers suggestion). This is because we do not believe the data used in this analysis is sufficient to answer such questions, lacking comprehensive data on faculty stress (Spoon et. al., Sci. Adv., 2023), parenthood status, etc.

What career age range did the model use?

The career age range observed in model outcomes are a function of the empirically derived attrition rates for faculty across academic fields. The highest career age observed in the AARC data was 80, and the faculty career ages that result from our model simulations and projections do not exceed 80.

We have also added the distribution of faculty across career ages for the projection scenario model outputs in the supplemental materials Fig. S3 (see response to your later comment regarding career age for further details). Looking at these distributions, it is observed that very few faculty have career age > 60, both in observation and in our simulations.

What was the initial condition for the model?

Empirical 2011 Faculty rosters are used as the initial conditions for the counterfactual analysis, and 2020 faculty rosters are these as the initial conditions for the projections analysis. This information has been added to the descriptions of methods in the main text.

Starting the model in 2011 how well does it fit the available data up to 2020?

Thank you for this suggestion. We ran this analysis for each field starting in 2011, and found that model outcomes were statistically indistinguishable from the observed 2020 faculty gender compositions for all 111 academic fields. This finding is not surprising, because the model is fit to the observed data, but it serves to validate the methods that we used to extract the model's parameters. We have added these results to the supplement (Fig. S2).

What are the sensitivity analysis results for the model? If you have made different fitting decisions how much would the results change? All this applied to both the hiring and attrition parameters estimates.

We model attrition and hiring using logistic regression, with career age included as an exogenous variable up to its fifth power. A natural question follows: what if we used a model with career age only to its first or third power? Or to higher powers? We performed this sensitivity analysis, and added three new figures to the supplement to present these findings:

First, we show the observed attrition probabilities at each career age, and four model fits to attrition data (Supplementary Figs S4 and S5). The first model includes career age only to its first power, and this model clearly does not capture the full career age / attrition correlation structure. The second model includes career age to its third power, which does a better job of fitting to the observed patterns. The third model includes career age up to its fifth power, which appears to very modestly improve upon the former model. The fourth model includes career age up to its seventh power, and the patterns captured by this model are largely the same as the 5th-power model up to career age 50, beyond which there are some notable differences in the inferred attrition probabilities. These differences would have relatively little impact on model outcomes because the vast majority of faculty have a career age below 50.

Second, we show the observed probability that hires are women, conditional on the career age of the hire. Once again, we fit four models to the data, and find that career age should be included at least up to its fifth order in order to capture the correlation structures between career age and the gender of new hires. However, limited differences result from including career age up to the 7th degree in the model (relative to the 5th degree).

As a final sensitivity analysis, we reproduce Fig. 2, but rather than including career age as an exogenous variable up to its fifth power in our models for hiring and attrition, we include career age up to its third power. Findings under this parameterization are qualitatively very similar to those presented in Fig. 2, indicating that the results are robust to modest changes to model parameterization (shown in supplement Fig. S6).

Far more detail in this and some interim results from each stage of the analysis would make the paper far more convincing. It currently has an air of "black box" too much of the analysis which would easily allow an unconvinced reader to discard the results.

We have added more detailed descriptions of the methods to the main text. We hope that the changes made will address these concerns.

Section C: You use the Leslie model to predict the future population. As the model is linear the population will either grow exponentially (most likely) or dwindle to zero. You mention you dealt with this by scaling the average value of H to keep the population at 2020 levels? This would change the ratio of hiring to attrition. How did this affect the timescale of the results. If a field had very minimal attrition (and hence grew massively over the time period of the dataset) the hiring rate would have to be very small too so there would be very little change in the gender balance. Did you consider running the model to steady state instead?

We chose the 40 year window (2020-2060) for this projection analysis because 40 years is roughly the timespan of a full-length faculty career. In other words, it will take around 40 years for most of the pre-existing faculty from 2020 to retire, such that the new, simulated faculty will have almost entirely replaced all former faculty by 2060.

For three out of five of our projection scenarios (OA, GNA, OA+ER), the point at which observed faculty are replaced by simulated faculty represents steady state. One way to check this intuition is to observe the asymptotic behavior of the trajectories in Fig. 3B; the slopes for these 3 scenarios nearly level out within 40 years.

The other two scenarios (OA + IR, GNA+IR) represent situations where women’s representation among new hires is increasing each year. These scenarios will not reach steady state until women represent 100% of faculty. Accordingly, the steady state outcomes for these scenarios would yield uninteresting results; instead, we argue that it is the relative timescales that are interesting.

What did you do to check that your predictions at least felt realistic under the fitted parameters? (see above for presenting the goodness of fit over the 10 years of the data).

We ran the analysis suggested in a prior comment (Starting the model in 2011 how well does it fit the available data up to 2020?) and found that model outcomes were statistically indistinguishable from the observed 2020 faculty gender compositions for all 111 academic fields, plus the “All STEM” and “All non-STEM” aggregations.

You only present the final proportion of women for each scenario. As mentioned earlier, models of this type have a tendency to lead to strange population distributions with wild age predictions and huge (or zero populations). Presenting more results here would assuage any worries the reader had about these problems. What is the predicted age distribution of men and women in the long term scenarios? Would a different method of keeping the total population in check have yielded different results? Interim results, especially from a model as complex as this one, rather than just presenting a final single number answer are a convincing validation that your model is a good one! Again, presenting this result will go a long way to convincing readers that your results are sound and rigorous.

Thank you for this suggestion. We now include a figure that presents faculty age distributions for each projection scenario at 2060 against the observed faculty age distribution in 2020 (pictured below, and as Fig. S3 in the supplementary materials). We find that the projected age distributions are very similar to the observed distributions for natural sciences (shown) and for the additional academic domains. We hope this additional validation will inspire confidence in our model of faculty hiring and attrition for the reviewer, and for future readers.

In Fig S3, line widths for the simulated scenarios span the central 95% of simulations.

Other people have reached almost identical conclusions (albeit it with smaller data sets) that hiring is more important than attrition. It would be good to compare your conclusions with their work in the Discussion.

We have revised the main text to cite the listed examples of similar studies. We thank the reviewer for bringing these relevant works to our attention.

General comments:

What thoughts have you given to non-binary individuals?

Be careful how you use the term "gender diversity"! In many countries "Gender diverse" is a term used in data collection for non-binary individuals, i.e. Male, female, gender diverse. The phrase "hiring more gender diverse faculty" can be read in different ways! If you are only considering men and women then gender balance may be a better framework to use.

We have added language to the main text which explicitly acknowledges that our analysis focuses on men and women due to limitations in our name-based gender tool, which only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

We have also taken additional care with referring to “gender diversity,” per reviewer 1’s point in their public review.

Reviewer #2 (Recommendations For The Authors):

Data availability: I did not see an indication that the dataset used here is publicly available, either in its raw format or as a summary dataset. Perhaps this is due to the sensitive nature of the data, but regardless of the underlying reason, the authors should include a note on data availability in the paper.

The dataset used for these analyses were obtained under a data use agreement with the Academic Analytics Research Center (AARC). While these data are not publicly available, researchers may apply for data access here: https://aarcresearch.com/access-our-data.

We also added a table to the supplemental materials (Tab. S3) that reports the estimated number of men and women in each of the 111 fields.

Additionally, a variety of summary statistics based on this dataset are available online, here: https://github.com/LarremoreLab/us-faculty-hiring-networks/tree/main

Gender classification: Was an existing package used to classify gender from names in the dataset, or did the authors develop custom code to do so? Either way, this code should be cited. I would also be curious to know what the error rate of these classifications are, and suggest that additional information on potential biases that might result from automated classifications be included in the discussion, under the section describing data limitations. The reliability of name-based gender classification is particularly of interest, as external gender classifications such as those applied on the basis of an individual's name - may not reflect the gender with which an individual self-identifies. In other words, while for many people their names may reflect their true genders, for others those names may only reflect their gender assigned at birth and not their self-perceived or lived gender identity. Nonbinary faculty are in particular invisibilized here (and through any analysis that assigns binary gender on the basis of name). While these considerations do not detract from the main focus of the study - which was to utilize an existing dataset classified only on the basis of binary gender to assess trends for women faculty-these limitations should be addressed as they provide additional context for the interpretation of the results and suggest avenues for future research.

We use a free, open-source, and open-data python package called nomquamgender (Van Buskirk et al, 2023) to estimate the strengths of (culturally constructed) name-gender associations. For sufficiently strong associations with a binary gender, we apply those labels to the names in our data. We have updated the main text to make this approach more apparent.

We have also added language to the main text which explicitly acknowledges that our approach only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

As we mentioned in response to the public review, we use a free and open source python package called nomquamgender to estimate the strengths of name-gender associations, and we apply gender labels to the names with sufficiently strong associations with a binary gender. This package is based on a paper by Van Buskirk et. al. 2023, “An open-source cultural consensus approach to name-based gender classification,” which documents error rates and potential biases.

We have also added language to the main text which explicitly acknowledges that our approach only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

Page 1: The sentence beginning "A trend towards greater women's representation could be caused..." is missing a conjunction. It should likely read: "A trend towards greater women's representation could be caused entirely by attrition, e.g., if relatively more men than women leave a field, OR entirely by hiring..."

We have edited the paragraph to remove the sentence in question.

Pages 1-2: The sentence beginning "Although both types of strategy..." and ending with "may ultimately achieve gender parity" is a bit of a run-on; perhaps it would be best to split this into multiple sentences for ease of reading.

We have revised this run-on sentence.

Page 2: See comments in the public review about a methods section, the addition of which may help to improve clarity for the readers. Within the existing descriptions of what I consider to be methods (i.e., the first three paragraphs currently under "results"), some minor corrections could be added here. First, consider citing the source of the dataset in the line where it is first described (in the sentence "For these analyses, we exploit a census-level dataset of employment and education records for tenured and tenure-track faculty in 12,112 PhD-granting departments in the United States from 2011-2020.") It also may be helpful to include context here (or above, in the discussion about institutional analyses) about how "departments" can be interpreted. For example, how many institutions are represented across these departments? More information on how the authors eliminated the gendered aspect of patterns in their counterfactual model would be helpful as well; this is currently hinted at on page 4, but could instead be included in the methods section with a call-out to the relevant supplemental information section (S2B).

We have added a citation to Academic Analytics Research Center’s (AARC) list of available data elements to the data’s introduction sentence. We hope this will allow readers to familiarize themselves with the data used in our analysis.

Faculty department membership was determined by AARC based on online faculty rosters. 392 institutions are represented across the 12,112 departments present in our dataset. We have updated the main text to include this information.

Finally, we have added a methods section to the main text, which includes information on how the gendered aspect of attrition patterns were eliminated in the counterfactual model.

Page 2: Perhaps some indication of how many transitions from an out-of-sample institution might be helpful to readers hoping to understand "edge cases."

In our analysis, we consider all transitions from out-of-sample institutions to in-sample institutions as hires, and all transitions away from in-sample institutions–whether it be to an out of sample institution, or out of academia entirely–as attritions. We choose to restrict our analysis of hiring and attrition to PhD granting institutions in the U.S. in this way because our data do not support an analysis of other, out-of-sample institutions.

I also would have liked additional information on how many faculty switched institutions but remained "in-sample and in the same field" - and the gender breakdowns of these institutional changes, as this might be an interesting future direction for studies of gender parity. (For example, readers may be spurred to ask: if the majority of those who move institutions are women, what are the implications for tenure and promotion for these individuals?)

While these mid-career moves are not counted as attritions in the present analysis, a study of faculty who switch institutions but remain (in-sample) as faculty could shed light on issues of gendered faculty retention at the level of institutions. We share the reviewer’s interest in a more in depth study of mid-career moves and how these moves impact faculty careers, and we now discuss the potential value of such a study towards the end of the paper. In fact, this subject is the topic of a current investigation by the authors!

Page 3: I was confused by the statement that "of the three types of stable points, only the first point represents an equitable steady-state, in which men and women faculty have equal average career lengths and are hired in unchanging proportions." Here, for example, computer science appears to be close to the origin on Figure 1, suggesting that hiring has occurred in "unchanging proportions" over the study interval. However, upon analysis of Table S2, it appears that changes in hiring in Computer Science (+2.26 pp) are relatively large over the study interval compared to other fields. Perhaps I am reading too literally into the phrase that "men and women faculty are hired in unchanging proportions" - but I (and likely others) would benefit from additional clarity here.

We had created an arrow along with the computer science label in Fig. 1, but it was difficult to see, which is likely the source of this confusion. This was our fault, and we have moved the “Comp. Sci.” label and its corresponding arrow to be more visible in Figure 1.

Changes in women’s representation in Computer Science due to hiring over 2011 - 2020 was +2.26 pp as the reviewer points out, but, consulting Fig. 1 and the corresponding table in the supplement, we observe that this is a relatively small amount of change compared to most fields.

Page 3: If possible it may be helpful to cite a study (or multiple) that shows that "changes in women's representation across academic fields have been mostly positive." What does "positive" mean here, particularly when the changes the authors observe are modest? Perhaps by "positive" you mean "perceived as positive"?

We used the term positive in the mathematical sense, to mean greater than zero. We have reworded the sentence to read “women's representation across academic fields has been mostly increasing…” We hope this change clarifies our meaning to future readers.

Page 3: The sentence that ends with "even though men are more likely to be at or near retirement age than women faculty due to historical demographic trends" may benefit from a citation (of either Figure S3 or another source).

We now cite the corresponding figure in this sentence.

Page 4: The two sentences that begin with "The empirical probability that a person leaves their academic career" would benefit from an added citation.

We have added a citation to the sentences.

Figure 3: Which 10 academic domains are represented in Panel 3B? The colors in appear to correspond to the legend in Panel 3A, but no indication of which fields are represented is provided. If possible, please do so - it would be interesting and informative to be able to make these comparisons.

This was not clear in the initial version of Fig. 3B, so we now label each domain. For reference, the domains represented in 3B are (from top to bottom):

● Health

● Education

● Journalism, Media, Communication

● Humanities

● Social Sciences

● Public Administration and Policy

● Medicine

● Business

● Natural Sciences

● Mathematics and Computing

● Engineering

Page 6: Consider citing relevant figure(s) earlier up in paragraph 2 of the discussion. For example, the first sentence could refer to Figure 1 (rather than waiting until the bottom of the paragraph to cite it).

Thank you for this suggestion, we now cite Fig. 1 earlier in this discussion paragraph.

Page 10: A minor comment on the fraction of women faculty in any given year-the authors assume that the proportion of women in a field can be calculated from knowing the number of women in a field and the number of men. This is, again, true if assuming binary genders but not true if additional gender diversity is included. It is likely that the number of nonbinary faculty is quite low, and as such would not cause a large change in the overall proportions calculated here, but additional context within the first paragraph of S1 might be helpful for readers.

We have added additional context in the first paragraph of S1, explaining that an additional term could be added to the equation to account for nonbinary faculty representation if our data included nonbinary gender annotations. Thank you for making this point.

Page 10: Please include a range of values for the residual terms of the decomposition of hiring and attrition in the sentence that reads "In Figure S1 we show that the residual terms are small, and thus the decomposition is a good approximation of the total change in women's representation."

These residual terms range from -0.51pp to 1.14pp (median = 0.2pp). We have added this information to the sentence in question.

Page 12: It may be helpful to readers to include a description of the information contained in Table S2 in the supplemental text under section S3.

We refer to table S2 twice in the main text (once in the observational findings, and once for the counterfactual analysis), and the contents of table S2 are described thoroughly in the table caption.

Reviewer #3 (Recommendations For The Authors):

(1) There is a potential limitation in the generalizability of the findings, as the study focuses exclusively on US academia. Including international perspectives could have provided a more global understanding of the issues at hand.

The U.S. focus of this study limits the generalizability of our findings, as non-U.S. other faculty may exhibit differences in hiring patterns, retention patterns, and current demographic representations. We have added a discussion of this limitation to the manuscript. Unfortunately, our data do not support international analyses of hiring and attrition.

(2) I am not sure that everyone who disappeared from the AARC dataset could be count as "attrition" from academia. Indeed, some who disappeared might have completely left academia once they disappeared from the AARC dataset. Yet, there's also the possibility that some professors left for academic positions in countries outside of the US, or US institutions that are not included in the AARC dataset. These individuals didn't leave academia. Furthermore, it is also possible that these scholars who moved to an institution outside of US or not indexed by AARC are gender specific. Therefore, analyses that this study conducts should find a way to test whether the assumption that anyone who disappeared from AARC is indeed valid. If not, how will this potentially challenge the current conclusions?

The reviewer makes an important point: faculty who move to faculty positions in other countries and faculty who move to non-PhD granting institutions, or to institutions that are otherwise not included in the AARC data are all counted as attritions in our analysis. We intentionally define hiring and attrition broadly to include all cases in which faculty join or leave a field or domain within our dataset.

The types of transitions that faculty make out of the tenure track system at PhD granting institutions in the U.S. may correlate with faculty attributes, like gender. For example, women or men may be more likely to transition to tenure track positions at non-U.S. institutions. Nevertheless, these types of career transition represent an attrition for the system of study, and a hire for another system. Following this same logic, faculty who transition from one field to another field in our analysis are treated as an attrition from the first field and a hire into the new field.

By focusing on “all-cause” attrition in this way, we are able to make robust insights for the specific systems we consider (e.g.,, STEM and non-STEM faculty at U.S. PhD granting institutions), without being roadblocked by the task of annotating faculty departures and arbitrating which should constitute “valid” attritions.

(3) It would be very interesting to know how much of the attribution was due to tenure failure. Previous studies have suggested that women are less likely to be granted tenure, which makes me wonder about the role that tenure plays in the gendered patterns of attrition in academia.

We note that faculty attrition rates start low and then reach a peak around 5-7 years after earning PhD, and then decline until around 15-20 years post-PhD, after which, attrition rates increase as faculty approach retirement. The first local maximum appears to coincide roughly with the tenure clock timing, but we can only speculate that these attritions are tenure related. Our dataset is unfortunately not equipped to determine the causal mechanisms driving attrition.

We reproduce the attrition risk curve in the supplementary materials, Fig. S4:

(4) The dataset used doesn't fully capture the complexities of academic environments, particularly smaller or less research-intensive institutions (regional universities, historically black colleges and universities, and minority-serving institutions). This could be potentially added to the manuscript for discussions.

We have added this point to the description of this study’s limitations in the discussion.

Author response:

We thank the reviewers for their thoughtful consideration of our study and are delighted they found the findings to be important. In this initial response to the overall positive reviews, we want to address common themes raised, clarify points relevant to a few specific reviewer concerns, and frame plans for the revised manuscript.

(1) Analysis of data from human tissue: Reviewer 1 notes “In their analyses of enteric glia from existing single-cell transcriptomic data sets, it is stated that these come from 'non-diseased' humans. However, the data on the small intestine is obtained from children with functional gastrointestinal disorders (Zheng 2023). Data on colonic enteric glia was obtained from colorectal cancer patients (Lee 2020). Although here the cells were isolated from non-malignant regions, saying that the large intestines of these patients are non-diseased is probably an overstatement.

In the Zheng et al. dataset, “functional GI disorders” refers to biopsies from children that do not have any histopathologic evidence of digestive disease. The children do, however, have at least one GI symptom that prompted a diagnostic endoscopy with biopsies, leading to the designation of “functional” disorder. Given that diagnostic endoscopies are invasive procedures that necessitate anesthesia, obtaining biopsies from completely healthy, asymptomatic children without any clinical indication would not be allowable per most institutional review boards, leading the authors of that study to use these samples as a control group. We thus used the “non-diseased” label to encompass these samples as well as those from the unaffected regions of large intestine from colorectal cancer patients. We recognize, however, that this label might be misleading and will revise the manuscript to more accurately reflect the information on control tissue origin.

Another existing dataset including human mucosal enteric glia of healthy subjects is presented in Smillie et al (2019). It would be interesting to see how the current findings relate to the data from Smillie et al.” 

We thank the reviewer for directing us to the Smillie et al. 2019 dataset. This dataset derives from colonic mucosal biopsies from 12 healthy adults (8480 stromal cells) and 18 adults with ulcerative colitis (10,245 stromal cells from inflamed bowel segments and 13,146 from uninflamed), all between the ages of 20-77 years. Our preliminary analysis shows that the putative glial cluster in this dataset does not separate by inflammation or disease state based on the common glial genes: S100B, PLP1, and SOX10. PLP1 and S100B are broadly expressed across this cluster while GFAP is not detected in this dataset, consistent with our observations from the two other human datasets included in our manuscript. In the revised manuscript, we will include the Smillie et al. 2019 data in a supplemental figure as additional supportive evidence.

(2) Validation and further details of the Plp1CreER-DTA model for genetic depletion of enteric glia: Reviewer 1 notes “The time between enteric glia depletion and analyses (mouse sacrifice) must be a crucial determinant of the type of effects, and the timing thereof. In the current study 11 days after tamoxifen treatment was chosen as the time point for analyses, which is consistent with earlier work by the lab using the same model (Rao et al 2017). What would happen when they wait longer than 11 days after tamoxifen treatment?”  Reviewer 3 asks whether “the Plp1CreER Rosa26DTA/+ mice system established correctly” and raises concern about quantitative characterization.

In previous work, we discovered that the gene Plp1 is broadly expressed by enteric glia and, within the mouse intestine, is quite specific to glial cells (PMID: 26119414). We characterized the Plp1CreER mouse line as a genetic tool in detail in this initial study. Then in a subsequent study, we used Plp1CreER-DTA mice to genetically deplete enteric glia and study the consequences on epithelial barrier integrity, crypt cell proliferation, enteric neuronal health and gastrointestinal motility (PMID: 28711628). In this second study, we performed extensive validation of the Plp1CreER-DTA mouse model including detailed quantification of glial depletion in the small and large intestines across the myenteric, intramuscular and mucosa compartments by immunohistochemical (IHC) staining of whole tissue segments to sample thousands of cells. We found that the majority of S100B+ enteric glia were depleted within 5 days in both sexes, including more than 88% loss of mucosal glia, and that this loss was stable at 3 subsequent timepoints (7, 9 and 14 days post-tamoxifen induction of Cre activity). Glial loss was further confirmed by IHC for GFAP in the myenteric plexus, and by ultrastructural analysis of the small intestine to ensure cell depletion rather than simply loss of marker expression. Our group was the first to use this model to study enteric glia, and since then similar models and our key observations have been replicated by other groups (PMID: 33282743, 34550727). Thus, we consider this model to be well established.

Reviewer 1 raises an excellent question about examining epithelial health beyond 11 days post-tamoxifen (11dpt) in this model. Particularly given the longer-lived nature of Paneth cells relative to other epithelial cell types, this would be very interesting to explore. Through 11dpt, Cre+ mice are well-appearing and indistinguishable from their Cre-negative control littermates. Unfortunately, a limitation of the Plp1CreER-DTA model is that beyond 11dpt, Cre+ mice become anorexic, lose body weight, and have signs of neurologic debility such as hindlimb weakness and uncoordinated gait that are prominent by 14dpt. These phenotypes are likely the consequence of targeting Plp1+ glia outside the gut, such as Schwann cells and oligodendrocytes (as described in another study which used a similar model to study demyelination in the central nervous system, PMID: 20851998). Given these CNS effects and that starvation is well known to affect Paneth cell phenotypes (PMIDs: 1167179, 21986443), we elected not to examine timepoints beyond 11dpt. Technological advances that enable more selective cell depletion would allow study of more chronic effects of enteric glial loss.

(3) Sex differences in the microbiome data: All 3 reviewers queried whether there were sex differences in the microbiome data with Reviewer 1 explaining “Previously the authors showed that enteric glia regulation of intestinal motility is sex-dependent (Rao et al 2017). While enteric glia depletion caused dysmotility in female mice, it did not affect motility in males. For this reason, most experiments in the current study were conducted in male mice only. However, for the experiments focusing on the effect of enteric glia depletion on host-microbiome interactions and intestinal microbiota composition both male and female mice were used. In Figure 8A male and female mice are distinctly depicted but this was not done for Figure 8C. Separate characterization of the microbiome of male and female mice would have helped to figure out how much intestinal dysmotility (in females) contributes to the effect on gut microbial composition. This is an important exercise to confirm that the effect on the microbiome is indeed a consequence of altered Paneth cell function…”

In our microbiome analysis, we initially analyzed males and females separately but did not observe significant differences between the two sexes. Thus, we merged the data to increase the statistical power of the genotype comparisons. It was an oversight on our part to not label the female and male datapoints in Figure 8C as we did for the other data in the manuscript. We will update this graph and related supplemental figures in the revised version. Per Reviewer 2’s suggestion, we will also address this further in the Results and Discussion.

(4) Reconciling RNA-Seq identification of transcriptional changes in the colon, but not the small intestine, while the GSEA and downstream tissue level morphological and functional analyses detected phenotypes in the small intestine. Reviewers 1 and 3 raised this question with Reviewer 1 noting “…enteric glia depletion was found to affect Paneth cells structurally and functionally in the small intestine, where transcriptional changes were initially not identified. Only when performing GSEA with the in silico help of cell type-specific gene profiles, differences in Paneth cell transcriptional programs in the small intestine were uncovered. A comment on this discrepancy would be helpful, especially for the non-bioinformatician readers among us.” 

Standard differential gene expression analysis (DEG) of the effects of glial loss revealed significant differences only in the colon, and even there only a handful of genes were changed. These changes were not accompanied by corresponding changes at the protein level, at least as detectable by IHC. In the small intestine, there were no significant differences by standard DEG thresholds. Unlike DEG, gene set enrichment analyses (GSEA), provides a significance value based on whether there is a higher than chance number of genes that are changing in a uniform direction without consideration for the significance of the magnitude of change. Therefore, the GSEA detected that a significant number of genes in the curated Paneth cell gene list exhibited a positive fold change difference in the bulk RNA sequencing data. This prompted us to examine Paneth cells and other epithelial cell types in more detail by IHC, functional and ultrastructural analyses, which all converged on the observation that Paneth cells were relatively selectively disrupted in the epithelium of glial depleted mice.

(5) Other: We will address all remaining comments in our detailed author response that will accompany our revised manuscript. We thank Reviewer 2 for the very positive feedback overall and highlighting opportunities to better label findings in some of the figures. We will make these suggested changes in our revised manuscript.

Author response:

We thank the reviewers for their highly valuable comments and recommendations on our manuscript. We particularly appreciate receiving reviews from three distinct points of view, all highly relevant to our study (i.e. from an ecological, biomechanics, and evolutionary biology perspective).

We will now carefully address all reviewer comments and questions, and resubmit a revised version in due time. Again, we thank the reviewers for their rigorous assessment of our study, which will greatly help us improving our manuscript.

Author response:

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

eLife assessment

The findings in this study are useful and may have practical implications for predicting DLBCL risk subject to further validating the bioinformatics outcomes. We found the approach and data analysis solid. However, some concerns regarding the drug sensitivity prediction and the links between the selected genes for the risk scores have been raised that need to be addressed by further functional works.

Thanks for your high recognition for our study. In fact, we have searched the treatment information of DLBCL patients in our own cohort, however, unfortunately all patients were treated strictly according to the guidelines issued by authorities of China, which suit Chinese patients fine but do not include the drugs explored in the present study. Therefore, more further investigations should be designed and conducted to validate our conclusion. Here, we provided a possible direction for future studies base on large cohorts, which could not only provide more reliable conclusions, but gain more attentions to the role of tumor microenvironment in influencing outcome and drug sensitivity.

Public Reviews:

Sincere thanks for all reviewers’ positive comments on our study and their helpful recommendations for improving our manuscript. For this part, we have sorted out the comments and recommendations from all reviewers, and made corresponding revisions. And here are our responses.

(1) How did we determined the three genes (VCAN, C1QB and CD3G) in the prognostic model?

Just as was mentioned in the “Prognostic model” in Materials and Methods section, the gene was selected by “survival” package in R. After we obtained the nine genes, we input the expression value of them, and analyzed with “survival” package in R. And the function “step” in that package can optimize the model, that is, to construct a model with as less factors as possible, and the finally enrolled factors were representative and presented the least collinearity. Through this way, the prognostic model we got could be more practical in clinical practice.

(2) Different centers have different protocols of IHC, so how could we put this model into clinical practice under this circumstance?

Not only did different centers have different protocols, the materials like antibodies also vary. Therefore, there is actually a long way to go in putting our study into clinical practice. As far as we’re concerned, there are at least three problems to solve. First, diagnostic antibodies should be used in clinical practice, which usually manifest better specificity and sensitivity. And this may be the reason why the staining of VCAN and C1QB was strong and difficult to differentiate. Second, a standardized protocol should be made. Last but not least, more precise analyses and studies should be conducted to make it clear which type of cells specifically express these genes (just as was mentioned by Reviewer #2). We are now endeavoring to solve these problems by utilizing as many techniques as possible, like multi-omics and mIHC. From revealing the true expression pattern to developing high quality antibodies and even standardized test kit, we are looking forward to a clinical translation.

(3) The analyses about immune infiltration and the key genes in DLBCL were superficial, limited within the correlation analyses.

Due to the model constructed based on tumor purity of DLBCL, the risk score could be associated with the enrichment of cell functions. We conducted GSEA analysis based on the differentially expressed genes between high-risk group and low-risk group in the two datasets (Figure 5H-I). It showed that the extracellular organization and cellular adhesion were different between the two groups, in which way the immune infiltration and activity might be regulated owing to the motility of immune cells. Besides, we have validated the infiltration of M1 macrophages and M2 macrophages with our own cohort (Supplementary Figure 3P).

(4) The drug sensitivity was just analyzed based on the model, which should be validated in real world research or lab study. And the sensitivity score seemed not different too much in most cases, even though there were statistical significance.

We tried to search the treatment information of DLBCL patients in our own cohort, however, unfortunately all patients were treated strictly according to the guidelines issued by authorities of China, which suit Chinese patients fine but do not include the drugs explored in the present study. Therefore, more further investigations should be designed and conducted to validate our conclusion. Here, we provided a possible direction for future studies base on large cohorts, which could not only provide more reliable conclusions, but gain more attentions to the role of tumor microenvironment in influencing outcome and drug sensitivity. As for the differences between high- and low-risk group, as a matter of fact, sometimes a little dose of drug could have a huge effect, because the dose-effect curve is usually nonlinear. Therefore, reduce the dose, even just 1%, the adverse effects could be avoided. To sum up, the drug sensitivity analyses in our study could provide more possibility for clinical trial and practice, and we are taking it into consideration to design reasonable clinical research.

(5) C1QB was associated with decreased tumor purity and worse prognosis, but decreased tumor purity was related to better prognosis. How to elucidate the contradiction?

Just as discussed in Discussion section, previous studies have revealed the role of C1QB in promoting an immunosuppressive microenvironment in cancer (see reference 22-26). C1QB might recruit the infiltration of pro-tumor immune cells, resulting in a reducing tumor purity on its perspective. However, the immune microenvironment was regulated by multi factors which form a network and combat or synergize each other. The statistical analysis often gives a possible phenomenon, but could not provide mechanism explanation. Therefore, more mechanic studies are needed to reveal the connection and key node. This is exactly what we will explore next.

(6) Others:

(1) Line 51 has been rewritten.

(2) References for ESTIMATE algorithm (reference 16) and CD3G+ T cells has been added (reference 17).

(3) The illegible figure labels might be caused by the incompatibility between the PDF file we submitted and the submission system. We have provided the TIFF images in this revision, and the EPS file could be submitted to editors upon their requests.

(4) A supplement description has been added to the Figure legend of Figure 6 to make it clear.

(5) In order to explore the expression of key genes among different locations of DLBCL we performed analyses in Figure5 and supplementary Figure3. These results might be thought-provoking that the tumor microenvironment differs among DLBCLs even though they share similar histological characteristics.

Author response:

We thank the editors and reviewers for their thorough engagement with the manuscript and their well-informed comments on the Poseidon framework. We are pleased to note that they consider Poseidon a promising and timely attempt to resolve important issues in the archaeogenetics community. We also agree with the main challenges they raise, specifically the lack of long-term, independent infrastructure funding at the time of writing, and various aspects of Poseidon that bear the potential to further consolidate a de-facto alienation of the aDNA community from the wider field of genomics.

Poseidon is indeed dependent on the Department of Archaeogenetics at MPI-EVA. For the short to middle-term future (3-5 years) we consider this dependency beneficial, providing a reliable anchor point and direct integration with one of the most proficient data-producing institutions in archaeogenetics. For the long term, as stated in the discussion section of the manuscript, we hope for a snowball effect in the dissemination and adoption of Poseidon to establish it as a valuable community resource that automatically attracts working time and infrastructure donations. To kickstart this process we have already intensified our active community outreach and teach Poseidon explicitly to (early career) practitioners in the field. We are aware of options to apply for independent infrastructure funding, for example through the German National Research Data Infrastructure (NFDI) initiative, and we plan to explore them further.

As the reviewers have noted, key decisions in Poseidon’s data storage mechanism have been influenced by the special path archaeogenetics has taken compared to other areas of genomics. The founding goal of the framework was to integrate immediately with established workflows in the field. Nevertheless we appreciate the concrete suggestions on how to connect Poseidon better with the good practices that emerged elsewhere. We will explicitly address the European Variation Archive in a revised version of the manuscript, deliberate embedding the BioSamples ID of the INSDC databases more prominently in the .janno file, prioritise support for VCF next to EIGENSTRAT and PLINK and add an option to clearly document the relevant human reference genome on a per-sample level. In the revised version of the text we will also explain the treatment of non-overlapping SNPs between studies by trident’s forge algorithm and how we imagine the interplay of different call sets in the Poseidon framework in general.

Beyond these bigger concerns we will also consider and answer the various more detailed recommendations thankfully shared by the reviewers, not least the question how we imagine Poseidon to be used by archaeologists and for archaeological data.

Author response:

We wish to express our sincere acknowledgement to the reviewers and the editors for the time and the effort spent in reviewing our manuscript. We highly appreciate the positive feedback and the thorough and constructive comments.

We plan to conduct additional experiments to address the reviewers’ concerns.

(1) We plan to utilize the RIPK1 kinase dead mice to investigate the role of RIPK1 kinase activity in these metabolic stress responses.

(2) We plan to conduct flow cytometry analysis to detect the percentage or number of different cell types in fasted liver tissue, to provide more accurate and quantitative assessments of monocyte   recruitment.

(3) We plan to conduct more western blotting to detect the expression of related molecules in the signal transduction pathway, to further clarify the underlying mechanisms.

(4) Regarding the single-cell RNA sequencing analysis，we plan to conduct CellChat analysis to provide information about the interactions between different cell populations.

(5) We will fix the issues regarding the data graphs and image resolutions.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

This study is very well framed and the writing is very clear. The manuscript is well organized and easy to follow and overall the previous state of the art of the field is taken into account.  I only have a couple of minor comments 

(1) There is a preprint that uses single nuclei RNA-Seq and ST on human MS subcortical white matter lesions doi: https://doi.org/10.1101/2022.11.03.514906. This work needs to be included in the discussion of the results. 

(1.1) We appreciate the reviewer bringing up this important preprint, and we have referenced it in the Discussion section of our updated manuscript. 

(2) The discussion should include the overall limitations of the study and how much it can be translated to human MS. Specifically, the current work uses EAE and therefore different disease stages are not captured in this study. This point is also raised by other reviewers. 

(1.2) We thank the reviewer for raising this important point, and we have included additional discussion about the limitations of EAE and its disease relevance to MS.

Reviewer #2 (Recommendations For The Authors):

The authors state that this EAE model is better for studying cortical gradients because previous models "such as directly injecting inflammatory cytokines into the meninges/cortex" cause a traumatic injury. It needs to be discussed that these models have now been superseded by more refined models involving long-term overexpression of pro-inflammatory cytokines in the sub-arachnoid space, thereby avoiding traumatic injury. The current results should be discussed in light of these newer models (James et al, 2020; 2022), which are more similar to MS cortical pathology and do exhibit lymphoid-like structures. 

(2.1) We thank the reviewer for pointing out these relevant studies, and we agree they describe non-traumatic and more MS-relevant models of leptomeningeal inflammation. We have included discussion of these works in the updated manuscript.  

• The study will be substantially improved if some of the ST data is validated at least partially with some RNAscope or other in situ hybridization using a subset of probes that capture the take-home message of the paper. 

(2.2) We agree with the reviewer that validation of transcriptomics results is important to support our conclusions. In the updated manuscript Figure 5 and Supplemental Figure 6 we have added RNAscope results for relevant genes. In agreement with the trends noted in the manuscript, expression of genes related to antigen processing and presentation such as B2m decreases gradually with distance from LMI. We also have included a reference to a newly published manuscript from our group (Gupta et al., 2023, J. Neuroinflammation) that characterizes meningeal inflammation and sub-pial changes in the SJL EAE model. In that manuscript, IHC is used to show accumulation of B cells and T cells in the leptomeningeal space, increased microglial and astrocyte reactivity adjacent to leptomeningeal inflammation, and reduction of neuronal markers adjacent to leptomeningeal inflammation.  

• The lack of change in signaling pathways involved in B-cell/T-cell interaction and cytokine/chemokine signaling, which would be expected in areas of immune cell aggregation in the meninges, needs discussion. 

(2.3) While we detected significant upregulation in antigen presentation, complement activation, and humoral immune signaling, areas of meningeal inflammation identified as cluster 11 showed upregulation of numerous other GO gene sets associated with immune cell interaction and cytokine signaling, as described in supplementary table 3. These include T-cell receptor binding, CCR chemokine receptor binding, interleukin 8 production, response to interleukin 1, positive regulation of interleukin-6 production, tumor necrosis factor production, leukocyte cell-cell adhesion. Overall, we believe that the collection of enriched gene sets is consistent with peripheral myeloid and lymphoid infiltration and cytokine production, with the most prominent cytokine / pathways being interferon ɣ/antigen processing and presentation, complement, and humoral inflammation.

• Fig 4 subclusters includes T-cell activation, pos regulation of neuronal death, cellular response to IFNg, neg regulation of neuronal projections, Ig mediated immune response, cell killing, pos regulation of programmed cell death, pos regulation of apoptotic process, but none of these are discussed despite their obvious importance. 

(2.4) We agree with the reviewer that these upregulated genesets warrant additional discussion and have added additional reference to these genesets in the results section. Also, the genesets ‘positive regulation of programmed cell death’, ‘positive regulation of apoptotic process’, and ‘positive regulation of cell death’ were erroneously included in Figure 4F in the initial manuscript, as they are actually downregulated in cluster 1_4. This has been clarified in the text.

• Subcluster 11 appears spatially to represent the meninges, but what pathways are expressed there? 330 genes/pathways altered independent of other clusters - immune cell regulation? 

(2.5) We refer the reviewer to Supplementary Table 3, which contains a complete list of GO genesets enriched within cluster 11 spots.

• The surprising lack of immunoglobulin genes upregulated in the meninges of the mice, considering these are the genes most upregulated in the MS meninges. Should be pointed out and discussed. 

(2.6) We appreciate the reviewer bringing up immunoglobulin genes, which previous publications have shown are elevated in MS meninges and cortical grey matter lesions. Consistent with this, several immunoglobulin genes are elevated in cluster 11, including genes encoding IgG2b, IgA, and IgM. While these results were available within the original submission in Supplementary Table 2, we have included the graph in the updated Supplementary Figure 3.

• Meningeal signature may be poorly represented given the individual slices shown in suppl 3A, which suggests that only 3 of the EAE slices had significant meningeal infiltrates, indicated by cluster 11 genes.  

(2.7) There was heterogeneity in the location and extent of meningeal infiltrate / cluster 11 in the EAE slices, as the reviewer points out. 2 slices had severe inflammation, 2 had moderate inflammation, and 2 had relatively mild inflammation, but all EAE slices were enriched in inflammation relative to naïve as demonstrated not only through clustering, but also through enriched marker analysis between EAE and Naive and Progeny analysis.  

• The ST is not resolving the meningeal tissue and the immediate underlying grey matter, as demonstrated by a high signal for both CXCL13 and GFAP in cluster 11. 

(2.8) We agree that the spatial transcriptomics strategy applied here is inadequate to precisely delineate between meningeal inflammation and the underlying brain parenchyma, and that the elevation of markers such as GFAP in cluster 11 indicates some ‘contamination’ of parenchymal cells into cluster 11. We have clarified this in the text and discussed the limitation of the spatial transcriptomics method used.  

• More information is required concerning how many animals were used in this study, to meet the requirements for complying with the 3Rs. 

(2.9) A total of 4 mice were used per group. In the naïve group one mouse contributed two slices, for a total of 5 naïve slices. In the EAE group two mice contributed two slices, for a total of 6 EAE slices. We have clarified this in the methods section of the updated manuscript.

Reviewer #3 (Recommendations For The Authors):

The authors should provide a more thorough description of the methodology, and there are a few minor concerns about experimental details, data presentation, and description that need to be addressed. In the next few lines, I will highlight a few important aspects that need to be addressed, propose some changes to the main manuscript, and suggest some additional experiments that, if successful, could confirm/support/further strengthen the conclusions that are at this point purely based on transcriptomic data. 

Major comments/suggestions: 

• The main gene expression changes between the control and EAE groups obtained via spatial transcriptomics need to be validated with another technique, at least partially. I suggest performing RNAscope or immunofluorescence imaging using brain sections from a new and independent cohort of animals, where cell-specific markers can also be tested. This type of assessment would work as a validation method and could also inform about the cell-specific contribution to the observed transcriptomic changes. 

(3.1) Please refer to response 2.2 

• The representative qualitative spatial expression heatmaps for each gene in Fig. 1F should be accompanied by corresponding graphs with quantitative measurements. Similar to what is done regarding the data in Fig. 2B and D. 

(3.2) We agree with the reviewer that quantitative graphs were missing, and we have included them in the updated Supplementary Figure 1. 

• A supplementary table discriminating all the DEGs (132 up and 70 downregulated) between cluster 11 and the other clusters has to be provided. What is the contribution of recruited encephalitogenic adaptive immune cells to this cluster 11 gene signature? 

(3.3) These unfiltered results are provided in Supplementary Table 2, and to view the up and down regulated genes the reader can sort the table based on fold change and adjusted P value. We believe providing the complete table is more useful to the reader, since the fold change and

P value thresholds used to determine “significance” are arbitrary. Since the spatial transcriptomics method used in this work does not have single cell resolution, we cannot accurately estimate the contribution of encephalitogenic adaptive immune cells in cluster 11. However, given previously published work of lymphocyte infiltration into the subarachnoid space in SJL EAE (Gupta et al., 2023, J. Neuroinflammation) and the enrichment of Cd3e in cluster 11 (Log2FC 0.31, adjusted P-val 0.005) we assume some contribution of peripheral lymphocytes.

• The authors mention that there is grey matter pathology in this relapse model, and this has been shown in a previous publication (Bhargava et al., 2021). However, the regions analyzed in the present study are different from the ones shown in the referenced paper. Is there an overexpression of genes involved in, or gene modules indicative of, neuronal stress and/or death that spatially overlap with clusters 1 and 2? If so, it would be important to provide information about those gene modules in the main figures. It would also be quite relevant to show the levels of cell stress/death proteins and of axonal stress/damage, by APP and/or nonphosphorylated SMI-32 staining, in the deep brain regions (like the thalamus), to corroborate the link between these phenomena and the gene signatures of subclusters 1_3, 1_4, and 2_6. 

(3.4) We thank the review for this insightful comment. We have recently published a manuscript that histologically analyzes leptomeningeal inflammation in the SJL EAE model, specifically assessing the areas looked at in our submitted manuscript (Gupta et al., 2023, J. Neuroinflammation). In that manuscript, IHC is used to show accumulation of B cells and T cells in the leptomeningeal space, increased microglial and astrocyte reactivity adjacent to leptomeningeal inflammation, and reduction of neuronal markers adjacent to leptomeningeal inflammation. To further describe the gene modules in the inflammatory subclusters 1_3/1_4/2_6, we have now provided heatmaps of the selected genesets and their constituent genes (Supplementary Figure 5). 

• It would be important to provide heatmaps discriminating the DEGs that make the gene modules that are significantly altered in subclusters 1_3, 1_4, and 2_6. The gene ontology terms are sometimes ambiguous. For instance, it would be very informative to the reader (and to the field) to know which altered genes compose the "lysosome", "immune response", "response to stress", or "B cell meditated immunity" pathways that are altered in the EAE subcluster 1_3 (Fig. 4E). The same applies to the gene modules altered in the other subclusters of interest. Authors should also consider generating a Venn diagram with the DEGs from subclusters 1_3, 1_4, and 2_6, to complement the GO term Venn presented in Fig. 4H. Having these pieces of information readily available, either as main or supplementary figures, would be a great addition. 

(3.5) We agree with the reviewer on this point and have included these heatmaps in Supplementary Figure 5. 

• The role of IFN-gamma as well as B cells (and Igs) in myelination/remyelination is mentioned in the discussion. However, there is very little evidence that these cells or their cytokines/Igs are mediating the described transcriptomic signatures at the level of the brain parenchyma of EAE mice undergoing relapse. Do the "antigen processing and presentation, cell killing, interleukin 6 production, and interferon gamma response" go terms, which better fitted the trajectory analysis, in fact include genes expressed almost exclusively by T and/or B cells? Are there genes that are downstream of IFN type I or II signaling? 

(3.6) Pathways including antigen processing / presentation, humoral inflammation, complement, among others were enriched in areas of meningeal inflammation and adjacent areas of parenchyma. These signaling pathways are mediated by effector molecules, many of which are produced by lymphocytes, but that can act on cells within the CNS parenchyma. The heatmaps in Supplementary Figure 5 demonstrate the significant role of MHC and complement genes, which could be expressed by leukocytes as well as glia, on many of the pathways.

• Is the transcriptomic overlap between meningeal and brain parenchymal regions, or the appearance of signatures similar to the parenchymal subclusters 1_3, 1_4, and 2_6, prevented if the mice are treated with the murine versions of natalizumab or rituximab prior relapse? 

(3.6) We appreciate the reviewers suggestion. Our future directions for this work includes testing the effects of disease modifying therapies on spatial and single-cell transcriptomic readouts of disease in SJL EAE.

• Please clarify what control group was used in this study. Naïve mice are mentioned in the Results section, does this mean that control animals were not injected with CFA? Authors should also elaborate on the descriptive methodology employed for the analysis of the spatial

transcriptomics data - especially regarding the trajectory analysis. As is, overall, the methodology description might not favor reproducibility. 

(3.7) We appreciate the need for clarification here. Our control group in this study was naïve, not having received any CFA or pertussis toxin. While often used as the control in EAE studies focused on mechanisms of autoimmunity, CFA and pertussis toxin independently induce systemic inflammation. Since in this study we were interested in neuroinflammation broadly, we chose to use a naïve comparison group to maximize our ability to find genes enriched in neuroinflammation. We have elaborated our methods section, including methods related to trajectory analysis. 

Minor comments/suggestions: 

In Fig. 1D the indication of the rostral to ventral axis needs to be inverted. 

Addressed.

In Fig. 1E the authors should also include a representative H&E staining of the same region in a control animal. 

Addressed.

There is inconsistency in the number of clusters obtained after UMAP unbiased clustering of the spatial transcriptomic data: 

• Fig. 3A-E - twelve clusters are shown (cluster 0 to 11). 

• In the Results section eleven clusters are mentioned - "we performed unbiased UMAP clustering on the spatial transcriptomic dataset and identified 11 distinct clusters".

The text was incorrect, there were 12 distinct clusters. This has been corrected.

Considering the mice strain used was SJL/J mice, the peptide used to induce EAE should be PLP139-151, as mentioned in the Methods section "Induction of SJL EAE". However, the legend of Fig. 1 mentions "post immunization with MOG 35-55". Please correct this. 

Corrected.

In the Methods section it is mentioned "At 12 weeks post-immunization, animals were euthanized", however the Results section mentions that tissues were harvested at 11 weeks post-immunization - "Brain slices were collected from four naïve mice and four EAE mice 11 weeks postimmunization". Please correct this. 

The Methods were incorrect, this has now been fixed. 

Please clarify the number of animals used for spatial transcriptomic analysis: 

• Legend of Fig. 1 mentions "Red arrows indicate MRI time points, black arrow indicates time of tissue harvesting (N = 6)." Whilst in the Results section it states "Brain slices were collected from four naïve mice and four EAE mice". 

The figure one legend has now been corrected (N = 4). Additionally, we have added clarification about the number of animals / slices used in the Methods section (see response 2.9).

Please be consistent in the way of representing DEGs in the MA plots: 

• Fig. 3F shows the upregulated genes (in red) on the right and the downregulated genes (in blue) on the left. 

• Supplemental Fig. 2K shows the upregulated genes (in red) on the left and the downregulated genes (in blue) on the right. 

• Supplemental Fig. 4 shows the upregulated genes on the right in blue, while the downregulated genes are in red. 

This has been fixed.

The letters attributed to each subcluster in panels E-G of Fig. 4 are different from the respective figure legend. 

This has been fixed.

Correct the legend of supplemental figure 2: o "(G-H) Representative spatial feature plots of read count (F) and UMI (G) demonstrate expected anatomic variability in transcript amount and diversity.". 

This has been fixed.

In Supplemental Fig. 4G there is probably an error with the XX axis, since the significantly up and down-regulated genes are not visible. 

This has been fixed.

Author response:

Reviewer 1:

Summary:

In this manuscript by Bimbard et al., a new method to perform stable recordings over long periods of time with neuropixels, as well as the technical details on how the electrodes can be explanted for follow-up reuse, is provided. I think the description of all parts of the method is very clear, and the validation analyses (n of units per day over time, RMS over recording days...) are very convincing. I however missed a stronger emphasis on why this could provide a big impact on the ephys community, by enabling new analyses, new behavior correlation studies, or neurophysiological mechanisms across temporal scales

Strengths:

Open source method. Validation across laboratories. Across species (mice and rats) demonstration of its use and in different behavioral conditions (head-fixed and freely moving).

Weaknesses:

Weak emphasis on what can be enabled with this new method that didn't exist before.

We thank the reviewer for highlighting the limited discussion around scientific impact. Our implant has several advantages which combine to make it much more accessible than previous solutions. This enables a variety of recording configurations that would not have been possible with previous designs, facilitating recordings from a wider range of brain regions, animals, and experimental setups. In short, there are three key advances:

(1) Adaptability: The CAD files can be readily adapted to a wide range of configurations (implantation depth, angle, position of headstage, etc.). Labs have already, modified the design to optimise for their needs, and re-shared with the community.

(2) Weight:  Because of the lightweight design, experimenters can i) perform complex and demanding freely moving tasks as we exemplify in the manuscript, and ii) implant female and water restricted mice while respecting animal welfare weight limitations.

(3) Cost: At ~$10, our implant is significantly cheaper than published alternatives, which makes it affordable to more labs and means that testing modifications is cost-effective.

We will make these features clearer in the manuscript.

Reviewer 2:

Summary:

This work by Bimbard et al., introduces a new implant for Neuropixels probes. While Neuropixels probes have critically improved and extended our ability to record the activity of a large number of neurons with high temporal resolution, the use of these expensive devices in chronic experiments has so far been hampered by the difficulty of safely implanting them and, importantly, to explant and reuse them after conclusion of the experiment. The authors present a newly designed two-part implant, consisting of a docking and a payload module, that allows for secure implantation and straightforward recovery of the probes. The implant is lightweight, making it amenable for use in mice and rats, and customizable. The authors provide schematics and files for printing of the implants, which can be easily modified and adapted to custom experiments by researchers with little to no design experience. Importantly, the authors demonstrate the successful use of this implant across multiple use cases, in head-fixed and freely moving experiments, in mice and rats, with different versions of Neuropixels probes, and across 8 different labs. Taken together, the presented implants promise to make chronic Neuropixel recordings and long-term studies of neuronal activity significantly easier and attainable for both current and future Neuropixels users.

Strengths:

- The implants have been successfully tested across 8 different laboratories, in mice and rats, in head-fixed and freely moving conditions, and have been adapted in multiple ways for a number of distinct experiments.

- Implants are easily customizable and the authors provide a straightforward approach for customization across multiple design dimensions even for researchers not experienced in design.

- The authors provide clear and straightforward descriptions of the construction, implantation, and explant of the described implants.

- The split of the implant into a docking and payload module makes reuse even in different experiments (using different docking modules) easy.

- The authors demonstrate that implants can be re-used multiple times and still allow for high-quality recordings.

- The authors show that the chronic implantations allow for the tracking of individual neurons across days and weeks (using additional software tracking solutions), which is critical for a large number of experiments requiring the description of neuronal activity, e.g. throughout learning processes.

- The authors show that implanted animals can even perform complex behavioral tasks, with no apparent reduction in their performance.

Weaknesses:

- While implanted animals can still perform complex behavioral tasks, the authors describe that the implants may reduce the animals' mobility, as measured by prolonged reaction times. However, the presented data does not allow us to judge whether this effect is specifically due to the presented implant or whether any implant or just tethering of the animals per se would have the same effects.

The reviewer is correct: some of the differences in mouse reaction time could be due to the tether rather than the implant. As these experiments were also performed in water-restricted female mice with the heavier Neuropixels 1.0 implant, our data represent the maximal impact of the implant, and we will highlight this in the revision.

- While the authors make certain comparisons to other, previously published approaches for chronic implantation and re-use of Neuropixels probes, it is hard to make conclusive comparisons and judge the advantages of the current implant. For example, while the authors emphasize that the lower weight of their implant allows them to perform recordings in mice (and is surely advantageous), the previously described, heavier implants they mention (Steinmetz et al., 2021; van Daal et al., 2021), have also been used in mice. Whether the weight difference makes a difference in practice therefore remains somewhat unclear.

The reviewer is correct: without a direct comparison, we cannot be certain that our smaller, lighter implant improves behavioural results (although this is supported by the literature, e.g. Newman et al, 2023). However, the reduced weight of our implant is critical for several laboratories represented in this manuscript due to animal welfare requirements. Indeed, in Daal et al the authors “recommend a [mouse] weight of >25 g for implanting Neuropixels 1.0 probes.” This limit precludes using (the vast majority of) female mice, or water-restricted animals. Conversely, our implant can be routinely used with lighter, water-restricted male and female mice. We will emphasise this point in the revision.

- The non-permanent integration of the headstages into the implant, while allowing for the use of the same headstage for multiple animals in parallel, requires repeated connections and does not provide strong protection for the implant. This may especially be an issue for the use in rats, requiring additional protective components as in the presented rat experiments.

We apologise for not clarifying the various headstage options in the manuscript and we will address this in the revision. Our repository has headplate holder designs (in the XtraModifications/Mouse_FreelyMoving folder). This allows leaving the headstage on the implant, and thus minimize the number of connections (albeit increasing the weight for the mouse). Indeed, mice recorded while performing the task described in our manuscript had the head-stage semi-permanently integrated to the implant, and we will highlight this in the revision.

Reviewer 3:

Summary:

In this manuscript, Bimbard and colleagues describe a new implant apparatus called "Apollo Implant", which should facilitate recording in freely moving rodents (mice and rats) using Neuropixels probes. The authors collected data from both mice and rats, they used 3 different versions of Neuropixels, multiple labs have already adopted this method, which is impressive. They openly share their CAD designs and surgery protocol to further facilitate the adaptation of their method.

Strengths:

Overall, the "Apollo Implant" is easy to use and adapt, as it has been used in other laboratories successfully and custom modifications are already available. The device is reproducible using common 3D printing services and can be easily modified thanks to its CAD design (the video explaining this is extremely helpful). The weight and price are amazing compared to other systems for rigid silicon probes allowing a wide range of use of the "Apollo Implant".

Weaknesses:

The "Apollo Implant" can only handle Neuropixels probes. It cannot hold other widely used and commercially available silicon probes. Certain angles and distances are not possible in their current form (distance between probes 1.8 to 4mm, implantation depth 2-6.5 mm, or angle of insertion up to 20 degrees).

We appreciate the reviewer’s points, but as we will discuss in the revised manuscript, one implant accommodating the diversity of the existing probes is beyond the scope of this project. However, because the design is adaptable, groups should be able to modify the current version of the implant to adapt to their electrodes’ size and format (and can highlight any issues in the Github “Discussions” area).

With Neuropixels, the current range of depths covers practically all trajectories in the mouse brain. In rats, where deeper penetrations may be useful, the experimenter can attach the probe at a lower point in the payload module to increase the length of exposed shank. We now specify this in the Github repository.

We have now extended the range of inter-probe distances from a maximum of 4 mm to 6.5 mm, and this will be reflected in the revised manuscript. Distances beyond this may be better served by 2 implants, and smaller distances could be achieved by attaching two probes on the same side of the docking module. In the next revision, we will add these points to the discussion.

Author response:

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

eLife assessment

The authors report that optogenetic inhibition of hippocampal axon terminals in retrosplenial cortex impairs the performance of a delayed non-match to place task. The significance of findings elucidating the role of hippocampal projections to the retrosplenial cortex in memory and decision-making behaviors is important. However, the strength of evidence for the paper's claims is currently incomplete.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is a study on the role of the retrosplenial cortex (RSC) and the hippocampus in working memory. Working memory is a critical cognitive function that allows temporary retention of information for task execution. The RSC, which is functionally and anatomically connected to both primary sensory (especially visual) and higher cognitive areas, plays a key role in integrating spatial-temporal context and in goal-directed behaviors. However, the specific contributions of the RSC and the hippocampus in working memory-guided behaviors are not fully understood due to a lack of studies that experimentally disrupt the connection between these two regions during such behaviors.

In this study, researchers employed eArch3.0 to silence hippocampal axon terminals in the RSC, aiming to explore the roles of these brain regions in working memory. Experiments were conducted where animals with silenced hippocampal axon terminals in the RSC performed a delayed non-match to place (DNMP) task. The results indicated that this manipulation impaired memory retrieval, leading to decreased performance and quicker decision-making in the animals. Notably, the authors observed that the effects of this impairment persisted beyond the light-activation period of the opsin, affecting up to three subsequent trials. They suggest that disrupting the hippocampal-RSC connection has a significant and lasting impact on working memory performance.

Strengths:

They conducted a study exploring the impact of direct hippocampal inputs into the RSC, a region involved in encoding spatial-temporal context and transferring contextual information, on spatial working memory tasks. Utilizing eArch3.0 expressed in hippocampal neurons via the viral vector AAV5-hSyn1-eArch3.0, they aimed to bilaterally silence hippocampal terminals located at the RSC in rats pre-trained in a DNMP task. They discovered that silencing hippocampal terminals in the RSC significantly decreased working memory performance in eArch+ animals, especially during task interleaving sessions (TI) that alternated between trials with and without light delivery. This effect persisted even in non-illuminated trials, indicating a lasting impact beyond the periods of direct manipulation. Additionally, they observed a decreased likelihood of correct responses following TI trials and an increased error rate in eArch+ animals, even after incorrect responses, suggesting an impairment in error-corrective behavior. This contrasted with baseline sessions where no light was delivered, and both eArch+ and control animals showed low error rates.

Weaknesses:

While I agree with the authors that the role of hippocampal inputs to the RSC in spatial working memory is understudied and merits further investigation, I find that the optogenetic experiment, a core part of this manuscript that includes viral injections, could be improved. The effects were rather subtle, rendering some of the results barely significant and possibly too weak to support major conclusions.

We thank Reviewer#1 for carefully and critically reading our manuscript, and for the valuable comments provided. The judged “subtlety” of the effects stems from a perspective according to which a quantitatively lower effect bears less biological significance for cognition. We disagree with this perspective and find it rather reductive for several reasons.

Once seen in the context of the animal’s ecology, subtle impairments can be life-threatening precisely because of their subtlety, leading the animal to confidently rely on a defective capacity, for such events as remembering the habitual location of a predator, or food source.

Also, studies in animal cognition often undertake complete, rather than graded, suppression of a given mechanism (in the same sense as that of “knocking out” a gene that is relevant for behaviour), leading to a gravelly, rather that gradually, impaired model system, to the point of not allowing a hypothetical causal link to be mechanistically revealed beyond its mere presence. This often hinders a thorough interpretation of the perturbed factor’s role. If a caricatural analogy is allowed, it would be as if we were to study the role of an animal’s legs by chopping them both off and observing the resulting behaviour.

In our study we conclude that silencing HIPP inputs in RSC perturbs cognition enough to impair behaviour while not disabling the animal entirely, as such allowing for behaviour to proceed, and for our observation of graded, decreased (not absent), proficiency under optogenetic silencing. So rather than weak, we would say the results are statistically significant, and biologically realistic.

Additionally, no mechanistic investigation was conducted beyond referencing previous reports to interpret the core behavioral phenotypes.

We fully agree with this being a weakness, as we wish we could have done more mechanistic studies to find out exactly what is Arch activation doing to HIPP-RSC transmission, which neurons are being affected, and perhaps in the future dissect its circuit determinants. We have all these goals very present and hope we can address them soon.

Reviewer #2 (Public Review):

The authors examine the impact of optogenetic inhibition of hippocampal axon terminals in the retrosplenial cortex (RSP) during the performance of a working memory T-maze task. Performance on a delayed non-match-to-place task was impaired by such inhibition. The authors also report that inhibition is associated with faster decision-making and that the effects of inhibition can be observed over several subsequent trials. The work seems reasonably well done and the role of hippocampal projections to retrosplenial cortex in memory and decision-making is very relevant to multiple fields. However, the work should be expanded in several ways before one can make firm conclusions on the role of this projection in memory and behavior.

We thank Reviewer#2 for carefully and critically reading our manuscript, and for the valuable comments provided.

(1) The work is very singular in its message and the experimentation. Further, the impact of the inhibition on behaviour is very moderate. In this sense, the results do not support the conclusion that the hippocampal projection to retrosplenial cortex is key to working memory in a navigational setting.

As we have mentioned in response to Reviewer#1, the judged “very moderate” effect stems from a perspective according to which a quantitatively lower effect bears less biological significance for cognition, precluding its consideration as “key” for behaviour. We disagree with this perspective and find it rather reductive for several reasons. Once seen in the context of the animal’s ecology, quantitatively lower impairments in working memory are no less key for this cognitive capacity, and can be life-threatening precisely because of their subtlety, leading the animal to confidently rely on a defective capacity, for such events as remembering the habitual location of a predator, or food source. Furthermore, studies in animal cognition often undertake complete, rather than graded, suppression of a given mechanism (in the same sense as “knocking out” a gene that is relevant for behaviour), leading to a gravelly, rather that gradually, impaired model system, to the point of not allowing a hypothetical causal link to be mechanistically revealed beyond its mere presence. This often hinders a thorough interpretation of its role.

In our study we conclude that silencing HIPP inputs in RSC perturbs behaviour enough to impair behaviour while not disabling the animal entirely, as such allowing for behaviour to proceed, and our observation of graded, decreased (not absent), proficiency under optogenetic silencing. So rather than weak, we would say the results are statistically significant, and biologically realistic.

(2) There are no experiments examining other types of behavior or working memory. Given that the animals used in the studies could be put through a large number of different tasks, this is surprising. There is no control navigational task. There is no working memory test that is non-spatial. Such results should be presented in order to put the main finding in context.

It is hard to gainsay this point. The more thorough and complete a behavioural characterization is, the more informative is the study, from every angle you look at it. While we agree that other forms of WM would be quite interesting in this context, we also cannot ignore the fact that DNMP is widely tested as a WM task, one that is biologically plausible, sensitive to perturbations of neural circuitry know to be at play therein, and fully accepted in the field. Faced with the impossibility of running further studies, for lack of additional funding and human resources, we chose to run this task.

A control navigational task would, in our understanding, be used to assess whether silencing HIPP projections to RSC would affect (spatial?) navigation, rather than WM, thus explaining the observed impairment. To this we have the following to say: Spatial Navigation is a very basic cognitive function, one that relies on body orientation relative to spatial context, on keeping an updated representation of such spatial context, (“alas”, as memory), and on guiding behaviour according to acquired knowledge about spatial context. Some of these functions are integral to spatial working memory, as such, they might indeed be affected.

Dissecting the determinants of spatial WM is indeed an ongoing effort, one that was not the intention of the current study, but also one that we have very present, in hope we can address in the future.

A non-spatial WM task would indeed vastly solidify our claims beyond spatial WM, onto WM. We have, for this reason, changed the title of the manuscript which now reads “spatial working memory”.

(3) The actual impact of the inhibition on activity in RSP is not provided. While this may not be strictly necessary, it is relevant that the hippocampal projection to RSP includes, and is perhaps dominated by inhibitory inputs. I wonder why the authors chose to manipulate hippocampal inputs to RSP when the subiculum stands as a much stronger source of afferents to RSP and has been shown to exhibit spatial and directional tuning of activity. The points here are that we cannot be sure what the manipulation is really accomplishing in terms of inhibiting RSP activity (perhaps this explains the moderate impact on behavior) and that the effect of inhibiting hippocampal inputs is not an effective means by which to study how RSP is responsive to inputs that reflect environmental locations.

We fully agree that neural recordings addressing the effect of silencing on RSC neural activity is relevant. We do wish we could have provided more mechanistic studies, to find out exactly what is Arch activation doing to HIPP-RSC transmission, which neurons are being affected, and thus dissecting its circuit determinants. We have all these goals very present and hope we can address them soon. Subiculum, which we mention in the Introduction, is indeed a key player in this complex circuitry, one whose hypothetical influence is the subject of experimental studies which will certainly reveal many other key elements.

(4) The impact of inhibition on trials subsequent to the trial during which optical stimulation was actually supplied seems trivial. The authors themselves point to evidence that activation of the hyperpolarizing proton pump is rather long-lasting in its action. Further, each sample-test trial pairing is independent of the prior or subsequent trials. This finding is presented as a major finding of the work, but would normally be relegated to supplemental data as an expected outcome given the dynamics of the pump when activated.

We disagree that this finding is “trivial”, and object to the considerations of “normalcy”, which we are left wondering about.

In lack of neurophysiological experiments (for the reasons stated above) to address this interesting finding, we chose to interpret it in light of (the few) published observations, such being the logical course of action in scientific reporting, given the present circumstances.

Evidence for such a prolonged effect in the context of behaviour is scarce (to our knowledge only the one we cite in the manuscript). As such, it is highly relevant to report it, and give it the relevance we do in our manuscript, rather than “relegating it to supplementary data”, as the reviewer considers being “normal”.

In the DNMP task the consecutive sample-test pairs are explicitly not independent, as they are part of the same behavioural session. This is illustrated by the simple phenomenon of learning, namely the intra-session learning curves, and the well-known behavioral trial-history effects. The brain does not simply erase such information during the ITI.

(5) In the middle of the first paragraph of the discussion, the authors make reference to work showing RSP responses to "contextual information in egocentric and allocentric reference frames". The citations here are clearly deficient. How is the Nitzan 2020 paper at all relevant here?

Nitzan 2020 reports the propagation of information from HIPP to CTX via SUB and RSC, thus providing a conduit for mnemonic information between the two structures, alternative to the one we target, thus providing thorough information concerning the HIPP-RSC circuitry at play during behaviour.

Alexander and Nitz 2015 precisely cite the encoding, and conjunction, of two types of contextual information, internal (ego-) and external (allocentric).

The subsequent reference is indeed superfluous here.

We thank the Reviewer#2 for calling our attention to the fact that references for this information are inadequate and lacking. We have now cited (Gill et al., 2011; Miller et al., 2019; Vedder et al., 2017) and refer readers to the review (Alexander et al., 2023)  for the purpose of illustrating the encoding of information in the two reference frames. In addition, we have substantially edited the Introduction and Discussion sections, and suppressed unnecessary passages.

(6) The manuscript is deficient in referencing and discussing data from the Smith laboratory that is similar. The discussion reads mainly like a repeat of the results section.

Please see above. We thank Reviewer#2 for this comment, we have now re-written the Discussion such that it is less of a summary of the Results and more focused on their implications and future directions.

Response to recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major

Line 101: Even with the tapered lambda fibre optic stub, if the fibre optics were longitudinally staggered by 2 millimetres, they would deliver light to diagonal regions in the horizontal plane rather than covering the full length of the RSC. Is this staggering pattern randomized or fixed? Additionally, Figure 1C is a bit misleading, as the light distribution pattern from the tapered fibre optic is likely to be more concentrated near the surface of the fibre, rather than spreading widely in a large spherical pattern.

The staggering is fixed. The elliptical (not spherical) contour in Fig 1C is not meant to convey any quantitative information, but rather to visually orient the reader towards the directions into which light will likely propagate, the effects of which we do not attempt to estimate here. We have made this contour smaller.

Line 119: The authors demonstrate the viral expression pattern of a representative animal and the overall expression patterns of all other animals in Figure 1 and the Supplementary Figures. However, numerous cases in the Supplementary Figures exhibit viral leakages and strong expressions in adjacent cortical and thalamic areas. Although there is a magnified view of the RSC's expression pattern in Figure 1, authors should show the same way in the supplemental data as well. Additionally, the degree of viral expression in the hippocampal subregions varies substantially across animals. This variation is concerning and impacts the interpretation of the results.

The viral construct was injected in the HIPP at coordinates based on our previous work (Ferreira-Fernandes et al., 2019) wherein injections of a similar vector in mid-dorsal HIPP resulted in widespread expression throughout the medial mesocortex AP extent, RSC through CG, as well as other areas in which HIPP establishes synapses. These were studied in detail then, by estimating the density of axon terminals. In the present work we did not acquire high-mag images of all slices, since they were too expensive, and we had this information from the study above. Still, we have now added further examples of high-mag images taken from eArch and CTRL animals.

We believe it is important here to mention the fact that the virus we use, AAV5, only travels anterograde and is static (i.e. it does not travel transynaptically).

Variations in viral expression are to be expected even if injections happen in the exact same way. It is crucial then, that fibre positioning is constant across animals, to guarantee that its relationship with viral expression is thence consistent, and to render irrelevant whatever off-target expression of the viral construct. We have ascertained this condition post-mortem in all our animals.

Line 124: Another point regarding the viral expressions and optical fibre implants used to inhibit the HIPP-RSC pathway is that the RSC and HIPP extend substantially along the anterior-posterior axis. The authors should demonstrate how the viral expression is distributed along this axis and indicate where the tip of the tapered optical fibre ended by marking it in the histological images. This information is crucial to confirm the authors' claim that the hippocampal projection terminals were indeed modulated by optical light. Also, the manuscript would benefit from details about the power/duration and/or modulation of the light used.

In both Figures 1 and S1 panels we can clearly see the tracks formed by the fibres. This provides examples of such dual angle placement vis a vis the expression of the construct, demonstrating that the former is fully targeted towards the latter. We have added markers to highlight these tracks and an example of a “full” track in figure S1. We did not have animals deviating from this relative positioning to any significant extent. The methods section mentions illumination power as 240mA, and we have now added estimated illumination time as well.

Line 141: The authors should include data on task performance during learning and baseline sessions for each animal, to demonstrate that they fully grasped the task rules and that achieving a 75% performance ratio was sufficient.

DNMP is a standard WM task used for many decades, in which animals reach performances above 75% in 4-8 sessions. We have used it extensively, and never saw any deviations from this learning rate and curve. We ran daily sessions until animals reached 75%, and thereafter until they maintained this performance, or above, for three consecutive sessions (the data points we show). We saw no deviations from what is published, nor from what is our own extensive experience, and thence are fully confident that all animals included in this manuscript grasped task rules.

Line 146: While the study focused on inhibiting inputs during the test run (retrieval phase), it would be beneficial to also inhibit inputs during the sample run (encoding phase) and the delay period. This would help confirm whether the silencing affects only working memory retrieval, or if it also impacts encoding and maintenance.

We agree, it would be very interesting to determine if there are any effects of silencing HIPP RSC terminals during Sample. However, since there is a limit to the number of trials per session, and to the total number of sessions, we could not run the three manipulations within each session of our experimental design, as that would lower the number of trials per condition to an extent that would affect statistical power. Silencing HIPP RSC terminals during Sample would best be a separate experiment, asking a different question, and perhaps within an experimental design distinct from the one envisioned.

A very important point here relates to the fact that the effects of optogenetic manipulation do not limit themselves to the illumination epoch, in fact they extend far beyond onto the 3rd trial post-illumination. The insertion of Sample-illuminated trials interleaved in the same session would fundamentally affect the interpretation of experimental results, as we could not attribute lower performances to the effects in either or both manipulated epochs.

Line 225: Figure 5 illustrates that silencing the inputs results in an extended impairment of working memory performance. However, it's unclear if there are any behavioural changes during the sample run. The inhibition could potentially affect encoding in the subsequent sample run, considering the inter-trial interval (ITI) is only 20 seconds.

From the observation of behaviour and the analysis of our data, we saw no overt “behavioural changes during the sample run”, as latencies and speeds were essentially unchanged.

If what is meant by your comment is the effect of optogenetic manipulation being protracted from the Test towards the Sample epoch, we find this unlikely. Conservatively, we estimate the peak of our optogenetic manipulation to occur around the time light is delivered, the Test phase, rather than 20-30 secs later.

In theory, any effect of optogenetic silencing of HIPP terminals in RSC can cause disturbances in encoding or Sample, the ITI itself, and the epoch in which mnemonic information retrieved from the Sample epoch is confronted with the contextual information present during Test, leading to a decision. This is regardless of the illumination epoch, and even if the effect of optogenetic manipulation is not prolonged in time. 

Since in our experiments we specifically target the Test epoch, and there is, in all likelihood, a decaying magnitude of neurophysiological effects, manifest in the reported decaying nature of the manipulation mechanism, and in our observed decrease of behavioural proficiency from subsequent trials 1:4, we are convinced that a conservative interpretation is that our major effect is concentrated in the epoch in which we deliver light - the Test epoch, the consequences of which (possibly related to short term plasticity events taking place within the HIPP-RSC neural circuit) extending further in time.

Line 410: The methods section on the surgical procedure could be clearer, particularly regarding the coordinates for microinjection and fibre implantation. A more precise description would aid reader comprehension.

The now-reported injection and implantation coordinates include the numbers corresponding to the distances, in mm, from Bregma to the targets, in the three stereotaxic dimensions considered: antero-posterior, medial-lateral left and right, and dorso-ventral, as well as the angle at which the fibres were positioned. We have added labels to the figures to highlight the fibreoptic track locations. We will be happy to provide further details as deemed necessary.

Line 461: It would be helpful to know if each animal displayed a preference for the left or right side. Including a description or figure showing that the performance ratio exceeded 75% in both left and right trials would provide a more comprehensive understanding of the animals' behaviour.

In the DNMP, an extensively used and documented WM task, it is an absolute pre-condition that no animals are biased to either side. As such, we did not use any animal that showed such bias.<br /> We have not observed this to be the case in any of our candidate animals, nor would we use any animal exhibiting such a preference.

Minor

Line 25: In the INTRODUCTION section, the authors introduce ego-centric and allocentric variables in the RSC. However, if they intend to discuss this feature, there is no supporting data for ego-centric or allocentric variables in the Results section.

We agree. The extent of the discussion of ego vs allo-centric variables in our manuscript might venture a bit out of the main subject. It was included to provide wider context to our reporting of the data, considering that spatial working memory is indeed one instance in which egocentric- and allocentric-referenced cognitive mechanisms confront each other, and one in which silencing the HIPP input to a cortical region thence involved would likely disturb ensuing computations. We have now substantially edited the manuscript’s Introduction and Discussion, sections, namely toning down this aspect.

Line 125: In the section title, DNMT -> DNMP obviously.

We have corrected this passage.

Figures: The quality of the figure panels does not meet the expected standards. For example, scale bars are missing in many panels (e.g., Figure 1A bottom, 1B, 1C, S1), figure labels are misaligned (as seen in Figure 3A-B compared to 3C, same with Figure 5), and there is inconsistency in color schemes (e.g., Figure 3C versus Figure 6, where 'Error' versus 'Correct' is depicted using green versus blue, respectively).

We have now corrected these inconsistencies and mistakes.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

…I find the concept and execution of the study very interesting and elegant. The paper is also commendably clear and readable. The differences between primary and higher cortex are compelling and I am largely convinced by the authors' claim that they have found evidence that broadly supports a mixed selectivity model of neural disentanglement along the lines of Rigotti et al (2013). I think that the increasing body of evidence for these kinds of representations is a significant development in our understanding of higher sensory representations. I also think that the dDR method is likely to be useful to researchers in a variety of fields who are looking to perform similar types of neural decoding analysis.

Thanks! We agree that questions around population coding and high-level representations are critical in the field of sensory systems.

Reviewer #2 (Public Review):

... This is a well-carried out study with thoughtful analyses which in large part achieves its aims to evaluate how task-engagement changes neural activity across multiple auditory regions. As with all work, there are several caveats or areas for future study/analysis. First, the sounds used here (tones, and narrow-band noise) are relatively simple sounds; previous work suggests that exactly what activity is observed within each region (e.g., sensory only, decision-related, etc) may depend in part upon what stimuli are used. Therefore, while the current study adds importantly to the literature, future work may consider the use of more varied stimuli. Second, the animals here were engaged in a behavioral task; but apart from an initial calculation of behavioral d', the task performance (and its effect on neural activity) is largely unaddressed.

The reviewer makes several important points that we hope we addressed in the specific changes detailed below. Indeed, it is important to recognize the possibility that the specific stimuli involved in a task may interact with the effects of behavioral state and that variability in task performance should be considered as an important aspect of behavioral state.

Reviewer #1 (Recommendations For The Authors):

I have a few minor comments and criticisms:

(1) Figure 1c. The choice of low-contrast grey text (e.g. "Target vs. target" is unfortunate, especially when printed, and should be replaced (e.g. with dark grey).

We have edited the figure to use a higher contrast (dark grey). Thanks for catching this.

(2) Figure 2 and Supplementary Figure 3. I think some indication of error or significance is required in all panels. Without this, it's hard to interpret any of these panels.

Thank you for this feedback. Including significance here was clarifying and helps to strengthen our claim that state-dependent changes in neural activity were smaller and more diverse for single neurons than at the population level. We modified Figure 2b-c to indicate whether each neuron’s response to the target stimulus was significantly different than its response to the catch stimulus. The same test was performed in Supplementary Figure 3. Additionally, we added a statistical test in Figure 2d-e to indicate, for each pair of target/catch stimuli, whether discrimination (d-prime) changed significantly between active and passive conditions. Furthermore, we modified the text of the second paragraph under the results heading: “Diverse effects of task engagement on single neurons in primary and non-primary auditory cortex” to reference and interpret the results of these significance tests. The new text reads as follows (L. 121):

“Sound-evoked spiking activity was compared between active and passive states to study the impact of task engagement on sound representation. In both A1 and dPEG, responses to target and catch stimuli were significantly discriminable for a subset of single neurons (about 25% in both areas, Figure 2A-C, Supplemental Figures 3-5, bootstrap test). This supports the idea that stimulus identity can be decoded in both brain regions, regardless of task performance. However, the fact that the responses of most neurons in both brain areas could not significantly discriminate target vs. catch stimuli also highlights the diversity of sound encoding observed at the level of single neurons. The accuracy of catch vs. target discrimination for each neuron was quantified using neural d-prime, the z-scored difference in target minus catch spiking response for each neuron (Methods: Single neuron PSTHs and d-prime (Niwa et al., 2012a)). Task engagement was associated with significant changes in catch vs. target d-prime for roughly 10% of neurons in both A1 (40 / 481 neurons, bootstrap test) and dPEG (33 / 377 neurons, bootstrap test). This included neurons that both increased their discriminability and decreased their discriminability (Figure 2D-E). Thus, the effects of task engagement at the level of single neurons were relatively mild and inconsistent across the population; many neurons showed no significant change and of those that did, effects were bidirectional (Figure 2D-E).”

We also included an additional methods paragraph in the “Statistical tests” section to describe the bootstrapping procedure used for these significance tests (L. 644):

“The one exception to this general approach is in Figure 2, where we analyzed the sound discrimination abilities of single neurons. In this case, we computed p-values for each neuron and stimulus independently. First, for each neuron and catch vs. target stimulus pair, we measured d-prime (see Methods: Single neuron evoked activity and d-prime). We generated a null distribution of d-prime values for each neuron-stimulus pair, under each experimental condition by shuffling stimulus identity across trials before computing d-prime (100 resamples). A neuron was determined to have a significant d-prime for a given target vs. catch pair if its actual measured d-prime was greater than the 95th percentile of the null d-prime distribution. Second, for each neuron and catch vs. target stimulus pair, we tested if d-prime was significantly different between active and passive conditions. To test this, we followed a similar procedure as above, however, rather than shuffle stimulus identity, we shuffled active vs. passive trial labels. This allowed us to generate a null distribution of active vs. passive d-prime difference for each neuron and stimulus pair. A neuron was determined to have a significant change in d-prime between conditions if the actual Δ d-prime lay outside the 95% confidence interval of the null Δ d-prime distribution.”

For Figure 2a, we chose not to indicate significance on the figure to avoid clutter, since the significance for all neurons in the population are shown in panels b-c anyway. Additionally, the difference plot shown in panel a is in units of z-scores, which we believe already gives a raw sense of the significance of the target vs. catch response change per neuron in this example dataset.

(3) Figure 2 and Supplementary Figure 3. I would consider including some more examples as a Supplementary Figure (and perhaps combining Supp Fig 3 with Fig 2 as a main figure).

We found no significant or apparent difference in single-neuron properties between A1 and dPEG. Therefore, we decided it is not helpful to plot both A1 and PEG examples in the main text. However, we agree that the ability to see more examples of the raw data could be useful. Therefore, we compiled two supplementary figures (Supplementary Figures 4 and 5) that replicate Figure 2a for all datasets, encompassing A1 and PEG.

(4) Figure 2a and Supp Fig 3a. I was initially confused that the "delta-spk/sec (z-score)" values had themselves been z-scored, but now I think that they are simply the differences of the two left hand sub-panels. This could be made clear in the figure legend.

The figure legends have been modified to state the procedure for computing “delta-spk/sec” more clearly. Specifically, we added the following information to the legend (L. 141):

“Difference is computed as the z-scored response to the target minus the z-scored catch response (resulting in a difference shown in units of z-score).”

(5) Figure 2b-e and Supp Fig 3b-e. Indicate the time window over which the responses were measured, and the number of neurons.

Figure legends have been modified to include a sentence clearly stating the time window over which responses were measured. The number of neurons is also now included in the legend and on the figure itself. Furthermore, a brief description of the new statistical testing procedure has been added here (L. 144).

“Responses were defined as the total number of spikes recorded during the 300 ms of sound presentation (area between dashed lines in panel A). Neurons with a significantly different response to the catch vs. target stimulus are indicated in black and quantified on the respective figure panel.”

(6) Figure 2. "singe" should read "single"

Typo in figure label has been fixed.

(7) Line 144. Figure number is missing (Figure 3B-C).

The missing figure number has been added to the text.

(8) Figure 3. Again, the low-contrast grey should be replaced.

The low-contrast grey has been replaced with dark grey.

Reviewer #2 (Recommendations For The Authors):

This study really nicely compares the activity and effects on activity in two areas of the auditory cortex in respect to task-engagement; I think it is, for the most part, very well done.

A couple of specific recommendations:

(1) Although I understand 'inf dB' as the SNR, including the actual dB level used in the experiments, would be useful, especially in the case of the inf dB.

Thank you for this feedback. We agree that clarification about the overall sound level used here would be helpful. We have modified the methods section “Behavioral paradigm” to include the following sentence (L. 450):

“That is, the masking noise (and distractor stimuli) were always presented with an overall sound level of 60 dB SPL. Infinite (inf) dB trials corresponded to trials where the target tone was presented at 60 dB SPL without any masking noise present, 0 dB to trials where the target was 60 dB SPL, -5 dB to trials where the target was presented at 55 dB SPL etc.”

In addition, we have modified the main text (L. 82):

“Animals reported the occurrence of a target tone in a sequence of narrowband noise distractors by licking a piezo spout (Figure 1A, Methods: Behavioral paradigm, distractor stimulus sound level: 60 dB SPL). … We describe SNR as the overall SPL of the target relative to distractor noise level. Thus, an SNR of –5 dB corresponds to a target level of 55 dB SPL while an Inf dB SNR corresponds to a target tone presented without any masking noise.”

And Figure legend 1 now explicitly states the sound level used in the experiments (L. 104):

“Variable SNR was achieved by varying overall SPL of the target relative to the fixed (60 dB SPL) distractor noise, e.g., -5 dB SNR corresponds to a 55 dB SPL target with 60 dB SPL masking noise. Infinite (inf) dB SNR corresponds to a target tone presented in isolation (60 dB SPL).”

(2) I very much appreciate the attempt to disentangle task engagement from generalized arousal state, and specifically, addressing this through the use of pupillometry. However, by focusing the discussion of pupil dynamics solely on the arousal-state aspects of pupil size, the paper doesn't address the increasing evidence suggests that pupil size may fluctuate based upon a lot of other things, including perceptual events (see Kronemer et al, 2022 for a recent human paper; for auditory: Zekveld et al 2018 (review) and Montes-Lourido et al, 2021; but many many others, too). It would be nice to see either a bit more nuanced discussion of what pupil size may be indicating (easier), or analyzing the behavior in the context of pupil dynamics (a heavier lift).

This is a good point. We agree that it is worth mentioning these more nuanced aspects of cognition that may be reflected by pupil size. Therefore, we also analyzed pupil size in the context of behavioral performance (see Supplemental Figure 6) and added the following text to the results (L. 193).

“In addition to reflecting overall arousal level, pupil size has also been reported to reflect more nuanced cognitive variables such as, for example, listening effort (Zekveld et al., 2014). Furthermore, rodent data suggests that optimal sensory detection is associated with intermediate pupil size (McGinley et al., 2015), consistent with the hypothesis of an inverted-U relationship between arousal and behavioral performance (Zekveld et al., 2014). To determine if this pattern was true for the animals in our task, we measured the dynamics of pupil size in the context of behavioral performance. Across animals, task stimuli evoked robust pupil dilation that varied with trial outcome (Supplemental Figure 6b-c). Notably, pre-trial pupil size was significantly different between correct (hit and correct reject), hit, and miss trials (Supplemental Figure 6b-c), recapitulating the finding of an inverted-U relationship to performance in rodents (McGinley et al., 2015).  Since we focused only on correct trials in our decoding analysis, these outcome-dependent differences in pupil size are unlikely to contribute to the emergent decoding selectivity in dPEG.”

(3) I think it would make this paper shine that much more if behavioral performance were not subsumed into the overall label of task engagement. You've already established you have performance that varies as a function of SNR; I would love to see the neural d' and covariability related to the behavioral d' (in the comparisons where this is possible). I would also love to see a more direct measure of choice for those stimuli that show variable behavior (e.g., a choice probability analysis or something of the like would seem to be easily applied to the target SNRs of -5 and 0 dB); and compare task engaged activity of hits vs misses vs passive listening to those same stimuli. You discuss previous studies looking at choice-related/decision-related activity and draw parallels to this work-given that there is the opportunity with this data set to *directly* assess choice-related activity, the absence of such an analysis seems like a missed opportunity.

Thank you for this feedback. We agree that “task engagement” is not a unimodal state and that a more fine-grained analysis of task-engaged neural activity, according to behavioral choice, could be informative.

First, we would like to point out that in Figure 4 we did already compare behavioral d’ to delta neural d’. We found that the two were significantly correlated in dPEG, but not in A1. This suggests that task-dependent changes in stimulus decoding in dPEG, but not A1, are predictive of behavioral performance. This is consistent with the finding that task-relevant stimulus representations were selectively enhanced in dPEG, but not in A1.

Second, we added a choice decoding analysis to address whether auditory cortex represents the animal’s choice in our task. The results of this analysis are summarized in Supplemental Figure 8 and are discussed under the results section: “Behavioral performance is correlated with neural coding changes in non-primary auditory cortex only.” (L. 226):

“The previous analysis suggests that the task-dependent increase in stimulus information present in dPEG population activity is predictive of overall task performance. Next, we asked whether the population activity in either brain region was directly predictive of behavioral choice on single hit vs. miss trials. To do this, we conducted a choice probability analysis (Methods). We found that in both brain regions choice could be decoded well above chance level (Supplemental Figure 8). Choice information was present throughout the entire trial and did not increase during the target stimulus presentation. This suggests that the difference in population activity primarily reflects a cognitive state associated with the probability of licking on a given trial, or “impulsivity” rather than “choice.” This interpretation is consistent with our finding that baseline pupil size on each trial is predictive of trial outcome (Supplemental Figure 6b).”

To keep our decoding approach consistent throughout the manuscript, we followed the same approach for choice decoding as we did for stimulus decoding (perform dDR then calculate neural d-prime in the dimensionality reduced space). To make the results more interpretable, we converted choice d-prime to a choice probability (percent correctly decoded choices) using leave-one-out cross validation. (We note that d-prime and percent correct are very highly correlated statistics.) This is described in the methods as follows (L. 550):

“We performed a choice decoding analysis on hit vs. miss trials. We followed the same procedure as described above for stimulus decoding, where instead of a pair of stimuli our two classes to be decoded were “hit trial” vs. “miss trial”. That is, for each target stimulus we computed the optimal linear discrimination axis separating hit vs. miss trials (Abbott and Dayan, 1999) in the reduced dimensionality space identified with dDR (Heller and David, 2022). For the sake of interpretability with respect to previous work we reported choice probability as the percentage of correctly decoded trial outcomes rather than d-prime. Percent correct was calculated by projecting the population activity onto the optimal discrimination axis and using leave-one-out cross validation to measure the number of correct classifications.”

(4) It would also be interesting to look at population coding across sessions (although the point is taken that within a session allows the opportunity to assess covariability). Minorly self-servingly but very much related to the above point, Christison-Lagay et al, 2017 employed a similar detect-in-noise task, analyzed single neurons and population level activity, and looked at putative choice-related activity. The current study has the opportunity to expand on that kind of analysis that much more by looking across multiple sites vs within a given recording site; and compare across regions.

Thank you for highlighting this point, we agree that it is important. When studying population coding it is critical to consider the impact of covariability between neurons. Therefore, it is worthwhile to revisit our interpretations of prior results, e.g., Christison-Lagay et al, 2017, which studied population coding by combining neurons across different sessions, given that we now have access to simultaneously recorded population data.

First, we would like to point out that this was the primary motivation for our simulation analyses presented in Figure 5. Using simulations, we found that task-dependent gain modulation (which can be observed across sessions) was sufficient to explain our primary finding – selective enhancement in decoding of behaviorally relevant sound stimuli in dPEG.

Second, to address the question about how covariability affects choice-related information in auditory cortex and compare our findings with prior studies, we performed the same set of simulations for choice probability analysis. We found that, again, choice-dependent gain modulation was sufficient to explain our findings. That is, simulations with hit- vs. miss-dependent gain changes, but fixed covariability, closely mirrored the choice probability we observed in the raw data. An additional simulation where covariability between all neurons was set to zero also recapitulated our findings in the raw data. Collectively, this suggests that covariability does not play a significant role in shaping the choice information present in A1 and dPEG during this task. We have added the following text to the manuscript to summarize this finding (L. 293):

“Finally, we used the same simulation approach to determine what aspects of population activity carry the “choice” related information we observed in A1 and dPEG (Figure 4 – figure supplement 1). Similar to our findings for stimulus decoding, we found that gain modulation alone was sufficient to recapitulate the choice information present in the raw data for this task. This helps frame prior work that pooled neurons across sessions to study population coding of choice in similar auditory discrimination tasks (Christison-Lagay et al, 2017).”

Author response:

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

eLife assessment

This manuscript presents a solid and generally convincing set of experiments to address the question of whether the lateral parafacial area (pFL) is active in controlling active expiration, which is particularly important in patient populations that rely on active exhalation to maintain breathing (eg, COPD, ALS, muscular dystrophy). This study presents a valuable finding by pharmacologically mapping the core medullary region that contributes to active expiration and addresses the question of where these regions lie anatomically. Results from these experiments will be of value to those interested in the neural control of breathing and other neuroscientists as a framework for how to perform pharmacological mapping experiments in the future.

Thanks for the positive feedback on our study, as well as the assessment of the novelty of our investigation and the advancements to the field that these results will bring in the future.

We have addressed the specific comments and made changes to the manuscript as indicated below.

Public Reviews:

Reviewer #1 (Public Review):

The main focus of the current study is to identify the anatomical core of an expiratory oscillator in the medulla using pharmacological disinhibition. Although expiration is passive in normal eupneic conditions, activation of the parafacial (pFL) region is believed to evoke active expiration in conditions of elevated ventilatory demands. The authors and others in the field have previously attempted to map this region using pharmacological, optogenetic, and chemogenetic approaches, which present their own challenges.

In the present study, the authors take a systematic approach to determine the precise anatomical location within the ventral medulla's rostrocaudal axis where the expiratory oscillator is located. The authors used a bicuculline (a GABA-A receptor antagonist) and fluorobeads solution at 5 distinct anatomical locations to study the effects on neuronal excitability and functional circuitry in the pFL. The effects of bicuculline on different phases of the respiratory cycle were characterized using a multidimensional cycle-by-cycle analysis. This analysis involved measuring the differences in airflow, diaphragm electromyography (EMG), and abdominal EMG signals, as well as using a phase-plane analysis to analyze the combined differences of these respiratory signals. Anatomical immunostaining techniques were also used to complement the functional mapping of the pFL.

Major strengths of this work include a robust study design, complementary neurophysiological and immunohistochemical methods, and the use of a novel phase-plane analysis. The authors construct a comprehensive functional map revealing functional nuances in respiratory responses to bicuculline along the rostrocaudal axis of the parafacial region. They convincingly show that although bicuculline injections at all coordinates of the pFL generated an expiratory response, the most rostral locations in the lateral parafacial region play the strongest role in generating active expiration. These were characterized by a strong impact on the duration and strength of ABD activation and a robust change in tidal volume and minute ventilation. The authors also confirmed histologically that none of the injection sites overlapped grossly with PHOX2B+ neurons, thus confirming the specificity of the injections in the pFL and not the neighboring RTN.

Collectively, these findings advance our understanding of the presumed expiratory oscillator, the pFL, and highlight the functional heterogeneity in the functional response of this anatomical structure.

Thanks for the positive feedback on the results presented in the current manuscript.

Reviewer #2 (Public Review):

Summary:

Pisanski and colleagues map regions of the brainstem that produce the rhythm for active expiratory breathing movements and influence their motor patterns. While the neural origins of inspiration are very well understood, the neural bases for expiration lag considerably. The problem is important and new knowledge pertaining to the neural origins of expiration is welcome.

The authors perturb the parafacial lateral (pFL) respiratory group of the brainstem with microinjection of bicuculline, to elucidate how disinhibition in specific locations of the pFL influences active expiration (and breathing in general) in anesthetized rats. They provide valuable, if not definitive, evidence that the borders of the pFL appear to extend more rostrally than previously appreciated. Prior research suggests that the expiratory pFL exists at the caudal pole of the facial cranial nucleus (VIIc). Here, the authors show that its borders probably extend as much as 1 mm rostral to VIIc. The evidence is convincing albeit with caveats.

Strengths:

The authors achieve their aim in terms of showing that the borders of the expiratory pFL are not well understood at present and that it (the pFL) extends more rostrally. The results support that point. The data are strong enough to cause many respiratory neurobiologists to look at the sites rostral to the VIIc for expiratory rhythmogenic neurons and characterize their properties and mechanisms. At present my view is that most respiratory neurobiologists overlook the regions rostral to VIIc in their studies of expiratory rhythm and pattern.

Weaknesses:

The injection of bicuculline has indiscriminate effects on excitatory and inhibitory neurons, and the parafacial region is populated by excitatory neurons that are expiratory rhythmogenic and GABA and glycinergic neurons whose roles in producing active expiration are contradictory (Flor et al. J Physiol, 2020, DOI: 10.1113/JP280243). It remains unclear how the microinjections of bicuculline differentially affect all three populations. A more selective approach would be able to disinhibit the populations separately. Nevertheless, for the main point at hand, the data do suggest that we should reconsider the borders of the expiratory pFL nucleus and begin to examine its physiology up to 1 mm rostral to VIIc.

The control experiment showed that bicuculline microinjections induced cFos expression in the pFL, which is good, but again we don't know which neurons were disinhibited: glutamatergic, GABAergic, or glycinergic.

Thanks for sharing your excitement on the results of our study, and appreciating the thorough investigation performed with the use of bicuculline, an approach that was originally used in Pagliardini et al, 2011, PMID: 21414911) and then used by many other groups to generate and study active expiration in vivo.

In the current study we used the well known effect of Bicuculline to systematically test the area that is more sensitive to such a pharmacological effect, and hence may be the core for generating active expiration. While the use of GABA receptor antagonists may have an indiscriminate effect on GABA receptor expressing neurons with various phenotypes, anatomical assessment of inhibitory cells has shown very little distribution of GABAergic and glycinergic cells in the parafacial area (Tanaka et.al, 2003; PMID: 14512139) and it has been inferred in multiple publications (Huckstepp et al., 2015, PMID: 25609622; Huckstepp et al. 2016 PMID: 27300271; Huckstepp et al., 2018, PMID: 30096151; Flor et al., 2020, PMID: 32621515; Britto & Moraes, 2017; PMID: 28004411; Silva et al. 2016; PMID: 26900003) and demonstrated recently (Magalhaes et al.,  2021; PMID: 34510468) that late-E neurons in the parafacial region are excitatory and have a glutamatergic phenotype. We can’t exclude that a small fraction of neurons in the pFL area are inhibitory, and that they could influence recruitment of adjacent late-E expiratory neurons. A more selective activation of neuronal populations with different phenotype would be indeed interesting, nonetheless, if local inhibitory neurons have a role in the generation of active expiration, then their disinhibition could have either an inhibitory effect on late-E activity or stimulate expiration in a more indirect fashion.

While the effect of bicuculline on active expiration has been reported and replicated in multiple manuscripts, the source of inhibition across different phases of the respiratory cycle is still under investigation. Some studies suggest that GABAergic and glycinergic inhibition is not originated in pFL but rather in the BötC and preBötC areas (Flor et al., 2020, PMID: 32621515; Magalhaes et al., 2021; PMID: 34510468) and the effects of this inhibition across the respiratory cycle is debated. Future studies will be key to identify the source of pFL inhibition.

The manuscript characterizes how bicuculline microinjections affect breathing parameters such as tidal volume, frequency, ventilation, inspiratory and expiratory time, as well as oxygen consumption. Those aspects of the manuscript are a bit tedious and sometimes overanalyzed. Plus, there was no predictive framework established at the outset for how one should expect disinhibition to affect breathing parameters. In other words, if the authors are seeking to map the pFL borders, then why analyze the breathing patterns so much? Does doing so provide more insight into the borders of pFL? I did not think it was compellingly argued.

We have edited the introduction to address this comment and emphasize the rationale for the study. We also edited the results section to summarize our findings.

We continue to report our in-depth analysis of the perturbations induced by bicuculline injection over the various respiratory characteristics as this will be fundamental to determine the effects of our experiment not only on the activation of pFL and active expiration, but also on the respiratory network in general. In order to be fair and open about our findings we have reported the results of our analysis in detail. Of note, all sites generated active expiration, but since the objective of the study was to determine the sites with the most significant changes, a finer and multilevel analysis has been used.

Further, lines 382-386 make a point about decreasing inspiratory time even though the data do not meet the statistical threshold. In lines 386-395, the reporting appears to reach significance (line 388) but not reach significance (line 389). I had trouble making sense of that disparity.

The statistics were confirmed, and the lines edited as follows: “Interestingly, the duration of inspiration during the response was found to decrease in all groups relative to baseline respiration (Ti response = 0.279 ± 0.034s, Ti baseline = 0.318 ± 0.043s, Wilcoxon rank sum: Z = 3.24, p = 0.001). Contrary to this decrease in inspiratory duration, the total expiratory time was observed to increase in all groups and remained elevated compared to baseline (TE response = 1.313 ± 0.188s, TE baseline = 1.029 ± 0.161s, Wilcoxon rank sum: Z = 4.49, p = 0.001).”

The other statistical hiccups include "tended towards significance" (line 454), "were found to only reach significance for a short portion of the response" (line 486-7), "did not reach the level of significance" (line 506), which gives one the sense of cherry picking or over-analysis. Frankly, this reviewer finds the paper much more compelling when just asking whether the microinjections evoke active expiration. If yes, then the site is probably part of the pFL.

Statistical “tendencies” have been eliminated throughout the manuscript.

We have analyzed in details our results in order to determine changes and differential effects on respiration when comparing the 5 sites of injections. Although the presentation of the results may seem tedious, it has allowed us to highlight some interesting effects: first, the effects on respiratory frequency. It has been shown in the past that optogenetic stimulation of this area causes an increase in respiratory frequency (Pagliardini et al., 2011, PMID: 21414911), whereas a dishinibition with this same approach or stimulation of AMPAreceptor in pFL have shown a reduction in frequency or not a significant change in the response (Pagliardini et al., 2011, PMID: 21414911; Huckstepp et al., 2015, PMID: 25609622; Huckstepp et al. 2016 PMID: 27300271; Huckstepp et al., 2018, PMID: 30096151). Here, we suggest that the reduction in respiratory frequency is observed only in the caudal sites and could be attributed to BötC effects rather than the stimulation of the core of the pFL since no respiratory change was observe where the effect was more potent (rostral side). Another interesting point was the effects on O2 consumption, although difficult to interpret at this point, we found very interesting that hyperventilation occurred only at the most rostral injection sites.

I encourage the authors to consider the fickleness of p-values in general and urge them to consider not just p but also effect size.

Thank you for the feedback on our description of the statistical results and the suggestion of incorporating effect size. We have now included measurements of effect size in the results section.  Specifically, we calculated the effect size within each ANOVA using the value of eta squared for all data shown in Figures 3 and 4. Please note that in our phase-plane analysis (Fig. 5-6) the Mahalanobis distance is itself an effect size measure for multidimensional data. We also note that statistical evaluation using non-parametric analyses do not involve effect sizes.

Reviewer #3 (Public Review):

Summary:

The study conducted by Pisanski et al investigates the role of the lateral parafacial area (pFL) in controlling active expiration. Stereotactic injections of bicuculline were utilized to map various pFL sites and their impact on respiration. The results indicate that injections at more rostral pFL locations induce the most robust changes in tidal volume, minute ventilation, and combined respiratory responses. The study indicates that the rostrocaudal organization of the pFL and its influence on breathing is not simple and uniform.

Strengths:

The data provide novel insights into the importance of rostral locations in controlling active expiration. The authors use innovative analytic methods to characterize the respiratory effects of bicuculline injections into various areas of the pFL.

Weaknesses:

Bicuculline injections increase the excitability of neurons. Aside from blocking GABA receptors, bicuculline also inhibits calcium-activated potassium currents and potentiates NMDA current, thus insights into the role of GABAergic inhibition are limited.

Increasing the excitability of neurons provides little insights into the activity pattern and function of the activated neurons. Without recording from the activated neurons, it is impossible to know whether an effect on active expiration or any other respiratory phase is caused by bicuculline acting on rhythmogenic neurons or tonic neurons that modulate respiration. While this approach is inappropriate to study the functional extent of the conditional "oscillator" for active expiration, it provides valuable insights into this region's complex role in controlling breathing.

We have included a reflection of the weaknesses of our studies in the technical consideration section to address the possibility that bicuculline may induce active expiration through other mechanisms. Please note that the use of bicuculline was not to gain further insight on GABAergic inhibition of pFL but to adopt a tool to generate active expiration that has been extensively validated by our group and others.

Multiple studies have shown recruitment of excitatory late expiratory neurons with bicuculline injections. Although we did not record from late-E neurons in this study, we infer from the body of literature that disinhibition of neurons in this area will activate late-E neurons (as previously demonstrated) and generate active expiration. Although we see value in recording activity of single neurons (especially to study mechanisms of rhythmogenesis), we opted to measure the physiological response from respiratory muscles as an indication of active expiration recruitment in vivo. Recording from single neurons after bicuculline injections in each site would confirm the presence of expiratory neurons along the parafacial area, which is probably not surprising, since every site tested promoted active expiration. The focus of the study though was to determine the site with the strongest physiological response to disinhibition. Future studies will be key to determine whether all neurons along this column have similar electrophysiological rhythmic properties to the ones recently reported (Magalhaes et al., 2021; PMID: 34510468), or some of them simply provide tonic drive to late-E neurons located elsewhere.

We have discussed the issue as follows:

“Our experiments focused on determining the area in the pFL that is most effective in generating active expiration as measured by ABD EMG activity and expiratory flow. We did not attempt to record single cell neuronal activity at various locations as previously shown in other studies (Pagliardini et al 2011; Magalhaes et al., 2021), as this approach would most likely find some late-E neurons across the pFL and thus not effectively discriminate between areas of the pFL. Future studies involving multi-unit recordings or imaging of cell population activities will help to determine the firing pattern and population density of bicuculline-activated cells and further determine differences in distribution and function of late-E neurons across the region of the pFL.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Overall, the manuscript addresses an important question in the field, the anatomical location of the expiratory oscillator. I commend the authors for a well-thought-out and clearly presented study. However, a few small concerns deserve attention to improve the clarity of the report.

(1) The figures would benefit from a rostral-to-caudal representation of results instead of a caudal-to-rostral orientation. Example, Figure 2.

We opted for a caudal to rostral representation to progressively move away from the inspiratory oscillator (preBötC) and the anatomical reference point (the caudal tip of the facial nucleus) with our series of injections. 

(2) A discussion about how expiratory responses generated by these pharmacological approaches would compare to endogenous baseline conditions. The authors mention that bicuculline injections elicited a late-E downward inflection that was absent in baseline conditions. Thus, this raises the point of how these findings compare to awake freely moving animals or during different conditions of increased ventilatory demand.

This is an interesting question that has not yet been address in the field. As far as we know, there are no recordings of pFL neurons in freely behaving animals although recordings of pFL late-E neurons under elevated PaCO2 have shown a late-E activity in in situ preparations (Britto & Moraes, 2017; PMID: 28004411; Magalhaes et al., 2021; PMID: 34510468).

We have clarified this in the discussion as follows:

“At rest, respiratory activity does not present with active expiration (i.e, expiratory flow below its functional residual capacity in conjunction with expiratory-related ABD muscle recruitment) and expiratory flow occurs due to passive recoil of chest wall with no contribution of abdominal activity. Active expiration and abdominal recruitment can be spontaneously observed during sleep (in particular REM sleep, Andrews and Pagliardini, 2015; Pisanski et al., 2019) and can be triggered during increased respiratory drive (e.g. Hypercapnia, RTN stimulation, Abbott et al., 2011). Although never assessed in freely moving, unanesthetized rodents, bicuculline has been extensively used to generate active expiration and late-E neuron activity in both juvenile and adult anesthetized rats (Pagliardini et al., 2011; Huckstepp et al., 2015 Huckstepp et al., 2016; Huckstepp et al., 2018; De Britto and Moraes, 2017; Magalhaes et al., 2021). “

(3) In Figure 2A, there appears to be an injection site in the top right quadrant of the image, very distant from the intended site. Could the authors confirm if this is an artifact?

Yes, it is an artifact of image acquisition, we should have marked that in the figure. To avoid confusion and follow other reviewers’ suggestions we have edited he figure.

(4) A stylistic suggestion would be to include the subpanel of Figure 2C saline control injection as a graph of its own and also include the control anatomical location in 2B.

Thanks for the suggestion. Because of the complex organization of the figure we opted to leave it as a subpanel in order to not distract the reader from the 5 injection sites, but still provide information about vehicle injection and their lack of changes in respiratory response.

(5) The authors note that DIAm Area (norm.) during the inspiratory phase is increased in the +6 and +8mm groups. However, Figure 5E shows that the +8mm group is significantly reduced as compared to the +6mm group. Please clarify.

During the inspiratory phase we did not observe any significant change in the DIA Area (norm.). We realize that the description of this part of the results was confusing and therefore we have eliminated that section.

Reviewer #2 (Recommendations For The Authors):

I encourage the authors to consider the fickleness of p-values in general and urge them to consider not just p but also effect size. There is a valuable editorial in this week's J Physiology (https://doi.org/10.1113/JP285575) that may provide helpful guidance.

Thanks for this comments and the general assessment. We realized that the results section was dense and with a lot of information. We significantly slimmed the description of the results in order to facilitate the appreciation of the results and avoid confounding statement about significant vs non- significant results.

We have now included measurements of effect size in the results section.  Specifically, we calculated the effect size within each ANOVA using the value of eta squared for all data shown in Figures 3 and 4. Please note that in our phase-plane analysis (Fig. 5-6) the Mahalanobis distance is itself an effect size measure for multidimensional data. We also note that statistical evaluation using non-parametric analyses do not involve effect sizes.

The equipment and resources should be clearly identified and use RRIDs whenever possible. Resources like antibodies and other reagents (e.g., cryoprotectants) should be identified, not just by manufacturer, but also by specific part or product numbers or identifiers.

Manuscript has been edited to add these details.

The manuscript makes reference to ImageJ and Matlab routines, which must be public through GitHub or another stable repository.

Thanks for pointing this out. Image J analysis has been performed following scripts already available to users (no custom scripts). The Matlab scripts used for the multivariate analysis is now available at: https://github.com/mprosteb/Pisanski2024

The way that ABD-DIA coupling was assessed was unclear from the Methods.

The following text has been added to the methods: “The coupling between ABD and DIA signals was measured as a ratio and analyzed by quantifying the number of bursts of activity observed for the ABD and DIA EMG signals during the first 10 minutes of the response, excluding time bins at end of the response (due to fading and waning of the ABD response in those instances).”

Fig. 1A was never cited in the text.

It has been cited now.

Fig. 1A-C appears to be exactly the same as Fig. 5A-C.

The reviewer is correct. We have used figure 1 to describe and explain our analytical methods with sample data and Figure 5 describes our results. We have clarified that in: “Figure 5: Rostral injections elicit more prominent changes to respiration in each signal and sub-period. A-C: Is the same as Method Figure 1, has been included here for further clarity when analyzing the results.”

Late Expiratory airflow is given in units of volts (V) in lines 358-363 (Fig. 4C) but then in units of volts-seconds (V•s) in lines 363-367. Both units are problematic because the voltage is neither an air volume nor an air volume per unit time. Is there some conversion factor left out?

In this section of the results we describe the changes in expiratory peak amplitude (V) and expiratory peak flow (V•s). Since calibration of airflow was performed on the positive flow and for larger volumes, we prefer to use the original units to guarantee precise assessment of the change and avoid introducing potential errors. Since the analysis considers changes from baseline readings, converting to ml or ml*s would not affect our analysis.

Reviewer #3 (Recommendations For The Authors):

The study conducted by Pisanski et al investigates the role of the lateral parafacial area (pFL) in respiratory control, specifically in modulating active expiration. The precise location of this expiratory oscillator within the ventral medulla remains uncertain, with some studies indicating that the caudal tip of the facial nucleus (VIIc) forms the core while others propose more rostral areas. Bicuculline injections were utilized at various pFL sites to explore the impact of these injections on respiration. The authors use innovative and impressive analytic methods to characterize the effect on respiratory activity. The results indicate that injections at more rostral pFL locations induce the most robust changes in tidal volume, minute ventilation, and combined respiratory responses. The study will contribute to an enhanced understanding of the neural mechanisms controlling active expiration. The main message of the study is that the rostro-caudal organization of the pFL is not simple and uniform. The data provides novel insights into the importance of rostral locations in controlling active expiration (see e.g. lines 738-740).

The data and results of the paper are intriguing, and it appears that the experiments are well-managed and executed. However, there are several major and minor comments and suggestions that should be addressed by the authors:

(1) The study relies heavily on local injections into specific areas that are confirmed histologically. One potential concern is the injection volume of 200 nL in such a tiny area. The authors suggest that the drug did not spread to rostral/caudal areas outside the specified coordinate partly based on their cFOS staining. For example, the lack of cFOS activation in TH+ cells and Phox2B cells is interpreted as proof that bicuculline did not spread to these somas (Figure 2). The authors seem to use a similar argument as evidence that the pFL does not include Phox2B neurons in the RTN as discussed in the Discussion section (lines 830-847). However, it is very surprising that bicuculline injections into an area that is known to contain Phox2B and Th+ neurons do not activate these neurons as assessed by the cFOS staining. It seems puzzling to me that none of their injections shown in Figure 2 activated Phox2B or Th neurons. I assume that in targeting the pFL the authors must have sometimes hit areas that included neurons that define the RTN, which would have activated Phox2B or Th+ neurons. Did the authors find that these activations did not activate active expiration? Such negative "controls" would strengthen their argument that pFL is a separate and distinct region that selectively controls active expiration.

Thanks for the positive feedback on the manuscript. As it has been demonstrated and discussed in several previous publications, PHOX2B expressing neurons in this area of the brain are part of the RTN Neuromedin B positive neurons (more densely located in the ventral paraFacial rather than the lateral parafacial, our site of injection), the TH+ C1 neurons (located in a somewhat more caudal and medial position compared to our sites of injection, around the BötC/ preBötC area) and the large Facial MN (easily identifiable by their large size and compact location). Given this differential spatial distribution, and the controls described below, we believe we have reduced the possibility of the direct activation of these neurons, although we can’t exclude it in full.

There is now strong evidence about lack of PHOX2B expression in late E neuron in juvenile and adult rats (Magalhaes et al., 2021; PMID: 34510468). We realize that the microinjected solution could potentially diffuse in the brain and hit other areas, but we combined two strategies to verify our intention for a focal injection activating only a restricted area of the brain (i.e., the pFL): i) localization of fluorobeads that were diluted in the Bicuculline solution; ii) expression of cFos combined with anatomical markers, to identify activated cells. Fluorobeads have a very limited spread in the brain and therefore informed us of the site of the injection to differentiate between the five injections locations. Although we can’t assume that Bicuculline will have a similar spread (and it will also be quickly degraded in the tissue), the combination of this analysis with the localized expression of cFos cells has helped us to differentiate between injections site. Because of the proximity of PHOX2B cells in RTN and C1 neurons, we also combined cFos expression with immunohistochemistry to determine whether bicuculline activation was also visible in these two neuronal populations. Our results indicate that there is baseline cfos activity in RTN neurons (see vehicle injection) but the fraction of PHOX2B activated cells did not increase with bicuculline injections suggesting that these neurons were not the target of our injections. Please note that cfos expression has been extensively used to determine RTN neuron activation, especially following chemoreflex responses. 

(2) The authors refer to "the expiratory oscillator" throughout the manuscript (e.g. lines 58, 62, 65) as if there is only one expiratory oscillator i.e. "the expiratory oscillator". For some reason, the authors avoided citing and mentioning PiCo (Anderson et al. 2016), which is considered the oscillator for postinspiration. Since the present study focuses on the role of expiration, and since the authors describe convincing effects on postinspiration, considering this oscillator which is located dorsomedial to the VRC seems relevant for the present study.

Due to the limited and controversial literature that is currently present describing Pico as a third oscillator and the fact that our studies do not directly assess the post-inspiratory activity (as measure by the V nerve or laryngeal muscles) or Pico activity and location (which would be even more distant than the RTN, for example), we prefer to avoid commenting on the effects of this injection on Pico or the connectivity between Pico and pFL.

We have added this to the discussion:

“Therefore, although it has previously been described, it is currently unknown the exact mechanism by which this post-I activity in the ABD muscles is generated. For example the interplay between the rostral pFL and brainstem structures generating post-inspiratory activity, such as the proposed post-inspiratory oscillator (PiCo; Anderson et al., 2016) or pontine respiratory networks, could be reasonably involved in this process.”

(3) The authors do not specify what type of bicuculline they injected. Bicuculline is known to have significant effects on potassium channels. Thus, the effects reported here could be due to a non-specific change in excitability, rather than caused by a specific GABAergic blockade.

The authors also do not know what effects these injections cause in the neurons in vivo, since the injections are not accompanied by recordings from the respiratory neurons that they activate. This together with the non-specific bicuculline effects will affect the interpretation of the results. Thus, the authors need to be more careful when interpreting their effects as "GABAergic". The use of more specific blockers like gabazine could partly address this concern. The authors have to discuss this in a "limitation section".

Thanks for pointing that out, we have now clarified in the methods section that we used bicuculline methochloride. We can’t exclude that some side- effects could be present due to the use of this drug. For the purpose of this study though, we focused on using bicuculline as a tool to consistently generate active expiration since it has been extensively used by multiple laboratories to induce abdominal muscle recruitment and active expiration, as well as to directly record late-E neurons in this same area.

We have included in the discussion the following statement:

“Technical considerations

Bicuculline methiodide has previously been observed to exhibit inhibitory effects on Ca2+ activated K+ currents inducing non-specific potentiation of NMDA currents (Johnson and Seutin, 1997). Consequently, caution is warranted in attributing our findings solely to the GABAa antagonist properties of bicuculline. Previous work has demonstrated a temporal correlation between the onset of late-E neuron activity in the caudal parafacial region and ABD activity in response to bicuculline (Pagliardini et al., 2011; de Britto and Moraes, 2017; Magalhaes et al., 2021) as well as GABAergic sIPSCs in late-E neurons (Magalhaes et al., 2012). However, it is essential to note that the current study lacks single unit recording, preventing us from definitively confirming whether the observed activity stems from late-E neuronal GABAergic dishinibition or excitation through non GABAergic mechanisms.”

(4) I also caution the authors when stating that the bicuculline injections will reveal the precise location and functional boundaries of "the" expiratory oscillation within the pFL. Increasing the excitability with bicuculline is inappropriate to study the functional boundaries of an oscillator. It is particularly inappropriate to identify the boundaries of the pFL, a network that is normally inactive and activated only under certain behavioral and metabolic conditions. Because the injections are increasing the neuronal excitability unspecifically, and because the authors are not recording the activity of the neurons in the pFL region it is unclear what kind of neurons are activated. The cFOS staining may help to define whether these neurons are Phox2B or Th positive or negative, but they will not provide insights into the activity patterns of the activated neurons. Thus, it is fair to assume that these injections will likely include also tonic neurons that might indirectly control the activity of pFL neurons under certain metabolic or behavioral conditions without actually being involved in the rhythmogenesis of active expiration. Many of the effects peak after several minutes, and different regions cause differential effects with different time courses, which is difficult to interpret functionally. Thus, the "core" identified in the present study could consist of tonic neurons as opposed to rhythmic neurons generating active expiration.

We agree with the reviewer that our local injections may have activated an heterogeneous population of neurons. We do not claim that we only activated late-E rhythmogenic neurons but that our multiple sites of injections revealed the area that is generating the strongest excitation of ABD muscles and active expiration.

While the use of GABA receptor antagonists may have an indiscriminate effect on GABA receptor expressing neurons with various phenotypes, anatomical assessment of inhibitory cells has shown very little distribution of GABAergic and glycinergic cells in the parafacial area (Tanaka et.al, 2003; PMID: 14512139) and it has been inferred in multiple publications (Huckstepp et al., 2015, PMID: 25609622; Huckstepp et al. 2016 PMID: 27300271; Huckstepp et al., 2018, PMID: 30096151; Flor et al., 2020, PMID: 32621515; Britto & Moraes, 2017; PMID: 28004411; Silva et al. 2016; PMID: 26900003) and demonstrated recently (Magalhaes et al.,  2021; PMID: 34510468) that late-E neurons in the parafacial region are excitatory and have a glutamatergic phenotype

As suggested by the reviewer, it is possible that the bicuculline injection may have activated some tonic non rhythmogenic neurons which could activate the expiratory oscillator located elsewhere.

We have edited the introduction as follows:

“By strategically administering localized volumes of bicuculline at multiple rostrocaudal levels of the ventral brainstem, we aimed to selectively enhance the excitability of neurons driving active expiration, thereby revealing the extension of the pharmacological response and the most efficient site in generating active expiration.”

We have edited the results as follows:

“Importantly, the group with injection sites at +0.6 mm from VIIc exhibited the swiftest response onset, suggesting that this area is the most critical for the generation of active expiration, either through direct activation of the expiratory oscillator or, alternatively, for providing a strong tonic drive to late-E neurons located elsewhere.”

In the introduction, it should also be emphasized that the pharmacological approach used in the present study complements the existing elegant chemogenetic studies, rather than emphasizing primarily the limitations of the chemogenetic inhibitions. The conclusion should be that these studies together provide different, yet complementary insights: The chemogenetic approach by inhibiting neurons, the present study by exciting neurons, and all studies come with their own limitations.

Thanks for the suggestion, we have updated the manuscript as follows:

“Although both of these elegant chemogenetic studies have contributed extensively to our understanding of the pFL, the existing evidence suggests that the expiratory oscillator may expand beyond the limits of the viral expression achieved in said studies, as proposed by Huckstepp et al., (2015).”

Throughout the manuscript, the authors have to be cautious when implying that an excitatory effect relates to the activity of rhythmogenic pFL neurons. For example, on line 710 the authors state that "it is conceivable to infer that the rostral pFL is in the closest proximity to the cells responsible for the generation of active expiration". While it may indeed be "conceivable", the bicuculline injections themselves provide no insights into the location of neurons responsible for rhythmogenesis. It is equally "conceivable" that the excited neurons provide a tonic drive to the neurons without being involved in the generation of active expiration. These tonic neurons could be located at a distance from the presumed rhythmogenic core.

We have included the possibility of tonic excitation in the technical considerations section:

“However, our study did not include recording from late-E neurons following bicuculline injections, preventing us from definitively confirming whether the observed activity stems from late-E neuronal excitation or the potentiation of a tonic drive, particularly in the rostral areas.”

(5) It is intriguing that some of their injections (Fig.2D) evoked postinspiratory activity. This interesting finding should be discussed as it could provide important insights into the coordination of the different phases of expiration.

Thanks for the suggestion. We have included the following to the discussion:

“Therefore, although it has previously been described, the exact mechanism by which this post-I ABD activity is generated is unclear. This late-E/post-I pattern of activity is similar to what has been observed in in vitro preparations and in vivo recordings in juvenile rats (Janczewski et al., 2002; Janczewski et al., 2006).

“Therefore, although it has previously been described, it is currently unknown the exact mechanism by which this post-I activity in the ABD muscles is generated. For example the interplay between the rostral pFL and brainstem structures generating post-inspiratory activity, such as the proposed post-inspiratory oscillator (PiCo; Anderson et al., 2016) or pontine respiratory networks, could be reasonably involved in this process.”

(6) The authors conducted bilateral disinhibition of the pFL, but only a unilateral photomicrograph was shown. Figure 2 should include a representative bilateral photomicrograph along with a scatter plot for clarity and completeness.

We have edited figure 2 to include representative images of bilateral injections.

(7) Regarding the Bicuculline injections in the Methods section: Aside from specifying exactly what type of bicuculline was used, the authors should provide more information about the pFL location and landmarks used, including the missing medial-lateral coordinate. The fluorobead spread of approximately ~300 µm, as observed in Figure 2C, is crucial for the interpretation of the results and should be detailed. An alternative approach could involve e.g. calculating the area covered by fluorobeads in each group.

We have included the following in the text:

“Each rat was injected at 2.8 mm lateral from the midline and at a specific RC coordinate based on the following groups: -0.2 mm from the caudal tip of the facial nucleus (VIIc) (n=5), +0.1 mm from VIIc (n=7), +0.4 mm from VIIc (n=5), +0.6 mm from VIIc (n=6), +0.8 mm from VIIc (n=5)”

“These findings strongly suggest that bicuculline specifically activated cells within the vicinity of the injection sites which spread ~300 ìm (Figure 2C, horizontal lines) and did not activate PHOX2B+ cells in the RTN area, beyond their baseline level of activity.”

(8) In the Experimental Protocol, the authors should provide more details on how the parameters were determined. For example, specify the number of cycles included for Dia frequency/amplitude, Abd frequency/amplitude, and with regards to the averaging process, the authors should specify over how many cycles they obtained an average for Dia/Abd activity time and AUC. The authors should also provide information on the number of bicuculline injections that they repeated to average these values and they should report the coefficient of variation for repeated injections. Please clarify the method used to calculate AUC, considering the non-linear nature of the activity.

Only one bicuculline injection per rat was performed and the number of rats used for each injection site is indicated in the methods as follows:

“Each rat was injected at 2.8 mm lateral from the midline and at a specific RC coordinate based on the following groups: -0.2 mm from the caudal tip of the facial nucleus (VIIc) (n=5), +0.1 mm from VIIc (n=7), +0.4 mm from VIIc (n=5), +0.6 mm from VIIc (n=6), +0.8 mm from VIIc (n=5), and CTRL (n=7). We recorded the physiological responses to the injection for 20-25 min.”

We have clarified in the methods section the following:

“Respiratory data was tracked in time bins of 2-minute duration from the baseline period prior to injections and spanned 20 min of recording post-injection. Mean-cycle measurements for each signal were computed by averaging values across all cycles within a given time bin.”

Additional clarifications have been added:

“We then used the average calculations of respiratory rate (RR), tidal volume (VT), Minute Ventilation (Ve), expiratory ABD amplitude, expiratory ABD area, VO2, VE/VO2 to obtain values relative to the baseline period. Peak responses were identified as the time bin that produced the strongest changes relative to baseline.”

“Mean-cycle measurements for each signal were computed by averaging across all cycles within a given time bin. (~300 cycles in baseline, ~100 cycles per response time bin). We then used the average calculations of respiratory rate (RR), tidal volume (VT), Minute Ventilation (Ve), expiratory ABD amplitude, expiratory ABD area, VO2, VE/VO2 to obtain values relative to the baseline period. Peak responses were identified as the time bin that produced the strongest changes relative to baseline.”

“The Area under the curve (AUC) was measured during baseline and was subtracted from the corresponding AUC of the response for each time bin (Figure 1C). This AUC measure was computed as the sum of the signal in a given respiratory phase as all signals were sampled at the same rate. Note that areas calculated below the zero- (0) line, as would be expected from a negative airflow during expiration, yields negative AUC values.”

(9) The authors should explain how oxygen consumption was calculated-did it involve the Depocas & Hart (1957) formula? Please provide information on expiratory CO2, whether ventilation was adjusted to achieve consistent CO2 levels across animals, and ideally specify the end-tidal CO2 range for the experiments. Discuss the rationale behind the chosen CO2 levels and whether CO2-dependent pFL activity could have influenced results.

We have clarified in the measurement in the methods as follows:

“The gas analyzer measured fractional concentration of O2. Based on this and the flow rate at the level of the trachea (minute ventilation), we calculated O2 consumption according to Depocas and Hart (1957).”

We have also added to the methods section:

“During the entire experimental procedure, rats breathed spontaneously and end tidal CO2 was not adjusted through the experimental protocol.”

In terms of the CO2-dependent pFL activity possibly influencing the results: by inducing active expiration in conditions in which there is no physiological demand for it (i.e. no hypoxia or hypercapnia), it is likely that pCO2 is reduced, overall decreasing the drive for ABD activity which would suggest that our results are likely an underestimation of the response that would have been produced if we maintained the CO2 levels constant.

(10) The authors should address the discrepancy in fos-activated neurons between the control (44 neurons) and experimental animals (90-120 neurons per hemisection). Please explain the activation in the control group. Please also provide insights into how the authors interpret this difference in cfos-activated neurons between control and experimental groups.

The following paragraph has been added to the discussion:

“The assessment of cellular activity, quantified through cFos staining, unveiled the existence of basal activity in control rats. This observed baseline activity is likely emanating from subthreshold physiological processes within the parafacial area which do not culminate in ABD activity. Analysis of the cFos staining confirmed focal activation of neurons in the pFL of rats injected with bicuculline and minimal cFos expression in the PHOX2B+ cells in all groups as compared to the control group. These results confirm the very limited mediolateral spread of the drug from the core site of injection and back previous findings supporting the hypothesis that the majority of PHOX2B+ cells are more ventrally located in the parafacial area (pFV, Huckstepp et al., 2015) and PHOX2B+ cell recruitment is not necessary for active expiration (de Britto & Moraes, 2017; Magalhães et al., 2021).”

(11) In Figure 8, the authors plotted the relationship of each cycle correlated to the normalized area. Have you also calculated the same late-E, inspiratory, and post-I to fR or VT separately?

No, we only did the separated breathing phase (late-E, I, Post-I) analysis in the calculations of the DIA, airflow and ABD area, as well as on the Euclidean and Mahalanobis distances.

Minor comments:

Is there any specific reason for conducting these experiments exclusively in males?

No, we usually use male rats for this type of experiments. We use both male and female rats for other studies that concern the effects of sex hormones but in this case, we performed experiments only in male rats.

Page 13, Line 320: What is the duration of the bicuculline-induced effects?

This information is included in the results section as follows:

“Similarly, the ABD response duration was longer at the two most rostral locations (+0.6 mm = 17.6 ± 2.7 min; +0.8 = 17.1 ± 3.3 min) compared to the most caudal group (-0.2 mm = 2.4 ± 1.1 min; One-Way ANOVA p = 0.043; Tukey -0.2 mm vs +0.6 mm: p = 0.048; -0.2 mm vs +0.8 mm: p = 0.041; Figure 3E).”

Page 16, Line 400: Is there a rationale for the high tidal volume (VT) observed in these animals? A baseline VT of 7 ml/kg appears notably elevated.

Please note that rats were vagotomised and spontaneously breathing, hence the tidal volume is increased compared to non-vagotomised rats as seen in previous studies (Ouahchi et al., 2011).

Figure 2D: Could you provide longer recordings? Additionally, incorporating diaphragm (Dia) recordings would enhance the interpretation of abdominal (Abd) recordings.

Figure 3 A has a representative example of the 20 minute recordings for each location.

Page 18, Line 458: Please rectify "Dunn: p , 0.001" to the appropriate format, perhaps "Dunn: p < 0.001."

Thank you, edited.

Author response:

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

eLife assessment

This fundamental study investigates the transcriptional changes in neurons that underlie loss of learning and memory with age in C. elegans, and how cognition is maintained in insulin/IGF-1-like signaling mutants. The presented evidence is compelling, utilizing a cutting-edge method to isolate neurons from worms for genomics that is clearly conveyed with a rigorous experimental approach. Overall, this study supports that older daf-2 worms maintain cognitive function via mechanisms that are unique from younger wild type worms, which will be of great interest to neuroscientists and researchers studying ageing.

Public Reviews:

Reviewer #1 (Public Review):

The authors perform RNA-seq on FACS isolated neurons from adult worms at days 1 and 8 of adulthood to profile the gene expression changes that occur with cognitive decline. Supporting data are included indicating that by day 7 of adulthood, learning and memory are reduced, indicating that this timepoint or after represents cognitively aged worms. Neuronal identity genes are reduced in expression within the cognitively aged worms, whereas genes involved in proteostasis, transcription/chromatin, and the stress response are elevated. A number of specific examples are provided, representing markers of specific neuronal subtypes, and correlating expression changes to the erosion of particular functions (e.g. motor neurons, chemosensory neurons, aversive learning neurons, etc).

To investigate whether upregulation of genes in neurons with age is compensatory or deleterious, the authors reduced expression of a set of three significantly upregulated genes and performed behavioral assays in young adults. In each case, reduction of expression improved memory, consistent with a model in which age-associated increases impair neuronal function.

The authors then characterize learning and memory in wild type, daf-2, and daf-2/daf-16 worms with age and find that daf-2 worms have an extended ability to learn for approximately 10 days longer that wild types. This was daf-16 dependent. Memory was extended in daf-2 as well, and strikingly, daf-2;daf-16 had no short term memory even at day 1. Transcriptomic analysis of FACS-sorted neurons was performed on the three groups at day 8. The authors focus their analysis on daf-2 vs. daf-2;daf-16 and present evidence that daf-2 neurons express a stress-resistance gene program. They also find small differences between the N2 and daf-2;daf-16 neurons, which correlate with the observed behavioral differences, though these differences are modest.

The authors tested eight candidate genes that were more highly expressed in daf-2 neurons vs. daf-2;daf-16 and showed that reduction of 2 and 5 of these genes impaired learning and memory, respectively, in daf-2 worms. This finding implicates specific neuronal transcriptional targets of IIS in maintaining cognitive ability in daf-2 with age, which, importantly, are distinct from those in young wild type worms.

Overall, this is a strong study with rigorously performed experiments. The authors achieved their aim of identifying transcriptional changes in neurons that underlie loss of learning and memory in C. elegans, and how cognition is maintained in insulin/IGF-1-like signaling mutants. 

We thank you for the evaluation and response.

Reviewer #2 (Public Review):

Weng et al. perform a comprehensive study of gene expression changes in young and old animals, in wild-type and daf-2 insulin receptor mutants, in the whole animal and specifically in the nervous system. Using this data, they identify gene families that are correlated with neuronal ageing, as well as a distinct set of genes that are upregulated in neurons of aged daf-2 mutants. This is particularly interesting as daf-2 mutants show both extended lifespan and healthier neurons in aged animals, reflected by better learning/memory in older animals compared with wild-type controls. Indeed, knockdown of several of these upregulated genes resulted in poorer learning and memory. In addition, the authors showed that several genes upregulated during ageing in wild-type neurons also contribute to learning and memory; specifically, knockdown of these genes in young animals resulted in improved memory. This indicates that (at least in this small number of cases), genes that show increased transcript levels with age in the nervous system somehow suppress memory, potentially by having damaging effects on neuronal health.

Finally, from a resource perspective, the neuronal transcriptome provided here will be very useful for C. elegans researchers as it adds to other existing datasets by providing the transcriptome of older animals (animals at day 8 of adulthood) and demonstrating the benefits of performing tissue-specific RNAseq instead of whole-animal sequencing.

The work presented here is of high quality and the authors present convincing evidence supporting their conclusions. I only have a few comments/suggestions:

(1) Do the genes identified to decrease learning/memory capacity in daf-2 animals (Figure 4d/e) also impact neuronal health? daf-2 mutant worms show delayed onset of age-related changes to neuron structure (Tank et al., 2011, J Neurosci). Does knockdown of the genes shown to affect learning also affect neuron structure during ageing, potentially one mechanism through which they modulate learning/memory? 

(2) The learning and memory assay data presented in this study uses the butanone olfactory learning paradigm, which is well established by the same group. Have the authors tried other learning assays when testing for learning/memory changes after knockdown of candidate genes? Depending on the expression pattern of these genes, they may have more or less of an effect on olfactory learning versus for e.g. gustatory or mechanosensory-based learning.

(3) A comment on the 'compensatory vs dysregulatory' model as stated by the authors on page 7 - I understand that this model presents the two main options, but perhaps this is slightly too simplistic: gene expression that rises during ageing may be detrimental for memory (= dysregulatory), but at the same time may also be beneficial other physiological roles in other tissues (=compensatory). 

Thank you for your original suggestions; we addressed them in the previous version of response to the reviewers.

Comments on revised version:

I am satisfied with how the authors have addressed all my comments/suggestions. 

Thank you for your response!

Reviewer #3 (Public Review):

Summary

In this manuscript, Weng et al. identify the neuron specific transcriptome that impacts age dependent cognitive decline. The authors design a pipeline to profile neurons from wild type and long-lived insulin receptor/IGF-1 mutants using timepoints when memory functions are declining. They discover signatures unique to neurons which validates their approach. The authors identify that genes related to neuronal identity are lost with age in wild type worms. For example, old neurons reduce the expression of genes linked to synaptic function and neuropeptide signaling and increase the expression of chromatin regulators, insulin peptides and glycoproteins. Depletion of selected genes which are upregulated in old neurons (utx-1, ins-19 and nmgp-1) leads to improved short memory function. This indicates that some genes that increase with age have detrimental effects on learning and memory. The pipeline is then used to test neuronal profiles of long-lived insulin/IGF-1 daf-2 mutants. Genes related to stress response pathways are upregulated in long lived daf-2 mutants (e.g. dod-24, F08H9.4) and those genes are required for improved neuron function.

Strengths

The manuscript is well written, and the experiments are well described. The authors take great care to explain their reasoning for performing experiments in a specific way and guide the reader through the interpretation of the results, which makes this manuscript an enjoyable and interesting read. The authors discover novel regulators of learning and memory using neuron-specific transcriptomic analysis in aged animals, which underlines the importance of cell specific deep sequencing. The timepoints of the transcriptomic profiling are elegantly chosen, as they coincide with the loss of memory and can be used to specifically reveal gene expression profiles related to neuron function. The authors discuss on the dod-24 example how powerful this approach is. In daf-2 mutants whole-body dod-24 expression differs from neuron specific profiles, which underlines the importance of precise cell specific approaches. This dataset will provide a very useful resource for the C. elegans and aging community as it complements existing datasets with additional time points and neuron specific deep profiling.

Weakness

This study nicely describes the neuron specific profiles of aged long-lived daf-2 mutants. Selected neuronal genes that were upregulated in daf-2 mutants (e.g. F08H9.4, mtl-1, dod-24, alh-2, C44B7.5) decreased learning/memory when knocked down. However, the knock down of these genes was not specific to neurons. The authors use a neuron-sensitive RNAi strain to address this concern and acknowledge this caveat in the text. While it is likely that selected candidates act only in neurons it is possible that other tissues participate as well.

Thank you for pointing this caveat out. We have mentioned it in the figure legend.

Author response:

Reviewer #1 (Public Review):

Summary:

This computational modeling study builds on multiple previous lines of experimental and theoretical research to investigate how a single neuron can solve a nonlinear pattern classification task. The authors construct a detailed biophysical and morphological model of a single striatal medium spiny neuron, and endow excitatory and inhibitory synapses with dynamic synaptic plasticity mechanisms that are sensitive to (1) the presence or absence of a dopamine reward signal, and (2) spatiotemporal coincidence of synaptic activity in single dendritic branches. The latter coincidence is detected by voltage-dependent NMDA-type glutamate receptors, which can generate a type of dendritic spike referred to as a "plateau potential." The proposed mechanisms result in moderate performance on a nonlinear classification task when specific input features are segregated and clustered onto individual branches, but reduced performance when input features are randomly distributed across branches. Given the high level of complexity of all components of the model, it is not clear which features of which components are most important for its performance. There is also room for improvement in the narrative structure of the manuscript and the organization of concepts and data.

To begin with, we will better explain the goal of the study in the introduction and explain that it relies on earlier theoretical work. The goal of the study was to investigate whether and how detailed neuron models with biologically-based morphologies, membrane properties, ion channels, dendritic nonlinearities, and biologically plausible learning rules can quantitatively account for the theoretical results obtained with more abstract models.

We will further evaluate and clarify the roles of several components in our model regarding their impact on the results. These include a) the role of sufficiently robust and supralinear plateau potentials in computing the NFBP; and b) the importance of metaplasticity for individual synapses, allowing them to start or stop responding to relevant or irrelevant stimuli, respectively, over the training period.

Strengths:

The integrative aspect of this study is its major strength. It is challenging to relate low-level details such as electrical spine compartmentalization, extrasynaptic neurotransmitter concentrations, dendritic nonlinearities, spatial clustering of correlated inputs, and plasticity of excitatory and inhibitory synapses to high-level computations such as nonlinear feature classification. Due to high simulation costs, it is rare to see highly biophysical and morphological models used for learning studies that require repeated stimulus presentations over the course of a training procedure. The study aspires to prove the principle that experimentally-supported biological mechanisms can explain complex learning.

Weaknesses:

The high level of complexity of each component of the model makes it difficult to gain an intuition for which aspects of the model are essential for its performance, or responsible for its poor performance under certain conditions. Stripping down some of the biophysical detail and comparing it to a simpler model may help better understand each component in isolation. That said, the fundamental concepts behind nonlinear feature binding in neurons with compartmentalized dendrites have been explored in previous work, so it is not clear how this study represents a significant conceptual advance. Finally, the presentation of the model, the motivation and justification of each design choice, and the interpretation of each result could be restructured for clarity to be better received by a wider audience.

To achieve the goal of the study as described above, we chose to use a biophysically and morphologically detailed neuron model to see if it could quantitatively account for the theoretically-based nonlinear computations, for instance, those discussed in Tran-Van-Minh, A. et al. (2015).

We will explain the role of each component of the learning rule, as well as the dendritic nonlinearities, for the performance on the NFBP.

Reviewer #2 (Public Review):

Summary:

The study explores how single striatal projection neurons (SPNs) utilize dendritic nonlinearities to solve complex integration tasks. It introduces a calcium-based synaptic learning rule that incorporates local calcium dynamics and dopaminergic signals, along with metaplasticity to ensure stability for synaptic weights. Results show SPNs can solve the nonlinear feature binding problem and enhance computational efficiency through inhibitory plasticity in dendrites, emphasizing the significant computational potential of individual neurons. In summary, the study provides a more biologically plausible solution to single-neuron learning and gives further mechanical insights into complex computations at the single-neuron level.

Strengths:

The paper introduces a novel learning rule for training a single multicompartmental neuron model to perform nonlinear feature binding tasks (NFBP), highlighting two main strengths: the learning rule is local, calcium-based, and requires only sparse reward signals, making it highly biologically plausible, and it applies to detailed neuron models that effectively preserve dendritic nonlinearities, contrasting with many previous studies that use simplified models.

Indeed, the learning rule is local and reward-based, and we will highlight better in the paper that it is “always on”, i.e. there are no separate training and testing phases.

Weaknesses:

I am concerned that the manuscript was submitted too hastily, as evidenced by the quality and logic of the writing and the presentation of the figures. These issues may compromise the integrity of the work. I would recommend a substantial revision of the manuscript to improve the clarity of the writing, incorporate more experiments, and better define the goals of the study.

We will revise the manuscript thoroughly to better present the figures and writing (more detailed below). We will also show supplementary figures showcasing the role of the different components of the learning rule.

Major Points:

(1) Quality of Scientific Writing: The current draft does not meet the expected standards. Key issues include:

i. Mathematical and Implementation Details: The manuscript lacks comprehensive mathematical descriptions and implementation details for the plasticity models (LTP/LTD/Meta) and the SPN model. Given the complexity of the biophysically detailed multicompartment model and the associated learning rules, the inclusion of only nine abstract equations (Eq. 1-9) in the Methods section is insufficient. I was surprised to find no supplementary material providing these crucial details. What parameters were used for the SPN model? What are the mathematical specifics for the extra-synaptic NMDA receptors utilized in this study? For instance, Eq. 3 references [Ca2+]-does this refer to calcium ions influenced by extra-synaptic NMDARs, or does it apply to other standard NMDARs? I also suggest the authors provide pseudocodes for the entire learning process to further clarify the learning rules.

The detailed setup of the model is described in the referenced papers, including equations and parameter values. The model is downloadable on github. For this reason we did not repeat the information here. That said, we will go through the manuscript and clarify all details, and provide supplemental figures and a GitHub link where necessary for reproducing the results.

ii. Figure quality. The authors seem not to carefully typeset the images, resulting in overcrowding and varying font sizes in the figures. Some of the fonts are too small and hard to read. The text in many of the diagrams is confusing. For example, in Panel A of Figure 3, two flattened images are combined, leading to small, distorted font sizes. In Panels C and D of Figure 7, the inconsistent use of terminology such as "kernels" further complicates the clarity of the presentation. I recommend that the authors thoroughly review all figures and accompanying text to ensure they meet the expected standards of clarity and quality.

We will revise the figures for consistency and clarity.

iii. Writing clarity. The manuscript often includes excessive and irrelevant details, particularly in the mathematical discussions. On page 24, within the "Metaplasticity" section, the authors introduce the biological background to support the proposed metaplasticity equation (Eq. 5). However, much of this biological detail is hypothesized rather than experimentally verified. For instance, the claim that "a pause in dopamine triggers a shift towards higher calcium concentrations while a peak in dopamine pushes the LTP kernel in the opposite direction" lacks cited experimental evidence. If evidence exists, it should be clearly referenced; otherwise, these assertions should be presented as theoretical hypotheses. Generally, Eq. 5 and related discussions should be described more concisely, with only a loose connection to dopamine effects until more experimental findings are available.

The reviewer is correct; the cited text does not present experimental facts but rather illustrates how the learning rule operates. We will revise the section on the construction of learning rules to clarify which aspects are explicit assumptions and which are experimentally verified. In particular, we will provide a more detailed description and motivation for metaplasticity

(2) Goals of the Study: The authors need to clearly define the primary objective of their research. Is it to showcase the computational advantages of the local learning rule, or to elucidate biological functions?

Briefly, the goal of the study was to investigate whether earlier theoretical results with more abstract models can be quantitatively recapitulated in morphologically and biophysically detailed neuron models with dendritic nonlinearities and with biologically based learning rules. (similar response to Summary and Weaknesses to Reviewer #1). We will update the introduction with this information.

i. Computational Advantage: If the intent is to demonstrate computational advantages, the current experimental results appear inadequate. The learning rule introduced in this work can only solve for four features, whereas previous research (e.g., Bicknell and Hausser, 2021) has shown capability with over 100 features. It is crucial for the authors to extend their demonstrations to prove that their learning rule can handle more than just three features. Furthermore, the requirement to fine-tune the midpoint of the synapse function indicates that the rule modifies the "activation function" of the synapses, as opposed to merely adjusting synaptic weights. In machine learning, modifying weights directly is typically more efficient than altering activation functions during learning tasks. This might account for why the current learning rule is restricted to a limited number of tasks. The authors should critically evaluate whether the proposed local learning rule, including meta-plasticity, actually offers any computational advantage. This evaluation is essential to understand the practical implications and effectiveness of the proposed learning rule.

As mentioned above, our intent is not to demonstrate the computational advantages of the proposed learning rule but to investigate and illustrate how biophysically detailed neuron models that also display dendritic plateau potential mechanisms, together with biologically-based learning rules, can support the theoretically predicted computational requirements for complex neuronal processing (e.g., Tran-Van-Minh, A. et al., 2015), as well as the results obtained with more abstract neuron models and plateau potential mechanisms (e.g., Schiess et al., 2016; Legenstein and Maass, 2011).

In the revised manuscript, we will also discuss the differences between the supervised learning rule in Bicknell and Hausser (2021) and our local and reward-based learning rule. We will also show a critical evaluation of how our local learning rule and metaplasticity affect the synaptic weights and why the different components of the rule are needed.

ii. Biological Significance: If the goal is to interpret biological functions, the authors should dig deeper into the model behaviors to uncover their biological significance. This exploration should aim to link the observed computational features of the model more directly with biological mechanisms and outcomes.

We will make an attempt to better link the learning rule and dendritic supra-linearities and interpret their biological function.

Author response:

eLife assessment

“…The evidence however is incomplete, since the tai loss-of-clone phenotype is based on one allele and the mechanism involved in cell competition through Dlp and Wg lacks adequate supporting data.”

We agree with the need for a second allele and are adding supporting data from a new tai lof allele we have generated by Crispr.

We also agree that additional functional data would help demonstrate that differences in Dlp levels are required for the mechanism of Tai cell competition. Experiments are ongoing to test whether normalizing Dlp levels across clonal boundaries rescues elimination of Tai-low clones.

Reviewer #1:

Overall Statements:

“There is some data in the supplementary materials suggesting that Tai promotes dlp mRNA expression, but this was not compelling.”

We are currently testing effects on Tai on dlp and dally transcription using qPCR and reporter transgenes. As noted below, the effects of Tai on Dlp trafficking are ‘strong’, so resolving effects on Dlp transcription will complement this localization data.

“The authors don't further examine Dlp protein in tai clones.”

As noted by the Reviewer, we do examine Dlp levels and localization in tai-low clones (see Figure 9), but these experiments are challenging due to their very small size and the hypomorphic nature of the tai allele (tai[k15101]) that was used. Experiments are in progress to examine the effect of our Crispr null allele of tai on Dlp levels and localization in wing clones.

“In sum, the authors have uncovered some interesting results, but the story has some unresolved issues that, if addressed, could boost its impact. Additionally, the preprint seems to have 2 stories, one about tai and cell competition and the other about tai and Wg distribution. It would be helpful to reorder the figures and improve the narrative so that these are better integrated with each other.”

We agree. The results of our modifier screen required that we first understand how Tai regulates the Wg pathway before could apply this to understanding the competitive mechanism. Thus, the paper is composed of three sections: 1. the screen, 2. the Tai-Dlp-Wg connection in the absence of competition, and 3. the contribution of Dlp-Wg to the tai[low] ‘loser’ phenotype. These sections use different techniques (e.g., clonal mosaics with genomic alleles, Gal4/UAS and RNAi to define the effect of Tai loss on Wg and Dlp). Ongoing experiments return to clonal mosaics to test whether elevating Dlp can rescue tai lof clones in the same manner as Apc/Apc2 alleles (see Figs. 2-3), which elevate Wg pathway activity.

Specifics:

“It would be good to know whether the authors can rescue tai-low clones by over-expression UAS-Dlp.”

As noted above, experiments are ongoing to test whether normalizing Dlp levels across clonal boundaries rescues elimination of Tai-low clones.

“The data on Wg distribution seems disjointed from the data about cell competition. The authors could refocus the paper to emphasize the cell competition story. The role of Dlp in Wg distribution is well established, so the authors could remove or condense these results. The story really could be Figs 1, 2, 3 and 7 and keep the paper focused on cell competition. The authors could then discuss Dlp as needed for Wg signaling transduction, which is already established in the literature.”

We appreciate the suggestion to reorganize the figures to focus the first part of the story on competition, and then follow with the role of Tai in controlling Dlp. We will consider this approach pending the results of ongoing experiments.  

“The model of tai controlling dlp mRNA and Dlp protein distribution is confusing. In fact, the data for the former is weak, while the data for the latter is strong. I suggest that the authors focus on the altered Dlp protein distribution on tai-low clones. It would also be helpful to prove the Wg signaling is impeded in tai clones (see #5 below).”

We agree but are currently testing how dlp reporters and mRNA respond to Tai in order to rigorously test a Dlp transcriptional mechanism. To complement the ‘strong’ evidence that Tai regulates Dlp distribution, we are testing Dlp in clones of our Tai Crispr null. Since submission, we have also assessed the effect of blocking the endocytic factor shibire/dynamin in Dlp distribution in Tai deficient cells to complement the data on Pentagone that is already in the paper (see Fig. S3).

“I don't know if the Fz3-RFP reported for Wg signaling works in imaginal discs, but if it does then the authors could make clones in this background to prove that cell-autonomous Wg signaling is reduced in tai-low clones.”

We thank the reviewer for this suggestion, which we are now testing.

Reviewer #2

Overall Comments:

“While the authors present good evidence in support of most of their conclusions, there are alternative explanations in many cases that have not been excluded.”

We appreciate this point and are conducting experiments for a revised submission that will help test alternative mechanisms and clarify our conclusions.

Specifics:

“However, the experiments have been done with a single allele, and these experiments do not exclude the possibility that there is another mutation on the same chromosome arm that is responsible for the observed phenotype. Since the authors have a UAS-tai stock, they could strengthen their results using a MARCM experiment where they could test whether the expression of UAS-tai rescues the elimination of tai mutant clones. Alternatively, they could use a second (independent) allele to demonstrate that the phenotype can be attributed to a reduction in tai activity.”

As noted above, we agree with the need for a second allele and are adding supporting data from a new tai lof allele we have generated by Crispr.

The tai[k15101] allele acts as a tai hypomorph and has been shown to produce weaker phenotypes than the 61G1 strong lof in a number of papers (Bai et al, 2000; König et al, 2011, Luo et al, 2019, and Zhang et al, 2015). We agree that rescue of tai[k1501] with a UAS-Tai transgene would help rule out effects of second site mutations. We are currently pursuing the reviewer’s second suggestion of phenocopy with a different allele, our new tai Crispr lof.   

“The authors have screened a total of 21 chromosomes for modification and have not really explained which alleles are nulls and which are hypomorphs. The nature of each of the alleles screened needs to be explained better.”

We will update the text to better reflect what type of alleles were chosen. In most cases we preferred amorphs or null alleles over hypomorphs, however when the amorph option was not available, we used hypomorphs.

“Also, the absence of a dominant modification does not necessarily exclude a function of that gene or pathway in the process. This is especially relevant for the Spz/Toll pathway which the authors have previously implicated in the ability of tai-overexpressing cells to kill wild-type cells.”

We thank the reviewer for this completely accurate point. The dominant screen does not rule out effects of other pathways such as Spz/Toll. Indeed, we were surprised by the lack of dominant effects by Spz/Toll alleles on tai[low] competition given our prior work. The reciprocally clear dominant effect of Apc/Apc2 led us to consider that Wg signaling plays a role in this phenomenon, which then became the starting point of this study.

“The most important discovery from this screen is the modification by the Apc alleles. This part of the paper would be strengthened by testing for modification by other components of the Wingless pathway. The authors show modification by Apc[MI01007] and the double mutant Apc[Q8] Apc2[N175A]. Without showing the Apc[Q8] and Apc2[N175A] alleles separately, it is hard to know if the effect of the double mutant is due to Apc, Apc2,` or the combination.”

We agree that testing for modification with other components of the Wg pathway would be helpful to strengthen the connection between Tai low clonal elimination and Wg pathway biology. We also agree that separating Apc [Q8] and Apc2 [N175A] would be a good idea to check if both Apc proteins are equally important for rescuing Tai low cell death, and future experiments for the lab could investigate this distinction.

“RNAi of tai seems to block the formation of the Wg gradient. If so, one might expect a reduction in wing size. Indeed, this could explain why the wings of tai/Df flies are smaller. The authors mention briefly that the posterior compartment size is reduced when tai-RNAi is expressed in that compartment. However, this observation merits more emphasis since it could explain why tai/Df flies are smaller (Are their wings smaller?).”

We agree that this is an exciting possibility. Growth effects of Tai linked to interactions with Yorkie and EcR could be due to a distinct role in promoting Wg activity. Alternatively, Tai may cooperate with Yorkie or EcR to control Wg pathway. These are exciting possibilities that we are pursuing in future work

With regard to the “small size” effect of reducing Tai, we have previously shown that RNAi of Tai using engrailed-Gal4 causes the posterior compartment to shrink (Zhang et al. 2015, Figure 1C-F, H). In this paper, we also showed that tai[k15101]/Df animals are proportionally smaller than wildtype animals and quantified this by measuring 2D wing size (Zhang et al. 2015, Figure 1A and 1B)

“In Figure 7, the authors show the effect of manipulating Tai levels alone or in combination with increasing Dlp levels. However, they do not include images of Wg protein distribution upon increasing Dlp levels alone.”

We thank the reviewer for this reminder and have already generated these control images to include in a revised submission paper.

“In Figure 8, there is more Wg protein both at the DV boundary and spreading when tai is overexpressed in the source cells using bbg-Gal4. However, in an earlier experiment (Figure 5C) they show that the wg-lacZ reporter is downregulated at the DV boundary when tai is overexpressed using en-Gal4. They therefore conclude that wg is not transcriptionally upregulated but is, instead secreted at higher levels when tai is expressed in the source cells. Wg protein is reduced in the DV stripe with tai is overexpressed using the en-Gal4 driver (Figure 6B') and is increased at the same location when tai is overexpressed with the bbg-Gal4 driver. (Figure 8) I don't know how to reconcile these observations.”

We thank the reviewer for pressing us to develop an overall model explaining our results and how we envision Tai regulating Dlp and Wg. We are preparing a graphic abstract that illustrates this model and will be included in our revision.

Briefly, we favor a model in which Tai controls the rate of Wg spread via Dlp, without a significant effect on wg transcription. For example, the induction of Dlp across the ‘engrailed’ domain of en>Tai discs (Fig 7B-B”) allows Wg to spread rapidly across the flanks and moderately depletes it from the DV margin (Fig 6B-B”) as noted by the reviewer. Adding a UAS-Dlp transgene in the en>Tai background dramatically accelerates Wg spread and causes it to be depleted from the DV margin and build up at the far end of the gradient adjacent to the dorsal and ventral hinge. Significantly blocking endocytosis of Wg in en>Tai discs with a dominant negative shibire transgene also causes Wg to build up in the same location (new data to be added in a revision) consistent with enhanced spreading. The difference in the bbg-Gal4 experiment is that Tai is only overexpressed in DV margin cells, which constrains and concentrates Wg within this restricted domain; we are in the process of testing whether this effect on Wg is blocked by RNAi of Dlp in bbg>Tai discs.

“In Figure 9, the tai-low clones have elevated levels of Dlp. How can this be reconciled with the tai-RNAi knockdown shown in Figure 7C' where reducing tai levels causes a strong reduction in Dlp levels?”

We apologize for not explaining this data well enough. First, the tai[k15101] allele is a weak, viable hypomorph (as shown in our Zhang et al, 2015 paper) whereas the Tai RNAi line is lethal with most drivers (including en-Gal4) and thus a stronger lof. Second, Tai RNAi lower Dlp levels (Fig 7C) while tai[k15101] causes Dlp to accumulate intracellularly (see Fig. 9A-C). These data indicate that reduced Tai leads to a defect in Dlp intracellular trafficking while its loss reduces Dlp overall levels; these data can be explained by a single role for Tai in Dlp traffic to or from the cell membrane, or two roles, one in trafficking and one Dlp expression. As noted, we are investigating both possibilities using dlp reporter lines and our new tai null Crispr allele.

Reviewer #3:

Overall Weaknesses:

“The study has relatively weak evidence for the mechanism of cell competition mediated by Dlp and Wg.”

The screen and middle section of the paper provide genetic evidence that elevating Wg pathway activity rescues Tai[low} loser cells and that Tai controls levels/localization of Dlp and distribution of Wg in the developing wing disc. Our current work is focused on linking these two finding together in Tai “loser” clones.

“More evidence is required to support the claim that dlp transcription or endocytosis is affected in tai clones.”

As noted above, we are testing whether normalizing Dlp levels across clonal boundaries rescues tai[low] loser clones and assessing effects of Tai on dlp transcription and Dlp trafficking.

Specifics:

“Most of the rest of the study is not in the clonal context, and mainly relies on RNAi KD of tai in the posterior compartment, which is a relatively large group of cells. I understand why the authors chose a different approach to investigate the role of tai in cell competition. However because ubiquitous loss of tai results in smaller organs, it is important to determine to what extent reducing levels of tai in the entire posterior compartment compares with clonal elimination i.e. cell competition. This is important in order to determine to what extent the paradigm of Tai-mediated regulation of Dlp levels and by extension, Wg availability, can be extended as a general mechanism underlying competitive elimination of tai-low clones. If the authors want to make a case for mechanisms involved in the competitive elimination of tai clones, then they need to show that the KD of tai in the posterior compartment shows hallmarks of cell competition. Is there cell death along the A/P boundary? Or is the compartment smaller because those cells are growing slower?”

Based on data that cell competition does not occur over compartment boundaries (e.g., see review by L.A. Johnston, Science, 2009), we chose not to use UAS-Gal4 to assess competition, but rather to investigate underlying biology occurring between Tai, Wg, and Dlp.

“Are the levels of Myc/DIAP1, proteins required for fitness, affected in en>tai RNAi cells?”

This is, of course, an interesting question given that Myc is a well-studied competition factor and is proposed to be downstream of the Tai-interacting protein Yki. We are not currently focused on Myc, but plan to test its role in the Tai-Dlp-Wg pathway in future work.